Center for Excellence in Applied Computational Science and Engineering (CEACSE)

Request for Proposals: Applied AI & Industry Partnerships Initiative

Request for Proposals - CEACSE Research Innovation and Strategic Expansion (RISE)

Annual Report to the Tennessee Higher Education Commission: Fiscal Year 2023

 Mission

The mission of the Center of Excellence in Applied Computational Science and Engineering (CEACSE) is to establish and expand a cohesive multidisciplinary effort in applied computational science and engineering that is leveraged across UTC and produce sustained growth in research funding, excellence in integrated education and research, and to increase national and international stature and competitiveness in Tennessee.

These funds are awarded annually on a competitive basis. The primary goal of this program is to enable development of new capabilities and extramural projects in the area of Computational Sciences. Awards support CEACSE strategic priority areas of Mobility & Transportation and Quantum Technologies

Purpose

The overall purpose of the Center of Excellence in Applied Computational Science and Engineering is to establish a cohesive and expanding base of multidisciplinary research in applied computational science and engineering to produce sustained growth in research funding, excellence in integrated research and education, and increases in national and international stature and economic competitiveness for Tennessee.

Computational simulation is critically important for the analysis and design of future high technology products and systems in a competitive global marketplace. The future security and economic well being of our country will depend in part on an adequate supply of scientists and engineers who are highly skilled in the use of computers to solve important engineering problems using modeling and simulation.

This evolution is transforming the use of high technology by introducing computational simulation and design software that supplements experiments and testing to produce competitive advantages in critical areas such as price, time-to-market, life-cycle costs, and overhead. Although these benefits to industry are driving the changes in engineering practice, science education in the U.S. has not responded adequately to the challenge of providing graduates who are adequately prepared.

In view of the extensive use of computational methodologies in design by industry, there is a significant role for innovative programs of integrated research and graduate education (i.e., graduate research in an applications environment) that is distinct from traditional university research activity.

The use of computers to solve complex, large-scale, practical problems is a trend that will accelerate in years to come.

CEACSE at UTC

UTC has recognized that these prospects now offer a dramatic window of opportunity to provide the leadership in computational applications driven research and education needed for future competitiveness in the high-technology sector of the global economy. UTC has also positioned itself through past research and faculty additions to provide this leadership for Tennessee.

Allocability of GA Tuition and Stipends on Sponsored Projects
FY2023 Faculty Initiation and Career Opportunity
Center of Excellence Distinguished Lecture Series Grants
2022 Competition (Funding for FY2024)
The Center of Excellence in Applied Computational Science & Engineering Fillable Qtrly. Report Template Planning and Capacity Building for Collaborative Teams (PACCT) FY2024-25 Request for Proposals Planning and Capacity Building for Collaborative Teams (PACCT) FY2024-25 Information Session Slides

Awards and Reports

FY 2025 Awards (PACCT)

Project Title: “Enhancing the Capacity of Quantum Key Distribution Research and Education through an Integrated Approach”

Dr. Mengjun Xie (Computer Science and Engineering) and Dr. Tian Li (Chemistry and Physics)

Project Title: “Intelligent Reconfigurable Battery System for Enhanced and Robust Electric Mobility”

Dr. Dalei Wu (Computer Science and Engineering), Dr. Keenan Dungey (Chemistry and Physics), Dr. Yu Liang and Dr. Yukun Yuan (Computer Science and Engineering)

Project Title: “Power and Transportation System Co-optimization with Renewable Energy and Electric Vehicles via Dynamic Pricing and Charging Rate Control”

Dr. Yukun Yuan (Computer Science and Engineering), Dr. Feng Guo (Psychology), Dr. Yu Liang and Dr. Dalei Wu (Computer Science and Engineering)

Project Title: “Development of Blockchain-based Secure Data-Sharing Framework for Automated Guided Vehicles and Collaborative Robots”

Dr. Gokhan Erdemir (Engineering Management and Technology), Navid Aghakhani (Management), Dr. Erkan Kaplanoglu (Engineering Management and Technology)

FY 2024 Awards and Final Report

FY24 Final Report

Project Title: “Modeling heat generation and temperature variation in supercapacitors”

Dr. Murat Barisik, Lead PI

Abstract

Recent energy storage and conversion applications requires a thorough knowledge of the behavior at electrode/electrolyte interfaces. One of the most promising future technologies, supercapacitors, store energy by building up ionic layers near the electrode surface known as electric double layer (EDL). However, EDL formation through a nanoscale confined electrolyte is not well understood yet. Especially, there is very limited information about the variation of solution temperature during working conditions, and its influence on electric double layer formation. Both ionic layering and heat transfer behavior of a supercapacitor shows noncontinuum behavior as the molecular level mechanisms becomes dominant at nano-levels. For such a case, this research project will study the heat generation and nanoscale heat transfer through electric double layer at molecular level. The Molecular Dynamics (MD) simulations on high-performance computer clusters will be employed, which naturally accounts for molecular nature of both ionic transport and heat transfer. The thermal conductivity of nano-confined electrolytes and interfacial thermal resistance between electrode and electrolyte will be determined. The effects of electrolyte’s temperature on the structures of EDL will be measured. Results will be published in two high-impact journal and extended into one extramural grant proposals.

Project Title: “Dynamics Analysis of Online Social Network Models”

Dr. Lingju Kong, Lead PI

Abstract

In this project, by developing the data-driven deterministic differential equation compartment models, the PI proposes to study the OSN dynamics from two aspects: (a) User traffic dynamics of a single OSN and (b) Competition and coexistence principle of users among multiple OSNs. The proposed project consists of multiple research objectives. The first objective is the development of the user adoption and abandonment model for a single OSN. The model will contain a generalized nonlinear incidence, which is a function of the number of current OSN users. The second objective is to study the competitive exclusion and coexistence of users among multiple OSNs. Due to the physical meanings of the models, conditions for the competitive exclusion and coexistence when one OSN is initially dominant will be the main research focus. Theoretic and numerical analysis will be conducted to understand the model dynamics. The phenomenon of various bifurcations (supercritical, subcritical, or saddle-node, et.al.), sensitivity analysis, and optimal control of the models will also be studied in detail. Case studies combining the developed models and the real-world data will be carried out. These findings will be further applied to predict the evolution of OSN dynamics and derive actionable policies.

Project Title: “Heisenberg-Limited Quantum Sensing Across Entanglement Distributed Quantum Networks”

Dr. Tian Li, Lead PI in collaboration with Dr. Donald Reising

Abstract

In this project, we propose to: (1) experimentally demonstrate a table-top continuous-variable entangled quantum network based on a two-mode squeezed state for sensing the average of multiple phase shifts; (2) develop machine learning techniques to measure and control the excess noise distributed across the network, so that Heisenberg-limited parameter estimation can be achieved. Upon completion, our project will be the first experimental realization of machine learning-aided entangled quantum network sensing.

Project Title: “Machine Vision & AI Application for Damaged Solar Panels Detection”

Dr. Abdul Ofoli, Lead PI in collaboration with Dr. Vahid Disfani

Abstract

This project aims to develop machine vision and artificial intelligence solutions to detect damaged solar panels in huge solar farms to (1) increase the reliability of power cultivated from these solar farms and (2) to reduce the maintenance costs of the farms significantly. Solar panels have come a long way, the technology of these free-energy generators is constantly improving. While solar panels are meant to withstand most climates and are built to last 20 to 30 years, they’re still not immune to damage especially since they’re made from outwardfacing glass. Solar panels can reach their “end-of-life” (EOL) prematurely due to these physical damages and they will need replacements. But most importantly, not being able to capture the state of damaged PV panels can lead to erroneous solar energy forecasting from solar power plants or installations from actual energy productions. Thus, there is a critical need to develop a visual means of inspections and automatic classification of the physical state of installed solar panels to determine healthy and possibly damaged panels.

Project Title: “Creating a Socially Aware Efficient, Transparent, and Equitable 311 System for Smart Cities”

Dr. Yukun Yuan, Lead PIin collaboration with Drs. Joseph Dumas, Junrong Shi, and Prof. Feng Guo

Abstract

Urban 311 services have already been widely used by residents to report non-emergency service requests, e.g., graffiti removal. Researchers have accumulated extensive knowledge on the bias of submitting service requests resulting from persistent spatial, racial, and economic inequalities in cities. However, for residents with diverse social background, studies on the service quality provided by city departments are lacking. This project develops a data driven approach to promote efficient, transparent, and equitable 311 services for diverse communities in a city, by leveraging multi-source data from public socioeconomic and demographic data, city infrastructure, historical service requests, and self-reported survey findings. There are four tasks of this project: i) modeling residents' behavior profile, ii) analyzing community-level social disparity, iii) predicting response time of addressing issues, and iv) designing socially aware learning-based resource scheduling algorithm. Our project has broader impact on both training students and enhancing service quality for residents in real cities.

FY 2023 Awards and Final Report

FY23 Final Report

Project Title: “Synthesis of Novel Aerogels for use in Retrofit Window Treatments which are Inexpensively Manufactured, Maintain Transparency Standards, and Dramatically Reduce Heat Loss”

Dr. Sungwoo Yang, Lead PI

Abstract

More than a third of all windows in the United States are single-pane windows, which are the most energy-inefficient component of our building envelopes. The annual losses of single-pane windows due to high heat losses is about $12 billion. We propose a one-year project to demonstrate the feasibility of cheap, strong, transparent, insulating (CS-OTTI) retrofits for single-pane windows. A key innovation in our proposed concept is the ability to leverage our ambiently-dried transparent aerogel. It achieves 90% transmittance higher than the best transmittance in literature. We will investigate various novel functionalized silanes to further enhance its optical performance. Our transparent aerogel costs about $1/liter, which is significantly cheaper than conventional aerogel ($3/liter). The monolithic aerogel exhibits reduced effective heat transfer rates (< 0.03 W/mK) without compromising structural integrity. Experimental data and computational modeling will be used to describe thermal performance of the aerogel retrofits on a single-pane window. These innovations have anticipated winter U-factors of < 0.52 BTU/sf/hr/°F, which is close to the performance of expensive air-filled double glazing at significantly lower cost and easy installation. CS-OTTI aerogels will expedite the deployment of OTTI aerogel in practice, including in solar thermal convertors, solar desalination systems, and solar ovens.

 

Project Title: “Modelling Archaeo-Acoustic Phenomena as a Means of Developing a Method for Non-Invasive, Remote Detection of Underwater Archaeological Sites”

Dr. Morgan Smith, Lead PI in collaboration with Drs. Boris Belinskiy and Abi Arabshahi

Abstract

Our objective is to test, through numerical modeling and simulations, an experimental method of detecting anthropogenic lithic material (stone tools) remotely with a sub-bottom profiler (SBP) as a means of identifying underwater archaeological sites rapidly and non-invasively. We propose to use this funding to work toward the application of a computer code for modelling and simulating this phenomenon, with the eventual goal being to move toward machine learning and automation of this methodology. This project is necessary because the reliability of this method in the field is unknown, and laboratory tests are needed to control for environmental variables which cannot be mitigated in the field (water temperature, density, turbidity, etc.). However, our project fulfills CEACSE’s primary funding goals, as this project concerns modelling, simulation, and machine learning; will result in high-impact peer-reviewed publications; and, if successful, will seed future long-term funding from external sources.

 

Project Title: “Study of Differential Diffusion Effects in Stratified Turbulent Flows using a Hybrid Multi-Scale Modeling Strategy”

Dr. Reetesh Ranjan, Lead PI

Abstract

Numerical investigation of stratified turbulent flows observed in engineering and geophysical flows is challenging due to the added complexity of the effects of stratification on turbulence. The challenges are increased further in the flows where differential diffusion effects are present due to the dependence of density stratification on temperature and salinity through a nonlinear equation of state (EoS). Large-eddy simulation (LES) is a computationally tractable approach for the investigation of such flows at practically relevant conditions. However, the subgrid-scale closures used in LES need to be robust and accurate to account for the effects of stratification on the subgrid processes. The proposed research aims to address some of the challenges associated with the modeling of differential diffusion effects in these flows. First, it will examine the effects of moderate levels of differential diffusion at moderate Reynolds number (Re) through direct numerical simulations, where the role of EoS will also be characterized. Secondly, a generalized hybrid multi-scale model leveraging the accuracy of the two-level simulation model and the efficiency of the LES model will be established for predictive LES capabilities. Finally, the established model will be used to study features of high Re axisymmetric towed wake at realistic flow conditions.

 

Project Title: “Integrating Google Trends Analytics into Geographically Weighted Model of Vaccine Hesitancy”

Dr. Lani Gao, Lead PI in collaboration with Drs. Nagwan Zahry and Ziwei Ma

Abstract

We propose a multiscale geographical weighted regression model integrated with Google Trends data analysis (MGWR-GT) to study COVID Vaccine Hesitancy (CVH). MGWR-GT model accounts for common factors association with CVH, as well as geographical and regional specifics of human behavior toward pandemic. The predicted CVH rate can guide COVID vaccine administration and distribution efforts at the state and local levels with consideration of disparities and vulnerable populations. Moreover, reliable and more accurate results given by the model would help public health decision makers respond to the public health crises more quickly, more efficiently, and more effectively. Furthermore, the proposed method can serve as a framework to predict human behavior toward public health crises by integrating traditional data and next-generation dynamic web search data. Finally, this research work will strengthen the real-time data analytics on health-related web queries in this big data era. Our project aims to achieve the following research goals:

• To examine and analyze the factors that influence vaccine hesitancy locally and globally
• To develop a conceptual framework of integrating seasonality of next-generation real-time dynamic data with traditional data analysis
• To explore the vital role of health communication in handling public health crises

 

Project Title: “Exploring entanglements in polymer network topologies with single-chain nanoparticles”

Dr. Meredith Barbee Lead PI in collaboration with Dr. Eleni Panagiotou

Abstract

The proposed research is focused on two main goals: (1) quantifying the effect of entanglements in polymer networks on the mechanical properties of the material and (2) developing design principles for an unexplored network topology in hydrogel materials based on the unfolding of single-chain polymer nanoparticles. Through a combination of simulations and characterization of synthetic materials, we hope to uncover fundamental relationships important in designing polymer networks and establish a framework for developing hydrogels that are highly extensible, overcoming limitations to the use in these typically brittle materials.

FY 2022 Awards and Final Report

FY22 Final Report

Project Title: “Identification and Prediction of Species Invasiveness Potential in the Gut Microbiome”

Dr. Fernando Alda, Lead PI in collaboration with Dr. Yu Liang

Abstract: Biological invasions are a well-recognized threat to biodiversity, human health, and the global economy. Though preventing invasions is often a top management priority, understanding invasion potential is not yet sufficient to predict the likelihood of invasion or to identify regions at risk. Studies suggest that gut microbial diversity and plasticity play a major role in determining invasion potential. So far, all of the evidence supporting the “gut microbial facilitation hypothesis” comes from work on invertebrates. We propose to determine whether it extends to vertebrate invasions and test the hypothesis through the use of deep learning computational analysis to assess whether there are microbial markers of invasive freshwater fishes. Invasive fish can destabilize valued inland fisheries and threaten local biodiversity, especially in global hotspots like the southeastern US. We will use a combination of high throughput sequencing metabarcoding and statistical graphic models to study—in the wild and experimentally—the gut microbiome of native and invasive fishes to (1) test whether there is a signature of invasiveness; and (2) to develop a model that can predict the origin and invasiveness potential of freshwater fishes, which will provide a basis to extrapolate a general theory on the success of biological invasions.

Project Title: “From in vitro to in silico: Exploring the Therapeutic Potential of Antimicrobial Peptides on Exogenous Fatty Acid Modification of Bacterial Membranes”

Dr. David Giles, Lead PI in collaboration with Drs. Steven Symes, and Bradley Harris

Abstract

Bacterial acquisition and utilization of fatty acids represents a pragmatic strategy for survival in various environments. It is now recognized that, in addition to their value as carbon sources, exogenous fatty acids can be recycled and assimilated into membrane phospholipids. The overall objective of this proposal is to investigate the nature of exogenous fatty acid-mediated membrane remodeling and antibiotic resistance in em>Vibrio cholerae. The rationale for the proposed research is that a better understanding of bacterial assimilation of fatty acids, particularly their impact on antibiotic resistance, could be used to manipulate pathogens in clinical and natural settings, resulting in new and innovative approaches for therapeutic intervention and environmental control. A combination of microbiological and biochemical methods are proposed to inform and complement the in silico approach. Computer simulation of membrane dynamics are integral for achieving experimental validity and applicability in predictive potential. This collaborative project represents an interdisciplinary approach, incorporating Biology, Chemistry and Engineering, to address biophysical, biochemical and physiological bacterial membrane dynamics associated with exogenous fatty acid utilization.

Project Title: “Degradation Modeling of Coated Magnesium Towards Patient-Specific Biomedical Implants”

Dr. Hamdy Ibrahim, Lead PI in collaboration with Dr. Mohamad Mahtabi

Abstract

Magnesium and its alloys have been under extensive research recently due to the high potential of their use for biomedical implant applications. For instance, the use of bone implants made of magnesium that can offer the required stability during the healing period and subsequently degrades is expected to result in a clinical breakthrough by eliminating the problems associated with the standard-of-care permanent implants such as stress shielding. Understanding the corrosion behavior of magnesium-based implants is crucial to assure the biomechanical performance of these new devices. Numerical simulation can serve as a useful tool to reduce the effort needed to achieve that goal. In this study, we propose to develop a numerical model based on the physical modeling approach that is capable of simulating the corrosion behavior of magnesium implants coated with biocompatible ceramic coatings. The developed model will, for the first time, simulate the effect of a ceramic coating layer on the surface of a magnesium-based implant. In vitro corrosion tests under conditions simulating those for the physiological environment will be performed to calibrate and validate the developed model. This model will serve as a future design tool for studying patient-specific biodegradable implants and support our project long-term goals.

Project Title: “Anti-Tamper IC Forensics and RF-Level DIscrimiNation FOR IMproved Trust (INFORM)”

Dr. Daniel Loveless, Lead PI in collaboration with Dr. Donald Reising

Abstract

Physical authenticity verification of integrated circuits is an unresolved but critical issue for commercial (IoT) and Department of Defense (DoD) systems. Currently, there exists no acceptable way to guarantee that an electronic part is not counterfeit or has not been tampered with, so the concept of "trust" in a mission-critical part, or one that may contain sensitive personal data, may be problematic. The majority of efforts to combat electronic counterfeiting have been focused on the digital layers. However, as wireless connectivity is becoming increasingly prevalent, there is a new opportunity to significantly enhance security through the RF, analog, and mixed-signal layers. This effort introduces a novel tamper forensic technique with the potential to impact all levels of electronics systems through verifiable trust. We will demonstrate a novel, non-destructive post-fabrication imaging technique coupled with RF measurement for initial identification and maintenance of trust through machine learning for enhanced trustworthiness at scale. The proposed activity pushes the boundaries of forensic analysis of the most advanced microelectronic fabrication technology nodes. Given complete development, this proposal's techniques will be applicable across nanometer-scale technologies, address security vulnerabilities at the physical device level, the RF waveform level, and propagate through the digital system level.

Project Title: “Development of Multi-Objectively Optimized Interatomic Potentials for Computational Design of High Temperature Actuator Materials”

Dr. Mohamad Mahtabi, Lead PI in collaboration with Dr. Hamdy Ibrahim

Abstract

With the rapid growth in computing power, multiscale simulations are now becoming a viable approach to calculate the properties of materials and design new materials. In this regard, ab initio simulations using Density Functional Theory (DFT), while ideal, are very limited in the size of the model due to computational costs. Molecular dynamics (MD) simulations present a promising alternative to DFT to computationally study the properties of materials. These methods are powerful because deformation properties of materials such as crack initiation and growth, dislocation behavior, polycrystal effects, effects of second phases, interface interactions, and so on, are naturally captured in these simulations. On the other hand, the accuracy of MD simulations heavily relies on the quality of the interatomic potentials. In this proposal, we propose the development of interatomic potentials, based on multi-objective optimization of parameters to present the mechanical properties of high temperature actuator materials (i.e. high temperature shape memory alloys, HTSMAs). The proposed research involves developing, calibrating, and validating interatomic potentials for MD simulations of alloys made of Ni, Ti, and Hf.

Project Title: “Decomposition Modeling of Microbial Mat Ecosystems to Quantify Earth's Early Fossil Record”

Dr. Ashley Manning-Berg, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi

Abstract

Most of our knowledge of early life on Earth is found in these silicified microbial mats, the oldest of which is found in 3.5 billion-year-old rocks composed of silica. The preserved organisms are identified and classified into taxa according to their morphology. However, silicified microfossils display a range of preserved morphologies, from well-preserved to unrecognizable. This range of morphology is the result of biologic decomposition, which is halted once the silicification process begins. Laboratory experiments suggest that morphologic changes occur rapidly during decomposition; therefore, preserved morphologies provide the timing of silica formation and indicate the amount of time an organism experienced decay prior to fossilization. The research proposed will expand on the research previously funded by the mini-CEACSE grant (FY2020) awarded to the PI. The research proposed here will create a way to predict the range of preserved microbial morphologies given a specific length of time that the microbes were exposed to during decomposition. Biomass accumulation within a mat will be calculated using a previously developed model and used in decomposition models to establish a relationship between biomass and time. The quality of microbial preservation can then be predicted based on the amount of time the mat was exposed to decomposition.

Project Title: “Addressing Sampling Biases in Genome-wide Association Study for SARS-CoV-2”

Dr. Hong Qin, Lead PI in collaboration with Drs. Ziwei Ma, and Azad Hossain

Abstract

Monitoring adaptive changes of SARS-CoV-2 is of critical importance to mitigate its transmission. Weather temperature and humidity have shown significant statistical associations with the transmission of SARS-CoV-2. Hence, temperature and humidity are proxies of environmental changes for SARS-CoV-2. Our preliminary analyses showed that the D614G mutant prefers drier weather than the reference strain. Genome-wide association study (GWAS) is a typical method to study association between genotype and phenotypic measures. However, GWAS typically requires random sampling. Currently, isolates of SARS-CoV-2 tend to be sequenced in regions with advanced research capacity, and most sequencing were reported during March 2020. These sampling biases pose challenges to genome-wide studies of SARS-CoV-2. Here, we propose to compare a suite of statistical and computational methods to mitigate the sampling biases of SARS-CoV-2, including model-based bootstrap, jackknifing resampling, skew normal distribution, skewed generalized t-distribution, and Gamma distribution. We will compare the accuracy and sensitivity of these methods based on simulation studies, and then apply to investigate the statistical association of nucleotide changes associated with weather temperature and humidity at the county-level. Our proposal will help us better understand the seasonality of SARS-CoV-2 and contribute to the national and global effort to address the pandemic.

Project Title: “Modeling of Transition to Turbulence in Large Eddy Simulation using the Two Level Simulation Approach”

Dr. Reetesh Ranjan, Lead PI

Abstract

Transition to turbulence is observed in several aerodynamic applications such as turbomachinery and flow past aircraft wings. Specifically, in wall-bounded flows, transition affects the skin friction coefficient, the wall heat flux, and the spectral and spatio-temporal characteristics. Therefore, an accurate prediction of the onset of transition and the corresponding flow features is important from the design perspective. The presence of such complexities makes the numerical investigation of transitional flows extremely challenging. While direct numerical simulation (DNS) can be used to examine fundamental features of such flows, large-eddy simulation (LES) tends to be more suitable for the investigation of practical applications. However, models for LES are usually derived for fully developed turbulence, thus requiring improved or alternate strategies. The proposed research will establish a hybrid modeling strategy for LES of transitional flows where the two-level simulation (TLS) model, a multi-scale model, is used for near-wall modeling. TLS model does not employ the notion of eddy viscosity, which allows it to capture small-scale physics such as anisotropy, vorticity dynamics, backscatter, and co-/counter-gradient transport. The hybrid modeling strategy will be first assessed through a series of well establish test cases, and then used for further investigation of controlled transition within the boundary layer.

FY 2021 Awards and Final Report

FY21 Final Report

Project Title: “Enhanced Eulerian-Lagrangian Formulation for Investigation of Turbulent Dispersed Multiphase flows”

Dr. Abdollah (Abi) Arabshahi, Lead PI, Research Professor, Mechanical Engineering

Dr. Reetesh Ranjan, Assistant Professor, Mechanical Engineering

Abstract

The proposed research focuses on further enhancements in the Eulerian-Lagrangian (EL) formulation for large-eddy simulation (LES) of turbulent dispersed multiphase flows. Such flows are observed in several engineering applications and natural systems, and are comprised of a carrier phase and a dispersed phase. We consider the point-particle-based approximation within the EL formulation, where the carrier phase is simulated using a Eulerian approach and the dispersed phase is tracked in a Lagrangian manner. While the EL formulation is well established, there are several challenges associated with the Lagrangian evolution of the dispersed phase, particularly in the context of LES. We will focus on two key challenges, which include subgrid turbulence dispersion modeling and accuracy and efficiency aspects of parcel-based Lagrangian tracking. We will extend and evaluate three different strategies for subgrid dispersion under the same numerical framework, which include localized multi-scale approach, fractal interpolation technique, and approximate deconvolution method. The accuracy and convergence aspects of the parcel-based approach will be assessed by using deterministic and stochastic parcel-number-density control algorithms. The enhanced EL framework will be verified and validated through well-established test cases and will be used to investigate spray combustion and drug delivery applications.

 

Project Title: "People, planet, and profits: Strategic planning for outdoor recreation, tourism and conservation"

Dr. Drew Bailey, Lead PI, UC Foundation Associate Professor and Program Coordinator Sport, Outdoor Recreation and Tourism Management

Dr. Greg Heath, Guerry Professor, Exercise Science

Charlie Mix, Director, GIS

Abstract

This project will develop a decision-making tool for long-term recreation, conservation, and tourism planning, utilizing machine learning on multi-level data. Data on recreational use patterns, economic impact, physical activity and public health, biodiversity and conservation, and urban development and climate modeling will be geospatially analyzed to establish areas of high value and high vulnerability. This information will assist regional planners and municipalities in the development of strategic approaches to address public health, protection of biodiversity, and sustainable economic development through recreational and tourism assets. The relationship between recreational assets (i.e., parks, trails, and greenways), tourism impacts, conservation of biodiversity, and physical and mental health has been firmly established in previous research. However, the combined influence of these elements is rarely considered in regional planning models, likely due to a lack of resources. Open source and localized data, and modern machine-learning and geospatial techniques, render it feasible to develop a decision-making tool that accounts for the long-term health of people, the planet, and profits in regional planning. Situated in an area of high recreational value, rare and vulnerable biodiversity, but poor mental and physical health, this research team has a unique opportunity to identify key factors influencing the triple-bottom-line in a geospatial context.

 

Project Title: "Real-Time Optimal Allocation of Adaptive Virtual Inertia in Power Systems with High Penetration of Distributed Energy Resources"

Dr. Vahid Disfani, Lead PI, Assistant Professor, Electrical Engineering

Dr. Raga Ahmed, Associate Professor, Electrical Engineering

Abstract

Grid integration of high penetration of distributed energy resources is expected to cause serious frequency excursions in power systems. These resources have highly intermittent power output and are connected through zero-inertia power electronic converters, both of which have adverse impacts on power system frequency. The main idea to resolve these issues is to emulate additional inertia through the same converters, which is referred to as virtual inertia. In addition to the total virtual inertia available throughout the power system, its allocation has a significant impacts on the frequency behavior of the power systems. This proposed project will develop real-time optimal allocation of virtual inertia in response to real-time forecast for availability of distributed energy resources. Realistic models of virtual inertia by different technologies will be developed. The optimization platform will be finally tested via software and real-time digital simulation platforms.

 

Project Title: "Climate and social evolution: Using machine learning to improve dataset quality and to develop predictive models"

Dr. Loren Hayes, Lead PI, Associate Professor, Biology, Geology & Environmental Science

Dr. Craig Tanis, Assistant Professor, Computer Science & Engineering

Abstract

A fundamental goal of biology is to understand the evolution of animal social systems. Comparative studies have failed to account for intraspecific variation in social organization (e.g., a species may live in groups or alone in different populations). Accounting for intraspecific variation in comparative studies is critical because the ability to change social organization may improve species resilience in the face of climate change. We aim to: (i) build a dataset on mammalian social organization that accounts for intraspecific variation and (ii) conduct a preliminary analysis to determine the impact of rainfall and temperature trends on artiodactyl social evolution. We focus on artiodactyls because the PI has completed manual data collection for this Order. We will conduct a semantic analysis of the literature, applying machine learning techniques to improve the consistency and speed of data collection (aim 1). We will use classical regression methods and machine learning–based predictive methods to test the hypothesis that variable rainfall and temperature are associated with variable social organization (aim 2). We will use the results of this study to strengthen a National Science Foundation proposal to conduct a comparative analysis of how climatic variation influences the evolution of mammalian (~5,500 species) social organization.

 

Project Title: "Development of an Integrated Human-in-the-Loop Simulation Platform for Smart City Applications"

Dr. Osama Osman, Lead PI, Assistant Professor, Civil Engineering

Dr. Farah Kandah, UC Foundation Associate Professor and Graduate Program Coordinator, Computer Science & Engineering

Abstract

The proposed research includes modeling, simulation, and computational performance analytics and optimization. The proposed research aims to enable application of Virtual Reality (VR) in a multi-player game setting for a wide spectrum of research applications at the University of Tennessee at Chattanooga. Specifically, an integrated multidisciplinary human-in-the-loop simulation platform will be developed to enable studying micro-level interactions between multiple heterogeneous road users in a VR multi-player setting. The research objectives are to: (a) develop an integrated simulator for heterogeneous road users that capitalizes on VR technology; (b) develop a behavioral data collection and visualization tool for the integrated simulator; and (c) demonstrate the capabilities of the integrated platform. The proposed integrated simulation platform will enable experimental research and training in highly controllable conditions. Additionally, the integrated platform will combine the advantages of various research methods: pedestrian-in-the-loop simulation for testing of pedestrian behavior in a wide range of applications, driver-in-the-loop simulation for experimental investigation of driver behavior in various scenarios, and data analytics and visualization techniques of behavioral data. The integrated platform will add a high degree of realism since assumptions and mathematical models of road user behaviors will not be the basis of simulation.

 

Project Title: "Topological design of porous metals for biomedical applications"

Dr. Eleni Panagiotou, Lead PI, Assistant Professor, Mathematics

Dr. Hamdy Ibrahim, Assistant Professor, Mechanical Engineering

Abstract

This proposed research is focused on the creation of optimal biodegradable metal material for biomedical applications using tools from topology. In particular, we focus on the development of such materials for the use in bone implants. It has been shown that the distribution of porosity in bones and their geometry plays a fundamental role in their ability to bear the load of the body. With this research we test the hypothesis that the overall topology of the porous structure, and not only the average size or distance, can provide more refined information to characterize different structures and to provide optimal structures. We will combine computer simulations and topological data analysis, as well as tools from braid theory and graph theoretical approaches.  We will propose optimal structures of controlled topology that will be created in the laboratory with established modern techniques, such as 3D printing, and with new methods, such as entangled metal wires. Our approach is expected to provide a new systematic way of studying biodegradable metal material for bone implant applications. This will lead to applications for external funding to study such material at a bigger scale in order to make impacts on medicine and industry.

 

Project Title: “An Efficient Framework for Numerical Investigation of Turbulent Combustion using Detailed Finite-Rate Chemistry”

Dr. Reetesh Ranjan, Lead PI, Assistant Professor, Mechanical Engineering

Abstract

Combustion devices such as liquid-fueled propulsion and gas turbine systems operating under lean conditions are desirable due to their low emission characteristics. Accurate prediction of complex physical processes observed in these devices—such as ignition, extinction, pollutant emissions, combustion instability, etc.—over a wide range of operating conditions requires the use of detailed finite-rate chemistry. Although recent computational advancements have enabled the use of detailed finite-rate chemistry while performing large-eddy simulation (LES) of such systems, the computational expense tends to be huge, thus requiring further strategies for efficient computation. The proposed research focuses on establishing a novel computationally efficient framework for the investigation of turbulent combustion using detailed finite-rate chemistry. The framework will combine the two well-established approaches, namely the dynamic adaptive chemistry (DAC) approach with the hybrid transported-tabulated chemistry (HTTC) approach. While the DAC technique focuses on reducing the computational cost of the chemistry source term, the HTCC strategy reduces the total number of the transport equations by using self-similar profiles for the minor species while transporting only the major species. The novel computational framework will be verified and validated through well-established test cases corresponding to both premixed and non-premixed combustion configurations.

 

Project Title: “A Low-Cost, Passive Solar Process Heat System”

Dr. Sungwoo Yang, Lead PI, Assistant Professor, Civil and Chemical Engineering

Abstract

Process heating constitutes nearly 70% of the total process energy consumed in the U.S. manufacturing sector, which is almost entirely extracted from fossil fuels. The demand for heating is particularly important for the food processing and beverage industry, which consumes 340 TBtu produced using natural gas annually for process heating. Solar thermal energy is an ideal natural gas substitute for heat generation in the food processing industry. However, the high-cost and complexity of existing concentrated solar-powered industrial process heat systems have prevented their widespread adoption in food processing plants. We propose a low-cost, passive solar process heat system capable of reaching high temperatures and pressures (up to 200 °C, 15 bar) without the need for expensive solar tracking concentrators. The key technological innovation that enables our flat-plate type solar receivers to reach relatively high temperatures relevant for the food processing industry (100-200 °C) is the optically transparent, thermally insulating monolithic silica aerogel developed in our lab. These novel aerogel layers allow transmission of >96% incident solar energy while minimizing heat losses, resulting in efficiencies as high as 75% even without solar concentration. 

FY 2020 Awards and Final Report

FY20 Final Report

Project Title: “Optimization of Sunlight Powered Water Harvesting from Air by Characterization and Modeling”

Dr. Sungwoo Yang, Lead PI, Assistant Professor, Civil and Chemical Engineering

Abstract

Two-thirds of the world's population in cities is experiencing a water shortage. Current techniques, such as dewing and fog capture, can be used only in locations where the humidity is high. We propose a novel and efficient zeolite-based water harvesting system. A key innovation is the ability to leverage our development of lightweight carbon network as flexible thermal additive and theoretical model to optimize the design of the water-harvesting process via solving the energy/concentration conservation equations. Our novel approach can achieve effective and efficient water harvesting, with a >2.5x higher energy efficiency compared to conventional water generators. The PI is currently the Pl on the UTC Faculty Pre-Tenure Enhancement Program. This project has been a successful interdisciplinary collaboration with researchers in UTC Mechanical Engineering, Civil Engineering, and Computational Engineering and Oak Ridge National Labs. The funds requested here would allow the Pl to continue previous research by performing detailed experiments to characterize and enhance the vapor diffusion characteristics (inter-/intra-crystalline diffusion) in carbon network and zeolite composites, which dictate water harvesting rates. The Pl also performs systematic optimizations of the overall system architecture by leveraging the team’s expertise in the development of high-fidelity adsorption computational models.

 

Project Title: “Alkynyl tetrafluoro-pyridyl ligands: computational studies, synthesis, and characterization”

Dr. Jared Pienkos, Lead PI, Assistant Professor of Chemistry, Chemistry and Physics

Abstract

Alkynyl compounds can be used to tune the absorption and emission properties of their corresponding transition metal complexes. Herein, we will describe the computational characterization and the synthesis of electron deficient alkynyl tetrafluoro-pyridyl ligands. Iridium, cobalt, and chromium alkynyl tetrafluoro-pyridyl metal ligand interactions will be modeled using a variety of basis sets and functionals supported by the Gaussian/WebMO interface. Concurrent with these computational studies, representative iridium, cobalt, and chromium compounds will be synthesized. Following their synthesis and characterization, modulation of electrochemical and emissive properties will be performed by exploiting secondary binding sites. All of these interactions will be computationally modeled with the Gaussian/WebMO interface using resources within the SimCenter. Computational tools used in this project will also be implemented for educational outreach activities within the community.

 

Project Title: “A study on the local and global effects of polymer entanglement in material properties and biological functions”

Dr. Eleni Panagiotou, Lead PI, Assistant Professor, Mathematics

Dr. Jin Wang, Professor and UNUM Chair of Excellence in Applied Mathematics, Department of Mathematics

Dr. Wang-Yong Yang, Assistant Professor of Chemistry, Chemistry and Physics

Dr. Ethan Hereth

Dr. Abi Arabshahi, Research Professor, Mechanical Engineering

Abstract

This proposed research is focused on making the connection between microscopic and macroscopic properties in polymers and biopolymers. First, we propose to use Molecular Dynamics (MD) simulations of coarse-grained models of linear polymer chains in a melt, for various molecular weights, and examine how the entanglement affects the mechanical properties of the material. We will also examine the role of the fluid-structure interactions, and our results will be compared to experimental data. Our results will show how local/global interactions affect material properties, a fundamental question in materials science and in the study of biological systems like the cytoskeleton. Second, we propose to use MD simulations of RNA, which include expanded r(AUUCU) repeats (responsible for spinocerebellar ataxia) to identify special characteristics of their 3D structure. We also study the dimeric compound 2AU-2 that is known to target the pathogenic RNA and model its binding by accounting for fluid-structure interactions. We use topology to study these and to suggest other molecules that would have the same effect. We check our results experimentally. Our results will show how geometry/ topology can be used to create site-specific molecules and could be applied to other extended repeats and lead to site-specific drug delivery methods.

 

Project Title: “Simulating bio-environmental interactions using –omics approaches”

Dr. Francesca Leasi, Lead PI, Assistant Professor, Biology, Geology and Environmental Sciences

Dr. Jejal Bathi, Visiting Assistant Professor, Civil and Chemical Engineering

Dr. Cuilan (Lani) Gao, Associate Professor, Department of Mathematics

Dr. Hong Qin, Associate Professor, Computer Science and Engineering

Abstract

Biological community assemblages are diverse, maximizing opportunities for species-specific responses to individual components of contamination, and community changes are highly specific to the type and severity of contamination, as well as the interaction of the two. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated ecological and trophic features. The goal of this project is to build mathematical models to further our understanding and prediction of the structure, function, and shifting of biological communities in aquatic ecosystems. The proposed approach is driven by recent advances in DNA sequencing technology and represents a potentially transformative application of those advances to environmental simulation and modeling. The study will contribute to fundamental knowledge of ecosystem interactions and how communities respond to disturbance. This project fosters collaboration among researchers in environmental engineering, biology/environmental science, biostatistics, and computational biology and provides interdisciplinary research opportunities for both undergraduate and graduate students. The combined results of this study will be used to develop simulation models using large, biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex ecosystem.

 

Project Title: “Decentralized and Scalable Trust Management Approach via Blockchain for Connected Vehicles in Smart Cities”

Dr. Farah Kandah, Lead PI, Assistant Professor, Computer Science and Engineering

Dr. Mina Sartipi, UC Foundation Professor, Computer Science and Engineering

Abstract

Intelligent transportation system/Connected vehicles are among the key components contributing to the Smart Cities, where vehicles are able to sense their surroundings and communicate with their peers, roadside units, and the infrastructure to share vital transportation information such as road conditions, crashes, and traffic jams. The advancement in this technology creates new cybersecurity requirements, where collaborative entities such as connected vehicles are required to maintain a high level of trust among them to ensure the validity and the credibility of the messages exchanged in the network. Therefore, there is both acritical and urgent need to design, prototype, validate, and demonstrate an integrated system that is better able to build a distributed, tamperproof, and consistent trust-based management system that is able to validate the trust among network entities to add a dimension of assurance, and to ensure that the exchanged data has a quantitative metric of trustworthiness, which will play a vital role in maintaining the safety of the system. In the absence of such information, comprehensive prevention of trust attacks will be impossible, threatening human lives and inhibiting the further development and expansion of the connected vehicle industry.

 

Project Title: “Corrosion modeling of magnesium-based fixation hardware for mandibular reconstruction surgeries”

Dr. Hamdy Ibrahim, Lead PI, Assistant Professor, Mechanical Engineering

Dr. Mohammad Mahtabi, Assistant Professor, Mechanical Engineering

Abstract

Standard-of-care fixation hardware used for orthopedic skeletal fixation applications are made of stiff metallic alloys that result in several long-term problems such as stress shielding, tissue irritation, and subsequent fixation failure. These poor clinical outcomes often require a second fixation removal surgery. The use of biodegradable fixation hardware made of magnesium that can offer the required stability during the healing period and subsequently degrades is expected to solve these problems and result in a clinical breakthrough. Despite the current interest in biodegradable bone implants, there is still a need to assess the biomechanical performance of these new devices for various bone fixation applications while considering the effect of degradation. In this study, we propose to develop a subroutine and a continuum damage mechanism (CDM) FE model to phenomenologically predict the corrosion rate of our strengthened biocompatible Mg-Zn-Ca-Mn alloy. The developed FE model parameters will be calibrated by conducting a series of in vitro tests on our Mg alloy in conditions simulating the physiological environment. Finally, the developed FE model will be used to compare the biomechanical performance of our Mg alloy with that for an off-the-shelf fixation hardware using a previously-developed 3D model for a mandibular reconstruction surgery.

 

Project Title: “Integration of Satellite Observations with Numerical Watershed and Hydrodynamic Models for Surface Water Quality Studies”

Dr. Azad Hossain, Lead PI, Assistant Professor, Biology, Geology & Environmental Science

Dr. Mark Schorr, Professor, Biology, Geology and Environmental Science

Dr. Jejal Bathi, Visiting Assistant Professor, Civil and Chemical Engineering

Abstract

Satellite observations have been used for water quality studies for many years, but they provide only surface observations and challenges related to cloud coverage and ground truthing, and variable spatial and temporal resolutions remain. Numerical models can provide hydrodynamically computed water quality data on the water surface as well as in the water column, but they have issues with initializations, boundary conditions, calibration, and validation. Although both methods have weaknesses, when used together, they can become a powerful tool to study surface water quality. The proof of concept of this capability was demonstrated in Enid Lake, MS, Lake Pontchartrain, LA, and the Mississippi River using CCHE2D Flow and Water Quality models developed in the National Center for Computational Hydroscience and Engineering at the University of Mississippi. The proposed study aims to further explore this capability at the University of Tennessee at Chattanooga by using the EPA’s Better Assessment Science Integrating Point and Non-point Sources (BASINS) model coupled with NASA’s Earth observation satellite imagery and near-real-time field measurements to study the spatio-temporal variability of hydrodynamically computed surface water quality parameters in the watersheds of southeast Tennessee.

 

Project Title: “The Use of Augmented Reality–Delivered Feedback to Train Neurocognitive and Neuromuscular Deficits: A Preliminary Investigation”

Dr. Jennifer Hogg, Lead PI in collaboration with Dr. Shellie Acocello, Dr. Gary Wilkerson, Dr. Yu Liang, & Dr. Dalei Wu

Abstract

Ways in which the neuromuscular system can be leveraged to prevent both initial musculoskeletal and concussive injury and re-injury has been a key area of focus in the literature due to poor injury outcomes. Recent advances in scientific thought suggest that the central nervous system and neurocognition play a greater role in peripheral neuromuscular control than was previously assumed, which presents an opportunity for the development of injury management programs. Because the brain is highly plastic and therefore trainable, research in this area will allow clinicians to take advantage of the brain's modifiable characteristics and devise rehabilitation strategies to improve both lower extremity injury outcomes and post-concussion management. The use of augmented reality–delivered, real-time feedback may serve to improve biomechanical and cognitive outcomes. The current proposal aims to fill that gap by determining the efficacy of using augmented reality to deliver movement feedback and the effect of the delivered feedback on neuromuscular activation, movement kinematics, and cognitive flexibility. Doing so will allow future studies to use the identified parameters to ultimately develop effective injury reduction and management programs.

 

Project Title: “The Impact of Membrane Phospholipid Remodeling on Pathogen Survival and Persistence”

Dr. Bradley Harris, Lead PI in collaboration with Dr. David Giles, & Ethan Hereth

Abstract

Bacterial pathogens are increasingly developing resistance to conventional antibiotics and represent a mounting threat to public health worldwide. This trend is due in part to the ability of these microorganisms to sense and adapt to their environment through endogenous membrane remodeling strategies. However, the ability of bacteria to adapt their membranes through uptake and assimilation of exogenous fatty acids remains largely unexplored. This project aims to quantify the extent to which exogenous fatty acids contribute to bacterial survival and persistence and to determine the molecular mechanisms by which fatty acid assimilation impacts membrane behavior. Uncovering this information will vertically advance our understanding of how fatty acids may serve as vital molecules that guide bacterial environmental adaptation and pathogen success. This research could also lead to the development of novel preventatives and therapeutics for antibiotic-resistant infections. Overall, the proposed project will contribute to capacity building and strategic excellence in computational science at UTC and potentially improve public health at large.

FY 2019 Awards and Final Report

FY19 Final Report

Project Title: “Urban Electric Vehicle Charging Markets: Computational Modeling and Optimal Design”

Dr. Vahid Rasouli Disfani, Lead PI in collaboration with Dr. Mina Sartipi, and Dr. M. Ahmadi

Abstract

Maximizing utilization of electric vehicle supply equipment (EVSE)—or electric vehicle (EV) charging stations—is still a challenge for cities like Chattanooga despite the emergence of EV station locators like PlugShare and ChargeHub. The missing key element in this market is the lack of data from the demand side of EVs, which often leaves EVs desperate for charging not connected while EVSEs are available nearby. This project computationally models and designs an infrastructure that simultaneously gathers demand (EV) data—including desired destinations, connection period, and energy demand—as well as EVSE availability data to optimally match them to maximize social welfare.

 

Project Title: “3D Drone Delivery Transportation Problem”

Dr. Ignatius Formunung, Lead PI in collaboration with Dr. Mbakisya A. Onyango, Dr. Arash Ghasemi, and Dr. Joseph Owino

Abstract

In this work, we consider the realistic model of the three-dimensional motion of a self-controlled drone in a densely populated urban environment. The objective is to deliver packages from multiple points to the multiple destinations using a connected group of drones. The cityscape is first modeled accurately using a tetrahedral grid generated around the GIS data. This grid is then used to determine the connectivity of the destination points. A recent algorithm developed by our team will be utilized to find the minimum routing for each drone. These routes are then corrected by incorporating the wind forces obtained using a computational fluid dynamic (CFD) solver. The idea is to weight the graph such that the drones travel in the wakes of the buildings to have minimum fuel (energy) consumption. We utilize our CFD solver to achieve this goal. Also, we use a 6DOF model for drone and aerodynamic forces obtained by the CFD solver. A simple PID controller is used in each drone to augment the path. The results have vital applications in military data collection by flying spy drones, optimized package delivery using drones, and smart and futuristic cities (where cars can fly!).

 

Project Title: “Estimating the Youden Index under the Multivariate ROC Curve in the Presence of Missing Values of Mass Diseased and Healthy Biomarker Data”

Dr. Sumith Gunasekera, Lead PI in collaboration with Dr. L. Weerasena, Dr. H. Qin, and Mr. Aruna Saram

Abstract

In the context of Binary classification, Receiver Operating Characteristic curves have played an important role in classifying individuals/objects into one of the two predefined classes/populations. These procedures explain how to estimate the Youden index that measures the accuracy of a diagnostic test. However, problem arises when data contains missing values. The proposed research demonstrates how the Youden index for the diseased and healthy subjects can be extended to multi-biomarkers in the higher-dimensional space by analytic and extensive computational continuation of the mass missing data of multi-biomarkers from breast cancer and by intensive and extensive computations of the simulated mass data with the aid of generalized variable method. This computational-extensive mass-data-based procedure is novel and reduces the high number of unnecessary breast biopsies by helping physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. This goal is accomplished by the comparison of classical and generalized variable procedures for the multivariate Youden Index for the multi-biomarkers with missing data, where missing data are cleaned or tackled with the aid of imputation using parallel programming procedures in machine learning.

 

Project Title: “TC3: A Smart Trust-based Connected Autonomous Collaborative Communities”

Dr.Farah Kandah, Lead PI in collaboration with Dr. Mina Sartipi

Abstract

Connected autonomous vehicles (CAV) are among the key components contributing to Smart City initiatives. Besides communication protocols, securing the network and establishing trust between network entities are among the main challenges that need to be addressed in the field. Securing the network against outsiders’ attacks—trying to bypass the authentication scheme—as well as insiders’ attacks—trying to pollute the network with forged information—are essences to be addressed. Thus, there is both a critical and urgent need to design, prototype, validate, and demonstrate an integrated, real-time system that is better able to ensure the safety of the system by identifying, reporting, and isolating suspicious activities that require immediate attention. In the absence of such information, comprehensive prevention of trust attacks will be impossible, threatening human lives and inhibiting the further development and expansion of the connected and autonomous vehicle industry. The PIs at the University of Tennessee at Chattanooga (UTC) are uniquely qualified to address the proposed research. Prior work by the team has produced significant early findings that enabled the PIs to design and prototype an effective system. Specific strengths in software-defined networking (SDN) and mmWave enables the team to introduce those concepts as key to improving the proposed trust approach.

 

Project Title: “Using Computational Tools to Understand the Fundamental Rules of Life”

Dr. Hope Klug, Lead PI in collaboration with Dr. Jennifer Boyd, Dr. Azad Hossain and Dr. Hong Qin

Abstract

A fundamental goal in biology is to understand the diversity of life in relation to interactions among organisms and their environment. Most biological studies thus far have involved the analysis of relatively small data sets. To understand diversity on a large scale, we need to shift our focus to the analysis of large datasets. To address the question of why we see striking variation in living organisms, we will use big data and cutting-edge computational tools to: 1) enhance our understanding of biological robustness by examining gene/protein interaction networks; 2) explore the factors that make some species rare and other species common; and 3) investigate how abiotic and biotic factors drive the evolution of individual-level traits. In all cases, we will evaluate species network configurations using environmental fluctuations across spatial and temporal scales.

 

Project Title: “Modeling Online Social Network Dynamics and Predicting Information Diffusion with Fractional Differential Equations”

Dr. Lingju Kong, Lead PI in collaboration with Dr. John R. Graef, and Dr. Andrew Ledoan

Abstract

The use of social media has been spreading at an accelerated rate in the last decade. Today, there are many social media platforms such as blogs and social network sites. While the dynamics of online social networks have been studied using several models formulated via classical derivatives, these models are local, fail to capture the memory of the system, and have some other deficiencies. The aim of the proposed project is to improve on these studies by utilizing the theory of fractional calculus. Two new dynamic mathematical models based on fractional calculus will be proposed to serve as effective tools for analyzing the mechanisms of online social networks. More precisely, the investigators will first use fractional ordinary differential equations to construct a model to better understand the adoption and abandonment of a social network. Next, they will employ a fractional partial differential equation to model the spatial and temporal characteristics of information diffusion. These models will be compared with real datasets from selected networks. Various model properties such as existence, uniqueness, and stability of solutions will be investigated. Moreover, extensive numerical simulations will be performed to facilitate the analysis and refinement of these models.

 

Project Title: “Ionizing Radiation Effects Spectroscopy for Secure Space and Defense Communications”

Dr. T. Daniel Loveless, Lead PI in collaboration with Dr. Donald R. Reising

Abstract

Process-induced variability and device-level reliability have been identified as bottlenecks to system reliability, introducing a stochastic nature chip functionality. This disruption necessitates (1) new techniques for measurement of stochastic time-dependent defects; (2) a framework for understanding the dominant device-level reliability failure mechanisms in emerging and disruptive technologies for higher-fidelity predictions of lifetime; and (3) a fundamental understanding of the interplay of variability, operational constraints, and device performance for development of future electronics infrastructure with clear applications in Internet-of-Things and Space and Defense systems. These goals will be accomplished through the integration of computational modeling techniques and experimental measurements. We will (1) perform time-dependent defect measurements on advanced FinFET devices; (2) develop stochastic-based models that describe the reliability failure mechanisms and compact models of the time-dependent defects for integration into device and circuit simulators; and (3) provide a novel tool, Ionizing Radiation Effects Spectroscopy (IRES), for measuring the impact of such effects in operational communications systems in situ. This work will offer a fundamentally new approach to evaluating system-level reliability vulnerabilities and has the potential for transforming the way industry assesses electronic device, component, and system reliability.

 

Project Title: “Investigating the Flow of Nanodrugs through Bio-Inspired Hydrogel Channels”

Dr. Soubantika Palchoudhury, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi

Abstract

Nanodrugs are highly attractive for next-generation medicine because they can be selectively targeted to diseased sites, provide diagnostic capability, and show better solubility compared to conventional therapeutics. However, their transport properties and accumulation within the body are largely unknown, due to experimental challenges in imaging the nanodrugs in complex medium. Recently, we developed a combined experimental and computational fluid dynamic approach at UTC to predict the velocity of a new Pt-iron oxide nanodrug through channels of different shapes. In this project, we aim to answer the fundamental question about transport behavior of the nanodrug through custom-designed channels made of materials that closely mimic bronchial airway. The channels will be experimentally developed through two novel approaches: 3D bioprinting and growing different hydrogels within the channel walls. We will develop a computational fluid dynamic model to predict the flow of nanodrug through these bio-inspired channels for the first time in-house at UTC. The proposed project will have two major outcomes. The computational fluid dynamic model will be a significant breakthrough in drug development and delivery, and using bio-inspired engineering to develop the flow path for nanodrugs will be a key experimental achievement. The project will be used to develop external proposals and publications.

 

Project Title: “Analyzing Bioimage Big Data with Deep Learning Neural Networks”

Dr. Hong Qin, Lead PI in collaboration with Dr. Joey Shaw, Dr. Yu Liang and Dr. Craig Tanis

Abstract

Our goal is to develop state-of-the-art deep convolutional neural networks models (CNNs) to transform two fields of biological research: cellular aging and plant species identification. For cellular aging, we plan to first develop supervised machine learning methods to cluster and label microscopic image for dividing yeast cells. We will then use these labeled images to train CNNs to automatically infer cell division events. For plant species identification, we plan to develop two CNN models and apply them sequentially: the first model will identify plant object regions from herbarium sheets, and the second model will use these objects to classify plant samples into meaningful clusters. Our proposed research will significantly advance the current bioimage big data analytics in these two fields.

 

Project Title: “Improving Post-Stroke Management Efficiency and Patient Outcomes through Analytics”

Dr. Mina Sartipi, Lead PI in collaboration with Dr. Nancy Fell

Abstract

For this CEACSE research project, our multidisciplinary team of academic researchers from the Computer Science and Engineering and Physical Therapy Departments will work together to develop a data-driven precision healthcare ecosystem for the management of stroke, the leading cause of long-term disability in the United States. This problem also aligns with the recently launched “big data to knowledge” initiative by NIH. Large-scale multi-modal heterogeneous data and big data analytics are the body and soul of the proposed research, respectively. Data preprocessing, predictive modeling, and prescriptive analytics will be explored and exploited to close the loop of big data analytics for precision healthcare. The computationally intensive concepts, models, algorithms, and functions will be designed and developed to transfer rich data to knowledge—and further to personalized decision support. The proposed inter-professional research will benefit both academic and healthcare communities.

 

Project Title: “Urban Resilience in the Post-Evacuation Age: Combining CFD and ABM for Megacities”

Dr. Kidambi Sreenivas, Lead PI in collaboration with Dr. Abdollah Arabshahi and Dr. Ethan Hereth

Abstract

The overarching goal of the proposed project is to reconstitute the capability (at the SimCenter) to carry out city-scale simulations such that evacuation planning can be carried out. This work will be carried out in collaboration with Dr. Epstein from NYU. The simulations will be carried out using technology developed at the SimCenter, while the agent-based models (ABM) will use agents developed by Dr. Epstein. Upon successful completion, results from this project will be used for an article that is to appear in Science. This approach of coupling computational fluid dynamics (CFD) and ABM has applications beyond the proposed project and can be used, for example, to track the spread of pandemics, etc.

 

Project Title: “Waterborne Infections and Pathogen Dynamics: Modeling, Experimentation, and Large-Scale Computation”

Dr. Jin Wang, Lead PI in collaboration with Dr. David Giles and Dr. Bradley Harris

Abstract

Waterborne infectious diseases remain a significant public heath burden worldwide. In particular, cholera, a severe intestinal infection caused by virulent strains of the bacterium Vibrio cholerae, has expanded in Africa and South Asia and re-emerged in the Americas in recent years as a serious health threat, with an estimated 2-4 million of cases per year reported by the World Health Organization. Effective outbreak response and control strategies for waterborne diseases rely on a deep understanding of the pathogen dynamics in reference to the epidemiologic triad of agent, host, and environment. The proposed research aims to establish a new mathematical and computational framework to investigate the pathogen dynamics related to waterborne infections, with a focus on cholera, and to make new discoveries regarding disease transmission and pathogen evolution. The project will combine mathematical models, biological experiments, and advanced numerical methods, with an emphasis on large-scale computation for model implementation and realistic application. The project belongs to the Health/Biomedical priority area.

 

Project Title: “Modeling Fate and Transport of Engineered Nanomaterial in Surface Water Systems”

Dr. Weidong Wu, Lead PI in collaboration with Dr. Jejal Reddy Bathi and Dr. Robert Webster

Abstract

Unique properties of engineered nanomaterials (ENM) have resulted in their increased production. However, it is unclear how these emerging ENM will move and react once released to the environment. One approach for addressing possible exposure of ENM in surface waters is by using numerical, mechanistic fate and transport models. There are no reliable fate models currently available that have the ability to simulate ENM behavior in the environment. Our proposed research will explore capabilities of the Environmental Fluid Dynamic Code (EFDC) model, originally developed by the U.S. Environmental Protection Agency (EPA) for simulating hydrodynamics of surface waters, for simulating ENM. We will examine the model algorithms to address the processes governing ENM in aqueous media. Since the literature pertaining to type and quantity of ENM in surface water environment is limited, as the first phase of the proposed research, a systematic evaluation of available literature to identify expected ENM and their physical, chemical, and biological properties that are important in pollutants fate assessment will be conducted. Second and third phases of the proposed research will include development of a calibrated EFDC model for a river hydraulics and ENM fate simulation under varied scenarios of changed river flows and pollutant loads.

FY 2018 Awards and Final Report

FY18 Final Report

Project Title: “A Computational Study of the Impact of Fatty Acid Substitutions on the Vibrio cholerae Outer and Inner Membranes”

Dr. Feng Bao, Lead PI in collaboration with Dr. Kidambi Sreenivas and Dr. Jin Wang

Project Title: “Computational Modeling and Uncertainty Quantification for Wave Energy”

Dr. Bradley Harris, Lead PI in collaboration with Dr. David Giles and Dr. Ethan Hereth

Project Title: “The Development and Application of Computational Tools to Address Fundamental Questions in Ecology and Evolution”

Dr. Hope Klug, Lead PI in collaboration with Dr. Jennifer Boyd and Dr. Hong Qin

Project Title:“Computational Fluid Dynamic Approach to Predict Transport and Distribution of Nanodrugs”

Dr. Soubantika Palchoudhury, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi

Project Title: “Connecting the Control Theory of Engineering to a Network Theory of Cellular Aging in Biology”

Dr. Hong Qin, Lead PI in collaboration with Dr. Craig Tanis

Project Title: “Unlocking the Secrets of RF-DNA Fingerprinting”

Dr. Donald Reising, Lead PI in collaboration with Dr. Daniel Loveless

Project Title: “Enabling Wireless 3C Technologies for Smart and Connected Cities”

Dr. Mina Sartipi, Lead PI in collaboration with Dr. Farah Kandah and Dr. Zhen Hu

Project Title: “Development of Computational Aeroacoustics Capability for Aerospace/Defense Applications”

Dr. Kidambi Sreenivas, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi

Project Title: “Robust Multifactor Framework for Large-scale Fault Detection and Diagnosis in Energy Systems of the U.S. Commercial Buildings”

Dr. Endong Wang, Lead PI in collaboration with Dr. Neslihan Alp

FY 2017 Awards and Final Report

FY17 Final Report

Project Title:“Computational Simulations of the Aerothermal Environment of Hypersonic Flight Vehicles”

Dr. Abdollah (Abi) Arabshahi, Lead PI in collaboration with Dr. Robert S. Webster

Project Title:“Investigation of Resources and Planning for Advanced Manufacturing Applications Center (AMAC) at UT Chattanooga”

Dr. Trevor S. Elliott, Lead PI
Other Personnel: Chase Dobbins – Undergraduate student

Project Title: “Healthy and Intelligent Transportation Planning: Estimating Return on Investment Associated with Improved Infrastructure for Bicycling and Walking and Decreased Physical Inactivity in Chattanooga/Hamilton County”

Dr. Gregory W. Heath, Lead PI in collaboration with Dr. Mina Sartipi and Dr. James Newman
Other Personnel: Mr. Andrew Mindermann – GIS Technician, Dr. Guijing Wang – Health economist – CDC, Mr. Eric Asboe – Transportation Planner- City of Chattanooga

Project Title:“Modeling Space and Defense Environmental Effects in Emerging Integrated Circuit Technologies”

Dr. T. Daniel Loveless, Lead PI
Other Personnel: Amee Patel, Matthew Joplin - Graduate Students; Ellis Richards, Ryan Boggs - Undergraduate Students

Project Title: “FUNSAFE Framework Development for Enhanced Multidisciplinary and Multiphysics Simulations”

Dr. James C. Newman III, Lead PI in collaboration with Dr. Kidambi Sreenivas, Dr. Robert Webster, and Dr. Abdollah Arabshahi

Project Title: “Smart Buildings Through Smarter Models”

Dr. Donald Reising, Lead PI in collaboration with Dr. Mina Sartipi and Dr. T. Daniel Loveless
Other Personnel: Mohammed Fadul, Amee Patel, Jin Cho - Graduate Students

Project Title: “Smart Urban Connectivity Powered by Mobility-on-Demand Public Transportation and Citywide Public Communications”

Dr. Mina Sartipi, Lead PI in collaboration with Dr. Craig Tanis
Other Personnel: Hector Suarez, Austin Harris - Graduate Students; Robert Barber, Caleb Campbell - Undergraduate Students

Project Title: “Near Real-time Detection of Anomalous Power Consumption in Smart Power Distribution Networks”

Dr. Nur Sisworahardjo, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi and Dr. Kidambi Sreenivas
Other Personnel: Akram Saad - Graduate student

Project Title: “Towards simulation of vertical axis wind turbines in offshore settings”

Dr. Kidambi Sreenivas, Lead PI in collaboration with Dr. Abi Arabshahi and Dr. Robert Webster

FY 2016 Awards and Final Report

FY16 Final Report

Project Title: “Numerical Simulation of Airflow in the Small Human Airways”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Physics Based Prediction of Stability and Control Characteristics Using Sensitivity-Enhanced Reduced Order Models”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Energy Performance of Residential Building Using Simple-Normalization Based Two-Stage Data Envelopment Analysis”

Dr. Neslihan Alp and Dr. Endong Wang, Lead PIs

Project Title: “A Tailored & Computational Data Analytics Approach for Improved Stroke Care in South East US (Southeast TN and North GA)”

Dr. Ashish Gupta*, Lead PI

Project Title: “Thermal Runaway Modeling of Li-Ion Batteries”

Dr. Sagar Kapadia*, Lead PI

Project Title: “Continued Development of Higher-Order Adaptive-Overset Dynamic Grid Capability within the FUNSAFE Framework”

Dr. James C. Newman III, Lead PI

Project Title: “Next Generation Drag Devices for Trucks and Intermodal (ISO) Containers”

Dr. Ramesh Pankajakshan*, Lead PI

Project Title: “Simulations of Highly Localized Drug Delivery to the Human Lung”

Dr. Ramesh Pankajakshan*, Lead PI

Project Title: “Multi-modality Heterogeneous Data Analytics for Smart Health”

Dr. Mina Sartipi, Lead PI

Project Title: “Sensing Communications, and Analysis in Smart Grid”

Dr. Mina Sartipi, Lead PI

Project Title: “Making FUNSAFE Capable of Running on Heterogeneous Architectures”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Novel Passive Flow Control Device Concept for Extending Stall Margin in Axial-Flow Compressors”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Computation and Application to Renewable Energy”

Dr. Jin Wang, Lead PI

Project Title: “High-Order Multiscale Finite element Modeling for Acoustics”

Dr. Li Wang*, Lead PI

Project Title: “Automatic High-Order Mesh Generation for Complex Geometries”

Dr. Li Wang*, Lead PI

Project Title: “Computational Simulation of the Purdue 3-stage Experimental Core Compressor”

Dr. Robert Webster, Lead PI

Project Title: “Computational Simulation of a Blow-down Tunnel for Turbine Testing at Purdue”

Dr. Robert Webster, Lead PI

* Faculty member left the university during the reporting period.

FY 2015 Awards and Final Report

FY15 Final Report

Project Title: “Extension of Reduced Order Modeling Capabilities for Stability Derivative Evaluation and Computational Design”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Numerical Simulations of Airflow and Particle Transport in a CT-Based Human Airway Model”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Rupture Predictions for Aneurysms”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Utilization of Computational Design Optimization Technology for Sub-Model Parameter Optimization ”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Electromagnetic Simulation and Optimization of Metamaterials”

Dr. W. Kyle Anderson*, Lead PI (Project lead changed during the year to Dr. Li Wang)

Project Title: “An Application for on Demand Plume Tracking for Evacuation Planning”

Mr. Ethan Hereth, Lead PI

Project Title: “Research into Tetrahedral Grids Produced From Physics-Based Point Placement”

Mr. C. Bruce Hilbert, Lead PI

Project Title: “Travel for Presentations and Networking at the Pointwise User Group Meeting”

Mr. C. Bruce Hilbert, Lead PI

Project Title: “A Robust Network Design in Cognitive Radio”

Dr. Farah Kandah, Lead PI

Project Title: “Exascale Computing Leadership Class Machines Using FUNSAFE Framework”

Dr. Sagar Kapadia, Lead PI

Project Title: “Harnessing the Power of Big Data in Arial Network Authentication and Medical Analysis and Predictions”

Dr. Joseph Kizza, Lead PI

Project Title: “High-Order Adaptive-Overset Dynamic Grid Development”

Dr. James Newman, Lead P

Project Title: “A Prototype Disaster Management System for Hazardous Material Releases”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Algorithms for Index Case Identification and Exposure Prediction in Infectious Disease Epidemics”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Standards for Numerical Simulations of Drag Reduction Devices for Class 8 Trucks”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Improvement in the Thermodynamic Performance of Steam Turbines”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Mitigating Wind Effect on Air Cooled Condensers”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Combined Spectral Element/Pseudo-Spectral Method for Complex Three-Dimensional Geometries”

Dr. Lafayette Taylor, Lead PI

Project Title: “Towards Accurate and Efficient Hybrid RANS/LES Modeling for Acoustic Noise Prediction Using High-Order Multi-scale Finite Elements”

Dr. Li Wang, Lead PI

Project Title: “Large Scale Simulation of Low-Pressure Compression System of the Energy Efficient Engine (E³)”

Dr. Robert Webster, Lead PI

Project Title: “Extending Stall Margin of Axial-Flow Turbomachines Through the Use of Passive Flow Control Devices”

Dr. Robert Webster, Lead PI

Project Title: “Fully Conservative Semi-Lagrangian Methods for Viscous the Energy Efficient Engine Flow Simulations”

Dr. Robert Wilson*, Lead PI

Project Title: “Development of Free Surface Interface Models for Higher-Order Finite Element Methods”

Dr. Robert Wilson*, Lead PI

Project Title: “Development of a Fully-Coupled Fluid-Structure Interaction Approach for Hydrodynamic Application”

Dr. Robert Wilson*, Lead PI

Project Title: “Securing Internet of Things by Capability-Based Access Control”

Dr. Li Yang, Lead PI

Project Title: “Big Data Solution for Improved Mental Health Management”

Dr. Ashish Gupta, Lead PI

Project Title: “Spectral and Energy-Efficient Distributed Multicast for Wireless Networks”

Dr. Mina Sartipi, Lead PI

Project Title: “Trust Propagation and Distrust (Rumor/Second Hand Trust) in Web of Trust (WOT) and Airborne Networks Authentication”

Dr. Joseph Kizza, Lead PI

*Faculty Member left the university during the reporting period.

FY 2014 Awards and Final Report

FY14 Final Report

Project Title: “Development of Reduced Order Modeling Capabilities with Applications to Stability Derivative Evaluation and Computational Design”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Numerical Simulation of Flow Structure and Transport/Deposition of Particles in Pulmonary Airways”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Electromagnetic Simulations for Metamaterials and Frequency-Selective Surface”

Dr. W. Kyle Anderson, Lead PI

Project Title: “An Exploration of the Efficacy of HUGG Style Meshes on Turbo-Machinery”

Mr. C. Bruce Hilbert, Lead PI

Project Title: “Refactoring and Optimization of the Tenasi Tool Suite Code”

Dr. Daniel Hyams, Lead PI

Project Title: “Quality of Service Assurance using GENI”

Dr. Farah Kandah, Lead PI

Project Title: “Numerical Simulation of Lithium-Ion Batteries”

Dr. Sagar Kapadia, Lead PI

Project Title: “Molecular Dynamics-Based Point Generation and Radial Basis Flow Solver”

Dr. Steve Karman, Lead PI

Project Title: “High-Order Space-Time Approach”

Dr. Lafayette Taylor, Lead PI

Project Title: “Technology Development for Multiphysics Simulation, Sensitivity, and Design”

Dr. James C. Newman III, Lead PI

Project Title: “Transition Modeling for Improved Heat Transfer Computations for Turbomachinery”

Dr. D. Stephen Nichols, Lead PI

Project Title: “Incompressible Multi-Species Flow Regime with Total Energy Conservation”

Dr. D. Stephen Nichols, Lead PI

Project Title: “Design of a Coronary Stent for Reduced Failure Rates”

Dr. D. Stephen Nichols, Lead PI

Project Title:“Validation and Application of the Tenasi Particle Module”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Communication and Data processing Tools for Automated Fall Risk Assessment System”

Dr. Mina Sartipi, Lead PI

Project Title: “Data Acquisition and Communication in Smart Grid Networks”

Dr. Mina Sartipi, Lead PI

Project Title: “High-Order Methods for the Compressible Navier-Stokes Equations”

Drs. Li Wang and Kyle Anderson, Lead PIs

Project Title: “Aero-elastic Study of the Turbofan Stage for the Energy Efficient Engine (E³)”

Dr. Robert Webster, Lead PI

Project Title: “Validation Simulations of the Turbofan and Boost Stages of the Energy Efficient Engine (E³)”

Dr. Robert Webster, Lead PI

Project Title: “Solver Enhancements for Simulation of Objects in Dynamic Contact Using Combined Immersed Boundary and Overset Grid Methods”

Dr. Robert Wilson, Lead PI

Project Title: “Development of a Fully-Coupled Fluid Structure Interaction Approach for Hydrodynamic Applications”

Dr. Robert Wilson, Lead PI

Project Title: “Arbitrary Lagragian-Eulerian Method for Blast Simulations”

Dr. Robert Wilson, Lead PI

Project Title: “Bioinformatics Analysis of Human Genes Associated with Diseases at Higher Rates in African Americans”

Dr. Li Yang, Lead PI

Project Title: “Zero-based Knowledge Authentication in Aerial Networks”

Ms. Katherine Winters, Lead PI

Project Title: “Undergraduate Research Assistantship Program in Computational Science and Engineering”

Dr. Louie Elliott, Lead PI

Project Title: “Stent Design Proposal Preparation”

Dr. Robert Melnik, Lead PI

Project Title: “Rapid Generation of Animations from Tenasi Simulations”

Dr. Ramesh Pankajakshan, Lead PI

FY 2013 Awards and Final Report

FY13 Final Report

Project Title: “Numerical Simulation of Respiratory Flow Patterns Within Human Lung”

Dr. Abdollah (Abi) Arabshahi, Lead PI

Project Title: “Sensitivity Analysis and Shape Design for Turbomachinery using Sliding Interfaces”

Dr. Chad Burdyshaw, Lead PI

Project Title: “Development of a Generalized Fluid-Structure Interaction Interface for SimCenter Software”

Dr. James C. Newman III, Lead PI

Project Title: “Multiwavelet Discontinuous Galerkin Method”

Dr. Lafayette K. Taylor, Lead PI

Project Title: “Authentication in Mobile Platforms”

Dr. Li Yang, Lead PI

Project Title: “A Power Efficient Multicasting Scheme Using Compressive Sensing”

Dr. Mina Sartipi, Lead PI

Project Title: “Shape optimization for flows with particles”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Conjugate Heat Transfer Analysis of Turbine Vane Cascade”

Dr. Robert Webster, Lead PI

Project Title: “Validation of Centrifugal Compressor Performance Using Tenasi”

Dr. Robert Webster, Lead PI

Project Title: “Development of a Multi-Regime Solution Capability for Tenasi Flow Solver”

Dr. Robert Wilson, Lead PI

Project Title: “Design of Stents for Bifurcated and Limb Arteries”

Dr. Steve Karman, Lead PI

Project Title: “Computational Modeling of Physiological Data using Inexpensive and Unobtrusive Sensors: A New Paradigm for Computational Physiology”

Dr. Yu Cao, Lead PI

Project Title: “Numerical Solution of Lithium Batteries”

Dr. W. Kyle Anderson, Lead PI

Project Title: “Extended Capabilities for Electromagnetic Simulations”

Dr. W. Kyle Anderson, Lead PI

Project Title: “Navier-Stokes Utilizing Discontinuous Galerkin/Petrov Galerkin (DF/PG) Approaches”

Dr. W. Kyle Anderson, Lead PI

FY 2012 Awards and Final Report

FY12 Final Report

Project Title: “Development of an Overset Grid Approach for the Tenasi Flow Solver”

Drs. Kidambi Sreenivas & Robert Wilson, Co-PIs

Project Title: “Multi-Regime Solution Capability via Ghost-Fluid Method”

Drs. Kidambi Sreenivas & Robert Wilson, Co-PIs

Project Title: “Shape and Topology Optimization using the UT-Tenasi Code”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Numerical Simulation of Respiratory Flow Patterns within Human Lung”

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Development and Verification of an Analytical Wake Model for Wind Farm Optimization”

Dr. Lafayette Taylor, Lead PI

Project Title: “Applications of SimCenter Hybrid RANS/LES Code”

Dr. D. Stephen Nichols, Lead PI

Project Title: “A Validation Study of Tenasi’s Conjugate Heat Transfer”

Dr. Robert Webster, Lead PI

Project Title: “Analysis and Design of Biological Stent Implants”

Dr. Steve Karman, Lead PI

Project Title: “Enhanced Compression in Distributed Sensing Applications”

Dr. Mina Sartipi, Lead PI

Project Title: “Online Opinion Mining on Social Media”

Dr. Li Yang, Lead PI

Project Title: “Large-Scale Medical Image Modeling for Intelligent Medical Information Retrieval”

Dr. Yu Cao, Lead PI

Project Title: “Tenasi Cloud Computing Initiative”

Dr. Daniel Hyams, Lead PI

FY 2011 Awards and Final Report

FY11 Final Report

Project Title: “Unstructured Elliptic Smoothing”

Dr. Steve Karman, Lead PI

Project Title: “Tetrahedral Mesh Creation/Optimization Using Edge/Face Flips”

Mr. C. Bruce Hilbert, Lead PI

Project Title: “Data Acquisition in Wireless Sensor Networks Using Distributed Rateless Codes”

Dr. Mina Sartipi, Lead PI

Project Title: “Modeling Turbulence Kinetic Energy for High Energy Flows”

Dr. D. Stephen Nichols, Lead PI

Project Title: “Targeted Mesh Adaptation for Finite Element Based Electro-Magnetics Field Solvers”

Dr. Chad Burdyshaw, Lead PI

Project Title: “Spray Modeling Enhancements to the UTC Tenasi Lagrangian Particle Tracking Module”

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Profiling and Predicting Behaviors of Network-based Intrusions”

Dr. Li Yang, Lead PI

Project Title: “Investigation of Boundary Conditions for Optimal Domain Size”

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Investigation of Reduced Mesh Density for Resolution of Air/Water Interfaces”

Dr. Robert Wilson, Lead PI

Project Title: “Navier-Stokes Utilizing Discontinuous Galerkin/Petrov Galerkin (DG/PG) Approaches”

Dr. Li Wang, Lead PI

Project Title: “Investigation of Local Low Mach Number Preconditioning Schemes”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Physics-Based Modeling for Multi-material Interfaces”

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Enhancing Scalability of Tenasi”

Dr. Daniel Hyams, Lead PI

Project Title: “LES of Chemically Reacting Flows”

Dr. Lafayette Taylor, Lead PI

Project Title: “Direct Numerical Simulation (DNS) for a priori Large-Eddy Simulation (LES) Sub-grid Model Evaluation”

Dr. Lafayette Taylor, Lead PI

Project Title: “Propulsion Sub-System Integration Using Tenasi”

Dr. Robert Webster, Lead PI

Project Title: “Electromagnetic Simulations for Non-linear Materials”

Drs. Kyle Anderson & Li Wang, Co-PIs

FY 2010 Awards and Final Report

FY10 Final Report

Project Title: “Numerical Simulation of Lithium-Ion Batteries

Dr. Kyle Anderson and Dr. Sagar Kapadia, Co-PIs

Project Title: “Large Eddy Simulation of Internal Turbulent Flows"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Implementation of the Hydrodynamic and Control System Design Technology into the Tenasi Unstructured Flow Solver"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Development and Analysis of Solution Algorithms for Field Simulation Problems"

Dr. Roger Briley and Dr. David Whitfield, Co-PIs

Project Title: “Generic Interface Methodology for Multi-Physics Applications"

Dr. Daniel Hyams, Lead PI

Project Title: “Tetrahedral Mesh Creation/Optimization Using Edge/Face Flips"

Dr. Steve Karman, Lead PI

Project Title: “Unstructured Elliptic Smoothing"

Dr. Steve Karman, Lead PI

Project Title: “CFD Based Two-Phase Loss Analysis for Solid Rocket Motors"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Robust and Efficient Rate Adaptation in Vehicle Ad-hoc Networks"

Dr. Mina Sartipi, Lead PI

Project Title: “Validation of Rotorcraft Simulations using Tenasi"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Simulations of Interactions Between Multiple Moving Bodies Using Overset Techniques"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Petro-Galerkin and Discontinuous Galerkin Methods for Unstructured Flow Solvers"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Diffuse Interface Methods for Wind-Ocean Wave Interactions"

Dr. Lafayette Taylor, Lead PI

Project Title: “Tank Sloshing Simulations in Microgravity Environments"

Dr. Robert Wilson, Lead PI

Project Title: "Emerging Infectious Disease: A Computational Multi-agent Model"

Dr. Li Yang, Lead PI

FY 2009 Awards and Final Report

FY09 Final Report

Project Title: “Computational Analysis and Design of Fuel Cell Components”

Dr. Kyle Anderson, Lead PI

Project Title: “Computational Simulation of an Experimental Knudsen Compressor"

Dr. Glenn Brook, Lead PI

Project Title: “Tenasi Performance Enhancement for Petascale Computing"

Dr. Daniel Hyams, Lead PI

Project Title: “Extension of the SimCenter Agent Based Modeling Code to biological systems, transportation and risk management"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “A panic model for the SimCenter Agent Based Modeling code"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “An agglomeration model for the Tenasi particle module"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “TinyID: A Revolutionized Warehouse Management Tool"

Dr. Mina Sartipi, Lead PI

Project Title: “Implementation of an arbitrary equation of state into the Tenasi family of flow solvers"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Improving the order of accuracy for unstructured flow solvers"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Implementation of the Phase Field Approach into the Tenasi Unstructured Solver"

Dr. Robert Wilson, Lead PI.

Project Title: “Level set approach for chemical etching and deposition"

Dr. Robert Wilson, Lead PI

Project Title: “Fluid-Structure interaction for ship hydrodynamics"

Dr. Robert Wilson, Lead PI

Project Title: “A Fast Response and Planning System in Disaster Management"

Dr. Li Yang, Lead PI

FY 2008 Awards and Final Report

FY08 Final Report

Project Title: “Entanglement, Decoherence, and Quantum Feedback”

Dr. Jin Wang, Lead PI

Project Title: “Multicast Protocol on Intel Mote 2 Sensor Network Platform"

Dr. Mina Sartipi, Lead PI

Project Title: “A Secure and Reliable Wireless Ad-Hoc Network in Disaster Management"

Dr. Li Yang and Dr. Joseph Kizza, Co-PIs

Project Title: “Simulation of Biodiesel Production by Microreaction Systems"

Dr. Frank Jones, Lead PI

Project Title: "Analysis and Sensitivity Derivatives for Plasma Simulations"

Dr. Kyle Anderson, Lead PI

Project Title: “Computational Analysis and Design of Fuel Cell Components"

Dr. Kyle Anderson, Lead PI

Project Title: “Adjoint-Based System for Design Optimization"

Dr. Chad Burdyshaw and Dr. Kyle Anderson, Co-PIs

Project Title: “Physical/Mathematical Modeling and Solution of Field Simulation Problems"

Dr. Roger Briley, Dr. David Whitfield and Dr. Ramesh Pankajakshan, Co-PIs

Project Title: “A Droplet Splatter Model for the Tenasi Particle Module"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “An Agent-Based Simulation Module in Tenasi"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Turbulence Modeling for Multi-Speed Flows"

Dr. Stephen Nichols, Lead PI

Project Title: “Development of a Deforming Mesh Capability for Unstructured Meshes"

Dr. Kidambi Sreenivas, Lead PI

Project Title: “Kinetic Simulation of Chemically Reactive Gas Flows on Unstructured Grids"

Mr. Glenn Brook, Lead PI

Project Title: “Hybrid Turbulence Models for Vortex and Separated Flows"

Dr. Lafe Taylor, Lead PI

Project Title: “Modeling and Analysis of Combustion Instability in Rocket Engines and Motors"

Dr. Robert Webster, Lead PI

FY 2007 Awards and Final Report

FY07 Final Report

Project Title: “Kinetic Simulation of Multispecies Gas Flows on Unstructured Grids”

Mr. R. Glenn Brook, Lead PI

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Extended Research Activities Funded by the Center in Fiscal Year 2005-2006 and Completed in Fiscal Year 2006-2007:

Project Title: “Information Communication Mediator Model in Disaster Management"

Dr. Li Yang and Dr. Joseph Kizza, Co-PIs

Project Title: “Multiprocessor Objective-C computer Systems for High Performance Computing"

Dr. Andrew Novobilski, Lead PI

Project Title: “Computational Methods for Field Simulations"

Dr. W. Roger Briley, Lead PI

Project Title: “Advanced Turbulence Modeling for Unstructured Topologies"

Dr. D. Stephen Nichols III, Lead PI

Project Title: “Unstructured Solution Algorithm System Integration, Design and Testing"

Dr. Daniel Hyams, Lead PI

Project Title: “Development of Parallel Eulerian-Lagrangian Two-Phase Flow Solvers"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Quantum Measurement and Feedback in Atomic Systems"

Dr. Jin Wang, Lead PI

Project Title: “Evaluation and Enhancement of an Unstructured Grid Algorithm for Free Surface"

Dr. Robert Wilson, Lead PI

Project Title: “Computation of Dynamic Stability and Control Devices"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “A Fundamental Study of the Effects of Design on Heterogeneous Biocatalysts"

Dr. Frank Jones, Lead PI

Project Title: “Geometry Manipulation and Visualization, Computational Simulation and Design"

Ms. Dawn Ellis and Dr. Steve Karman, Co-PIs

Project Title: “Extensible Adjoint Methods for Sensitivity Analysis, Error Estimation, and Adaptive Meshing"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Adjoint Method for Magnetohydrodynamic Simulations"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Computational Analysis and Design of Fuel Cell Components"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Computational Engineering with Solid Oxide Fuel Cells"

Dr. James Henry, Lead PI

Project Title: “Advancement and Verification of the Navier-Stokes Flow Solver for Rocket Motor Internal Flows"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Development of an Unstructured Grid Algorithm for Turbomachinery"

Dr. Kidambi Sreenivas, Lead PI

FY 2006 Awards and Final Report

FY06 Final Report

Project Title: “Information Communication Mediator Model in Disaster Management"

Dr. Li Yang and Dr. Joseph Kizza, Co-PIs

Project Title: “Multiprocessor Objective-C computer Systems for High Performance Computing"

Dr. Andrew Novobilski, Lead PI

Project Title: “Computational Methods for Field Simulations"

Dr. W. Roger Briley, Lead PI

Project Title: “Advanced Turbulence Modeling for Unstructured Topologies"

Dr. D. Stephen Nichols III, Lead PI

Project Title: “Unstructured Solution Algorithm System Integration, Design and Testing"

Dr. Daniel Hyams, Lead PI

Project Title: “Development of Parallel Eulerian-Lagrangian Two-Phase Flow Solvers"

Dr. Ramesh Pankajakshan, Lead PI

Project Title: “Quantum Measurement and Feedback in Atomic Systems"

Dr. Jin Wang, Lead PI

Project Title: “Evaluation and Enhancement of an Unstructured Grid Algorithm for Free Surface"

Dr. Robert Wilson, Lead PI

Project Title: “Computation of Dynamic Stability and Control Devices"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “A Fundamental Study of the Effects of Design on Heterogeneous Biocatalysts"

Dr. Frank Jones, Lead PI

Project Title: “Geometry Manipulation and Visualization, Computational Simulation and Design"

Ms. Dawn Ellis and Dr. Steve Karman, Co-PIs

Project Title: “Extensible Adjoint Methods for Sensitivity Analysis, Error Estimation, and Adaptive Meshing"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Adjoint Method for Magnetohydrodynamic Simulations"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Computational Analysis and Design of Fuel Cell Components"

Dr. W. Kyle Anderson and Dr. Steve Karman, Co-PIs

Project Title: “Computational Engineering with Solid Oxide Fuel Cells"

Dr. James Henry, Lead PI

Project Title: “Advancement and Verification of the Navier-Stokes Flow Solver for Rocket Motor Internal Flows"

Dr. Abdollah Arabshahi, Lead PI

Project Title: “Numerical Solution of the Boltzmann Equation with BGK Approximation"

Dr. Mr. R. Glenn Brook, Lead PI

Project Title: “Development of an Unstructured Grid Algorithm for Turbomachinery"

Dr. Kidambi Sreenivas, Lead PI