CEACSE (Center of Excellence in Applied Computational Science and Engineering)
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 Urban Science, Energy & Environment, Defense/Aerospace, and Biomedical research.
The overall purpose of the Center of Excellence in Applied Computational Science 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.
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.
This year a total of $684,332 was awarded to nine lead principal investigators and twelve collaborating investigators across eight different departments.
2017-2018 CEACSE Awardees
Project Title: “Computational Modeling and Uncertainty Quantification for Wave Energy”
Dr. Feng Bao, Lead PI in collaboration with Dr. Kidambi Sreenivas and Dr. Jin Wang
Award Amount: $84,771.00
Ocean waves, generated by wind blowing over the water surface, have tremendous energy which can be captured and converted into electricity. With the rising demand for energy, growing consumption of oil and gas, and increasing global warming, waves offer an attractive green energy source and have generated considerable interest in research, development and testing in recent years. The research carried out in this work focuses on deriving mathematical and computational methods which describe structure motions occur between ocean wave, wind wave and solid energy converters. The research activities conducted in this project will establish interdisciplinary collaborations between Department of Mathematics and SimCenter at UTC, and will also build a research direction for future Ph.D. students in the Ph.D. program in Computational Science with concentration in Computational Mathematics within Department of Mathematics.
Project Title: “A Computational Study of the Impact of Fatty Acid Substitutions on the Vibrio cholerae Outer and Inner Membranes”
Dr. Bradley Harris, Lead PI in collaboration with Dr. David Giles and Dr. Ethan Hereth
Award Amount: $27, 481
Food- and waterborne enteric pathogens kill approximately 2 million people each year, and the ways in which these organisms uptake and utilize fatty acids are critical to their ability to spread disease. One of the most extensively studied of these pathogens is Vibrio cholerae, the Gramnegative bacterium responsible for the acute intestinal infection known as cholera. The ability of this pathogen to uptake fatty acids from its environment may contribute to its ability to survive as it passes through the human gastrointestinal tract. The objective of this project is to build computational models to further our understanding of the structure and function of bacterial membranes and provide new insights relevant to the prevention and treatment of this disease. This project fosters collaboration among researchers in biology, chemical engineering, and computational science. The combined results of this study will serve to establish this research team as investigators in the field, and will be used to support the pursuit of external funding through agencies such as the National Institutes of Health and the National Science Foundation.
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
Award Amount: $88,998
In recent years, funding agencies and journals have required researchers to deposit data in depositories, which has led to large datasets that can potentially be used to answer fundamental biological questions about the astounding diversity of life in relation to interactions among organisms and their environment (i.e., ecology) and changes across generations in the genetic and phenotypic makeup of populations (i.e., evolution) on a broad scale. We will develop and utilize novel computational tools that allow us to effectively analyze large datasets extracted from biological databases to investigate the link between biological traits and species’ rarity, as well as climate change vulnerability. We will also investigate how life-history traits, ecological conditions, and sociality interact to influence mating and parental dynamics. The proposed research will allow us to utilize the high performance computing resources to address pressing questions in ecology and evolution, expand the research programs in two departments and colleges and allow non-computer scientists to collaborate with a computer scientist. This work will lead to high-impact publications and grant submissions, and facilitate the research training of numerous undergraduate and graduate students.
Dr. Soubantika Palchoudhury, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi
“Computational Fluid Dynamic Approach to Predict Transport and Distribution of Nanodrugs”
Award Amount: $89,211
Nanodrugs are seen as next-generation solution in the field of biomedicine, particularly for their use as chemotherapeutic and drug delivery agents. The key advantage of nanodrugs is their ability to selectively reach the diseased site without affecting the healthy tissues. In nanomedicine, a computational approach is used to predict the transport and distribution profile of nanodrugs inside the body, but the method is still in its developmental stages. Transport of nanodrugs is a complex process due to the combined involvement of hydrodynamic forces, chemical interaction of the surface, magnetic attraction, adhesion to the cell wall, and Brownian forces. The goal of this project is to develop a robust computational fluid dynamics model for predicting the transport of a new Pt-iron oxide nanodrug synthesized at CECS, and to determine the factors dominating the drug’s transport. The project will put the SimCenter at the forefront of emerging innovation in the field of Health and Biological Systems. In addition, the project has tremendous potential for publication in high-impact journals like Nano Letters, Chemical Communications, and ACS Nano due to its novelty. This research will also serve to provide preliminary data for extramural funding opportunities.
Dr. Hong Qin, Lead PI in collaboration with Dr. Craig Tanis.
“Connecting the Control Theory of Engineering to a Network Theory of Cellular Aging in Biology”
Award Amount: $91,906
In Engineering, control theory studies how a system can be tuned to desirable behavior with given input through feedback. Applying control theory to gene networks is a promising new direction in systems biology and precision medicine because it can improve targeted gene therapies. We recently developed a network model for cellular aging which uses the same graph models with network control studies. We propose to apply network control theory in our gene network model of cellular aging, thereby identify critical genes and gene interactions required for longevity. Methods developed through this pilot project will establish UTC in an important new research direction on complex networks and will enhance research across disciplines on campus.
Dr. Donald Reising, Lead PI in collaboration with Dr. Daniel Loveless
“Unlocking the Secrets of RF-DNA Fingerprinting”
Award Amount: $91,978
Wireless communication networks are seamlessly used to not only conduct personal communication, but also by businesses to carry out daily operations that are essential to their success. Therefore, it is imperative that these networks employ sufficient security measures essential to providing a trusted exchange of information while simultaneously protecting and safeguarding both users and associated information. Digital techniques such as encryption and authentication are commonly attacked and compromised and they fail to leverage the naturally occurring discriminatory information contained within the wireless waveforms themselves. Radio Frequency (RF) fingerprinting is one technique that has been developed to leverage such discriminatory information as a means of enhancing wireless network security. However, the relationship between the RF hardware components and the exploited distinct and native attributes remains unexplored, because researchers traditionally treat this collection of components as a “black box” with little to no thought as to how they contribute and/or possibly hinder RF fingerprinting. The proposed effort looks to open the “black box” and investigate the connection between the waveform distinct and native attributes exploited by the RF fingerprinting process, and the hardware components that are used in the construction of the wireless device. This work is integral to the development of secure wireless communication networks that will be deployed throughout the smart and connected communities of the future.
Dr. Mina Sartipi, Lead PI in collaboration with Dr. Farah Kandah and Dr. Zhen Hu
“Enabling Wireless 3C Technologies for Smart and Connected Cities”
Award Amount: $92,00
There is an unstoppable trend sweeping the globe for smart and connected cities (S&CCs) that are increasingly revolutionizing our lives, with enormous benefits. In order to achieve S&CCs, we need a powerful infrastructure/backbone to facilitate high-performance data transmission, data analysis, and data storage in the Age of Big Data. Due to the prevalence of mobile/Internet-of-Things devices and emerging applications, wireless technologies and mobile communications plays an irreplaceable role. Thus, by combining data with mobility, we propose to design a fundamental wireless infrastructure and to promote novel wireless 3C (Communication, Computing, and Caching) technologies to support the whole data ecosystem in S&CCs. The proposed infrastructure will enable multiple heterogeneous radio access technologies and hierarchical computing/caching modalities. Our proposed research will exploit the state-of-theart mathematical programming and big data analytics and leverage the theoretical/applied computational science and engineering in the S&CCs design, development, and optimization. Our achievements can contribute to the advancement of 5G technologies and foster the further study on future wireless. Meanwhile, our research can attract the extensive collaborations among academic scholars, industrial partners, and community stakeholders, and have a great potential to transform Chattanooga, TN from Gig City to Wireless Gig City, and eventually to a truly smart and connected city by taking advantage of EPB’s gigabit fiber optics in Chattanooga, TN.
Dr. Kidambi Sreenivas, Lead PI in collaboration with Dr. Abdollah (Abi) Arabshahi
“Development of Computational Aeroacoustics Capability for Aerospace/Defense Applications”
Award Amount: $68,085
Noise from various sources is a part of everyday life. The ability to simulate the generation and propagation of noise is a significant challenge. This is primarily because acoustic waves are a perturbation (very small changes) of the ambient pressure. Consequently, significant computational resources are needed in order to resolve these waves accurately. A recent advance in high-order algorithms enables one to increase the order of accuracy (instead of or in addition to increasing spatial resolution) locally. This could have significant implications for acoustic wave propagation as it could drive down the cost of these simulations. The proposed research will focus on applying high-order techniques to canonical and practical problems in aeroacoustics.
Dr. Endong Wang, Lead PI in collaboration with Dr. Neslihan Alp
“Robust Multifactor Framework for Large-scale Fault Detection and Diagnosis in Energy Systems of the U.S. Commercial Buildings”
Award Amount: $49,902
In the U.S., existing commercial buildings, such as shopping centers, office buildings, and warehouses, account for around 38% of the total energy consumed. Reducing energy usage through various renovation measures in commercial buildings is an important opportunity to substantially reduce energy use and thereby, mitigate possible environmental deterioration. Accurately identifying the sources contributing to energy loss and waste of building energy systems is the first step to reduce energy consumption. Fault detection and diagnosis remains a significant challenge in the domain due to the complexity of building energy systems. Energy benchmarking, which essentially contrasts a target building against referential peers to locate deficiencies, has been frequently adopted in both academia and industry to identify energy system faults for building renovation. Existing multi-criteria benchmarking procedures tend to ignore the inherent interactions between factors or subsystems, e.g. occupants and building structures, which could lead to serious decision errors. We have performed some work to improve this issue for residential buildings. Combining information theory, this proposed project intends to further expand our model to a generalized framework by overcoming algorithm deficiencies to be more functional. It aims at developing an efficient energy decision analysis instrument which expects to facilitate energy retrofitting success both locally and nationally to lower energy use in commercial buildings.