| NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
| Recipient: |
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| Initial Amendment Date: | January 23, 2024 |
| Latest Amendment Date: | January 23, 2024 |
| Award Number: | 2330054 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Rohit Ramachandran
rramacha@nsf.gov (703)292-7258 CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
| Start Date: | March 1, 2024 |
| End Date: | February 28, 2027 (Estimated) |
| Total Intended Award Amount: | $346,983.00 |
| Total Awarded Amount to Date: | $346,983.00 |
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| Recipient Sponsored Research Office: |
438 WHITNEY RD EXTENSION UNIT 1133 STORRS CT US 06269-9018 (860)486-3622 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
438 WHITNEY RD EXTENSION UNIT 1133 STORRS CT US 06269-1133 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| NSF Program(s): | Proc Sys, Reac Eng & Mol Therm |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Complex systems are everywhere: from agricultural supply chains, wastewater treatment and public water systems, to the energy/power infrastructure that heats, cools, and light residential and industrial buildings. Decarbonizing process industries, especially as they relate to food, energy, and water, is of particular and timely importance. Engineers have an ever-constant mission of improving the health, safety, and robustness of these complex systems that support and improve society. However, innovation inherently increases complexity and, therefore, the efforts to solve complex and interconnected challenges, which include designing new systems and improving existing systems, rely heavily on computational modeling, simulation, and optimization-based approaches. There are two major challenges that this proposal aims to address: (1) the current performance of computational optimization approaches limits their applicability to simplified, lower-complexity problems, and (2) university engineering programs often lack cohesive computational-thinking activities throughout their curricula. Solving (1) will alleviate the current computational bottlenecks and broaden the scale and scope of complex problems that can be solved. Solving (2) will not only help train the next generation of engineers on computational modeling approaches but improve their overall problem-solving skills.
The research objective of this project is to develop scalable deterministic global optimization (DGO) algorithms and open-source software implementations by exploiting alternative stream computing architectures for parallelization, to enable the solution of higher complexity models that include first-principles models and machine learning elements involving nonlinear (partial) differential and algebraic equations. The significance of the proposed work lies in unlocking the massive parallel computing performance of graphical processing units (GPUs) for DGO with the development of a new branch-and-bound deterministic search algorithm. The result will be a significant speedup over the current state of the art, which will enable the solution of larger-scale higher-complexity problems that arise in food-energy-water (FEW) applications, among others. The first major innovation is a method for automatically generating source code representations of convex/concave relaxations of nonconvex functions in the optimization formulation, and subgradients thereof, on arbitrary domains of interest. The second major innovation is a scalable GPU-compatible parallel DGO algorithm and open-source software implementation for the guaranteed solution of nonconvex programs. This project will align the proposed research with educational activities aimed at transforming a diverse cohort of students into skilled computational thinkers. This project will support the training of students to understand the complexity of systems models from an optimization context to better understand the practicality of optimization-based approaches. This project will deliver methods, tools, and training modules to serve the immediate and future technology workforce training needs of engineering fields that will increasingly depend on optimization for innovation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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