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Research Spending & Results

Award Detail

Doing Business As Name:University of South Carolina at Columbia
  • Chen Li
  • (803) 777-7155
  • Yan Tong
Award Date:06/16/2021
Estimated Total Award Amount: $ 400,000
Funds Obligated to Date: $ 400,000
  • FY 2021=$400,000
Start Date:07/01/2021
End Date:06/30/2024
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:ISS: Understanding the Gravity Effect on Flow Boiling Through High-Resolution Experiments and Machine Learning
Federal Award ID Number:2126437
DUNS ID:041387846
Parent DUNS ID:041387846
Program:TTP-Thermal Transport Process
Program Officer:
  • Ying Sun
  • (703) 292-7443

Awardee Location

Street:Sponsored Awards Management
Awardee Cong. District:06

Primary Place of Performance

Organization Name:University of South Carolina at Columbia
Cong. District:06

Abstract at Time of Award

Flow boiling plays an essential role in energy-water nexus in both terrestrial and space applications. These applications include thermoelectric power generation, thermal management of power electronics and microelectronics, water purification, and heating, cooling and air-conditioning systems. However, flow boiling is significantly affected by five major forces such as surface tension, inertia, shear, vapor evaporation momentum, and gravitational force. The significant changes of channel sizes and working conditions (such as flow rate, heat load, and temperature) result in various contributions of these five forces and hence drastic changes of flow boiling patterns and performance. In addition, it is extremely challenging to conduct experiments of flow boiling in a wide range of channel sizes and working conditions due to the prohibitive costs and efforts. In this project, a package of “Deep Models of Flow Boiling” will be developed to understand the effects of these major forces on flow boiling through the combined use of ground and microgravity experiments and the machine learning based techniques. The models are aimed to not only predicting flow boiling characteristics but also creating synthetic images of flow patterns. This project will pave the way for performing virtual flow boiling experiments under a wide range of working conditions. Furthermore, it would provide a powerful platform to study and design flow boiling-based water-energy systems in a significantly more comprehensive and economic way. The challenging objective of developing the deep models of flow boiling will be achieved by three major research tasks. First, high-resolution experiments and dataset will be constructed. In order to assure accurate and more continuous inputs for machine learning, a complete and accurate data pool of flow boiling will be built through high-resolution experiments under a wide range of working conditions in terrestrial conditions on a test setup that is identical with the test section of the NASA Flow Boiling and Condensation Experiment (FBCE) on the International Space Station (ISS). Experimental data on the FBCE in ISS will be also collected to provide a quality dataset in microgravity. Second, modeling of the force effect on physical variables will be achieved by machine learning. An end-to-end Multi-Target Hybrid Deep Regression (MTHDR) framework will be built to predict physical variables of flow boiling using the collected datasets from both ground and ISS experiments. Third, image synthesis will be performed for two-phase flow patterns. A generative adversarial network (GAN)-based model will be developed to create images of two-phase flow patterns so as to establish a framework to understand and even quantify the effects of major forces on extremely complex two-phase flow patterns. 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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