NSF Org:	CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems
Recipient:	TEXAS A&M ENGINEERING EXPERIMENT STATION
Initial Amendment Date:	May 21, 2019
Latest Amendment Date:	May 21, 2019
Award Number:	1926882
Award Instrument:	Standard Grant
Program Manager:	Ying Sun CBET  Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG  Directorate for Engineering
Start Date:	June 1, 2019
End Date:	November 30, 2020 (Estimated)
Total Intended Award Amount:	$165,812.00
Total Awarded Amount to Date:	$165,812.00
Funds Obligated to Date:	FY 2019 = $22,087.00
History of Investigator:	Shima Hajimirza (Principal Investigator) shima.hajimirza@stevens.edu
Recipient Sponsored Research Office:	Texas A&M Engineering Experiment Station 3124 TAMU COLLEGE STATION TX  US  77843-3124 (979)862-6777
Sponsor Congressional District:	10
Primary Place of Performance:	Texas A&M Engineering Experiment Station 3123 TAMU College Station TX  US  77843-3123
Primary Place of Performance Congressional District:	10
Unique Entity Identifier (UEI):	QD1MX6N5YTN4
Parent UEI:	QD1MX6N5YTN4
NSF Program(s):	TTP-Thermal Transport Process
Primary Program Source:	01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7916
Program Element Code(s):	140600
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.041

ABSTRACT

Radiative heat transfer in porous media is complex and ambiguous; yet, measuring the radiative properties of a target porous media is of vital importance. Measurement and prediction of the radiative properties is critical for simulation and design of energy technologies that involve porous material structures, including pebble beds in nuclear reactors, selective laser sintering technology, solar absorbers, solar thermochemical reactors, biological tissues, thermal barriers for jet engines and space vehicles, ceramic foams for catalytic combustion and many more. At present, predicting radiative properties of randomly packed beds requires large time-consuming ray-tracing simulations. This project replaces these computations with efficient machine learning based methods to revolutionize a wide range of related applications and underlying technologies.

This transformative project demonstrates that surrogate models can approximate and predict the probability distribution functions of radiative properties of randomly packed structures reliably and efficiently. Large time-consuming ray-tracing Monte Carlo simulations are replaced by predictive models based on machine learning methods. The inputs to the models are statistics of a wide range of variables pertaining to the physical configurations of void, solid, boundary conditions and dimensions and the medium shape. Various learning models are studied for data fitting, and an analysis of accuracy versus the cost of computation is performed for each. Data sampling, model selection and model fitting are all engineered to render surrogate models that are accurate, efficient, scalable and generalizable. Sampling, design of experiment and model fitting is studied for each surrogate model to reduce the computational load while minimizing the cost of data collection and learning. The practical accuracy of the proposed models is validated based on comparison with direct Monte Carlo simulations and previously established laboratory-based experiments. The proposed predictive models are applied in computed tomography for inference of porous media structures in various applications.

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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