Developing new, environmentally friendly energy sources is one of the grand engineering challenges faced by society. Thermophotovoltaic devices convert waste heat into usable electricity and have attracted a great research interest to satify the increasing need for electrical power. Thermophotovoltaic (TPV) systems can take advantage of many energy sources, including solar energy and waste heat from fossil fuels and industrial processes. TPV systems could enable low-weight, versatile and compact electricity generators that are noiseless, low-maintenance and energy-efficient. Realizing high-efficiency TPV systems requires advancing fundamental knowledge of materials, photonics, and design. A key challenge is to optimize multi-functional TPV components and their constituent materials for stable operation under environment and extreme temperatures. This project will use artificial intelligenc (machine learning) to merge the knowledge of optical materials with advanced optimization to achieve highly efficient TPV systems. The project will create a fundamentally new, machine-learning-assisted optimization framework for the realization of advanced TPV components. This project will leverage the extended knowledge and database of tailorable optical materials and integrate machine-learning algorithms with photonic designs.

Technical Abstract

In the recent years, there has been significant research interest in engineering the optical and spectral properties of materials through the use of photonic metasurfaces for efficient energy conversion, including thermophotovoltaics. The proposed program merges advanced photonic topology optimization with deep-learning-based inverse design methods and a comprehensive material database to unlock unorthodox optical designs for the realization of highly-efficient components for TPV applications. This effort will expand the design parameter space and incorporate machine-learning approaches to achieve the dramatic improvement of the speed and efficiency of topology optimization, as well as to build a large documented materials database. Through unconventional optical design, the program aims to develop highly efficient TPV energy conversion approaches by enhancing radiative heat transfer process. The proposed TPV device could enable unparalleled energy conversion efficiency potentially exceeding 50% by matching the emissivity of the emitter to the bandgap of commercial photovoltaic cells such as silicon, gallium antimonide, indium gallium arsenide. This approach could elevate nanophotonic designs into previously unavailable regimes and can be applied to photonic systems beyond TPV.

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.

Project Start
Project End
Budget Start
2020-09-15
Budget End
2023-08-31
Support Year
Fiscal Year
2020
Total Cost
$450,000
Indirect Cost
Name
Purdue University
Department
Type
DUNS #
City
West Lafayette
State
IN
Country
United States
Zip Code
47907