Prediction of Thermionic Energy Conversion Performance and Parametric Effects Using Genetic Algorithms to Fit Physics-Inspired Model Equations to Prototype Test Data

Abstract

Thermionic converters have potential as an energy conversion technology for high-temperature space and terrestrial applications using concentrated solar, nuclear reaction, and combustion processes as the heat source. Recent studies have generated experimental performance data for narrow-gap thermionic energy conversion devices. This investigation explores the use of genetic algorithms to fit existing data with physics-inspired model equations. The resulting model equations can be used for performance prediction for system design optimization or to explore parametric effects on performance. The model equations incorporate Richardson’s law for current density, including both the saturated and Boltzmann regimes, with appropriate relations for power delivered to the external load. The transition regime is characterized using two separate models, each accounting for nonuniformity in emission surfaces and irregularities in the manufacturing process. The trained models enable performance prediction of small-gap thermionic energy conversion devices. In this study, data were fitted for two different prototype designs. The prototype test data and postulated values for the work functions and a transition regime parameter are substituted into physics-inspired model equations, yielding performance models with three adjustable constants. Optimized values of these constants are determined using a genetic algorithm to best fit the experimentally determined performance data for prototype thermionic conversion devices tested in earlier studies. This approach is demonstrated to fit the performance data to within 9%. This methodology also allows the user to back-infer the effective work function values, which were found in this study to be consistent with independent measurement.