Session: 10-04: Alternative Energy Conversion Technology (including Wind, Geothermal, Hydro, and Ocean)

Paper Number: 130516

130516 - Physical Versus Data-Driven Modeling of Thermionic Device Performance Over the Full Range of Power Generation Operating Conditions 

Abstract: 

Ideal thermionic energy converters may operate in either the saturated or the Boltzmann regimes. The operating mode of the thermionic converter is determined by the operating conditions of the device and the device specifications. These conditions and specifications include the operating voltage, the device geometry, and the electrode material properties. Low operating voltages for the ideal thermionic converter correspond to performance behavior in the saturated regime and high operating voltages correspond to performance in the Boltzmann regime.  

 

For real thermionic devices, performance characteristics asymptotically approach these two ideal curves at high and low voltages. The behavior of real devices presents a transition region between the two regimes where the current density from the emitter to the collector electrode is less than the ideal behavior predicted by the saturated and Boltzmann regime governing equations. The focus of this paper is to develop a physics-based model for the transition between the two regimes and to incorporate data from real devices. In real devices, the most efficient device performance and highest output power generally corresponds to this transition regime, where the density of electrons in the gap is significant and is therefore of central importance.

 

To develop a physics-based framework, we based the modeling on classical models of current transport between electrodes. The saturated regime is characterized by the Child-Langmuir law for thermionic emission and Langmuir space-charge theory is implemented for the charge-affected regime to characterize the transition behavior in real diodes. The framework developed in this work is based on these classical approximations in the power-generating operating mode for the thermionic diode. The classical models are used to establish a general functional form for the current flux variation with voltage in the transition region. With that functional form, we then utilize machine learning tools to interpret the behavior of real data in the context of the developed framework, and to develop a performance predictive tool that leverages the advantages of a physics-inspired machine learning methodology.

 

The framework developed in this work is shown to fit performance data for real, small gap, vacuum thermionic devices to within 5%. We took the framework and the functional form suggested in tandem with experimental data to develop a transition model which reflects the underlying physics and is consistent with performance trends in the data for real devices. The implications of this type of model for use in design optimization studies and space power applications are also discussed.

Presenting Author: Elizabeth Juette University of California, Berkeley

Presenting Author Biography: Elizabeth Juette is a Ph.D. student at the University of California, Berkeley in the Department of Mechanical
Engineering with a focus on Energy Sciences (heat transfer). She is currently in her second year of graduate school
in the Energy and Multiphase Transport Laboratory and is working on thermionic energy conversion for her
NASA Space Technology Graduate Research Opportunity (NSTGRO) fellowship.

Authors:

Elizabeth Juette University of California, Berkeley
Van P. Carey University of California, Berkeley
Jean-Pierre Fleurial NASA Jet Propulsion Laboratory