Modelling and optimization of a Radioisotope Thermoelectric Generator (RTG)

Kurzfassung

Lunar exploration requires a continuous supply of electrical power, independent of solar irradiation availability, which can be provided by Radioisotope Thermoelectric Generators (RTGs). RTGs have been deployed by NASA in various deep-space missions, proving their reliability and longevity. ESA is currently deploying its own RTG, based on 200 Wth 241Am-based radioisotope fuel. Design and operation of RTGs is a complex multi-disciplinary challenge, as they are comprised of multiple components that affect their performance. Their inner core uses multi-layer containment to prevent radioisotope fuel leakage. In contact with the core, thermoelectric generators (TEGs) produce electric power, while in the areas not covered by TEGs thermal insulation is placed. The design of each RTG component affects the heat distribution and thermoelectric performance, thus requiring multi-parametric tools for design optimization. Here, a 1D thermoelectric network model is developed, coupled with a genetic algorithm (GA) tool, and deployed to maximize the specific power of the RTG. Using an iterative process, the 1D model calculates the heat flux through each RTG component, and thus the temperature difference, necessary to evaluate the thermoelectric performance of the TEGs. Using the GA, an optimization study investigates the RTG design that achieves maximum specific power while maintaining safe operating limits on the RTG components.