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Coupling of a Genetic Algorithm and a Thermoelectric Network Model for Radioisotope Thermoelectric Generator (RTG) Optimization

Kyrimis, Stylianos und Lallemand, Elliott und Stiewe, Christian und Ziolkowski, Pawel und Müller, Eckhard (2025) Coupling of a Genetic Algorithm and a Thermoelectric Network Model for Radioisotope Thermoelectric Generator (RTG) Optimization. In: Advances in Artificial Intelligence for Aerospace Engineering 5th workshop. AI for Aerospace Engineering, 2025-05-19 - 2025-05-21, Toulouse, France.

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Offizielle URL: https://elib.dlr.de/214649/1/abstractsAI4A5thworkshop_external.pdf

Kurzfassung

Lunar exploration requires a continuous supply of electrical power, independent of solar irradiation availability, which can be provided by radioisotope thermoelectric generators (RTG). RTG contain a radioisotope heating unit, enclosed in an aeroshell core and coupled with thermoelectric modules (TEM). TEM utilize the temperature difference developed between their hot side, adjacent to the RTG core, and their cold side, connected to radiation fins attached to the outer RTG surface, to convert part of the released heat into electricity through the Seebeck effect. Design and operation of RTG is a complex multi-disciplinary challenge, as they are comprised of multiple components that affect performance. The surfaces of the aeroshell not connected to TEM are covered by thermal insulation, driving most of the source heat through the TEM. Excessive insulation however, could undesirably increase the system mass which should be minimized. The materials and geometry of the TEM affect the heat-to-electricity conversion efficiency as well as the temperature at its hot side which should not exceed the TEM’s safe operating temperature to prevent device failure. To address these challenges and optimize the RTG for lunar operation, we developed a multi-physics and multi-parametric thermoelectric (TE) network model, which represents the RTG as a series of thermal resistors and TE elements. By solving the heat transfer equation, the temperature difference on all resistors can be calculated, while the TE heat transfer equations on the TEM provide an estimate of the RTG’s performance. We then coupled this model with a Genetic Algorithm for rapid design investigations to identify the RTG design which maximises the RTG’s specific power, i.e. electrical power output over system mass, while maintaining safe operating temperatures on the TEM. The optimized RTG design, which utilizes a Bi2Te3 TEM, achieves a specific power of 1.42 W/kg, with an electrical power of 13.9 W and a conversion efficiency of 6.9%. This is a significant improvement over the pre-defined specifications of the European Space Agency (ESA), i.e power of 10 W with a 5% conversion efficiency [1]. Our model can further adapt the RTG to incorporate TEM which can operate at higher temperatures, such as Skutterudites or Half-Heusler materials, thus allowing for higher performances.

elib-URL des Eintrags:https://elib.dlr.de/220336/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Coupling of a Genetic Algorithm and a Thermoelectric Network Model for Radioisotope Thermoelectric Generator (RTG) Optimization
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Kyrimis, StylianosStylianos.Kyrimis (at) dlr.dehttps://orcid.org/0000-0002-6195-9421199256807
Lallemand, Elliotteliott.lallemand (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Stiewe, ChristianChristian.Stiewe (at) dlr.dehttps://orcid.org/0000-0002-1895-9258NICHT SPEZIFIZIERT
Ziolkowski, PawelPawel.Ziolkowski (at) dlr.dehttps://orcid.org/0000-0003-1519-6803199256808
Müller, EckhardEckhard.Muller (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:20 Mai 2025
Erschienen in:Advances in Artificial Intelligence for Aerospace Engineering 5th workshop
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Waxenegger-Wilfing, GüntherGuenther.Waxenegger (at) dlr.dehttps://orcid.org/0000-0001-5381-6431NICHT SPEZIFIZIERT
Durak, UmutUmut.Durak (at) dlr.dehttps://orcid.org/0000-0002-2928-1710199256809
Kintscher, Markusmarkus.kintscher (at) dlr.dehttps://orcid.org/0000-0003-0600-0135NICHT SPEZIFIZIERT
Bidaud, PhilippePhilippe.Bidaud (at) onera.frNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Herbin, StéphaneStephane.Herbin (at) onera.frNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Farges, Jean Loupjean-loup.farges (at) onera.frNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Name der Reihe:Abstracts of “Advances in Artificial Intelligence for Aerospace Engineering” 5th workshop
Status:veröffentlicht
Stichwörter:Radioisotope Thermoelectric Generator (RTG), Thermoelectric Network Model, Genetic Algorithm (GA), Lunar exploration, Deep space exploration
Veranstaltungstitel:AI for Aerospace Engineering
Veranstaltungsort:Toulouse, France
Veranstaltungsart:Workshop
Veranstaltungsbeginn:19 Mai 2025
Veranstaltungsende:21 Mai 2025
Veranstalter :ONERA
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - Radiothermogeneratoren als autarke Stromquellen Mondbasis
Standort: Köln-Porz
Institute & Einrichtungen:Institut für Werkstoff-Forschung > Thermoelektrische Materialien und Systeme
Hinterlegt von: Kyrimis, Stylianos
Hinterlegt am:10 Dez 2025 11:50
Letzte Änderung:10 Dez 2025 11:50

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