Multi-Objective Robust Design of Segmented Thermoelectric–Thermal Protection Structures for Hypersonic Vehicles Using a High-Fidelity Thermal Network

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-11-25
DOI
https://doi.org/10.3390/app152312482
Primary Topic
Aerodynamics and Fluid Dynamics Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Objective Robust Design of Segmented Thermoelectric–Thermal Protection Structures for Hypersonic Vehicles Using a High-Fidelity Thermal Network

Kongjun Zhu, Hao Dong, Keming Cheng, Tianyu Xia et al.
Applied Sciences
Aerodynamics and Fluid Dynamics Research
article

Multi-Objective Robust Design of Segmented Thermoelectric–Thermal Protection Structures for Hypersonic Vehicles Using a High-Fidelity Thermal Network

Kongjun Zhu, Hao Dong, Keming Cheng, Tianyu Xia, Yidi Zhao
article en

Abstract

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development.

Applied SciencesVol. 15(23)
Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 53%
Aerodynamics and Fluid Dynamics Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.