Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical and Civil Engineering

First Advisor

Hamidreza Najafi

Second Advisor

Darshan G. Pahinkar

Third Advisor

Jian Du

Fourth Advisor

Ashok Pandit


Thermal protection system (TPS) plays a pivotal role in safeguarding space vehicles from extreme aerothermal heating during atmospheric entry. To ensure effective thermal management, Hybrid Thermal Protection seamlessly integrating both active and passive TPS is an innovative solution. The passive TPS consists of an insulative layer on vehicle skin to counteract the aerothermal heat. Although a thicker layer enhances insulation, a trade-off arises between the thickness of the passive layer and the spacecraft’s weight. Beneath the passive TPS layer lies the active TPS functioning as a heat exchanger where the vehicle fuel liquid is considered as coolant. The effectiveness of the active TPS is influenced by parameters such as the coolant flowrate and the design of the flow channel. In this paper, two potential designs for the active TPS are considered and the resulting hybrid systems are studied through parametric analysis and optimization using multi-objective optimization through genetic algorithm. Two objective functions including the total weight of the system as well as the required pumping power are considered and parameters including coolant velocity, thickness of passive TPS layer and thickness of gyroid are used as the optimization variables. The methodology used and results produced from this study provide insights regarding the design and optimization of hybrid TPS and the potentials for using triply iv periodic minimal surfaces to achieve enhanced heat transfer performance. The gyroid configuration demonstrated enhanced performance for both temperature and heat flux within the range of design parameters that were used for this study. The measurement of incoming heat flux is an important parameter of TPS but this is not directly possible. This study also includes a solution to inverse heat transfer problem using a deep learning model that can predict the incoming heat flux on top of a hybrid TPS layer based on the bottom surface temperature.

Available for download on Sunday, May 31, 2026