Theses and Dissertations
Advisor
Sescu, Adrian
Committee Member
Bhushan, Shanti
Committee Member
Burgreen, Greg
Committee Member
Dettwiller, Ian
Date of Degree
12-12-2025
Original embargo terms
Embargo 2 years
Document Type
Dissertation - Open Access
Major
Computational Engineering
Degree Name
Doctor of Philosophy (Ph.D.)
College
James Worth Bagley College of Engineering
Department
Computational Engineering Program
Abstract
Accurate and rapid prediction of aerothermodynamic loads remains an ongoing challenge for intelligent trajectory design and optimization for hypersonic vehicles. Existing approaches rely on engineering approximations to estimate aerothermal heating and mechanical loading, which are valid only for a limited range of flow conditions and geometric shapes. Direct use of computational fluid dynamics (CFD) simulations to support trajectory optimization is infeasible considering the computational expense associated with calculating a numerical solution to the Navier-Stokes equations, even under the assumption of steady-state flow. To address this shortcoming, I introduce an aerothermodynamic digital twin (AT-DT) framework which enables rapid prediction of aerothermodynamic loads and thermal response along the surface of a hypersonic vehicle. The framework uses a data-driven surrogate model to predict the aerothermodynamic loading anywhere on the vehicle surface as a function of freestream conditions, vehicle attitude, and wall temperature. The surrogate model is coupled with a lightweight, one-dimensional thermal solver to analyze heat soak into the structure. The AT-DT model is validated using experimental data from the HIFiRE-5b flight test. The results indicate the framework is able to accurately predict aerothermodynamic loads and changes in the vehicle wall temperature along the reentry trajectory. Using an AT-DT, thermal analysis of a trajectory can be performed in a matter of seconds, compared to hours or days for traditional conjugate heat transfer (CHT) analysis techniques. This novel framework is coupled with a gradient-based trajectory optimization algorithm that leverages modern GPU computing hardware and open-source deep learning libraries. Unlike many gradient-based algorithms which use finite differencing to estimate the derivatives of the loss functions and constraints, the proposed methodology leverages automatic differentiation to rapidly calculate gradients with a high degree of accuracy. This architecture is used to determine a series of active maneuvers which minimize heating at specific locations on the vehicle while still achieving desired terminal location constraints. Unlike many trajectory optimization approaches, this framework can be used to determine optimal trajectories based on the design characteristics and limitations of individual vehicle components, enabling a more sophisticated approach to maximizing performance and ensuring survivability of hypersonic glide vehicles.
Sponsorship (Optional)
U.S. Army Engineer Research Development Center (#W912HZ25C0012)
Recommended Citation
Way, Alexander Spiller, "Design and validation of an aerothermodynamic digital twin framework for hypersonic glide vehicles" (2025). Theses and Dissertations. 6731.
https://scholarsjunction.msstate.edu/td/6731
