Advisor: Dr. John Dec

Milestone: MS Thesis Proposal

Degree Program: Aerospace Engineering

Title: Improving Atmospheric Entry Environment Prediction Using Machine-Learning Surrogate Models

Abstract: The expansion of the space economy, driven by both private and public sectors, has increased the demand for atmospheric entry prediction. Accurate predictions in early mission analysis enable guidance and control design, establishment of mission requirements, landing footprint estimation, and space debris risk assessment. Current early mission analysis tools face a fundamental trade-off: simplified models are computationally fast but neglect key physics such as aerodynamics and ablation; high-fidelity multi-physics simulations are accurate but computationally expensive for uncertainty studies. This work proposes a mission-driven framework that bridges this gap by coupling a 3-DOF non-planar trajectory propagator with FUN3D computational fluid dynamics and FEAR ablation analysis to generate high-fidelity training data, which is then used to develop machine-learned surrogate models for aerodynamic coefficients and ablation response. Training data is concentrated near a nominal trajectory; its plausible dispersions with altitude, velocity, and vehicle geometry are used as inputs, and lift coefficient, drag coefficient, mass loss, and frontal area recession are used as outputs. The coupled Trajectory-CFD Solver has been developed and validated against the Stardust sample return capsule re-entry. The framework predicts the landing site within 41 miles of the actual location, reducing the prediction error by nearly half compared to a conventional low-fidelity trajectory propagator. Remaining work includes data generation improvements, surrogate model training and deployment, integration of the surrogates into the trajectory propagator, and Monte Carlo uncertainty quantification of landing footprints. The resulting framework will demonstrate how mission-specific surrogate modeling can preserve the fidelity of high-fidelity solvers at a fraction of the computational cost, advancing early-phase reentry mission analysis.

Date and time: 2026-05-04, 11:00 AM

Location: ESM 108

Committee:
Dr. John Dec (advisor), School of Aerospace Engineering
Dr. Krish Ahuja, School of Aerospace Engineering
Dr. Alvaro Romero-Calvo, School of Aerospace Engineering
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