Student Name: Furkan Gonul

Advisor: Dr. Dimitri Mavris

Milestone: MS Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: SURROGATE MODELING FOR LOW THRUST EARTH TO MOON SPIRAL TRAJECTORY DESIGN AND PRELIMINARY MISSION FEASIBILITY ASSESSMENT

Abstract: Low thrust electric propulsion offers propellant savings. However, designing such transfers is computationally expensive. Formal optimization techniques for multi revolution spiral trajectories often take hours per trajectory and require manual initial guess calculations which makes it difficult to explore the design space in the preliminary mission design stage where engineers need to evaluate hundreds to thousands of candidate designs. Machine learning surrogate models offer an approximation that can deliver mission feasibility predictions in milliseconds rather than hours after training. Despite its potential, the use of such surrogates for low thrust spiral Earth to Moon trajectories has not been investigated thoroughly. This thesis asks which representation of the transfer makes the surrogate trainable, whether a trained model with a feasibility classifier can serve as a sizing and screening tool in preliminary mission design. Also under which conditions a physics informed neural network behaves better than a purely data driven one. Thesis uses a dataset of 30,000 planar transfers flown by the Q-law guidance law in modified equinoctial elements with the Moon as a third body. Sampling the transfer once per revolution, at perigee, is the lead contribution. It removes the oscillation within each revolution and makes the surrogate more accurate. The surrogate’s error in the propellant of an unseen design is 3.2 kg on average, 0.7 percent of the spacecraft’s initial mass and one prediction takes about 3 milliseconds. Therefore, the surrogate’s final mass is a conservative estimate of the mission and its prediction served as a starting guess from which the optimizer converged. The physics informed loss, tested on a second model, helps when training transfers are few or noisy. The results show that a neural network surrogate model can be used to investigate the design space in milliseconds for preliminary mission design.

Date and time: 2026-11-17, 8.00 - 10.00 AM

Location: CoVE (Collaborative Visualization Environment - ASDL)

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Graeme James Kennedy, School of Aerospace Engineering
Dr. Tristan Sarton du Jonchay,