Surrogate-based Design Optimization for a Long-Range Mars Rotorcraft

This project aims to develop a multidisciplinary surrogate model-based optimization framework for aerospace vehicle design; building on a previous award-winning machine learning framework. The new framework will incorporate flight dynamics and controls for a long-range Mars rotorcraft; enabling higher-fidelity simulations; reducing risk; and expanding mission possibilities across NASA.

This project benefits NASA by developing a machine learning-based optimization framework for designing long-range Mars rotorcraft. The framework enhances mission design by reducing risks through higher-fidelity simulations; enabling exploration of complex Martian environments. This methodology can also be applied to other planetary missions; advancing aerospace design across NASA.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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