Machine Learning-Augmented Far-Field Noise Prediction for Distributed Electric Propulsion Aircraft

The growing adoption of Distributed Electric Propulsion (DEP) aircraft presents new challenges in aeroacoustic modeling, as multi-propeller interactions, airframe effects, and noise certification requirements become critical barriers to large-scale deployment. Current low-fidelity noise models fail to capture complex noise sources, while high-fidelity CFD-based simulations are computationally prohibitive for rapid design iteration. To address this, we propose a machine learning-driven surrogate modeling framework that enables real-time, high-accuracy noise prediction for DEP aircraft. This framework integrates Graph Neural Networks (GNNs) and Fourier Neural Operators (FNOs) to predict full flow-fields and far-field noise signatures, rather than relying solely on empirical regression of noise metrics. Compared to traditional scale-resolving CFD, this approach achieves 1,000x faster computations while maintaining high accuracy (5% MSE with a few hundred simulations). By leveraging multi-fidelity aeroacoustic data sources (FW-H, VPM, CFD) and embedding physics constraints, the model can generalize across various DEP configurations, enabling faster aircraft design, optimization, and certification. Phase I funding will be used to develop and validate the FNO-GNN prediction methodology, starting with single-propeller and wing interactions before expanding to multi-propeller DEP configurations in Phase II. The technology targets NASA aeronautics programs, OEMs (Airbus, Boeing, Joby, Archer, Lilium), defense contractors (Lockheed Martin, Northrop Grumman), and regulatory agencies (FAA, ICAO, EASA), supporting urban air mobility (UAM), hybrid-electric regional aircraft, and UAV applications. By accelerating DEP aircraft noise prediction and mitigation, this innovation directly supports NASA’s Sustainable Aviation and Advanced Air Mobility (AAM) initiatives, providing a scalable, high-impact solution for future electric aviation.

The proposal responds to the sought outcomes of SBIR A1.02 by developing an ML-driven surrogate modeling framework that enables aeroacoustic analysis for DEP aircraft with a focus on source identification and noise prediction. The framework can predict noise generated by aerodynamic interactions, aligning with NASA’s goals for improved airframe noise characterization and design integration. The proposal directly supports NASA’s Aeronautics Research Mission Directorate (ARMD) Strategic Thrusts by enabling the design of quieter, more efficient aircraft and improving noise-aware autonomous flight planning for urban air mobility (UAM) and hybrid-electric regional aircraft. This work aligns with Strategic Thrust 3: Ultra-Efficient Subsonic Transport, and Strategic Thrust 4: Safe, Quiet, and Affordable Vertical Lift Air Vehicles, addressing near-term (2025-2035) regulatory and design objectives while laying the foundation for mid- and far-term sustainable aviation innovations (2035-2045+). At the program level, this research enhances NASA’s Transformative Aeronautics Concepts Program (TACP) and Advanced Air Vehicles Program (AAVP) by advancing DEP noise modeling, a key challenge for integrating next-generation sustainable aircraft into the National Airspace System (NAS). It provides critical insights into DEP configurations, novel engine placements, and airframe-propulsion noise interactions. At the project level, the proposed AI-driven acoustic modeling tools support NASA’s Transformational Tools and Technologies (TTT) Project by improving early-stage noise assessment methodologies. The work also benefits the Advanced Air Transport Technology (AATT) Project, integrating noise-mitigating design strategies into conceptual aircraft development. Additionally, this research aligns with Convergent Aeronautics Solutions (CAS) Project, accelerating technology transition and commercial adoption of low-noise DEP aircraft. The growing adoption of electric and hybrid-electric propulsion in aviation has intensified the need for advanced aeroacoustic simulation tools to address noise certification, aircraft design optimization, and urban air mobility (UAM) integration challenges. Current noise prediction methods rely on high-fidelity Computational Aeroacoustics (CAA) and wind tunnel testing, which are computationally expensive, time-consuming, and difficult to scale for iterative design and certification efforts. As regulatory agencies impose stricter noise limits, and urban planners assess the impact of eVTOL operations in dense environments, there is a pressing demand for real-time, scalable noise modeling solutions that enable quieter, more efficient aircraft development. The proposed machine learning-driven surrogate modeling framework addresses these challenges by reducing computational costs, accelerating noise assessment timelines, and improving design optimization capabilities for DEP aircraft. Aircraft OEMs and EVTOL developers (e.g., Airbus, Boeing, Joby) can integrate this technology into their design workflows, allowing them to optimize propeller configurations, reduce certification risks, and decrease reliance on costly flight testing. Aerospace defense contractors (e.g., Lockheed Martin, Northrop Grumman) can apply the framework to low-noise UAV development, where acoustic signature management is critical for stealth and tactical operations. Beyond manufacturers, regulatory agencies require data-driven tools to evaluate compliance with FAA Part 36 and emerging noise regulations for electric aviation. Airports and urban planners will also benefit from predictive noise assessments for vertiports and flight corridors, helping mitigate community noise concerns as advanced air mobility networks expand. This bridges a critical gap in the industry, providing a scalable, real-time noise prediction solution that aligns with the rapid evolution of next-gen aviation technology.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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