What is it about?
This study explores how well a computational tool called Flow360 can predict noise generated by Joby Aviation’s full-scale, five-bladed electric vertical takeoff and landing (eVTOL) propeller. eVTOL aircraft represent a new and rapidly developing category of urban air vehicles. Because these aircraft operate in cities, noise pollution is a major concern for public acceptance and regulatory approval. Traditional helicopter noise models do not accurately capture the unique noise characteristics of eVTOL propellers, which have different designs featuring multiple, slower-turning blades. The research team used high-fidelity numerical simulations, specifically Delayed Detached-Eddy Simulations (DDES), combined with a Ffowcs Williams–Hawkings (FW–H) acoustic analogy, to predict both tonal (consistent frequencies related to blade passing) and broadband (turbulence-driven) noise components from the propeller. To validate these predictions, they compared their results against measurements collected at NASA’s National Full-Scale Aerodynamics Complex (NFAC) during tests of the propeller in hover and pure edgewise flight (where the propeller moves forward). Key to the analysis was the use of the Vold–Kalman order-tracking filter (VKF) to separate tonal noise from broadband noise, enabling detailed comparisons of different blade pitch angles and flight conditions. The researchers also performed source-noise analyses to identify which parts of the propeller’s surface contributed most to noise generation, revealing underlying flow mechanisms such as the roll-up of tip vortices and crossflow-induced separations. This study validates Flow360 as a high-fidelity tool for predicting the aerodynamic performance and aeroacoustic behavior of a full-scale five-bladed eVTOL propeller in hover and edgewise flight, with good agreement against NASA NFAC measurements. Overall, the study shows that Flow360 can capture thrust, torque, blade-passing harmonics, broadband spectral content, and directivity trends across multiple blade pitch settings and flight conditions. The result is a validated computational framework for studying full-scale eVTOL propeller noise and supporting quieter vehicle design. To organize the findings, the team highlights a combined computational–experimental framework showing how high-fidelity simulations and full-scale test data can complement each other in aeroacoustic analysis, design optimization, and community noise assessment for eVTOL vehicles
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Photo by pay dhp on Unsplash
Why is it important?
As eVTOL aircraft become closer to widespread adoption, the challenge of managing and reducing their noise footprint in populated areas grows more urgent. Unlike traditional helicopters, eVTOL designs often use multiple propellers with slower blade speeds and complex airflow interactions, creating noise signatures that are not well understood or predicted using legacy models. Inaccurate noise prediction can lead to poor design choices or community resistance, slowing down the deployment of these new aircraft. By validating Flow360's capability to reliably predict the tonal and broadband noise of complex, full-scale eVTOL propellers, this study provides a valuable tool for engineers designing quieter vehicles. Understanding the specific flow phenomena behind noise generation enables targeted design modifications and informs noise mitigation strategies. Moreover, the research emphasizes the importance of coupling advanced simulations with experimental data in realistic testing environments like NASA’s NFAC. This combined approach builds confidence in predictions across different flight regimes (hover, transition, edgewise flight), critical for certifying eVTOLs and improving their public acceptance. Ultimately, this work supports the broader goals of urban air mobility, helping to integrate new flight technologies into cities with minimal disturbance. Accurate aeroacoustic prediction tools like Flow360 will be essential to meeting regulatory noise limits and achieving sustainable, community-friendly eVTOL operations in the near future. Key takeaways: 1. Flow360, using high-fidelity computational fluid dynamics and aeroacoustic models, accurately simulates both tonal and broadband noise from Joby’s five-bladed eVTOL propeller. 2. Validation against full-scale measurements from NASA’s NFAC shows strong agreement in thrust, torque, noise frequencies, spectral levels, and directional noise patterns in hover and edgewise flight modes. 3. Advanced filtering techniques enable clear separation of tonal noise (linked to blade passing) from broadband noise (due to turbulence), deepening understanding of noise sources. 4. Noise arises from multiple aerodynamic phenomena including tip-vortex roll-up in hover and flow separation in forward flight, informing targeted noise mitigation approaches. 5. This study establishes a validated CFD-CAA framework to support quieter eVTOL propeller designs and improved community noise engagement as urban air mobility advances.
Perspectives
Years of working at the intersection of high-fidelity simulation and real engineering needs have shown me that predictive aeroacoustics must be both physically rigorous and practically usable. This conviction motivated the present study: to show that advanced CFD-CAA methods can move beyond theory and become reliable tools for understanding and reducing the noise of next-generation eVTOL propellers. For eVTOL systems, where noise is central to public acceptance, computation must do more than generate results; it must explain the underlying physics and stand up against experiment. By connecting detailed simulation with full-scale validation, I hope this work helps build confidence in simulation-driven design and contributes to quieter, more practical, and more community-compatible electric aircraft.
Payam Dehpanah
Flexcompute Inc
Read the Original
This page is a summary of: Aeroacoustic Validation of Flow360 for Joby’s Five-Bladed eVTOL Propeller Using Scale-Resolving Simulations and NFAC Experiments, January 2026, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2026-2079.
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