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Borer, N.K., Derlaga, J.M., Deere, K.A., Carter, M.B., Viken, S., Patterson, M.D., et al. (2017) Comparison of Aero-Propulsive Performance Predictions for Distributed Propulsion Configurations. 55th AIAA Aerospace Sciences Meeting, Grapevine, 9-13 January 2017.
https://doi.org/10.2514/6.2017-0209

has been cited by the following article:

• TITLE: Optimizing Computational Efficiency with Preliminary 2D CFD for Ground-Effect Flight Transit Vehicle Design

  AUTHORS: Adam B. Suppes, Galen J. Suppes

  KEYWORDS: Lift, Drag, Computational Fluid Dynamics, Energy Analysis, L/D Efficiency

  JOURNAL NAME: Advances in Aerospace Science and Technology, Vol.10 No.4, December 2, 2025

  ABSTRACT: Early stages of ground-effect flight transit vehicle (GEFT) development identify benchmark digital prototype performances with approaches to reduce energy consumption by 20% to 80%. 80% reductions are possible when aerodynamic suspension mitigates rolling losses. Applications range from reducing drag for automobiles to railcars operating in ground-effect flight at greater speeds on existing track infrastructure. The technology utilizes a trailing-section upper-surface ducted fan, a trailing tapered surface, and a lower cavity comprising side fences and a trailing flap. These additional features both enable higher levels of performance and increase the degrees of freedom for computational optimization. This paper uses a minimum in total Computational Fluid Dynamics (CFD) mesh turbulence as a method to identify the local optimal ducted fan power setting and to compare different designs. Three-dimensional simulations identified a range of viable design applications for constant airfoil cross sections. Applications are limited by the vehicles’ ratios of thickness to length (thickness ratio) and the ratios of width to length (aspect ratio). Analysis is limited to vehicles 2.6 m wide for compatibility with multimodal operation on existing highway and railway corridors. The work creates the opportunity to test the limits of modern computational methods to rapidly advance designs through the application of extrapolating 2D CFD performances to 3D designs and understanding how air flow transforms into lift pressures.