Recent Advances in Applied Aerodynamics (2nd Edition)

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Guest Editor
Department of Aeronautical and Mechanical Design Engineering, Korea National University of Transportation, Chungju-si 27496, Republic of Korea
Interests: low-speed aerodynamics; unsteady aerodynamics; formation flight; wing-in-ground effect aircraft design; biomimetics
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Special Issue Information

Dear Colleagues,

Applied aerodynamics seeks to understand and utilize the fundamental aspects of fluid flow in the analysis, design, and integration of aerodynamic geometries. This field covers a broad range of applications, generally involving any object that experiences aerodynamic forces in fluid flow, though common applications include fixed-wing or rotary-wing aircraft, wind turbines and propellers, ground and marine vehicles, internal flows, avian and insect flight, and atmospheric flows. We are seeking papers on theoretical, experimental, and computational approaches to aerodynamics applications. Areas of interest include, but are not limited to, flight or ground vehicle aerodynamic design, the analysis of wing/rotor/vehicle aerodynamic performance, methods for modeling aerodynamic bodies, and novel studies or technological applications related to aerodynamic applications. Specific areas of interest are listed below but work in related areas is also encouraged.

  • Aerodynamic design: Analysis, methodologies, and optimization techniques;
  • Aerodynamic flow control: Analytical, computational, and experimental;
  • Aerodynamic testing: Ground, wind tunnel, and flight testing;
  • Aero-propulsive interactions and aerodynamics of integrated propeller systems;
  • Airfoil/wing/configuration aerodynamics;
  • Applied aeroelasticity and aerodynamic–structural dynamics interaction;
  • Applied computational fluid dynamics;
  • Boundary layer transition for aerodynamic applications;
  • CFD methods for aerodynamics applications;
  • Propeller/rotorcraft/wind turbine aerodynamics;
  • Reduced-order aerodynamics modeling and system identification;
  • Transonic and supersonic aerodynamics;
  • Unsteady aerodynamics and massively separated flows.

Prof. Dr. Cheolheui Han
Guest Editor

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Keywords

  • CFD
  • aerodynamic testing
  • design optimization
  • flow control
  • flow–structure interaction
  • boundary layer transition

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Related Special Issue

Published Papers (1 paper)

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Research

18 pages, 4564 KiB  
Article
Multi-Fidelity Modeling of Isolated Hovering Rotors
by Jason Cornelius, Nicholas Peters, Tove Ågren and Hugo Hjelm
Aerospace 2025, 12(8), 650; https://doi.org/10.3390/aerospace12080650 - 22 Jul 2025
Abstract
Surrogate modeling has been rapidly evolving in the field of aerospace engineering, further reducing the cost of computational analyses. These models often require large amounts of information to learn the underlying process, which is at odds with obtaining and using the highest-fidelity data. [...] Read more.
Surrogate modeling has been rapidly evolving in the field of aerospace engineering, further reducing the cost of computational analyses. These models often require large amounts of information to learn the underlying process, which is at odds with obtaining and using the highest-fidelity data. This study assesses the efficacy of multi-fidelity modeling (MFM) to improve simulation accuracy while reducing computational cost. A database of hovering rotor simulations with perturbations of the rotor design and operating conditions was first generated using two different fidelity levels of the OVERFLOW 2.4D Computational Fluid Dynamics software. MFM was then used to quantify the effectiveness of this approach for the development of accurate surrogate models. Multi-fidelity models based on Gaussian Process Regression (GPR) were derived for hovering rotor performance prediction given the geometric rotor blade inputs that currently include twist, planform, airfoil, and the collective pitch angle. The MFM approach was consistently more accurate at predicting the hold-out test data than the surrogate model with high-fidelity data alone. An MFM using just 20% of the available high-fidelity training data was as accurate as a solely high-fidelity model trained on 80% of the available data, representing an approximate fourfold reduction in computational cost. Full article
(This article belongs to the Special Issue Recent Advances in Applied Aerodynamics (2nd Edition))
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