Multi-Fidelity Approaches for Modeling, Simulation and Validation in Fluid Dynamics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Fluid Science and Technology".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 22
Special Issue Editors
Interests: aerodynamics; computational fluid dynamics; fluid mechanics; wind tunnel modeling; wind tunnel testing; aeroelasticity; multibody system dynamics
Interests: aerodynamics; rotorcraft; wind tunnel tests; computational fluid dynamics; flow control; fluid structure interactions; wind turbine; eVTOLs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue highlights the use of multi-fidelity approaches in the modeling, simulation, and validation of fluid dynamics problems. It encourages studies that combine high-fidelity simulations with reduced- or mid-fidelity models, particularly those supported by experimental validation. Contributions may address model fusion, surrogate modeling, uncertainty quantification, and digital twin development. We also welcome innovative methodologies that leverage artificial intelligence and machine learning for reduced-order modeling, data assimilation, or hybrid physics-informed approaches. A special focus will be given to complex, multi-scale problems involving strong interactions such as fluid–structure interaction, aerodynamic interference effects, and unsteady flow phenomena, where multi-fidelity strategies can play a key role in balancing accuracy and computational efficiency. Both methodological innovations and application-driven research across different fluid dynamic contexts are welcome, including aerospace, energy, and environmental flows.
Dr. Alberto Savino
Dr. Alex Zanotti
Guest Editors
Manuscript Submission Information
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Keywords
- multi-fidelity
- mid-fidelity
- fluid dynamics
- simulation
- model validation
- wind tunnel
- surrogate modeling
- digital twin
- reduced-order models
- experimental correlation
- uncertainty quantification
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