Stability Derivatives Prediction Using Reduced-Order Models on Triple Delta Wing
The numerical estimation of stability derivatives is a rather difficult task for delta wing configurations due to their inherent vortex-dominated flow topology. It requires high fidelity computational fluid dynamics (CFD) simulations to resolve the relevant flow characteristics. In particular, at early design stages, often only limited CFD data are available, which gives rise to the application of reduced-order modeling techniques. Stability derivatives are estimated using two widely used frameworks for simulating unsteady flows, DoD High Performance Computing Modernization Program CREATE TM -AV/Kestrel and the DLR, German Aerospace Center (DLR) TAU code, in conjunction with a classical stability derivatives method and are compared with three different reduced-order modeling approaches. The DLR TAU code contains a linearized version of the discrete unsteady Reynolds-averaged Navier–Stokes equations based on the small perturbation approach and are solved in the frequency domain. This physics-based reduced-order model computes stability derivatives directly. Indicial response modeling and system identification belong to the group of data-driven approaches that are based on Kestrel CFD simulations. The weaknesses and strengths of the individual approaches of reduced-order models are shown, and, in particular, the efficiency of the methods is outlined. The use case is the ONERA_DLR_421 model, a generic research wind-tunnel model of a triple delta wing fighter type aircraft configuration, at transonic flow conditions for various angles of attack.
Authors
- Jürgen Seidel (ORCID: https://orcid.org/0000-0001-5690-447X)
- Adam Jirásek
- Markus Widhalm (ORCID: https://orcid.org/0000-0003-2085-9413)
- Mario Stradtner (ORCID: https://orcid.org/0000-0001-8038-9420)
- Mehdi Ghoreyshi (ORCID: https://orcid.org/0000-0003-2338-8645)
- Andreas Schütte
Institutions
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- United States Air Force Academy (US)
Publication Details
- Journal
- Journal of Aircraft
- Published
- 2025-08-31
- DOI
- https://doi.org/10.2514/1.c038321
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 1.32