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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stability Derivatives Prediction Using Reduced-Order Models on Triple Delta Wing

Jürgen Seidel, Adam Jirásek, Markus Widhalm, Mario Stradtner et al.
1 citations
Journal of Aircraft
Model Reduction and Neural Networks
1.32
article

Stability Derivatives Prediction Using Reduced-Order Models on Triple Delta Wing

Jürgen Seidel, Adam Jirásek, Markus Widhalm, Mario Stradtner, Mehdi Ghoreyshi, Andreas Schütte
article en
1 citations

Abstract

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.

Journal of Aircraft
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), United States Air Force Academy (US)
Openalex Percentile: Top 16%
Model Reduction and Neural Networks
1.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Stability Derivatives Prediction Using Reduced-Order Models on Triple Delta Wing — Jürgen Seidel, Adam Jirásek, et al. · Journal of Aircraft (2025) | TGRS Research Map | TGRS