Normal Force and Pitching Moment Trend Sensing of Delta Wing Based on Flow Physical Topology with Sparse Pressure Measurements
At high angles of attack (AOAs), aircraft experience significant nonlinear aerodynamic forces, which are influenced predominantly by vortex structures. These unsteady forces and moments have a considerable impact on aircraft maneuverability and overall flight safety. In this study, a novel approach for sensing normal force and pitching moment trends using pressure is proposed, which evaluates the feasibility of utilizing sparsely measured pressure signatures to discern the influence of vortex structures on delta wing models. This physics-based methodology involves the strategic optimization of sparse pressure sensor placement to significantly augment the measurement reliability. In wind tunnel tests involving the 65° delta wing model, it was observed that the pressure-normal force and pitching moment trend sensing parameter (PNPS), denoted KPNPS, exhibited a strong correlation with both the pitching moment and the normal force of the model. Parameter KPNPS is defined as the spanwise integration of the pressure at a chord station, and is calculated specifically using the spanwise pressure data obtained at the chordwise station x/c=0.75. The test results confirm the viability of the proposed approach, indicating that sparse pressure measurements in conjunction with physics-based modeling offer valuable insights into the complex aerodynamic phenomena that influence the stability and control of modern aircraft. This novel method has the potential to be a useful tool to improve flight safety and refine aircraft design processes.
Authors
- Linkai Li (ORCID: https://orcid.org/0000-0002-3602-3365)
- Jinhan Guo (ORCID: https://orcid.org/0000-0002-4205-5566)
- Yunsong Gu (ORCID: https://orcid.org/0000-0002-3469-0092)
- Shuai Wang (ORCID: https://orcid.org/0000-0002-5698-725X)
- Yuzhe Zhang (ORCID: https://orcid.org/0000-0002-3975-4665)
Institutions
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- Journal of Aerospace Engineering
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1061/jaeeez.aseng-6178
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 1.32