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

Institutions

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

Normal Force and Pitching Moment Trend Sensing of Delta Wing Based on Flow Physical Topology with Sparse Pressure Measurements

Linkai Li, Jinhan Guo, Yunsong Gu, Shuai Wang et al.
1 citations
Journal of Aerospace Engineering
Model Reduction and Neural Networks
1.32
article

Normal Force and Pitching Moment Trend Sensing of Delta Wing Based on Flow Physical Topology with Sparse Pressure Measurements

Linkai Li, Jinhan Guo, Yunsong Gu, Shuai Wang, Yuzhe Zhang
article en
1 citations

Abstract

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.

Journal of Aerospace EngineeringVol. 38(5)
Nanjing University of Aeronautics and Astronautics (CN)
Climate action
Openalex Percentile: Top 19%
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.

Normal Force and Pitching Moment Trend Sensing of Delta Wing Based on Flow Physical Topology with Sparse Pressure Measurements — Linkai Li, Jinhan Guo, et al. · Journal of Aerospace Engineering (2025) | TGRS Research Map | TGRS