2016/12/20 by Christopher Wales, Ann Gaitonde, Dorian Jones · 2 citations
Decision Sciences · Engineering · Physics and Astronomy · #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Structural Health Monitoring Techniques
paper · doi:10.2514/1.c033765
openalex publication_date 2016/12/20 · crossref created 2016/12/20 · crossref issued 2017/07/01 · crossref published 2017/07/01 · crossref published-print 2017/07/01 · crossref deposited 2022/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01 · crossref indexed 2026/08/04
This paper describes two approaches to the construction of reduced-order models from computational fluid dynamics to predict the gust response of airfoils and wings. The first is a linear reduced-order model constructed using the eigensystem realization algorithm from pulse responses, and the second approach modifies the linear reduced-order model using steady-state data to introduce some nonlinearity into the reduced-order model. Results are presented for the Future Fast Aeroelastic Simulation Technologies wing and the Future Fast Aeroelastic Simulation Technologies crank airfoil. These show that for gusts of large amplitude in the transonic regime the response exhibits nonlinearity due to shock motion. This nonlinearity is not captured well by linear reduced-order models; however, the nonlinear reduced-order model shows better agreement with the full nonlinear simulation results.