Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework

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Constrained sparse Galerkin regression (2016, methodology): “Other nonlinear modelling techniques, such as NARMAX (Billings 2013), have been widely applied to problems in fluid mechanics, including modelling of wave packets in a turbulent jet (Semeraro et al. 2017), flutter instability (Zhang et al. 2012) and unsteady aerodynamics (Glaz et al. 2010).”
Machine Learning for Fluid Mechanics (2019, methodology): “Neural networks are also frequently employed in nonlinear system identiﬁcation techniques, such as NARMAX, which are often used to model ﬂuid systems (Glaz et al. 2010).”
Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics (2014, methodology): “This paper reviews the recent development and applications of Volterra theory, linear and nonlinear indicial response methods, RBF, and a SBRFmodel using CFD data.”
Evaluation of aerodynamic loads via reduced order methodology (2015, background): “Some recent pertinent examples in the context of aeroelasticity for helicopter rotor blades can be found in [25,26], whereas a first attempt to address the entire aerodynamic field around a rotor via modal ROM is presented in [27].”
Aeroelastic and Aerothermoelastic Analysis in Hypersonic Flow: Past, Present, and Future (2011, methodology): “Typical approaches for constructing aerodynamic ROMs are proper orthogonal decomposition (POD) [81–84], Volterra series [82,85], and surrogates [71,86–92].”