Fusing wind-tunnel measurements and CFD data using constrained gappy proper orthogonal decomposition

Mifsud, Michael; Vendl, Alexander; Hansen, Lars-Uwe; Görtz, Stefan

doi:10.1016/j.ast.2018.12.036

Cited by 42 publications

(22 citation statements)

References 16 publications

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“…However, the projection process is intrusive, requiring access to the codes that implement the high-dimensional operators of the original equations. One way to avoid the intrusive projection is to learn a map from input parameters to coefficients of the reduced basis, e.g., via gappy POD [5,6], or using radial basis functions [7], a neural network [8,9,10] or a nearest-neighbors method [10]. However, these approaches are agnostic to the dynamics of the system in that they do not model the evolution of the system state over time.…”

Section: Introductionmentioning

confidence: 99%

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Qian

Krämer

Peherstorfer

et al. 2020

Physica D: Nonlinear Phenomena

226

148

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We present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system's governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh-Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

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Section: Introductionmentioning

confidence: 99%

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Qian

Krämer

Peherstorfer

et al. 2020

Physica D: Nonlinear Phenomena

226

148

View full text Add to dashboard Cite

show abstract

“…For example, the low-fidelity empirical method computes aerodynamic coefficients much faster and covers a wider range compared to the data obtained by CFD or W/T test. Numerous data fusion techniques (8,9) have been developed to combine various sets of data with different levels of fidelity and distribution domain. This research tries to develop a linear correction technique to combine the existing database with the high-fidelity estimates from flight test data.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of light aircraft 6 DOF simulation using flight test data in longitudinal motion

et al. 2021

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This paper proposes a procedure to improve the accuracy of the light aircraft 6 DOF simulation model by implementing model tuning and aerodynamic database correction using flight test data. In this study, the full-scale flight testing of a 2-seater aircraft has been performed in specific longitudinal manoeuver for model enhancement and simulation validation purposes. The baseline simulation model database is constructed using multi-fidelity analysis methods such as wind tunnel (W/T) test, computational fluid dynamic (CFD) and empirical calculation. The enhancement process starts with identifying longitudinal equations of motion for sensitivity analysis, where the effect of crucial parameters is analysed and then adjusted using the model tuning technique. Next, the classical Maximum Likelihood (ML) estimation method is applied to calculate aerodynamic derivatives from flight test data, these parameters are utilised to correct the initial aerodynamic table. A simulation validation process is introduced to evaluate the accuracy of the enhanced 6 DOF simulation model. The presented results demonstrate that the applied enhancement procedure has improved the simulation accuracy in longitudinal motion. The discrepancy between the simulation and flight test response showed significant improvement, which satisfies the regulation tolerance.

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“…It was originally proposed for the reconstruction of human face images in [13] and later further developed for fluid dynamic applications in [14]. Modified versions of the approach were introduced in [15][16][17] and successfully applied to reconstruct surface pressure coefficient distributions from wind tunnel sensor data and a set of pre-computed CFD simulations. The method was demonstrated to be able to accurately predict shock waves for industrial-relevant aircraft configurations in the transonic flow regime.…”

Section: Introductionmentioning

confidence: 99%

An alternative wind tunnel data correction based on CFD and experimental data in the transonic flow regime

Bertram

Hoffmann

Goertz

et al. 2021

Aiaa Aviation 2021 Forum

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This paper presents an alternative approach to correct wind tunnel data through the use of CFD solutions. The correction is based on the difference between the measured pressures in the wind tunnel and the pressures predicted with CFD in free flow condition, at angles of attack and Mach numbers surround the wind tunnel data point. Optimization is used to find the combination of angle of attack and Mach number that minimizes the difference between the measured pressures and the predicted pressures. A surrogate model is used to approximate the CFD data to enhance the computational efficiency of the approach. The result of the optimization is a corrected angle of attack and Mach number that corresponds to a pressure distribution under free flight conditions that best fits the wind tunnel experiment in terms of the objective function used. It is demonstrated that when all wing pressures are used in the objective function the resulting corrections are in agreement with wall interference corrections as predicted with a wall pressure signature method.

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Fusing wind-tunnel measurements and CFD data using constrained gappy proper orthogonal decomposition

Cited by 42 publications

References 16 publications

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

Enhancement of light aircraft 6 DOF simulation using flight test data in longitudinal motion

An alternative wind tunnel data correction based on CFD and experimental data in the transonic flow regime

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