Multifidelity coKriging for High-Dimensional Output Functions with Application to Hypersonic Airloads Computation

Rokita, Tomer; Friedmann, Peretz P.

doi:10.2514/1.j056620

Cited by 22 publications

(4 citation statements)

References 28 publications

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“…Co-Kriging, 2000 [22,24,30] √ Bayesian HK, 2012 [25] √ Bayesian MCK, 2012 [24] √ Bayesian IHK, 2018 [12] √ Bayesian MVCM, 2015 [19] √ BF GVFM, 2015 [20] √ BF co-BRF, 2017 [40] √ Bayesian MFGP, 2018 [41] √ Bayesian POD-co-Kriging, 2018 [42] √ Bayesian SM-VFM, 2018 [26] √ SM, BF MHK, 2020 [31,32] √ Bayesian MDNN, 2020 [13] √ BF GCK, 2020 [43,44] √ Bayesian TL-VFSM, 2021 [27] √ BF GAN-MDF, 2022 [29] √ BF MMGP, 2021 [45] √ Bayesian WS, PC-DIT, PC-CSC, 2016 [39] √ Bayesian ECK, 2018 [36] √ Bayesian LRMFS, 2018 [37] √ BF VWS-MFS, 2021 [46] √ Bayesian NHLF-co-Kriging, 2022 [38] √ Bayesian Our work (EHK)…”

Section: Granularity Of Lf Datasets Mfsm Framework Type Multi-level N...mentioning

confidence: 99%

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Pham,

Tyan,

Nguyen

et al. 2023

Aerospace

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Multi-fidelity surrogate modeling (MFSM) methods are gaining recognition for their effectiveness in addressing simulation-based design challenges. Prior approaches have typically relied on recursive techniques, combining a limited number of high-fidelity (HF) samples with multiple low-fidelity (LF) datasets structured in hierarchical levels to generate a precise HF approximation model. However, challenges arise when dealing with non-level LF datasets, where the fidelity levels of LF models are indistinguishable across the design space. In such cases, conventional methods employing recursive frameworks may lead to inefficient LF dataset utilization and substantial computational costs. To address these challenges, this work proposes the extended hierarchical Kriging (EHK) method, designed to simultaneously incorporate multiple non-level LF datasets for improved HF model construction, regardless of minor differences in fidelity levels. This method leverages a unique Bayesian-based MFSM framework, simultaneously combining non-level LF models using scaling factors to construct a global trend model. During model processing, unknown scaling factors are implicitly estimated through hyperparameter optimization, resulting in minimal computational costs during model processing, regardless of the number of LF datasets integrated, while maintaining the necessary accuracy in the resulting HF model. The advantages of the proposed EHK method are validated against state-of-the-art MFSM methods through various analytical examples and an engineering case study involving the construction of an aerodynamic database for the KP-2 eVTOL aircraft under various flying conditions. The results demonstrated the superiority of the proposed method in terms of computational cost and accuracy when generating aerodynamic models from the given multi-fidelity datasets.

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Section: Granularity Of Lf Datasets Mfsm Framework Type Multi-level N...mentioning

confidence: 99%

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Pham,

Tyan,

Nguyen

et al. 2023

Aerospace

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“…The accuracy of the surrogate can be significantly enhanced for the same computational budget if multiple fidelity models or grid levels are available [31][32][33]. Co-Kriging can be considered a powerful correction process which makes use of the correlation between cheap and expensive data to enhance the prediction accuracy.…”

Section: Surrogate Modelingmentioning

confidence: 99%

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

Wauters

Degroote

2021

Journal of Aircraft

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In this paper the application of a wing fence on an unmanned aerial vehicle (UAV) is examined. The UAV under consideration is characterized by flow separation initiating at the tip, leading to a loss of lift and controllability and the appearance of a nose-up pitching moment. A possible solution to this problem is the use of wing fences: plates placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. Firstly, existing theories in regard to the working of wing fences are brought together. Secondly, the sensitivity of stall speed and controllability to the design variables of the wing fence are laid bare. Finally, the aerodynamic and stability characteristics of the UAV as a function of the design variables are assessed. To accomplish the aforementioned three objectives in both an affordable and accurate manner, computational fluid dynamics (CFD) simulations using the − model to correctly model the low Reynolds effects that characterize the flow over a UAV and surrogate modeling in the form of regressive universal co-Kriging are brought together.

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“…Multifidelity methods have been used in many fields, such as mechanism analysis, optimization design [29][30][31], statistical inference, and uncertainty quantification [32]. In the field of fluid dynamics, some scholars have applied the multifidelity method to flapping wing dynamics analysis [33,34], aerodynamic optimization [35][36][37][38], flight simulation [28,39,40], hypersonic aerodynamic load prediction [41], low-fidelity turbulence model correction [42,43], and uncertainty quantification of fluid dynamics system [44][45][46][47]. It is worth noting that most of these studies focus on the evaluation of steady-state aerodynamics to achieve rapid simulation and optimization, while there are few studies on unsteady multifidelity aerodynamic modeling.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Unsteady Aerodynamics Reduced Order Model of Airfoils Based on Algorithm Fusion and Multifidelity Framework

Shi

Wan

et al. 2021

International Journal of Aerospace Engineering

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A reduced order modeling method based on algorithm fusion and multifidelity framework for nonlinear unsteady aerodynamics is proposed to obtain a low-cost and high-precision unsteady aerodynamic model. This method integrates the traditional algorithm, intelligent algorithm, and multifidelity data fusion algorithm. In this method, the traditional algorithm is based on separated flow theory, the intelligent algorithm refers to the nonlinear autoregressive (NARX) method, and the multifidelity data fusion algorithm uses different fidelity data for aerodynamic modeling, which can shorten the time cost of data acquisition. In the process of modeling, firstly, a multifidelity model with NARX description provides a general intelligent algorithm framework for unsteady aerodynamics. Then, based on the separated flow theory, the correction equation from low-fidelity model to high-fidelity result is constructed, and the cuckoo algorithm based on chaos optimization is used to identify the parameters. In order to verify the effectiveness of the method, an unsteady aerodynamic model of NACA0012 airfoil is established. Three kinds of data with low, medium, and high fidelity are used for modeling. The low-fidelity and medium-fidelity data is obtained from the CFD-Euler solver and CFD-RANS solver, respectively, while the high-fidelity data comes from the experimental results. Then, the model is established, and its prediction of unsteady aerodynamic coefficients is in good agreement with the CFD results and the experimental data. After that, the model is applied to a two-dimensional aeroelastic system, and the bifurcation and limit cycle response analysis are compared with the experimental results, which further shows that the model can accurately capture the main flow characteristics in the flow range of low speed and high angle of attack. In addition, the convergence of the model is studied; the accuracy and generalization ability as well as applicability scope of the model are compared with other aerodynamic models and finally discussed.

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Multifidelity coKriging for High-Dimensional Output Functions with Application to Hypersonic Airloads Computation

Cited by 22 publications

References 28 publications

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

Nonlinear Unsteady Aerodynamics Reduced Order Model of Airfoils Based on Algorithm Fusion and Multifidelity Framework

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