Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Pham, Van-Minh; Tyan, Maxim; Nguyen, Tuan Anh; Lee, C.J.; Nguyen, Luan Van; Lee, Jae-Woo

doi:10.1016/j.cja.2023.05.012

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…In order to estimate the precise location, identity, and real‐time situation of the target, information fusion of data from single or multiple sources is required. [ 1 ] As a typical link in the field of information fusion, situation awareness (SA) has produced many new structures, techniques, and methods in recent years. However, as the scale of the system expands and the complexity becomes higher and higher, it is urgent to introduce new methods and system structures to optimize the existing theoretical framework.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

Wang,

Yuan,

Jia

et al. 2024

Advcd Theory and Sims

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In order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.

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Section: Introductionmentioning

confidence: 99%

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

Wang,

Yuan,

Jia

et al. 2024

Advcd Theory and Sims

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“…Bu et al [32] utilized MHK for an efficient aerostructure optimization of helicopter rotors toward aeroacoustic noise reduction. Pham et al [33] proposed adaptive multi-fidelity data fusion strategy to incorporate multiple MFSM methods to efficiently handle data fusion problems featuring non-uniform aerodynamic data.…”

Section: Introductionmentioning

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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Reynolds number effect correction of multi-fidelity aerodynamic distributions from wind tunnel and simulation data

Yang,

Chen,

Gao

et al. 2023

Physics of Fluids

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Conventional wind tunnels cannot simulate actual flight Reynolds numbers because of testing condition constraints. The changes in aerodynamic characteristics caused by differences in the Reynolds number are especially sensitive on transonic supercritical airfoils and wings. Accordingly, this study proposes a correction method for the Reynolds number effect based on feature extraction of multi-fidelity aerodynamic distributions. Correcting wind tunnel test results at low Reynolds numbers with large amounts of numerical simulation data across various Reynolds numbers allows this method to achieve greater precision on airfoil pressure distribution predictions at high Reynolds numbers. The first step is to extract the primary features of the surface pressure distribution of the supercritical airfoil using the proper orthogonal decomposition technique. Next, a multi-fidelity neural network model is employed to relate the (low-fidelity) simulation data to the (high-fidelity) wind tunnel data acquired at low Reynolds numbers. Finally, the model is used to predict the airfoil pressure distributions at high Reynolds numbers, and the predictions are validated using wind tunnel test data collected from the RAE2822 airfoil. The results indicate that this approach can provide more accurate estimations than numerical simulations and single-fidelity models. Additionally, it can effectively reduce the impact of the Reynolds number difference on the transonic aerodynamic properties of supercritical airfoils.

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Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Cited by 4 publications

References 38 publications

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

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

Reynolds number effect correction of multi-fidelity aerodynamic distributions from wind tunnel and simulation data

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