A point cloud deep neural network metamodel method for aerodynamic prediction

Xiong, Fenfen; Zhang, Li; Hu, Xiao; Ren, Chengkun

doi:10.1016/j.cja.2022.11.025

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…With a sufficient amount of training data, these models possess powerful nonlinear fitting capabilities to establish the relationship between flight states and aerodynamic loads. Models such as support vector machines [7,8], random forest [9], and neural networks [10][11][12] have been employed for this purpose. Furthermore, recurrent neural networks improve accuracy by capturing the time lag effects of unsteady aerodynamics [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Dong,

Wang,

Lin

et al. 2024

Aerospace

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Unsteady aerodynamic prediction at high angles of attack is of great importance to the design and development of advanced fighters. In this paper, a weighted feature fusion model (WFFM) that combines the state-space model and neural networks is proposed to build an unsteady aerodynamic model for the precise simulation and control of post-stall maneuvers. In the proposed model, the influences of the physical model on neural networks are considered and adjusted by introducing a standardization layer and a new weighting method. A long short-term memory (LSTM) network is used to fuse two mappings: one from flight states to aerodynamic loads, and the other from low-fidelity data to high-fidelity data. Data from wind tunnel oscillation experiments at high angles of attack using a new kind of wire-driven parallel robot and the traditional tail support are used for verifying the proposed aerodynamic model. The output of the WFFM is also compared with predictions from other models, such as the state-space model, single LSTM model, and feature fusion model not including a feature weighting layer. Results demonstrate improved accuracy of the proposed model in the interpolation and extrapolation tests. Furthermore, the WFFM is applied to the flight simulation of F-16 with different control inputs. Compared with conventional models, the WFFM shows improved accuracy and better generalization capability.

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Section: Introductionmentioning

confidence: 99%

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Dong,

Wang,

Lin

et al. 2024

Aerospace

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“…Kashefi et al [39] proposed a novel deep-learning framework for predicting steady incompressible flow on multiple sets of irregular geometries based on PointNet and tested the effectiveness of the PIPN in the case of incompressible flows and thermal fields. To reduce the computational cost of numerical simulations, Xiong et al [40] designed a point-cloud deep neural network based on the PointNet architecture and established a mapping between the spatial position of the ONERA M6 wing and CFD calculation values to predict the aerodynamic characteristics of the three-dimensional geometry. The results indicate that the computational cost can be reduced by approximately 23% under comparable predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Transonic Flow over Cascades via Graph Embedding Methods on Large-Scale Point Clouds

Lan,

Wang,

Wang

et al. 2023

Aerospace

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In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design.

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Fast Prediction of Structural Stress Field Using Point Cloud Deep Learning

Yang,

Wang,

et al. 2024

Mechanisms and Machine Science

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A point cloud deep neural network metamodel method for aerodynamic prediction

Cited by 9 publications

References 32 publications

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Prediction of Transonic Flow over Cascades via Graph Embedding Methods on Large-Scale Point Clouds

Fast Prediction of Structural Stress Field Using Point Cloud Deep Learning

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