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Keywords

Finite element analysis, Machine learning, wind tunnel, stress distribution, Deformation

Document Type

Research Paper

Abstract

Wind tunnels are essential for examining aircraft model aerodynamics, accurately simulating real-world conditions, and enhancing design and performance evaluations. This study introduces a novel technique to improve the time and accuracy of stress distribution forecasts in wind tunnel simulations. This method combines Finite Element Analysis (FEA) with two regression models: Support Vector Machine (SVM) and k-Nearest Neighbors (kNN). The investigation begins with a thorough analysis of ANSYS fluent flow data, which reveals intricate fluid dynamics details within the wind tunnel. A comparative analysis of stress projections, supplemented by Root Mean Square Error (RMSE) metric, demonstrates the proposed methodology’s viability. High accuracy is noted in the SVM-based model, as evidenced by its 2.1% RMSE, which surpasses the kNN model's 5.6% RMSE. Notably, the stress distribution calculation took almost 2 hours in ANSYS.In contrast, it required only 10 seconds in SVM and 3 seconds in kNN, showcasing the time-efficient attributes of these models where they solely depend on the trained data. Moreover, the computational efficacy of the SVM and kNN models is highlighted, emphasizing their flexibility in stress analysis. This integrative approach introduces a promising potential in engineering simulations, yielding precise stress distribution forecasts that have the potential to advance aircraft design methodologies and wind tunnel evaluations.

References

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Highlights

A Finite Element Analysis, of an Aircraft model in a wind tunnel using ANSYS Fluent and Structural Analysis, is presented Artificial Intelligence-based machine learning models are presented, namely SVM and kNN regression models The stress distribution on the aircraft front wing is predicted by Machine Learning models, and RMSE  compared their accuracy

DOI

10.30684/etj.2023.142873.1552

First Page

135

Last Page

143

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