Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

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Review of deep learning-based aerodynamic shape surrogate models and optimization for airfoils and blade profiles

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(4)

Published: April 1, 2025

In recent years, deep learning technology has developed rapidly and shown great potential in the optimization of complex systems. aerodynamic shape optimization, traditional computational fluid dynamics experimental methods are limited due to issues efficiency cost. contrast, surrogate models have gradually become a new alternative their advantages nonlinear modeling, efficient computation, flexible design. These offer novel approaches through such as data regression, automatic differentiation, operator learning. This paper presents comprehensive review latest research progress field based on models, focusing key technologies, application cases, future development trends. The article first elaborates importance context airfoil blade profile introducing background motivation. Then, it discusses technologies challenges faced optimization. Subsequently, introduces detail model, including data- physics-drisven neural networks, Physics-Informed Neural Networks Deep Operator Networks, practical cases these networks Finally, looks into pointing out Kolmogorov–Arnold improving model accuracy interpretability, well types summarizes development.

Language: Английский

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Optimization of Structures and Composite Materials: A Brief Review

Eng—Advances in Engineering, Journal Year: 2024, Volume and Issue: 5(4), P. 3192 - 3211

Published: Dec. 2, 2024

Neural networks (NNs) have revolutionized various fields, including aeronautics where it is applied in computational fluid dynamics, finite element analysis, load prediction, and structural optimization. Particularly optimization, neural deep are extensively employed to enhance the efficiency of genetic algorithms because, with this tool, possible speed up analysis process, which will also optimization process. The main objective paper present how can help process optimizing geometries composition composite structures (dimension, topology, volume fractions, reinforcement architecture, matrix/reinforcement composition, etc.) compared traditional methods. This article stands out by showcasing not only studies related but those field mechanics, emphasizing that underlying principles shared applicable both domains. use NNs as a surrogate model has been demonstrated be great tool for process; some shown accurate their predictions, an MSE 1×10−5 MAE 0.007%. It observed its helps reduce time, such 47.5 times faster than full aeroelastic model.

Language: Английский

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Current Applications of Machine Learning in Additive Manufacturing: A Review on Challenges and Future Trends

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Language: Английский

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