Development of Smoothing Algorithm for Stabilization of the Material and Fluid–Solid Interaction Problems Using Machine Learning

Abstract

 In the application of machine learning algorithms has gained significant attention in various scientific and engineering disciplines. This study focuses on the development of a novel smoothing algorithm utilizing machine learning techniques for the stabilization of material and fluid-solid interaction problems. The interaction between materials and fluids presents complex challenges in numerous fields, including civil engineering, biomechanics, and aerospace engineering. Traditional numerical methods often struggle to accurately model and simulate these intricate interactions, leading to unstable and unreliable results. Therefore, the incorporation of machine learning techniques into the computational analysis of such problems has the potential to improve accuracy and efficiency.

The proposed smoothing algorithm aims to address these challenges by leveraging the power of machine learning. It involves training a model to recognize and capture the underlying patterns and behaviors of the material and fluid-solid interactions. The algorithm employs advanced techniques such as artificial neural networks and deep learning architectures to learn from the available data, adaptively adjust the simulation parameters, and stabilize the computation process. The development process encompasses several stages. Initially, a comprehensive dataset is collected, comprising a wide range of material and fluid-solid interaction scenarios. The dataset includes information on the properties of the materials, fluid dynamics, and the resulting solid responses. This data is then used to train the machine learning model, enabling it to learn the underlying physics and behavior of the interaction process. The algorithm operates in real-time during simulations, continuously adjusting and refining the solution to maintain stability. By effectively addressing the instability issues commonly encountered in material and fluid-solid interaction problems, the proposed algorithm enhances the reliability and accuracy of the simulation results.

The effectiveness of the developed smoothing algorithm is validated through extensive numerical experiments and comparisons with traditional methods. Performance metrics such as stability, accuracy, computational efficiency, and convergence are carefully assessed to evaluate the algorithm's effectiveness in stabilizing material and fluid-solid interaction problems. This research presents a pioneering approach in utilizing machine learning techniques to develop a novel smoothing algorithm for stabilizing material and fluid-solid interaction problems. The results obtained from this study have the potential to significantly advance the accuracy and reliability of computational simulations in various engineering and scientific domains, enabling more robust design and analysis of structures and systems affected by material and fluid-solid interactions.