NUMERICAL MODELING AND MACHINE LEARNING IN PREDICTING THE PROPERTIES OF BARITE COMPOSITES: A COMPREHENSIVE ANALYSIS
Keywords:
barite composite, numerical modeling, finite element method, machine learning, homogenization, mechanical properties, radiation protection, microstructure, optimizationAbstract
This paper presents a comprehensive analysis of modern approaches to the numerical modeling of the production processes and property prediction of cement-based barite composites. A detailed literature review of the last two decades is conducted, highlighting key trends in the application of the Finite Element Method (FEM) and Monte Carlo methods for assessing mechanical and radiation-shielding properties. Particular attention is paid to the influence of the volume fraction of barite on composite properties such as Young’s modulus and Poisson’s ratio. The growing role of the homogenization approach and multiphysics modeling for accounting for the real material microstructure is emphasized. The paper also explores the promising direction of integrating numerical methods with machine learning algorithms to create high-accuracy surrogate models, which can significantly reduce computational costs and optimize composite composition. It is noted that, despite significant progress, the challenge of rigorous model verification with experimental data remains relevant. The analysis results demonstrate the nonlinear dependence of mechanical properties on barite content and form the basis for the intelligent design of materials with specified performance characteristics.
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