Published September 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Advanced Computational Methods for Simulating and Optimizing Stochastic Fracture: A Systematic Literature Review

Creators

  • 1. Department of Civil Engineering, University of Gondar, Gondar, Ethiopia.

Description

Abstract: Stochastic fracture processes, pervasive in diverse natural and engineered systems, pose intricate challenges for accurate simulation and optimization. This systematic literature review surveys the landscape of advanced computational methodologies to unravel and optimize stochastic fracture phenomena. Grounded in multidisciplinary perspectives spanning engineering, physics, and applied mathematics, the review navigates through the intricacies of simulation techniques and optimizations methods. From Finite Element Method (FEM) to Molecular Dynamics (MD) simulations, the review delineates the evolution and application of computational frameworks. It scrutinizes optimization strategies ranging from evolutionary algorithms to surrogate-assisted techniques, illuminating their efficacy in optimizing fracture properties amidst stochasticity. Drawing from applications in geological formations, engineered materials, and biomechanics, the review elucidates the diverse realms where advanced computational methods find resonance. Despite strides in computational prowess, challenges loom large, including computational complexity, validation dilemmas, and interdisciplinary communication barriers. Looking ahead, the review prognosticates on the integration of machine learning, novel algorithmic developments, and standardization endeavor’s to propel the frontier of stochastic fracture simulations towards unprecedented realms of understanding and optimization. Through synthesis and critique, this review engenders a roadmap for future research and underscores the transformative potential of advanced computational methods in deciphering stochastic fracture phenomena.

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Additional details

Identifiers

DOI
10.35940/ijese.H2579.12100924
EISSN
2319-6378

Dates

Accepted
2024-09-15
Manuscript received on 09 July 2024 | Revised Manu-script received on 30 July 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024

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