Chenghuan Liu, Xiangbo Hu, Xiaogang Wang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.010910

Abstract Digital image correlation (DIC) is an emerging non-contact optical measurement method that tracks speckle patterns on the specimen surface to obtain the deformation, providing an advanced methodology for the quantitative evaluation of full-field strain. The present work focuses on the quantitative assessment of deformation from micro to macro scales based on the DIC method and examines damage evolution in metal materials under static and cyclic loading conditions. First, an SEM-based DIC method allowing high-resolution strain measurement at subgrain scales is developed for investigating strain partitioning in dual-phase steel. The results reveal that the strain distribution… More >

Weizheng Lu, Shuo Wang, Yang Chen, Jia Li^*, Qihong Fang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.010711

Abstract The multi-principal elemental alloys (MPEAs) exhibit excellent combinations of mechanical properties and radiation-resistant, are considered potential candidates for aerospace industries and advanced reactors. However, the quantitative contribution of microstructure on the strengthening mechanism remains challenging at the micro-scale, which greatly limits the long-term application. To address this, we developed a hierarchical multiscale simulation framework that covers potential physical mechanisms to explore the hardening effects of chemical short-range order (CSRO) and irradiation defects in MPEA. Firstly, by combining atomic simulation, discrete dislocation dynamics, and crystal plasticity finite element method, a hierarchical cross-scale model covering heterostructure lattice… More >

Zhuoyuan Zheng^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-1, 2025, DOI:10.32604/icces.2025.010939

Abstract Silicon (Si) anodes are promising candidates for lithium-ion batteries due to their high theoretical capacity and low operating voltage. However, the significant volume expansion that occurs during lithiation presents challenges, including material degradation and decreased cycle life. This study employs an electrochemical-mechanical-thermal coupled finite element model, supported by experimental validation, to investigate the impact of lithiation-induced deformation on the energy dissipation of Si anodes. We quantitatively investigate the effects of several key design parameters—C-rate, Si layer thickness, and lithiation depth—on energy losses resulting from various mechanisms, such as mechanical energy loss, polarization, and joule heating.… More >

Seunghwan Seo^1,2,*, Moonkyung Chung¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025

Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More > Graphic Abstract

Siying Zhu^1,#, Weijian Gao^2,#, Min Yi^1,2,*, Zhuhua Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 657-679, 2025, DOI:10.32604/cmc.2025.068242 - 29 August 2025

Abstract Microstructure topology evolution during severe plastic deformation (SPD) is crucial for understanding and optimising the mechanical properties of metallic materials, though its prediction remains challenging. Herein, we combine discrete cell complexes (DCC), a fully discrete algebraic topology model—with finite element analysis (FEA) to simulate and analyse the microstructure topology of pure copper under SPD. Using DCC, we model the evolution of microstructure topology characterised by Betti numbers (, , ) and Euler characteristic (). This captures key changes in GBNs and topological features within representative volume elements (RVEs) containing several hundred grains during SPD-induced recrystallisation.… More >

Zhi Yang^1,2, Xiaohui Zhang^1,2,*, Xinting Tong³, Yutang Zhao⁴, Teng Xia^1,2, Hua Wang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1133-1150, 2025, DOI:10.32604/fdmp.2025.061737 - 30 May 2025

Abstract In bottom-blown copper smelting processes, oxygen-enriched air is typically injected into the melt through a lance, generating bubbles that ascend and agitate the melt, enhancing mass, momentum, and heat transfer within the furnace. The melt’s viscosity, which varies across reaction stages, and the operating conditions influence bubble size and dynamics. This study investigates the interplay between melt viscosity and bubble diameter on bubble motion using numerical simulations and experiments. In particular, the volume of fluid (VOF) method and Ω-identification technique were employed to analyze bubble velocity, deformation, trajectories, and wake characteristics. The results showed that More >

Josef Walek^*, Petr Lichý

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3135-3153, 2025, DOI:10.32604/cmes.2025.059889 - 03 March 2025

Abstract Plastometric experiments, supplemented with numerical simulations using the finite element method (FEM), can be advantageously used to characterize the deformation behavior of metallic materials. The accuracy of such simulations predicting deformation behaviors of materials is, however, primarily affected by the applied rheology law. The presented study focuses on the characterization of the deformation behavior of AISI 1045 type carbon steel, widely used e.g., in automotive and power engineering, under extreme conditions (i.e., high temperatures, strain rates). The study consists of two main parts: experimentally analyzing the flow stress development of the steel under different thermomechanical… More >

Cheng Deng^1,2, Hui Zheng^2,*, Liangyong Gong¹, Rongping Zhang¹, Mengqi Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2147-2172, 2025, DOI:10.32604/cmes.2025.059750 - 27 January 2025

Abstract Since the plasticity of soil and the irregular shape of the excavation, the efficiency and stability of the traditional local radial basis function (RBF) collocation method (LRBFCM) are inadequate for analyzing three-dimensional (3D) deformation of deep excavation. In this work, the technique known as the direct method, where the local influence nodes are collocated on a straight line, is introduced to optimize the LRBFCM. The direct method can improve the accuracy of the partial derivative, reduce the size effect caused by the large length-width ratio, and weaken the influence of the shape parameters on the More >

Wenpeng Li¹, Zhenghe Liu¹, Yujing Ma¹, Zhuxuan Meng^2,*, Ji Ma³, Weisong Liu², Vinh Phu Nguyen⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1515-1543, 2025, DOI:10.32604/cmes.2025.059235 - 27 January 2025

Abstract This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior. Physical models involving deformation, such as collisions, vibrations, and penetration, are developed using the material point method. To reduce the computational cost of Monte Carlo simulations, response surface models are created as surrogate models for the material point system to approximate its dynamic behavior. An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order, effectively balancing the accuracy and computational efficiency of the surrogate model. Based on the sparse polynomial More >

Petr Opěla^1,*, Josef Walek^1,*, Jaromír Kopeček²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >