Guochen Ding¹, Jing Zhang¹, Shurong Ding^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011410

Abstract Zirconium alloys have high strength, high corrosion resistance and low neutron absorption cross section, widely served as the nuclear cladding tubes or some other structural components. During the storage stage of spent fuels or in the lower-temperature reactor-core locations, the hydrogen atoms within the zirconium alloy components would diffuse to the crack tip owing to stress concentration, possibly initiating delayed hydride cracking (DHC) and posing a potential threat to nuclear safety. In this study, the CDM (continuum damage mechanics)-based multi-field coupling computational models are developed, with the hydride-induced hardening and embrittlement, hydride orientation contributions and… More >

Ching-Chi Hsu^1,*, Hsin-Hao Lin¹, Kao-Shang Shih²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011792

Abstract Decompression surgery is one of the useful methods to relieve the pressure on the spinal cord and nerves [1]. In computational simulation, various bone material modelling strategies have been used to model cortical bone and cancellous bone of spinal vertebrae [2,3]. However, the effects of the bone material modelling strategies on the biomechanics of the thoracolumbar spine are unclear. Thus, this study aimed to investigate the biomechanics of the thoracolumbar spine with various bone modelling strategies using a patient-specific finite element modelling technique.
Three-dimensional finite element models of the human thoracolumbar spine were developed from the… More >

Yuhong Zhao^1,2,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012951

Abstract For a long time, the phase-field method has been considered as a mesoscale phenomenological method lacking physical accuracy and unable to be associated with the mechanical/functional properties of materials, etc. Some misunderstandings existing in these viewpoints need to be clarified. Therefore, it is necessary to propose or adopt the perspective of “unified or unifying phase-field modeling (UPFM)” to address these issues, which means that phase-field modeling has multiple unifications. Specifically, the phase-field method is the perfect unity of thermodynamics and kinetics, the unity of multi-scale models from micro- to meso- and then to macroscopic scale, More >

Yang Chen¹, Baobin Xie¹, Weizheng Lu¹, Jia Li^1,*, Qihong Fang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011624

Abstract In order to accurately predict and evaluate the mechanical properties of multi-principal element alloys (MPEAs), some new models and simulation methods need to be developed to solve the problems caused by its unique natural characteristics, such as severe lattice distortion. The existing models are based on the development of low concentration alloys, and cannot be well applied to MPEAs. Here, we develop i) the random field theory informed discrete dislocation dynamics simulations based on high-resolution transmission electron microscopy, to systematically clarify the role of heterogeneous lattice strain on the complex interactions between the dislocation loop… More >

Renzi Bai^1,2,*, Julien Colmars³, Hui Cheng^1,2, Kaifu Zhang^1,2, Philippe Boisse³

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011719

Abstract The increasing use of composite material require more efficient and inexpensive manufacturing process analysis method to optimize the product quality. The manufacture of textile reinforced composites often requires the preforming of a dry textile reinforcement and the subsequent injection of a resin in Liquid Composite Moulding processes (LCM). The composite can also be produced by thermoforming a prepreg consisting of a textile reinforcement incorporating the unhardened matrix, so that the composite can be formed. In both cases (LCM and prepreg), the forming process is driven by the deformation of the textile reinforcement which is influenced… More >

Ying Song¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011329

Abstract Granular and columnar sea ice formed random pores containing gas and brine while growing in a polar environment. Building an appropriate microstructure of sea ice model to reveal its material singularities using standard methods is difficult. In this study, we develop a porous sea ice model based on coupled thermos-mechanical peridynamics [1-3] by considering the fluid flow and material transport in pores. The novel features of using the porous sea ice peridynamic model are as follows: (1) To establish the porous sea ice model, the sea ice pore equation is combined with the peridynamic equations. More >

Lei Peng^1,*, Jin Zhou², Xianfeng Zhang³, Zhongwei Guan^4,5

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011196

Abstract This paper presents the numerical modelling of the ballistic response of hybrid composite structures subjected to 7.62 mm projectile impact. This study focuses on the modelling of composites made of various materials, including ceramics, Ultra-High-Molecular-Weight Polyethylene (UHMWPE), Kevlar, and compressed wood, with fabrication of hybrid laminated structures that offer promising ballistic resistance capabilities. By employing a range of constitutive models and failure criteria, the finite element model simulates the ballistic behaviors of the constituent materials, facilitating a comprehensive understanding of their performance under high-velocity impacts. The core of the study lies in the comparison between… More >

Ruiqi Pan¹, Wei Xiong², Liang Hao^1,*, Yan Li^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011651

Abstract As metal additive manufacturing (MAM) becomes more widely used in engineering, an increasing number of novel lattice structures are being developed. However, most recently developed lattice structures do not match the requirement of MAM efficiently. Based on the Design for Additive Manufacturing (DfAM), comparing the mainstream implicit and explicit modelling methods, it is proposed to introduce a Subdivisional (Sub-D) modelling method to model lattice structures with better modelling versatility, 3D printability, and mechanical properties. To this end, a novel negative Poisson's ratio (NPR) structure is developed as an example to demonstrate the efficient and wide… More >

Lijun Xiao^1,*, Weidong Song¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010981

Abstract Additively-manufactured microlattice materials have attracted much attention due to their outstanding mechanical properties and energy absorption capacity. Considering the high cost of 3D printing, numerical simulation has become the most common approach for predicting and optimizing the mechanical performance of micro-lattice materials. The current study provides an efficient method to incorporate the printing process induced geometric defects in the lattice models. Numerical simulations are performed to precisely predict the mechanical response of the printed microlattice materials under quasi-static and dynamic loadings. Furthermore, the microlattice structures are graphically represented based on their mesoscopic structural characteristics. Accordingly, More >

Grace Gau¹, Minerva Singh^2,3,*

Revue Internationale de Géomatique, Vol.33, pp. 427-443, 2024, DOI:10.32604/rig.2024.055752 - 11 October 2024

Abstract This study examines how socio-economic characteristics predict flood risk in London, England, using machine learning algorithms. The socio-economic variables considered included race, employment, crime and poverty measures. A stacked generalization (SG) model combines random forest (RF), support vector machine (SVM), and XGBoost. Binary classification issues employ RF as the basis model and SVM as the meta-model. In multiclass classification problems, RF and SVM are base models while XGBoost is meta-model. The study utilizes flood risk labels for London areas and census data to train these models. This study found that SVM performs well in binary… More >