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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Sun, Yanan Zhang, Guoyin Edwards, Michael G. Li, Chenfeng Beijing China Zienkiewicz Institute for Modelling Data and AI Swansea University Bay Campus SwanseaSA1 8EN United Kingdom

ISBN: (纸本)9783031829062

The study of hydraulic fracture patterns has important practical significance in oil and gas production. This research focuses on the study of hydraulic fracturing problems with peridynamics. A fully coupled fluid-filled explicit peridynamics model in saturated porous media is presented in this paper to study the two crack behaviours, including cracking branching in porous media under different loading conditions and the stepwise phenomenon. By comparing the numerical results, it is proved that this explicit peridynamics model is able to help understand the effect of the pore pressure in hydraulic fracturing branching and provide more evidence for stepwise phenomenon with peridynamics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Porous materials

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Implicit Bonded Discrete Element Method with Manifold Optimization

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ACM Transactions on Graphics 2025年第1期44卷 1-17页

作者： Lu, Jia-Ming Cao, Geng-Chen Li, Chenfeng Hu, Shi-Min BNRist Department of Computer Science and Technology Tsinghua University Beijing Beijing China Zienkiewicz Institute for Modelling Data and AI Swansea University Bay Campus Swansea United Kingdom BNRist Department of Computer Science and Technology Tsinghua University Beijing China

This article proposes a novel simulation approach that combines implicit integration with the Bonded Discrete Element Method (BDEM) to achieve faster, more stable, and more accurate fracture simulation. The new method leverages the efficiency of implicit schemes in dynamic simulation and the versatility of BDEM in fracture modeling. Specifically, an optimization-based integrator for BDEM is introduced and combined with a manifold optimization approach to accelerate the solution process of the quaternion-constrained system. Our comparative experiments indicate that our method offers better scale consistency and more realistic collision effects than finite element method and material point method fragmentation approaches. Additionally, our method achieves a computational speedup of 2.1 to 9.8 times over explicit BDEM methods. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Discrete element method fracture simulation optimization-based integrator manifold optimization

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Key Technologies and Future Development Trends of Intelligent Earth-Rock Dam Construction

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第3期1卷 1-18页

作者： Yujie Wang Yufei Zhao Biao Liu Naixin Wang Chenfeng Li State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower Research Beijing China Key Laboratory of Construction and Safety of Hydraulic Engineering of Ministry of Water Resources Beijing China Zienkiewicz Institute for Modelling Data and AI Swansea University Swansea UK

The rapid advancement of information technologies, such as cloud computing, internet of things (loT), and big data, has significantly enhanced the intelligence level in dam construction. Intelligent monitoring systems for dam filling have garnered considerable attention from water conservancy and hydropower engineering units, owing to their capability for refined management and quality control of layered and compartmentalized construction processes. The construction of earth-rock dams has undergone a transformation, shifting from manual management of dam compaction equipment, layer management, and quality control to a comprehensive intelligent approach. This new approach encompasses fine-grained control of unit engineering, dam material identification, unmanned driving technology, real-time assessment and monitoring of compaction quality, and early warning forecasting. This article presents a systematic overview of key technologies employed in the intelligent construction process of earth-rock dams. These technologies include the fine decomposition of building information modeling (BIM) models on web platforms, rapid identification of dam material particle size, the application of autonomous driving technology for intelligent dam filling, and real-time monitoring and evaluation of dam compaction characteristics. Additionally, the paper discusses the latest research results in the field, building upon existing technology progress, and concludes by exploring the future development trends of intelligent dam construction. The construction of intelligent earth-rock dams serve as a crucial reference and valuable lessons for the development and construction of high earth-rock dam projects.

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Neural networks meet hyperelasticity: A monotonic approach

arXiv

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arXiv 2025年

作者： Klein, Dominik K. Hossain, Mokarram Kikinov, Konstantin Kannapinn, Maximilian Rudykh, Stephan Gil, Antonio J. Cyber-Physical Simulation Department of Mechanical Engineering Technical University of Darmstadt Darmstadt64293 Germany Zienkiewicz Institute for Data Modelling and AI Faculty of Science and Engineering Swansea University SA1 8EN United Kingdom School of Mathematical and Statistical Sciences University of Galway Galway Ireland

We apply physics-augmented neural network (PANN) constitutive models to experimental uniaxial tensile data of rubber-like materials whose behavior depends on manufacturing parameters. For this, we conduct experimental investigations on a 3D printed digital material at different mix ratios and consider several datasets from literature, including Ecoflex at different Shore hardness and a photocured 3D printing material at different grayscale values. We introduce a parametrized hyperelastic PANN model which can represent material behavior at different manufacturing parameters. The proposed model fulfills common mechanical conditions of hyperelasticity. In addition, the hyperelastic potential of the proposed model is monotonic in isotropic isochoric strain invariants of the right Cauchy-Green tensor. In incompressible hyperelasticity, this is a relaxed version of the ellipticity (or rank-one convexity) condition. Using this relaxed ellipticity condition, the PANN model has enough flexibility to be applicable to a wide range of materials while having enough structure for a stable extrapolation outside the calibration data. The monotonic PANN yields excellent results for all materials studied and can represent a wide range of largely varying qualitative and quantitative stress behavior. Although calibrated on uniaxial tensile data only, it leads to a stable numerical behavior of 3D finite element simulations. The findings of our work suggest that monotonicity could play a key role in the formulation of very general yet robust and stable constitutive models applicable to materials with highly nonlinear and parametrized behavior. © 2025, CC BY-NC-ND.

关键词： Digital printing

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Graded porous scaffold mediates internal fluidic environment for 3D in vitro mechanobiology

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Computers in Biology and Medicine 2025年 186卷 109674-109674页

作者： De Rosa, Chiara Angela Wright, Christopher J. Xiong, Yi Del Giudice, Francesco Zhao, Feihu Department of Biomedical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom Zienkiewicz Institute for Modelling Data and AI Swansea University Swansea United Kingdom School of System Design and Intelligent Manufacturing Southern University of Science and Technology Guangdong Province Shenzhen China Complex Fluid Research Group Department of Chemical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom

Most cell types are mechanosensitive, their activities such as differentiation, proliferation and apoptosis, can be influenced by the mechanical environment through mechanical stimulation. In three dimensional (3D) mechanobiological in vitro studies, the porous structure of scaffold controls the local mechanical environment that applied to cells. Many previous studies have focused on the topological design of homogeneous scaffold struts. However, the impact of scaffold inhomogeneity on the mechanical environment, which is essential in mechanobiological application (e.g. for multi-cells co-culture), remains elusive. In this study, we use a computational fluid dynamics (CFD) approach together with data analysis to study the influence of a porosity gradient (10 %–30 % porosity difference) on the local and global mechanical environment (wall shear stress - WSS) within the commonly used structures of triple periodic minimal surfaces (TPMS). In addition, the anisotropy of internal WSS and scaffold permeability caused by the porosity gradient is investigated. It is found that the influence of anisotropy on the average WSS and permeability is up to 11 % and 31 %, respectively. These results, as theoretical references will be useful to tissue engineers and mechanobiologists for scaffold design and in vitro experiment planning such as integrated use of graded scaffold and bioreactors for specific cell types. © 2025 The Authors

关键词： Shear stress

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SoSpider:A bio-inspired multimodal untethered soft hexapod robot for planetary lava tube exploration

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Science China(Technological Sciences) 2023年第11期66卷 3090-3106页

作者： NIU LiZhou DING Liang ZHANG ShengJie YANG HuaiGuang GAO HaiBo DENG ZongQuan LIU GuangJun HOSSaiN Mokarram State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China Department of Aerospace Engineering Ryerson UniversityToronto ON M5B 2K3Canada Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUnited Kingdom

Soft robots have tremendous potential for applications in various fields,owing to their safety and flexibility embedded at the material *** robots,especially bio-inspired soft legged robots,have become one of the most active fields of current research in robotics thanks to their superior mobility and ability to face complex ***,it is arduous to establish a dynamic simulation model for soft robots,owing to their hyper-redundant degrees of freedom,hyper-elasticity,and nonlinearity of their soft *** this study,we designed,simulated,and fabricated a hexapod robot that achieves walking,crawling,pronking,and rolling with wheeled legs plus a soft body capable of shape change.A robot prototype was fabricated using 3D printing technology and soft silicone pneumatic ***,battery power,and control boards were integrated into the body of the robot for untethered *** have explored the capabilities of the robot in different conditions,especially in scenarios that simulate lunar and Martian environments,demonstrating the motion performance of the *** results have shown promising potentials of the developed robot for future applications in planetary lava tube *** experimental and simulation results also show good agreements that indicate the potential predictive roles of simulation tools for soft robot design,planning,and control.

关键词： multimodal soft robot pneumatic simulation planetary exploration

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A particle-resolved heat-particle-fluid coupling model by DEM-IMB-LBM

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第6期16卷 2267-2281页

作者： Ming Xia Jinlong Fu Y.T.Feng Fengqiang Gong Jin Yu Hunan Key Laboratory of Geomechanics and Engineering Safety Xiangtan UniversityXiangtan411105China Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwanseaSA18EPUK School of Civil Engineering Southeast UniversityNanjing211189China Fujian Research Center for Tunneling and Urban Underground Space Engineering Huaqiao UniversityXiamen361021China

Multifield coupling is frequently encountered and also an active area of research in geotechnical *** this work,a particle-resolved direct numerical simulation(PR-DNS)technique is extended to simulate particle-fluid interaction problems involving heat transfer at the grain *** this extended technique,an immersed moving boundary(IMB)scheme is used to couple the discrete element method(DEM)and lattice Boltzmann method(LBM),while a recently proposed Dirichlet-type thermal boundary condition is also adapted to account for heat transfer between fluid phase and solid *** resulting DEM-IBM-LBM model is robust to simulate moving curved boundaries with constant temperature in thermal *** facilitate the understanding and implementation of this coupled model for non-isothermal problems,a complete list is given for the conversion of relevant physical variables to lattice ***,benchmark tests,including a single-particle sedimentation and a two-particle drafting-kissing-tumbling(DKT)simulation with heat transfer,are carried out to validate the accuracy of our coupled *** further investigate the role of heat transfer in particle-laden flows,two multiple-particle problems with heat transfer are *** examples demonstrate that the proposed coupling model is a promising high-resolution approach for simulating the heat-particle-fluid coupling at the grain level.

关键词： Particle-fluid interaction Heat transfer Discrete element method(DEM) Lattice Boltzmann method(LBM) Dirichlet-type thermal boundary Direct numerical simulation

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Large strain constitutive modelling of soft compressible and incompressible solids: Generalised isotropic and anisotropic viscoelasticity

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Journal of the Mechanics and Physics of Solids 2025年

作者： Zeng Liu Rogelio Ortigosa Antonio J. Gil Javier Bonet Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE) Barcelona Spain Departament de Enginyeria Civil i Ambiental (DECA) Universitat Politècnica de Catalunya Barcelona Spain Computational Mechanics and Scientific Computing Group Technical University of Cartagena Campus Muralla del Mar 30202 Cartagena (Murcia) Spain Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus SA1 8EN United Kingdom

This paper discusses a new phenomenological continuum formulation for the constitutive modelling of viscoelastic materials at large strains. Following pioneering works in Sidoroff (1974), Lubliner (1985), Bergström (1998) and Reese and Govindjee (1997), the formulation shares some common ingredients with other phenomenological approaches, including the multiplicative decomposition of the deformation gradient into viscous and elastic contributions, the additive Maxwell-type decomposition of the strain energy density, and the definition of a set of kinematic internal state variables with their associated evolution laws. Our formulation departs from other state-of-the-art methodologies via three distinct novelties. First, and revisiting previous work by Bonet (2001), the paper introduces a thermodynamically consistent linear rate type evolution law in terms of stress-type variables, which resembles the return mapping algorithm typically used in elastoplasticity, facilitating the modelling link between both inelastic constitutive models. In this sense, the proposed viscoelastic evolution law can be identified with a classical plastic flow rule. Very importantly, the evolution law is shown to be compatible with the second law of thermodynamics by construction and have a closed-form solution in the case of incompressible viscoelasticity when using a prototypical neo-Hookean type of non-equilibrium strain energy density. Moreover, the paper shows how using the concept of a stress-driven dissipative potential, more general non-linear type of stress evolution laws can be straightforwardly constructed. Second, to facilitate the joint consideration of anisotropy and thermodynamic equilibrium, a frame indifferent stress free configuration is introduced which facilitates the definition of objective strain measures. Third, the methodology is extended from isotropy to transverse isotropy via the consideration of the appropriate structural tensor. The formulation is first displayed

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A novel, finite-element-based framework for sparse data solution reconstruction and multiple choices

arXiv

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arXiv 2024年

作者： Bielajewa, Wiera Baxter, Michelle Nithiarasu, Perumal Zienkiewicz Institute for Modelling Data and AI Swansea University Bay Campus SwanseaSA1 8EN United Kingdom AbingdonOX14 3DB United Kingdom

Digital twinning is gaining widespread popularity across various areas of engineering, and indeed it offers a capability of effective real-time monitoring and control, which are vital for cost-intensive experimental facilities, particularly the ones where extreme conditions exist. Sparse experimental measurements collected by various diagnostic sensors are usually the only source of information available during the course of a physical experiment. Consequently, in order to enable monitoring and control of the experiment (digital twinning), the ability to perform inverse analysis, facilitating the full field solution reconstruction from the sparse experimental data in real time, is crucial. Such solution reconstruction might be necessary to control a system, if a parameter to be controlled cannot be directly derived from the sparse measurements alone, as oftentimes is the case, for instance maximum temperature within a test piece. This paper shows for the first time that it is possible to directly solve inverse problems, such as solution reconstruction, where some or all boundary conditions (BCs) are unknown, by purely using a finite-element (FE) approach, without needing to employ any traditional inverse analysis techniques or any machine learning models, as is normally done in the field. This novel and efficient FE-based inverse analysis framework employs a conven- Copyright © 2024, The Authors. All rights reserved.

关键词： Cost engineering

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Machine learning for modelling unstructured grid data in computational physics: A review

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Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： Reinforcement learning

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