Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Abstract: Quantum computing has the potential to exponentially surpass classical computing in terms of computational power, but its practical applications need further expansion. At the same time, computational mechanics offers a wide range of applications, but faces challenges of significant computational power requirements arising from multi-scale, multi-physics, and extreme conditions, among others. Therefore, the complementary development of quantum computing and computational mechanics holds great promise. This paper reviews the current state of quantum computing applications in computational mechanics and discusses future trends in this field.
Xu Y C, Kuang Z T, Huang Q, Yang J, Hu H. Quantum computing: The new focus in computational mechanics. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-039.
Abstract: This paper reviews the research progress on symmetry and conservation laws in mechanical analysis. It begins by introducing Lie group symmetries in continuous systems, including the symmetries of differential equations, partial differential equations, functionals, and approximate Lie symmetries of perturbed differential equations, with practical applications demonstrated through examples. The paper then explores symmetries and conservation laws in discrete systems, focusing on the dynamics equations, Noether symmetries, Lie symmetries, and Mei symmetries, with explanations supported by specific application examples. Finally, it reviews symmetries and conservation laws in stochastic systems, discussing the symmetries of Ito and Stratonovich stochastic differential equations, particularly in the statistical sense. The aim of this paper is to provide theoretical references for subsequent research and to advance the development of related fields.
Qiu Z P, Qiu Y, Zhang P X. A review of research advances in analytical methods for symmetry and conservation laws in mechanical analysis. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-033.
Abstract: Biological intelligence, which includes features such as perception, memory, learning, problem-solving and decision-making, is widely observed in humans, animals and other higher organisms with nervous systems. Recent studies have shown that single cells also exhibit behaviours that resemble human-like intelligence in their interactions with the microenvironment, such as “multimodal perception”, “problem solving”, “learning and memory”, and “evolutionary adaptation”. Cellular intelligence, as a newly proposed and disruptive theoretical concept, raises fundamental questions, including the principles underlying the emergence of cellular intelligence, the mechanisms by which collective cell behaviour emerges as collective intelligence, and the evolutionary drivers for single cells to evolve into multicellular life forms. As the fields of biomechanics and mechanobiology have advanced, numerous studies have demonstrated the significant influence of the mechanical microenvironment on cellular physiological behaviour. Under mechanical stimulation, even single cells exhibit intelligent behaviours similar to those observed in higher organisms. Based on this, the concept of “cellular mechanical intelligence” is proposed in this paper. We summarise the characteristics of intelligent behaviours in terms of mechanical perception, mechanical decision making, mechanical memory and mechanical learning, with the aim of providing new insights and perspectives on the mechanisms underlying cellular mechanical intelligence and its potential applications, such as in cellular intelligent medicine.
Cheng B, Lu M N, Jia Y B, Xu F. Cellular Mechanical Intelligence. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-028.
Abstract: On-orbit assembly of ultra-large space structures serves as the technological foundation for future space missions including high-capacity space-based communications, high-precision space-based observations and space-based solar power stations. It holds significant scientific and engineering values. Addressing the demands for assembling ultra-large structures like 100-meter parabolic antennas on orbit, this review article surveys the research progress and challenges in the dynamics and control of ultra-large space structures assembled on orbit. The article focuses on five key aspects, including the overall assembly design and its dynamic problems, the dynamic modeling and computation of flexible multibody systems, the motion planning and control of robots, the dynamic verification and adjustment of assembly outcomes, and the ground simulation experiments. It highlights the necessity of solving critical issues such as the multi-scaled spatiotemporal coupling dynamics of flexible components undergoing large overall motions, the efficient motion planning and accurate control method of robots, and the thermal-mechanically coupled error verification and adjustment strategies. As such, the necessity requires a comprehensive research framework integrating theoretical analysis, numerical simulation, and ground experimental validation to realize the ultra-large space structures in a scale from 100 meters to 1000 meters. Finally, the article outlines research priorities for the next decade, including the efficient dynamics modeling, the motion planning and control of robots in complex environments, the dynamic prediction and adjustment of multi-module closed-loop assembly, and the earth-space consistent experiment validation systems, providing systematic suggestions for promoting the on-orbit assembly technology of ultra-large space structures.
Hu H Y, Tian Q, Wen H, Luo K, Ma X F. Dynamics and Control of On-Orbit Assembly of Ultra-Large Space Structures. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-044.
Abstract: Fatigue life models are fundamental when assessing the integrity and reliability of engineering components made of metallic materials. Hence, a plethora of domain knowledge-driven models have been developed over the past centuries, pursuing the consistency with fatigue failure mechanisms and the rationality of mathematical expressions. They generally demonstrate physical significance and can describe the complex processes of fatigue damage evolution explicitly and comprehensively. However, with the increasing demand for the operational safety of critical components and high-performance structural materials emerging constantly, they are facing limitations in the aspects of predictive capability, application scope, and engineering practicality. As an alternative, data-driven models, under the impetus of Artificial Intelligence tide, have attained growing attention and found increasing applications in life-prediction issues under various loading patterns. Data-driven models feature their powerful ability to derive optimal explicit/implicit relationships between fatigue life with numerous influential factors, without suffering from human errors. Moreover, they can quickly discover the physical laws governing fatigue failure which are difficult to be clarified by domain knowledge-driven models. Nowadays, data-driven models are recognized as opening a new pathway for fatigue damage analysis and life prediction, being a hot spot in fatigue research. This paper reviews the progress of research in developing data-driven models for predicting the fatigue life of metallic materials. Different types of data-driven models, including pure data-driven models and knowledge informed data-driven models, are summarized, along with their distinct construction methodologies and application advantages. The future prospects and challenges in this field are also discussed.
Gan L, Wu H, Zhong Z. Advances in data-driven models for fatigue life prediction of metallic materials. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-025.
Abstract: Ventilated supercavity drag reduction is a key technology to break through the traditional underwater speed limit and achieve high-speed operation of underwater vehicles, which has important engineering application value. The navigation stability of underwater vehicles is a bottleneck problem that restricts the development of supercavitating vehicles, which is closely related to the stability of ventilated supercavity. Therefore, accurate prediction and control of supercavity shape are one of the key factors in the overall design of supercavitating vehicles. This paper first introduces the research progress on the flow morphology characteristics of ventilated supercavities under different flow conditions, and further sorts out the key scientific issues that affect the flow morphology, including the characteristics and stability mechanism of the cavity interface, the closure mechanism of the supercavity, and the interaction between the jet and the supercavity. Finally, based on the understanding and recognition of the morphology of ventilated supercavities, a method for achieving flow control of ventilated supercavities is introduced.
Wang Z Y, Wang J Z, Huang J, Wang Z, Wang Y W. Research progress on the stability mechanism and control of ventilated supercavitation. AdvancesinMechanics, 2025, 55(1): 1-43. doi: 10.6052/1000-0992-24-024.
Abstract: Direct time integration methods play a critical role in the numerical computation of large-scale nonlinear dynamic systems, particularly in the field of engineering simulation and design. The self-starting single-solve explicit time integration methods have become essential tools in this domain due to their efficiency and reliability in handling complex nonlinear systems. However, as these algorithms continue to evolve and diversify, their performance varies significantly, underscoring the urgent need for a systematic review and in-depth analysis of their capabilities. This paper first introduces the key performance metrics for evaluating time integration methods, including accuracy, stability, amplitude and phase error, providing a theoretical foundation for readers. It then offers a detailed review of the development of self-starting single-solve explicit time integration methods, systematically tracing the evolution of various algorithms. Finally, the performance of several self-starting single-solve explicit methods is compared in terms of spectral properties, accuracy, stability, and error characteristics, with numerical verification performed using typical examples and engineering structures. The paper highlights two explicit methods that currently exhibit superior performance: the fully explicit GSSE method and the velocity-implicit GSSI method. Both methods are characterized by their self-starting capability, single-solution, explicitness, maximized conditional stability, controllable numerical dissipation (over the full range), and identical second-order accuracy. The primary distinction between the two lies in the computational effort required for damping problems and the size of the conditional stability domain in the presence of damping. The paper also explores future research directions for explicit time integration methods, emphasizing the potential for further optimization and development.
Li J Z, Liu Y K, Yu K P, Cui N G. Research progress and performance evaluations for self-starting single-solve explicit time integrators. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-030.
Abstract: This paper is intended to reconcile the stress-based and strain-based formulations for material failure criteria, where a longstanding and deep division is present. The two approaches do not naturally agree with each other, and they not genuinely complement each other, either. Most popular criteria are stress-based when originally proposed, including the maximum stress, Tresca, von Mises, Raghava-Caddell-Yeh and the Mohr criteria. Their formulations are unique and self-consistent, i.e. capable of reproducing the input data. Their strain-based counterparts, with the maximum strain criterion being considered as the strain-based counterpart of the maximum stress criterion, are neither unique nor necessarily self-consistent. It has been proven that the self-consistent ones reproduce their respective stress-based counterparts identically in effect with a disadvantage of requiring an additional material property to apply, without a single benefit. For the Mohr criterion as a special case, a strain-based counterpart is simply infeasible in general. All undesirable features of strain-based criteria are rooted in a single source: the failure strains can only be measured under a uniaxial stress state, which corresponds to a combined strain state in general, not a uniaxial strain state! Given the arguments presented, the reconciliation proves to be biased completely towards the stress-based side if mathematics, logic and common sense prevail over perception and prejudice.
Li S G. Stress or Strain?. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-035.
Abstract: Slender pipes conveying fluid are an important structure in various engineering equipment systems such as engine hydraulic device, aviation tanker, nuclear heat exchanger and offshore drilling platform. When the flow velocity is sufficiently high, the slender pipe may be subjected to flow-induced instability including buckling and flutter, which may lead to safety accidents in serious cases. Flow-induced instability and nonlinear vibration of pipes conveying fluid are typical fluid-structure interaction behaviors, and have become a generic paradigm and fertile dynamics problem in nonlinear dynamics and fluid-structure interaction mechanics. After establishing governing equation, clarifying the stability mechanism and analyzing the nonlinear vibration mechanism of pipes conveying fluid, much attention has been payed to the large-deformation dynamics of this dynamical system in recent years. In this review, the research progress of nonlinear vibrations, especially the large-deformation bending dynamics of slender pipes are systematically introduced. Firstly, the nonlinear characteristics and classification of the fluid-conveying pipe system are summarized, and the applicability of some common assumptions is briefly analyzed. Secondly, the Taylor expansion approximation model, geometrically exact model, absolute node coordinate formulation model, data-driven model and other related modeling and solving methods are reviewed. Then, the nonlinear dynamics mechanism and evolution law of cantilevered and supported pipes are reviewed, and some recent research progress of cantilevered pipes from small-deformation hypothesis to large-deformation response is emphasized. On this basis, several typical methods of improving the stability of the pipe, suppressing the nonlinear vibrations of the pipe and utilizing the large-deformation response of the pipe are also introduced. Finally, the research status of large-deformation dynamics of slender pipe conveying fluid is summarized, and several basic scientific problems worthy of attention are pointed out.
Chen W, Cao R Q, Hu J C, Dai H L, Wang L. Recent advances in research on large-deformation dynamics of slender pipes conveying fluid. AdvancesinMechanics, in press. doi: 10.6052/1000-0992-24-027.
Abstract: In recent years, deep learning has become ubiquitous and is empowering various fields. In particular, the combination of artificial intelligence and traditional science (AI for science, AI4Science) has attracted widespread attention. In the field of AI4Science, the use of artificial intelligence algorithms to solve partial differential equations (AI4PDEs) has become the focus of computational mechanics research. The core of AI4PDEs is to fuse data with equations and can solve almost any PDEs. Due to the advantages of AI4PDEs in data fusion, computational efficiency using AI4PDEs is usually increased by tens of thousands of times compared to traditional algorithms. Therefore, this article comprehensively reviews the research on AI4PDEs, summarizes the existing AI4PDEs algorithms and theories, discusses its application in solid mechanics, including forward and inverse problems, and outlines future research directions, especially the foundation model of computational mechanics. Existing algorithms of AI4PDEs include physics-informed neural networks (PINNs), deep energy methods (DEM), operator learning, and (physics-informed neural operator, PINO). AI4PDEs has numerous applications in scientific computing, and this paper focuses on application of AI4PDEs in the forward and inverse problems of solid mechanics. The forward problems include linear elasticity, elasto-plasticity, hyperelasticity, and fracture mechanics; while the inverse problems encompass the identification of material parameters, constitutive laws, defect recognition, and topology optimization. AI4PDEs represents a novel method of scientific simulation, which offers approximate solutions for specific problems by leveraging large datasets and then fine-tunes according to the specific physical equations, avoiding the need to start calculations from scratch as traditional algorithms do. Thus, AI4PDEs is a prototype for the foundation model of computational mechanics in the future, capable of significantly accelerating traditional numerical methods. We believe that utilizing artificial intelligence to empower scientific computing is not only a vital direction for the future of computation but also a dawn of humanity in scientific research, laying the foundation for mankind to reach new heights in scientific development.
Wang Y Z, Zhuang X Y, Timon R, Liu Y H. AI for PDEs in solid mechanics: A review. AdvancesinMechanics, 2024, 54(4): 1-57. doi: 10.6052/1000-0992-24-016.
Abstract
HTML
PDF
Cited by
Email Alert
RSS