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2026, 56(1): 1-50.
doi: 10.6052/1000-0992-25-018
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Understanding the relationships between material microstructures and their mechanical performance and using them to make predictions are pivotal topics in solid mechanics. From Galileo’s beam bending analysis, Cauchy’s stress definition to Arrhenius-based creep laws, theoretical and simulation frameworks find great success in addressing engineering problems. Yet, the spatiotemporal complexity challenges the conventional ‘observation-hypothesis-model’ approach for structural integrity in key industrial sectors such as aerospace, nuclear energy, and semiconductors. Recent progress and fusion of high-performance computing, high-throughput experiments, data science, and artificial intelligence provide a complementary solution to scientific discovery and engineering deployment on these issues. However, unlike their applications in vision and language domains, engineering science demands stronger data-model inference capabilities. High-quality, physically consistent databases and digital libraries are needed to enhance model performance, generalization, and interpretability. Concepts such as “physics transfer” and “reality reconstruction” offer guiding principles for modeling and predicting complex behaviors. With further support from cognitive science, intelligent agents and physical intelligence are increasingly capable of assisting, or even replacing, researchers in conducting exploration and reasoning in complex, dynamic scenarios. This paper reviews key insights of the complexities in solid mechanics and discusses active research areas through the lenses of learning theory and open science, with particular emphasis on multiscale mechanics and the long-term service behavior of materials and structures.
Understanding the relationships between material microstructures and their mechanical performance and using them to make predictions are pivotal topics in solid mechanics. From Galileo’s beam bending analysis, Cauchy’s stress definition to Arrhenius-based creep laws, theoretical and simulation frameworks find great success in addressing engineering problems. Yet, the spatiotemporal complexity challenges the conventional ‘observation-hypothesis-model’ approach for structural integrity in key industrial sectors such as aerospace, nuclear energy, and semiconductors. Recent progress and fusion of high-performance computing, high-throughput experiments, data science, and artificial intelligence provide a complementary solution to scientific discovery and engineering deployment on these issues. However, unlike their applications in vision and language domains, engineering science demands stronger data-model inference capabilities. High-quality, physically consistent databases and digital libraries are needed to enhance model performance, generalization, and interpretability. Concepts such as “physics transfer” and “reality reconstruction” offer guiding principles for modeling and predicting complex behaviors. With further support from cognitive science, intelligent agents and physical intelligence are increasingly capable of assisting, or even replacing, researchers in conducting exploration and reasoning in complex, dynamic scenarios. This paper reviews key insights of the complexities in solid mechanics and discusses active research areas through the lenses of learning theory and open science, with particular emphasis on multiscale mechanics and the long-term service behavior of materials and structures.
2026, 56(1): 51-122.
doi: 10.6052/1000-0992-25-024
Abstract:
Thin-walled structures are commonly found in aircraft. As advanced aircraft evolve to meet the demands of wide speed ranges, transmedium capabilities, and large sizes, the vibro-acoustic environments of thin-walled structures have become increasingly complex. Consequently, there is a pressing need for low-frequency, wide-band, and time-varying vibro-acoustic control. The rapid advancement of metastructures/metamaterials has opened new opportunities for breakthroughs in air-vehicle technologies. Thin-walled metastructures based on the local-resonance mechanism offer significant advantages in addressing the challenges of vibro-acoustic control of aircraft. This paper reviews the progress of passive and piezoelectric thin-walled metastructures, focusing on their vibration suppression and sound insulation capabilities, and provides a comparative analysis of their evolutionary process and technical features. It offers guidelines for designing thin-walled metastructures in advanced aircraft. First, the mechanisms of local-resonance bandgaps in both passive and piezoelectric thin-walled metastructures are explained, along with their sound-insulation mechanisms, which lays the theoretical foundation for introducing research progress of this area. Additionally, the research progress of thin-walled metastructures for vibration suppression and sound insulation is reviewed, with particular attention to nonlinear thin-walled metastructures. Subsequently, the applications of thin-walled metastructures in addressing vibro-acoustic control issues of air vehicles are discussed. Finally, this paper offers future outlooks for thin-walled metastructures in air vehicles, focusing on optimal design, intelligent tuning, multifunctional integration, adaptability to extreme environments and precision manufacturing.
Thin-walled structures are commonly found in aircraft. As advanced aircraft evolve to meet the demands of wide speed ranges, transmedium capabilities, and large sizes, the vibro-acoustic environments of thin-walled structures have become increasingly complex. Consequently, there is a pressing need for low-frequency, wide-band, and time-varying vibro-acoustic control. The rapid advancement of metastructures/metamaterials has opened new opportunities for breakthroughs in air-vehicle technologies. Thin-walled metastructures based on the local-resonance mechanism offer significant advantages in addressing the challenges of vibro-acoustic control of aircraft. This paper reviews the progress of passive and piezoelectric thin-walled metastructures, focusing on their vibration suppression and sound insulation capabilities, and provides a comparative analysis of their evolutionary process and technical features. It offers guidelines for designing thin-walled metastructures in advanced aircraft. First, the mechanisms of local-resonance bandgaps in both passive and piezoelectric thin-walled metastructures are explained, along with their sound-insulation mechanisms, which lays the theoretical foundation for introducing research progress of this area. Additionally, the research progress of thin-walled metastructures for vibration suppression and sound insulation is reviewed, with particular attention to nonlinear thin-walled metastructures. Subsequently, the applications of thin-walled metastructures in addressing vibro-acoustic control issues of air vehicles are discussed. Finally, this paper offers future outlooks for thin-walled metastructures in air vehicles, focusing on optimal design, intelligent tuning, multifunctional integration, adaptability to extreme environments and precision manufacturing.
2026, 56(1): 123-168.
doi: 10.6052/1000-0992-25-026
Abstract:
The rapid advancement of machine learning is transforming the research paradigm of mechanical properties of metallic materials from experience-driven to data-driven. This review systematically summarizes recent progress and challenges in machine learning based prediction of mechanical properties in metallic materials. We first outline commonly used ML algorithms and workflows, with an emphasis on cutting-edge methods such as explainable AI and physics-informed machine learning. We then review typical applications and predictive performance of ML models across three scales: micro/mesoscopic properties (e.g., microstructural evolution, fracture behavior), macroscopic properties (e.g., hardness, stress response, fatigue life), and cross-scale coupling properties (e.g., flow stress, yield strength, constitutive parameter inversion), highlighting their advantages in high-throughput computation and multi-scale modeling. Finally, we identify persistent challenges such as data scarcity, heterogeneity, and insufficient accuracy under wide temperature/strain-rate ranges, and propose potential solutions including transfer learning, large language models, and multi-modal fusion. Looking forward, we outline a technical pathway integrating multi-modal data and physical mechanisms for accurate prediction of mechanical behavior under extreme conditions, aiming to advance materials mechanics toward digitalization and intelligence.
The rapid advancement of machine learning is transforming the research paradigm of mechanical properties of metallic materials from experience-driven to data-driven. This review systematically summarizes recent progress and challenges in machine learning based prediction of mechanical properties in metallic materials. We first outline commonly used ML algorithms and workflows, with an emphasis on cutting-edge methods such as explainable AI and physics-informed machine learning. We then review typical applications and predictive performance of ML models across three scales: micro/mesoscopic properties (e.g., microstructural evolution, fracture behavior), macroscopic properties (e.g., hardness, stress response, fatigue life), and cross-scale coupling properties (e.g., flow stress, yield strength, constitutive parameter inversion), highlighting their advantages in high-throughput computation and multi-scale modeling. Finally, we identify persistent challenges such as data scarcity, heterogeneity, and insufficient accuracy under wide temperature/strain-rate ranges, and propose potential solutions including transfer learning, large language models, and multi-modal fusion. Looking forward, we outline a technical pathway integrating multi-modal data and physical mechanisms for accurate prediction of mechanical behavior under extreme conditions, aiming to advance materials mechanics toward digitalization and intelligence.
2026, 56(1): 169-202.
doi: 10.6052/1000-0992-25-025
Abstract:
The construction background of three full scale low speed wind tunnels in the United States and Russia is briefly introduced. Then, emphasis on tests performed in these wind tunnels are presented, including test operation mode, test model types, test technologies, etc, especially for special test techniques of the full scale wind tunnel. The future development trend of the test techniques is concluded. Research results show that the construction need of full scale wind tunnel is mainly originated from large model aerodynamic test and some related technology development. During model test process, more special attentions are paid to the installation of very large model and the treatment of test failure. The models tested in full scale wind tunnel mainly include airplanes, aerospace vehicles, and energy infrastructures. Additionally, fundamental aerodynamic problem such as rotor flow, acoustic noise can also be resolved in such kind of wind tunnel. As far as the test technique is concerned, conventional measurement method such as force balance, pressure transducer and hot wire anemometer can be used. More importantly, special test techniques developed for full scale model tilting test apparatus, test benches with very large angle of attack, model free flight mechanism, non-intrusive optical measurement and bad weather simulation facility are also been described. The general development trend of test technique is obtained, including going along a direction of providing data with high precision, combining and utilizing various test methods, enabling development with big data in depth, integrating multidiscipline research, developing virtual and augmented reality, etc. Finally, some enlightenments and suggestions are put forward, such as developing test techniques step by step, building professional experimental stand, and highlighting the advantages of large scale and detailed measurements.
The construction background of three full scale low speed wind tunnels in the United States and Russia is briefly introduced. Then, emphasis on tests performed in these wind tunnels are presented, including test operation mode, test model types, test technologies, etc, especially for special test techniques of the full scale wind tunnel. The future development trend of the test techniques is concluded. Research results show that the construction need of full scale wind tunnel is mainly originated from large model aerodynamic test and some related technology development. During model test process, more special attentions are paid to the installation of very large model and the treatment of test failure. The models tested in full scale wind tunnel mainly include airplanes, aerospace vehicles, and energy infrastructures. Additionally, fundamental aerodynamic problem such as rotor flow, acoustic noise can also be resolved in such kind of wind tunnel. As far as the test technique is concerned, conventional measurement method such as force balance, pressure transducer and hot wire anemometer can be used. More importantly, special test techniques developed for full scale model tilting test apparatus, test benches with very large angle of attack, model free flight mechanism, non-intrusive optical measurement and bad weather simulation facility are also been described. The general development trend of test technique is obtained, including going along a direction of providing data with high precision, combining and utilizing various test methods, enabling development with big data in depth, integrating multidiscipline research, developing virtual and augmented reality, etc. Finally, some enlightenments and suggestions are put forward, such as developing test techniques step by step, building professional experimental stand, and highlighting the advantages of large scale and detailed measurements.
2026, 56(1): 203-259.
doi: 10.6052/1000-0992-25-015
Abstract:
Amorphous alloys exhibit complex viscoelastic behaviors due to their unique atomic structure, characterized by dynamic relaxation and static hysteresis features. This not only serves as a core entry point for in-depth understanding of fundamental physical issues such as glass transition, plastic deformation mechanisms, and dynamic heterogeneity, but also provides key theoretical support for the development and engineering application of high-performance amorphous alloys. Currently, how to construct a theoretical framework from the microscopic mechanism that can uniformly describe and predict their complex viscoelastic behaviors remains a core challenge in this field. This paper focuses on the core role and latest progress of the quasi-point defect (QPD) theory in systematically analyzing the viscoelastic behaviors. It deeply explores the application of the QPD theory in analyzing dynamic relaxation and reveals the intrinsic consistency between this theory and fractional models. On this basis, it reviews the intrinsic connection between dynamic relaxation and macroscopic quasi-static viscoelastic deformation, and explains the physical mechanisms behind phenomena such as two-step relaxation and creep, which are dominated by defect movements at different scales. Regarding creep behavior, it particularly discusses the understanding of defect evolution and multi-level power-law creep mechanisms. Additionally, this paper systematically expounds the mechanism of regulating the energy state of amorphous alloys through viscoelastic deformation and how this regulation changes the dynamic relaxation of the material by influencing the concentration, distribution, and cooperative movement of quasi-point defects. This paper aims to demonstrate how to establish the correlation between the microstructure heterogeneity, defect dynamics, and viscoelastic response of amorphous alloys based on the QPD theory, providing a theoretical perspective for understanding and predicting their complex mechanical behaviors.
Amorphous alloys exhibit complex viscoelastic behaviors due to their unique atomic structure, characterized by dynamic relaxation and static hysteresis features. This not only serves as a core entry point for in-depth understanding of fundamental physical issues such as glass transition, plastic deformation mechanisms, and dynamic heterogeneity, but also provides key theoretical support for the development and engineering application of high-performance amorphous alloys. Currently, how to construct a theoretical framework from the microscopic mechanism that can uniformly describe and predict their complex viscoelastic behaviors remains a core challenge in this field. This paper focuses on the core role and latest progress of the quasi-point defect (QPD) theory in systematically analyzing the viscoelastic behaviors. It deeply explores the application of the QPD theory in analyzing dynamic relaxation and reveals the intrinsic consistency between this theory and fractional models. On this basis, it reviews the intrinsic connection between dynamic relaxation and macroscopic quasi-static viscoelastic deformation, and explains the physical mechanisms behind phenomena such as two-step relaxation and creep, which are dominated by defect movements at different scales. Regarding creep behavior, it particularly discusses the understanding of defect evolution and multi-level power-law creep mechanisms. Additionally, this paper systematically expounds the mechanism of regulating the energy state of amorphous alloys through viscoelastic deformation and how this regulation changes the dynamic relaxation of the material by influencing the concentration, distribution, and cooperative movement of quasi-point defects. This paper aims to demonstrate how to establish the correlation between the microstructure heterogeneity, defect dynamics, and viscoelastic response of amorphous alloys based on the QPD theory, providing a theoretical perspective for understanding and predicting their complex mechanical behaviors.
2026, 56(1): 260-274.
doi: 10.6052/1000-0992-25-020
Abstract:
The interactions of coaxial vortex rings are the typical flow in subsonic jets and the significant sources of jet noise. Controlling the acceleration and deceleration of vortex rings during the interactions is critical to noise reduction. Previous studies have shown that the radial acceleration of the weaker ring is the dominant contributor to high-amplitude, low-frequency noise. In this work, the conditions under which this phenomenon occurs and the physical laws that govern it are investigated based on Dyson thin-core vortex ring model. By decomposing the acoustic source into the product of the vortex rings’ axial and radial kinematic parameters, the interactions of vortex rings are analyzed under various initial circulation and radius ratios. A critical initial radius ratio is identified, below which the source term related to the radial acceleration of the weaker ring contributes more to the total noise source than that of the stronger ring. Through quantitative analysis of the vortex ring interaction dynamics, the correlation between the peak value of the noise pulse and the peak values of axial velocity and radial acceleration of the rings is established. Moreover, the reverse motion of the stronger ring can induce an out-of-phase pulse in its corresponding acoustic source term.
The interactions of coaxial vortex rings are the typical flow in subsonic jets and the significant sources of jet noise. Controlling the acceleration and deceleration of vortex rings during the interactions is critical to noise reduction. Previous studies have shown that the radial acceleration of the weaker ring is the dominant contributor to high-amplitude, low-frequency noise. In this work, the conditions under which this phenomenon occurs and the physical laws that govern it are investigated based on Dyson thin-core vortex ring model. By decomposing the acoustic source into the product of the vortex rings’ axial and radial kinematic parameters, the interactions of vortex rings are analyzed under various initial circulation and radius ratios. A critical initial radius ratio is identified, below which the source term related to the radial acceleration of the weaker ring contributes more to the total noise source than that of the stronger ring. Through quantitative analysis of the vortex ring interaction dynamics, the correlation between the peak value of the noise pulse and the peak values of axial velocity and radial acceleration of the rings is established. Moreover, the reverse motion of the stronger ring can induce an out-of-phase pulse in its corresponding acoustic source term.
2026, 56(1): 275-283.
doi: 10.6052/1000-0992-26-010
Abstract:
This paper summarizes and statistically analyzes the applications, peer reviews and the funding of the discipline of mechanics of National Natural Science Foundation of China in 2025, and also reports the progress reforms and problems during the project review. The objective is to draw wide attention from scientists in the mechanics community, and to work together to promote the funding progress in the discipline of mechanics of NSFC.
This paper summarizes and statistically analyzes the applications, peer reviews and the funding of the discipline of mechanics of National Natural Science Foundation of China in 2025, and also reports the progress reforms and problems during the project review. The objective is to draw wide attention from scientists in the mechanics community, and to work together to promote the funding progress in the discipline of mechanics of NSFC.
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