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2026, 56(3): 535-569.
doi: 10.6052/1000-0992-25-032
Abstract:
Uncertainty quantification (UQ) and uncertainty-based design optimization (UBDO), as an emerging design paradigm for flight vehicles, provide a systematic methodological framework for addressing the precise characterization, propagation, and design optimization of uncertainties. This paper reviews the core concepts and key technologies in this field. It summarizes the uncertainty challenges associated with critical systems and significant environmental conditions of flight vehicles. Based on the latest research progress, five key research directions are identified: (1) High-dimensional uncertainty quantification and efficient propagation: Constructing an adaptive high-dimensional UQ framework by integrating techniques such as dimensionality reduction, compressed sensing, and low-rank tensor decomposition to effectively address the “curse of dimensionality”. (2) Hybrid uncertainty quantification and efficient propagation: A unified framework is established to accommodate various types of uncertainties—including probabilistic, interval, fuzzy, and evidence theory. The computational efficiency for complex, multi-source uncertainty problems is further enhanced by incorporating surrogate modeling and active learning strategies. (3) Multi-level and multi-fidelity UQ framework: Achieving dynamic and optimal allocation of computational resources across models of varying fidelities by integrating techniques like generalized approximate control variates and adaptive multi-index stochastic collocation. (4) Uncertainty-based design optimization algorithms and frameworks: Unifying probabilistic constraints and robustness metrics within a multi-objective optimization and decision-making framework under uncertainty, enabling trade-off optimization among performance, reliability, and robustness through single-loop and decoupled optimization strategies. (5) Uncertainty design and analysis based on artificial intelligence techniques: Centered on physics-informed neural networks, this direction incorporates physical knowledge and multi-source data to establish intelligent frameworks for uncertainty quantification and optimization.
Uncertainty quantification (UQ) and uncertainty-based design optimization (UBDO), as an emerging design paradigm for flight vehicles, provide a systematic methodological framework for addressing the precise characterization, propagation, and design optimization of uncertainties. This paper reviews the core concepts and key technologies in this field. It summarizes the uncertainty challenges associated with critical systems and significant environmental conditions of flight vehicles. Based on the latest research progress, five key research directions are identified: (1) High-dimensional uncertainty quantification and efficient propagation: Constructing an adaptive high-dimensional UQ framework by integrating techniques such as dimensionality reduction, compressed sensing, and low-rank tensor decomposition to effectively address the “curse of dimensionality”. (2) Hybrid uncertainty quantification and efficient propagation: A unified framework is established to accommodate various types of uncertainties—including probabilistic, interval, fuzzy, and evidence theory. The computational efficiency for complex, multi-source uncertainty problems is further enhanced by incorporating surrogate modeling and active learning strategies. (3) Multi-level and multi-fidelity UQ framework: Achieving dynamic and optimal allocation of computational resources across models of varying fidelities by integrating techniques like generalized approximate control variates and adaptive multi-index stochastic collocation. (4) Uncertainty-based design optimization algorithms and frameworks: Unifying probabilistic constraints and robustness metrics within a multi-objective optimization and decision-making framework under uncertainty, enabling trade-off optimization among performance, reliability, and robustness through single-loop and decoupled optimization strategies. (5) Uncertainty design and analysis based on artificial intelligence techniques: Centered on physics-informed neural networks, this direction incorporates physical knowledge and multi-source data to establish intelligent frameworks for uncertainty quantification and optimization.
2026, 56(3): 570-599.
doi: 10.6052/1000-0992-25-029
Abstract:
With the global aging population and high incidence of chronic diseases, major intractable conditions such as cardiovascular diseases, tumors, and diabetes have become primary challenges to public health and socioeconomic development worldwide. Their pathological processes are often accompanied by abnormal remodeling of the extracellular matrix (ECM) and disruption of mechanical homeostasis, rendering traditional treatments ineffective in reversing these conditions. Recent studies reveal that actively modulating the mechanical properties of the ECM through principles of materials science and engineering to precisely mediate cellular behavior can effectively activate endogenous tissue repair, significantly promoting tissue regeneration. This research strategy, termed force-materials science, involves actively designing materials to leverage force−structure−function relationships for proactive control of the mechanical environment within biological systems. Based on this concept, this paper proposes: systematically identifying the molecular composition of the ECM from a matrixomics perspective and deconstructing its mechanical information encoding; utilizing matrix biomechanics to understand cell−ECM interaction mechanisms and decipher pathological ECM “re-encoding” processes; and, grounded in deep understanding of the ECM’s mechanical microenvironment, exploring matrix engineering technologies for “de-encoding” abnormal ECM and restoring function by integrating matrix biomechanics principles, ultimately achieving the goal of matrix therapy for endogenous tissue repair. Specifically, this paper introduces the composition and dynamic coding of the ECM, systematically summarizes the physiological/pathological changes in abnormal ECM mechanical microenvironments, and emphasizes the proposal and construction of novel matrix engineering and therapeutic strategies based on molecular targeting and material reconstruction. These efforts aim to provide new theoretical foundations and innovative approaches for the intervention of major intractable diseases and the advancement of regenerative medicine.
With the global aging population and high incidence of chronic diseases, major intractable conditions such as cardiovascular diseases, tumors, and diabetes have become primary challenges to public health and socioeconomic development worldwide. Their pathological processes are often accompanied by abnormal remodeling of the extracellular matrix (ECM) and disruption of mechanical homeostasis, rendering traditional treatments ineffective in reversing these conditions. Recent studies reveal that actively modulating the mechanical properties of the ECM through principles of materials science and engineering to precisely mediate cellular behavior can effectively activate endogenous tissue repair, significantly promoting tissue regeneration. This research strategy, termed force-materials science, involves actively designing materials to leverage force−structure−function relationships for proactive control of the mechanical environment within biological systems. Based on this concept, this paper proposes: systematically identifying the molecular composition of the ECM from a matrixomics perspective and deconstructing its mechanical information encoding; utilizing matrix biomechanics to understand cell−ECM interaction mechanisms and decipher pathological ECM “re-encoding” processes; and, grounded in deep understanding of the ECM’s mechanical microenvironment, exploring matrix engineering technologies for “de-encoding” abnormal ECM and restoring function by integrating matrix biomechanics principles, ultimately achieving the goal of matrix therapy for endogenous tissue repair. Specifically, this paper introduces the composition and dynamic coding of the ECM, systematically summarizes the physiological/pathological changes in abnormal ECM mechanical microenvironments, and emphasizes the proposal and construction of novel matrix engineering and therapeutic strategies based on molecular targeting and material reconstruction. These efforts aim to provide new theoretical foundations and innovative approaches for the intervention of major intractable diseases and the advancement of regenerative medicine.
2026, 56(3): 600-661.
doi: 10.6052/1000-0992-25-022
Abstract:
In complex unsteady wakes, the fluid–structure interaction (FSI) modes can differ significantly from those under uniform flows, often involving rich physical mechanisms. This paper reviews recent advances in three representative FSI phenomena: Vortex-induced vibration (VIV) of cylinders, flapping of flexible plates, and locomotion of swimming/flying organisms. These phenomena are widely observed in both nature and engineering applications and span self-excited, active, and hybrid FSI modes. First, we compare the response modes under uniform and unsteady wake inflows. Results show that incoming vortices can substantially amplify vibration amplitudes of cylinders and plates, potentially triggering new instabilities. In contrast, biological swimmers may actively exploit incoming vortices by modulating their motions to enhance propulsion efficiency. Furthermore, this paper discusses potential applications of such FSI modes in complex wake flows, including enhanced energy harvesting from flow-induced vibrations and the development of bioinspired robots with improved sensing and decision-making capabilities. Finally, the challenges and future research directions in this area are outlined to guide further exploration.
In complex unsteady wakes, the fluid–structure interaction (FSI) modes can differ significantly from those under uniform flows, often involving rich physical mechanisms. This paper reviews recent advances in three representative FSI phenomena: Vortex-induced vibration (VIV) of cylinders, flapping of flexible plates, and locomotion of swimming/flying organisms. These phenomena are widely observed in both nature and engineering applications and span self-excited, active, and hybrid FSI modes. First, we compare the response modes under uniform and unsteady wake inflows. Results show that incoming vortices can substantially amplify vibration amplitudes of cylinders and plates, potentially triggering new instabilities. In contrast, biological swimmers may actively exploit incoming vortices by modulating their motions to enhance propulsion efficiency. Furthermore, this paper discusses potential applications of such FSI modes in complex wake flows, including enhanced energy harvesting from flow-induced vibrations and the development of bioinspired robots with improved sensing and decision-making capabilities. Finally, the challenges and future research directions in this area are outlined to guide further exploration.
2026, 56(3): 662-692.
doi: 10.6052/1000-0992-25-037
Abstract:
With their uniquely three-dimensional, wavy whiskers, harbor seals (Phoca vitulina) exhibit exceptional underwater sensing capabilities. Studies have shown that harbor seals can detect weak vortices with flow velocities as low as 245 μm·s−1 and can track hydrodynamic trails left by targets up to 180 m away and as long as 35 s earlier. These abilities highlight the remarkable advantages of harbor seal whiskers in underwater vortex sensing and hydrodynamic trail tracking. Bio-inspired sensor designs based on harbor seal whiskers have thus become a research hotspot in biomimetic science and engineering, demonstrating promising applications in underwater target detection and recognition. This paper first reviews research progress on the morphological characteristics and geometric modeling of harbor seal whiskers, summarizing and comparing the strengths and limitations of different simplified models. It then provides an overview of advances in the hydrodynamic characteristics of biomimetic whisker models, covering wake features and vibration responses of such models in uniform and wake flows, the sensing mechanisms of harbor seal whiskers, interactions within whisker arrays, and applications of artificial intelligence methods in sensing-signal recognition. Finally, based on the shortcomings and key open questions in existing research, the paper outlines several research directions that warrant attention for advancing biomimetic science and engineering applications of harbor seal whiskers.
With their uniquely three-dimensional, wavy whiskers, harbor seals (Phoca vitulina) exhibit exceptional underwater sensing capabilities. Studies have shown that harbor seals can detect weak vortices with flow velocities as low as 245 μm·s−1 and can track hydrodynamic trails left by targets up to 180 m away and as long as 35 s earlier. These abilities highlight the remarkable advantages of harbor seal whiskers in underwater vortex sensing and hydrodynamic trail tracking. Bio-inspired sensor designs based on harbor seal whiskers have thus become a research hotspot in biomimetic science and engineering, demonstrating promising applications in underwater target detection and recognition. This paper first reviews research progress on the morphological characteristics and geometric modeling of harbor seal whiskers, summarizing and comparing the strengths and limitations of different simplified models. It then provides an overview of advances in the hydrodynamic characteristics of biomimetic whisker models, covering wake features and vibration responses of such models in uniform and wake flows, the sensing mechanisms of harbor seal whiskers, interactions within whisker arrays, and applications of artificial intelligence methods in sensing-signal recognition. Finally, based on the shortcomings and key open questions in existing research, the paper outlines several research directions that warrant attention for advancing biomimetic science and engineering applications of harbor seal whiskers.
2026, 56(3): 693-743.
doi: 10.6052/1000-0992-25-034
Abstract:
Nanofluidics studies flow and mass transport in confined systems with characteristic dimensions ranging from about 100 nm down to the sub-nanometer scale. Nanofluidics is not simply a scaled-down version of macroscopic flow, where the predominant forces and boundary conditions are fundamentally different from the macroscopic flows, giving rise to new phenomena and enabling new applications. The development of nanotechnology enables the fabrication of nano- and even sub-nanometer structures, enabling to investigate the flow and transport in such small scales. This review summarizes the state-of-art in nanofluidics, including concepts, open questions, experiments advances, and examples of application. First, we outline the key scientific questions in nanofluidics, including boundary slip in nanochannels, coupling between flow and mass transport, two-phase flow in confinement, and the breakdown of continuum descriptions at the smallest scale. Second, we summarize key nanofabrication techniques and experimental methods used to probe flow and transport in such small confinement. Third, we describe multiscale simulation approaches used in nanofluidics—from continuum models to molecular dynamics and the ab initio simulations—and illustrate how they unvail the mechanisms of flow in nanoscale. Finally, we discuss emerging application areas in nanofluidics, such as drag reduction, energy conversion, chemical engineering, artificial intelligence, advanced manufacturing, and diagnosis. Overall, by bridging molecular-scale dynamics and macroscopic transport, nanofluidics has emerged as an important direction in fluid mechanics with broad interdisciplinary applications.
Nanofluidics studies flow and mass transport in confined systems with characteristic dimensions ranging from about 100 nm down to the sub-nanometer scale. Nanofluidics is not simply a scaled-down version of macroscopic flow, where the predominant forces and boundary conditions are fundamentally different from the macroscopic flows, giving rise to new phenomena and enabling new applications. The development of nanotechnology enables the fabrication of nano- and even sub-nanometer structures, enabling to investigate the flow and transport in such small scales. This review summarizes the state-of-art in nanofluidics, including concepts, open questions, experiments advances, and examples of application. First, we outline the key scientific questions in nanofluidics, including boundary slip in nanochannels, coupling between flow and mass transport, two-phase flow in confinement, and the breakdown of continuum descriptions at the smallest scale. Second, we summarize key nanofabrication techniques and experimental methods used to probe flow and transport in such small confinement. Third, we describe multiscale simulation approaches used in nanofluidics—from continuum models to molecular dynamics and the ab initio simulations—and illustrate how they unvail the mechanisms of flow in nanoscale. Finally, we discuss emerging application areas in nanofluidics, such as drag reduction, energy conversion, chemical engineering, artificial intelligence, advanced manufacturing, and diagnosis. Overall, by bridging molecular-scale dynamics and macroscopic transport, nanofluidics has emerged as an important direction in fluid mechanics with broad interdisciplinary applications.
2026, 56(3): 744-782.
doi: 10.6052/1000-0992-25-027
Abstract:
Metal laser additive manufacturing technology exhibits the capability for precision forming of complex components in high-end fields such as the aerospace, defense, and medicine. However, its broader application in advanced engineering fields remains constrained by manufacturing defects; fluctuations in power density and cooling rate during processing can readily induce defects such as thermally induced porosity and high-stress cracking, thereby posing interdisciplinary challenges for in-situ monitoring and quality control. This paper reviews the establishment of a multi-dimensional detection technology system and systematizes the advancements in key testing methods and technologies. Specifically, mainstream sensing technologies, including optical and acoustic sensing, when integrated with advanced intelligent algorithms, facilitate dynamic identification of surface defects and extraction of internal defect characteristics. Multi-source data fusion further establishes a collaborative analysis framework linking microscopic molten pool behavior to macroscopic geometric accuracy. Additionally, emerging techniques, such as high-speed synchrotron radiation imaging, offer accurate cross-scale online observation tools for investigating the initiation and evolution mechanisms of defects. Current technologies are constrained by challenges such as multi-source noise interference and low synchronization efficiency of multi-physics field data. Future research should focus on the in-depth integration of multi-sensor detection technology with machine learning, explore online intelligent detection approaches, and develop full-process quality prediction models driven by digital twins. This study intends to provide theoretical synthesis and technical pathway analysis to address the common challenges of defect monitoring and forming accuracy control in the additive manufacturing process.
Metal laser additive manufacturing technology exhibits the capability for precision forming of complex components in high-end fields such as the aerospace, defense, and medicine. However, its broader application in advanced engineering fields remains constrained by manufacturing defects; fluctuations in power density and cooling rate during processing can readily induce defects such as thermally induced porosity and high-stress cracking, thereby posing interdisciplinary challenges for in-situ monitoring and quality control. This paper reviews the establishment of a multi-dimensional detection technology system and systematizes the advancements in key testing methods and technologies. Specifically, mainstream sensing technologies, including optical and acoustic sensing, when integrated with advanced intelligent algorithms, facilitate dynamic identification of surface defects and extraction of internal defect characteristics. Multi-source data fusion further establishes a collaborative analysis framework linking microscopic molten pool behavior to macroscopic geometric accuracy. Additionally, emerging techniques, such as high-speed synchrotron radiation imaging, offer accurate cross-scale online observation tools for investigating the initiation and evolution mechanisms of defects. Current technologies are constrained by challenges such as multi-source noise interference and low synchronization efficiency of multi-physics field data. Future research should focus on the in-depth integration of multi-sensor detection technology with machine learning, explore online intelligent detection approaches, and develop full-process quality prediction models driven by digital twins. This study intends to provide theoretical synthesis and technical pathway analysis to address the common challenges of defect monitoring and forming accuracy control in the additive manufacturing process.
2026, 56(3): 783-850.
doi: 10.6052/1000-0992-25-033
Abstract:
Injury biomechanics primarily investigates human responses and injury outcomes based on mechanical principles. A thorough understanding of specific injury mechanisms and associated tolerance limits is essential for improving human protection. Crash test dummies, serving as anthropomorphic substitutes that replicate human biomechanical responses during impact, are widely applied in automotive safety, sports rehabilitation, forensic analysis, military protection, and aerospace engineering. In the field of automotive safety, crash test dummies constitute essential tools for injury assessment and are generally categorized into physical dummies and computational human surrogates. This paper reviews the development history of both physical and virtual dummies, with a particular focus on parametric design methodologies used in automotive crash dummy development. Moreover, the paper discusses future trends in injury assessment techniques, with the aim of contributing to the advancement of injury biomechanics and supporting technological progress in automotive safety.
Injury biomechanics primarily investigates human responses and injury outcomes based on mechanical principles. A thorough understanding of specific injury mechanisms and associated tolerance limits is essential for improving human protection. Crash test dummies, serving as anthropomorphic substitutes that replicate human biomechanical responses during impact, are widely applied in automotive safety, sports rehabilitation, forensic analysis, military protection, and aerospace engineering. In the field of automotive safety, crash test dummies constitute essential tools for injury assessment and are generally categorized into physical dummies and computational human surrogates. This paper reviews the development history of both physical and virtual dummies, with a particular focus on parametric design methodologies used in automotive crash dummy development. Moreover, the paper discusses future trends in injury assessment techniques, with the aim of contributing to the advancement of injury biomechanics and supporting technological progress in automotive safety.
2026, 56(3): 851-868.
doi: 10.6052/1000-0992-25-045
Abstract:
The rise of artificial intelligence (AI), particularly its transformative advance in algorithm and large-scale data processing, has provided a new path for humanity to address the energy crisis. As the ultimate form of future energy, nuclear fusion has advanced from basic research to the commercialization threshold after more than 70 years of development. This paper systematically elaborates on the current development status and key challenges of global nuclear fusion research, deeply analyzes the application scenarios and practical achievements of AI technology in key fields such as nuclear fusion device control, data processing, model optimization, and risk management, discusses the transformative impact of the integration of AI and nuclear fusion on the global energy pattern, and finally looks forward to the future development direction and industrial layout of this field, providing a reference for promoting the energy revolution and scientific and technological progress.
The rise of artificial intelligence (AI), particularly its transformative advance in algorithm and large-scale data processing, has provided a new path for humanity to address the energy crisis. As the ultimate form of future energy, nuclear fusion has advanced from basic research to the commercialization threshold after more than 70 years of development. This paper systematically elaborates on the current development status and key challenges of global nuclear fusion research, deeply analyzes the application scenarios and practical achievements of AI technology in key fields such as nuclear fusion device control, data processing, model optimization, and risk management, discusses the transformative impact of the integration of AI and nuclear fusion on the global energy pattern, and finally looks forward to the future development direction and industrial layout of this field, providing a reference for promoting the energy revolution and scientific and technological progress.
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