Institute of Mechanics,
Chinese Academy of Sciences
2025 Vol.15(5)
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Theoretical and Applied Mechanics Letters 15 (2025) 100598.
doi: 10.1016/j.taml.2025.100598
Abstract:
This study explores the combination of ultrasound technology with a detection algorithm to categorize flow regimes in bubble columns used for aeration in aquaculture. An ultrasonic velocity profiler is used to obtain the standard deviation of the bubble velocity distributed throughout the column. The bubble velocity data for three known flow regimes were used to develop a probability density function (PDF) classification model. The experimental apparatus consisted of a circular tank equipped with a bubble generator and gas hold-up monitoring systems. The flow regimes of the experimental fluid were determined, and the classification was conducted via the PDF method. The results demonstrate that the classification accuracy is not lower than that of traditional machine learning methods.
This study explores the combination of ultrasound technology with a detection algorithm to categorize flow regimes in bubble columns used for aeration in aquaculture. An ultrasonic velocity profiler is used to obtain the standard deviation of the bubble velocity distributed throughout the column. The bubble velocity data for three known flow regimes were used to develop a probability density function (PDF) classification model. The experimental apparatus consisted of a circular tank equipped with a bubble generator and gas hold-up monitoring systems. The flow regimes of the experimental fluid were determined, and the classification was conducted via the PDF method. The results demonstrate that the classification accuracy is not lower than that of traditional machine learning methods.
Theoretical and Applied Mechanics Letters 15 (2025) 100599.
doi: 10.1016/j.taml.2025.100599
Abstract:
Flying insects demonstrate remarkable control over their body movements and orientation, enabling them to perform rapid maneuvers and withstand external disturbances in just a few wing beats. This fast flight stabilization mechanism has captured the interest of biologists and engineers, driving the exploration of flapping-wing flight control systems and their potential applications in bioinspired flying robots. While many control models have been developed within a rigorous mathematical framework using linear feedback systems, such as proportional (P), integral (I), and derivative (D)-based controllers, the exact mechanisms by which insects achieve the fastest stabilization—despite constraints such as passive aerodynamic damping and feedback delay—remain unclear. In this study, we demonstrate that flying insects employ a novel strategy for fast flight stabilization by minimizing the restoration time under external perturbations. We introduce a versatile PD-based control model that solves the closed-loop dynamics of insect flight and optimizes flight stabilization within a mathematical framework. Our findings reveal that passive aerodynamic damping plays a crucial role in stabilizing flight, acting as derivative feedback without delay, whereas feedback delay hinders stabilization. Additionally, we show that minimizing the restoring time leads to the fastest flight stabilization. Hovering flight analyses of fruit flies, honeybees, hawkmoths, and hummingbirds suggest that restoring time minimization through dynamic oscillatory modes rather than closed-loop time constants is a common strategy among small bioflies for effective maneuvering against disturbances. This strategy, which spans a broad range of Reynolds numbers (on the order of 102 to 104), could offer valuable insights for designing flight controllers in bioinspired flying robots.
Flying insects demonstrate remarkable control over their body movements and orientation, enabling them to perform rapid maneuvers and withstand external disturbances in just a few wing beats. This fast flight stabilization mechanism has captured the interest of biologists and engineers, driving the exploration of flapping-wing flight control systems and their potential applications in bioinspired flying robots. While many control models have been developed within a rigorous mathematical framework using linear feedback systems, such as proportional (P), integral (I), and derivative (D)-based controllers, the exact mechanisms by which insects achieve the fastest stabilization—despite constraints such as passive aerodynamic damping and feedback delay—remain unclear. In this study, we demonstrate that flying insects employ a novel strategy for fast flight stabilization by minimizing the restoration time under external perturbations. We introduce a versatile PD-based control model that solves the closed-loop dynamics of insect flight and optimizes flight stabilization within a mathematical framework. Our findings reveal that passive aerodynamic damping plays a crucial role in stabilizing flight, acting as derivative feedback without delay, whereas feedback delay hinders stabilization. Additionally, we show that minimizing the restoring time leads to the fastest flight stabilization. Hovering flight analyses of fruit flies, honeybees, hawkmoths, and hummingbirds suggest that restoring time minimization through dynamic oscillatory modes rather than closed-loop time constants is a common strategy among small bioflies for effective maneuvering against disturbances. This strategy, which spans a broad range of Reynolds numbers (on the order of 102 to 104), could offer valuable insights for designing flight controllers in bioinspired flying robots.
Theoretical and Applied Mechanics Letters 15 (2025) 100600.
doi: 10.1016/j.taml.2025.100600
Abstract:
This study investigated the operation features of a dropshaft-tunnel system under varying downstream water levels through a large-scale physical model. In the experiments, the air pressure distribution in the system was measured, and the flow pattern was recorded by cameras. The results revealed that the air pressure in the dropshaft increased with increasing water flow rate under free outflow conditions but changed little when the outflow was submerged, even when the flow rate further increased. Additionally, there was a wavy flow in the tunnel under free outflow conditions, whereas plug flow with air pockets occurred under submerged outflow conditions. The downstream water level was found to affect the system through changing the linkage between the dropshaft and tunnel and the resistance to air release downstream. The findings of this study contribute to a more comprehensive understanding of the operation of deep tunnel systems.
This study investigated the operation features of a dropshaft-tunnel system under varying downstream water levels through a large-scale physical model. In the experiments, the air pressure distribution in the system was measured, and the flow pattern was recorded by cameras. The results revealed that the air pressure in the dropshaft increased with increasing water flow rate under free outflow conditions but changed little when the outflow was submerged, even when the flow rate further increased. Additionally, there was a wavy flow in the tunnel under free outflow conditions, whereas plug flow with air pockets occurred under submerged outflow conditions. The downstream water level was found to affect the system through changing the linkage between the dropshaft and tunnel and the resistance to air release downstream. The findings of this study contribute to a more comprehensive understanding of the operation of deep tunnel systems.
Theoretical and Applied Mechanics Letters 15 (2025) 100601.
doi: 10.1016/j.taml.2025.100601
Abstract:
Head injuries from vehicle collisions, falls, and sports are often the result of complex mechanisms involving both linear and angular forces. This study aims to quantitatively assess the effects of linear and angular force on the severity of traumatic brain injury in rats during collisions. An orthogonal experimental design was employed, facilitating the manipulation of linear velocity, rotational acceleration, and angle (light, medium, and heavy) across 54 rats. 24 hours post-injury, magnetic resonance imaging T2-weighted imaging, and diffusion tensor imaging were utilized to detect abnormal brain signals, with the fractional anisotropy value of the corpus callosum serving as the primary injury indicator. Anatomical analyses and immunohistological staining were conducted to measure the amyloid precursor protein (β-APP) accumulation, using integrated optical density as a secondary indicator. Entropy weighting was applied to derive index weights for the injury scoring system. Through analysis guided by analysis of variance and linear regression, it was determined that both linear and angular loadings significantly impacted brain injury severity. Increased rotational acceleration at constant linear velocities correlated with more severe injuries, whereas the rotation angle exhibited minimal effect. Linear velocity emerged as the primary determinant of injury severity, accounting for 91.5% of the variance, while rotational acceleration and rotation angle contributed 6.5% and 0.9%, respectively. These findings offer critical insights for developing protective measures against brain injuries in traffic accidents.
Head injuries from vehicle collisions, falls, and sports are often the result of complex mechanisms involving both linear and angular forces. This study aims to quantitatively assess the effects of linear and angular force on the severity of traumatic brain injury in rats during collisions. An orthogonal experimental design was employed, facilitating the manipulation of linear velocity, rotational acceleration, and angle (light, medium, and heavy) across 54 rats. 24 hours post-injury, magnetic resonance imaging T2-weighted imaging, and diffusion tensor imaging were utilized to detect abnormal brain signals, with the fractional anisotropy value of the corpus callosum serving as the primary injury indicator. Anatomical analyses and immunohistological staining were conducted to measure the amyloid precursor protein (β-APP) accumulation, using integrated optical density as a secondary indicator. Entropy weighting was applied to derive index weights for the injury scoring system. Through analysis guided by analysis of variance and linear regression, it was determined that both linear and angular loadings significantly impacted brain injury severity. Increased rotational acceleration at constant linear velocities correlated with more severe injuries, whereas the rotation angle exhibited minimal effect. Linear velocity emerged as the primary determinant of injury severity, accounting for 91.5% of the variance, while rotational acceleration and rotation angle contributed 6.5% and 0.9%, respectively. These findings offer critical insights for developing protective measures against brain injuries in traffic accidents.
Theoretical and Applied Mechanics Letters 15 (2025) 100602.
doi: 10.1016/j.taml.2025.100602
Abstract:
Recent advances in contrastive language-image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors’ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., “low-drag supercritical wing for transonic conditions”) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.
Recent advances in contrastive language-image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors’ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., “low-drag supercritical wing for transonic conditions”) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.
Theoretical and Applied Mechanics Letters 15 (2025) 100604.
doi: 10.1016/j.taml.2025.100604
Abstract:
With the growing demand for high-precision flow field simulations in computational science and engineering, the super-resolution reconstruction of physical fields has attracted considerable research interest. However, traditional numerical methods often entail high computational costs, involve complex data processing, and struggle to capture fine-scale high-frequency details. To address these challenges, we propose an innovative super-resolution reconstruction framework that integrates a Fourier neural operator (FNO) with an enhanced diffusion model. The framework employs an adaptively weighted FNO to process low-resolution flow field inputs, effectively capturing global dependencies and high-frequency features. Furthermore, a residual-guided diffusion model is introduced to further improve reconstruction performance. This model uses a Markov chain for noise injection in physical fields and integrates a reverse denoising procedure, efficiently solved by an adaptive time-step ordinary differential equation solver, thereby ensuring both stability and computational efficiency. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy and efficiency, offering a promising solution for fine-grained data reconstruction in scientific simulations.
With the growing demand for high-precision flow field simulations in computational science and engineering, the super-resolution reconstruction of physical fields has attracted considerable research interest. However, traditional numerical methods often entail high computational costs, involve complex data processing, and struggle to capture fine-scale high-frequency details. To address these challenges, we propose an innovative super-resolution reconstruction framework that integrates a Fourier neural operator (FNO) with an enhanced diffusion model. The framework employs an adaptively weighted FNO to process low-resolution flow field inputs, effectively capturing global dependencies and high-frequency features. Furthermore, a residual-guided diffusion model is introduced to further improve reconstruction performance. This model uses a Markov chain for noise injection in physical fields and integrates a reverse denoising procedure, efficiently solved by an adaptive time-step ordinary differential equation solver, thereby ensuring both stability and computational efficiency. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy and efficiency, offering a promising solution for fine-grained data reconstruction in scientific simulations.
Theoretical and Applied Mechanics Letters 15 (2025) 100605.
doi: 10.1016/j.taml.2025.100605
Abstract:
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[Abstract]
Theoretical and Applied Mechanics Letters 15 (2025) 100606.
doi: 10.1016/j.taml.2025.100606
Abstract:
A multiscale stochastic-deterministic coupling method is proposed to investigate the complex interactions between turbulent and rarefied gas flows within a unified framework. This method intermittently integrates the general synthetic iterative scheme with the shear stress transport turbulence model into the direct simulation Monte Carlo (DSMC) approach, enabling the simulation of gas flows across the free-molecular, transition, slip, and turbulent regimes. First, the macroscopic synthetic equations, derived directly from DSMC, are coupled with the turbulence model to establish a constitutive relation that incorporates not only turbulent and laminar transport coefficients but also higher-order terms accounting for rarefaction effects. Second, the macroscopic properties, statistically sampled over specific time intervals in DSMC, along with the turbulent properties provided by the turbulence model, serve as initial conditions for solving the macroscopic synthetic equations. Finally, the simulation particles in DSMC are updated based on the macroscopic properties obtained from the synthetic equations. Numerical simulations demonstrate that the proposed method asymptotically converges to the turbulence model in the continuum regime and to the DSMC method in the rarefied regime, depending on the Knudsen number. This coupling method is then applied to simulate a turbulent opposing jet surrounded by hypersonic rarefied gas flows, revealing significant variations in surface properties due to the interplay of turbulent and rarefied effects.
A multiscale stochastic-deterministic coupling method is proposed to investigate the complex interactions between turbulent and rarefied gas flows within a unified framework. This method intermittently integrates the general synthetic iterative scheme with the shear stress transport turbulence model into the direct simulation Monte Carlo (DSMC) approach, enabling the simulation of gas flows across the free-molecular, transition, slip, and turbulent regimes. First, the macroscopic synthetic equations, derived directly from DSMC, are coupled with the turbulence model to establish a constitutive relation that incorporates not only turbulent and laminar transport coefficients but also higher-order terms accounting for rarefaction effects. Second, the macroscopic properties, statistically sampled over specific time intervals in DSMC, along with the turbulent properties provided by the turbulence model, serve as initial conditions for solving the macroscopic synthetic equations. Finally, the simulation particles in DSMC are updated based on the macroscopic properties obtained from the synthetic equations. Numerical simulations demonstrate that the proposed method asymptotically converges to the turbulence model in the continuum regime and to the DSMC method in the rarefied regime, depending on the Knudsen number. This coupling method is then applied to simulate a turbulent opposing jet surrounded by hypersonic rarefied gas flows, revealing significant variations in surface properties due to the interplay of turbulent and rarefied effects.
Theoretical and Applied Mechanics Letters 15 (2025) 100607.
doi: 10.1016/j.taml.2025.100607
Abstract:
Surface energy is essential to the understanding of micro-mechanics for heterogeneous composites. To investigate the effective elasticity and fracture behaviors, we derive an effective surface energy based on Eshelby’s equivalent inclusion theory. Within a unified theoretical framework, the effective surface energy predicts the fundamentals from elasticity to fracture, and reproduces classical homogenization methods and phase field models. The influences of elastic heterogeneity and size effects are analyzed in depth. Using the surface energy formulation, a computational model is developed by minimizing the deviation of effective elastic modulus from experimental observation. To validate our theoretical prediction, numerical simulations under tension and shear loadings for monodisperse and bidisperse particulate systems are performed, which agree well with experimental evidences. Local debondings nucleate and initiate at the inclusion-matrix interfaces, then develop into multiple interacting cracks and shear bands, thereby greatly promotes the process of fracture.
Surface energy is essential to the understanding of micro-mechanics for heterogeneous composites. To investigate the effective elasticity and fracture behaviors, we derive an effective surface energy based on Eshelby’s equivalent inclusion theory. Within a unified theoretical framework, the effective surface energy predicts the fundamentals from elasticity to fracture, and reproduces classical homogenization methods and phase field models. The influences of elastic heterogeneity and size effects are analyzed in depth. Using the surface energy formulation, a computational model is developed by minimizing the deviation of effective elastic modulus from experimental observation. To validate our theoretical prediction, numerical simulations under tension and shear loadings for monodisperse and bidisperse particulate systems are performed, which agree well with experimental evidences. Local debondings nucleate and initiate at the inclusion-matrix interfaces, then develop into multiple interacting cracks and shear bands, thereby greatly promotes the process of fracture.
Theoretical and Applied Mechanics Letters 15 (2025) 100610.
doi: 10.1016/j.taml.2025.100610
Abstract:
Theoretical and Applied Mechanics Letters 15 (2025) 100612.
doi: 10.1016/j.taml.2025.100612
Abstract:
The stability of the plane grating monochromator in the Hefei Advanced Light Facility is highly important for beamline focusing, with angular vibration being a key indicator for assessing its stability. This paper proposes an elastic fitting method based on fifth-order polynomial fitting for the precise analysis of microangular vibrations on grating surfaces. Compared with the traditional rigid body method, this method fully considers the three major elastic characteristics exhibited by optical components during vibration: significant phase differences, nonuniform deformation gradients, and spatial distribution differences in angular deformation. The research results indicate that this method can accurately reflect the actual vibration state of the grating surface, not only enabling the quantitative prediction of local angular microvibration but also establishing a reliable theoretical analysis framework for the stability assessment of high-precision instruments.
The stability of the plane grating monochromator in the Hefei Advanced Light Facility is highly important for beamline focusing, with angular vibration being a key indicator for assessing its stability. This paper proposes an elastic fitting method based on fifth-order polynomial fitting for the precise analysis of microangular vibrations on grating surfaces. Compared with the traditional rigid body method, this method fully considers the three major elastic characteristics exhibited by optical components during vibration: significant phase differences, nonuniform deformation gradients, and spatial distribution differences in angular deformation. The research results indicate that this method can accurately reflect the actual vibration state of the grating surface, not only enabling the quantitative prediction of local angular microvibration but also establishing a reliable theoretical analysis framework for the stability assessment of high-precision instruments.
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