Acta Mechanica Sinica最新文献

Focused ultrasound (FUS) therapy generates sufficient heat for medical interventions like tumor ablation by concentrating energy at the focal point. The complex viscoelastic properties of biological tissues pose challenges in balancing focusing precision and penetration depth, impacting the safety of surrounding tissues and treatment efficacy. This study develops an acoustic-solid-thermal coupling computational model to elucidate the dynamic mechanical response and energy dissipation mechanisms of soft tissue during FUS thermal therapy using a hyper-viscoelastic constitutive model. Results indicate that the high compressibility and low shear resistance of biological tissues result in a unique shear dissipation mechanism. Energy dissipation efficiency per area is indirectly influenced by load frequency via its effect on the dynamic shear modulus and is directly proportional to load amplitude. Focusing precision, represented by the focal zone width, is inversely controlled by frequency via wavelength. A mathematical model for evaluating temperature rise efficiency is proposed, and an optimal frequency for efficient FUS thermal therapy in brain-like soft materials is identified. This research elucidates the link between viscoelastic tissue behavior and FUS treatment outcomes, offering insights for optimizing FUS applications in various medical fields.

Magnesium alloy, as a new material for vascular stents, possesses excellent mechanical properties, biocompatibility, and biodegradability. However, the mechanical properties of magnesium alloy stents exhibit relatively inferior performance compared to traditional metal stents with identical structural characteristics. Therefore, improving their mechanical properties is a key issue in the development of biodegradable magnesium alloy stents. In this study, three new stent structures (i.e., stent A, stent B, and stent C) were designed based on the typical structure of biodegradable stents. The changes made included altering the angle and arrangement of the support rings to create a support ring structure with alternating large and small angles, as well as modifying the position and shape of the link. Using finite element analysis, the compressive performance, expansion performance, bending flexibility performance, damage to blood vessels, and hemodynamic changes of the stent were used as evaluation indexes. The results of these comprehensive evaluations were utilized as the primary criteria for selecting the most suitable stent design. The results demonstrated that compared to the traditional stent, stents A, B, and C exhibited improvements in radial stiffness of 16.9%, 15.1%, and 37.8%, respectively; reductions in bending stiffness of 27.3%, 7.6%, and 38.1%, respectively; decreases in dog-boning rate of 5.1%, 93.9%, and 31.3%, respectively; as well as declines in the low wall shear stress region by 50.1%, 43.8%, and 36.2%, respectively. In comparison to traditional stents, a reduction in radial recoiling was observed for stents A and C, with decreases of 9.3% and 7.4%, respectively. Although there was a slight increase in vessel damage for stents A, B, and C compared to traditional stents, this difference was not significant to have an impact. The changes in intravascular blood flow rate were essentially the same after implantation of the four stents. A comparison of the four stents revealed that stents A and C exhibited superior overall mechanical properties and they have greater potential for clinical application. This study provides a reference for designing clinical stent structures.

A systematic verification and validation (V&V) of our previously proposed momentum source wave generation method is performed. Some settings of previous numerical wave tanks (NWTs) of regular and irregular waves have been optimized. The H2-5 V&V method involving five mesh sizes with mesh refinement ratio being 1.225 is used to verify the NWT of regular waves, in which the wave height and mass conservation are mainly considered based on a Lv3 (H_s = 0.75 m) and a Lv6 (H_s = 5 m) regular wave. Additionally, eight different sea states are chosen to validate the wave height, mass conservation and wave frequency of regular waves. Regarding the NWT of irregular waves, five different sea states with significant wave heights ranging from 0.09 m to 12.5 m are selected to validate the statistical characteristics of irregular waves, including the profile of the wave spectrum, peak frequency and significant wave height. Results show that the verification errors for Lv3 and Lv6 regular wave on the most refined grid are −0.018 and −0.35 for wave height, respectively, and −0.14 and for −0.17 mass conservation, respectively. The uncertainty estimation analysis shows that the numerical error could be partially balanced out by the modelling error to achieve a smaller validation error by adjusting the mesh size elaborately. And the validation errors of the wave height, mass conservation and dominant frequency of regular waves under different sea states are no more than 7%, 8% and 2%, respectively. For a Lv3 (H_s = 0.75 m) and a Lv6 (H_s = 5 m) regular wave, simulations are validated on the wave height in wave development section for safety factors FS ≈ 1 and FS ≈ 0.5–1, respectively. Regarding irregular waves, the validation errors of the significant wave height and peak frequency are both lower than 2%.

Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties. However, exploring the structure-property relationship in these materials, including forward prediction and inverse design, presents substantial challenges. The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches. Here, we establish a novel framework that integrates the machine learning (ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design. Unlike prior end-to-end ML methods limited to specific problems, our framework is both load-independent and geometry-independent. This means that a single training session for a constitutive model suffices to tackle various problems directly, eliminating the need for repeated data collection or training. We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures. Leveraging accelerated forward prediction, we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios, and extend this capability to multi-objective customization seamlessly. Moreover, we achieve topology optimization for stress alleviation at the crack tip, resulting in a significant reduction of Mises stress by up to 41.2% and yielding a theoretical interpretable pattern. This framework offers a general, efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.

The role of hydrodynamic effect in the meeting of multiple fish is a fascinating topic. The interactions of two self-propelled flexible plates swimming in opposite directions horizontally and maintaining a certain lateral distance are numerically simulated using a penalty-immersed boundary method. The effects of the flapping phase and lateral distance on the propulsive performance of two fish meetings are analyzed. Results show that, when two plates meet, if their leading edges diverge laterally, the individual plate can efficiently and rapidly move apart from the other horizontally. If their leading edges converge laterally, the plate motion can be retarded, leading to high energy consumption. Moreover, an increasing lateral distance between two plates significantly weakens the fluid-structure interactions, resulting in an exponential decline in mean cruising speed. A quantitative force analysis based on vortex dynamic theory is performed to gain physics insight into the hydrodynamic interaction mechanism. It is found that lateral separation between the two leading edges enhances the vorticity generation and boundary vorticity flux on the surface of the plate, subsequently reinforcing the thrust effect and increasing horizontal velocity. This study offers insight into the hydrodynamic mechanisms of the fluid-structure interactions among fish moving toward each other and suggests potential strategies for enhancing the maneuverability of robotic fish in complex environment.

Within the context of global energy transitions, many wind turbines have been installed in desert and Gobi regions. Nevertheless, the impact of turbulence characteristics in actual sand-laden atmospheric flows on the aerodynamic performance of wind turbines has not been evaluated. The current study employs the high-quality wind velocity data measured in the Qingtu Lake Observation Array station of Min Qin to reveal the effects of turbulence characteristics in sand-laden atmospheric flows on the power and loads of a small wind turbine. The results demonstrate that turbulent coherent structures under sand-laden conditions occur more frequently and with shorter durations than that under the unladen conditions, leading to frequent and large fluctuations of wind turbine loads, specifically, the power, thrust, and blade root flapwise moment increased by 238%, 167%, and 194%, respectively. The predictions by applying the extreme turbulence model suggested that the maximum extreme thrust, blade root flapwise moment, and blade root edgewise moment of wind turbine under sand-laden conditions are 23%, 19%, and 7% higher than that under unladen conditions. This study is expected to provide a basic supply for wind turbine design and siting decisions in sand-laden environment.

The flow control at low Reynolds numbers is one of the most promising technologies in the field of aerodynamics, and it is also an important source of the innovation for novel aircraft. In this study, a new way of nonlinear flow control by interaction between two flexible flaps is proposed, and their flow control mechanism is studied employing the self-constructed immersed boundary-lattice Boltzmann-finite element method (IB-LB-FEM). The effects of the difference in material properties and flap length between the two flexible flaps on the nonlinear flow control of the airfoil are discussed. It is suggested that the relationship between the deformation of the two flexible flaps and the evolution of the vortex under the fluid-structure interaction (FSI). It is shown that the upstream flexible flap plays a key role in the flow control of the two flexible flaps. The FSI effect of the upstream flexible flap will change the unsteady flow behind it and affect the deformation of the downstream flexible flap. Two flexible flaps with different material properties and different lengths will change their own FSI characteristics by the induced vortex, effectively suppressing the flow separation on the airfoil’s upper surface. The interaction of two flexible flaps plays an extremely important role in improving the autonomy and adjustability of flow control. The numerical results will provide a theoretical basis and technical guidance for the development and application of a new flap passive control technology.

Flexoelectricity refers to the link between electrical polarization and strain gradient fields in piezoelectric materials, particularly at the nano-scale. The present investigation aims to comprehensively focus on the static bending analysis of a piezoelectric sandwich functionally graded porous (FGP) double-curved shallow nanoshell based on the flexoelectric effect and nonlocal strain gradient theory. Two coefficients that reduce or increase the stiffness of the nanoshell, including nonlocal and length-scale parameters, are considered to change along the nanoshell thickness direction, and three different porosity rules are novel points in this study. The nanoshell structure is placed on a Pasternak elastic foundation and is made up of three separate layers of material. The outermost layers consist of piezoelectric smart material with flexoelectric effects, while the core layer is composed of FGP material. Hamilton’s principle was used in conjunction with a unique refined higher-order shear deformation theory to derive general equilibrium equations that provide more precise outcomes. The Navier and Galerkin-Vlasov methodology is used to get the static bending characteristics of nanoshells that have various boundary conditions. The program’s correctness is assessed by comparison with published dependable findings in specific instances of the model described in the article. In addition, the influence of parameters such as flexoelectric effect, nonlocal and length scale parameters, elastic foundation stiffness coefficient, porosity coefficient, and boundary conditions on the static bending response of the nanoshell is detected and comprehensively studied. The findings of this study have practical implications for the efficient design and control of comparable systems, such as micro-electromechanical and nano-electromechanical devices.

In thermal protection structures, controlling and optimizing the surface roughness of carbon/phenolic (C/Ph) composites can effectively improve thermal protection performance and ensure the safe operation of carriers in high-temperature environments. This paper introduces a machine learning (ML) framework to forecast the surface roughness of carbon-phenolic composites under various thermal conditions by employing an ML algorithm derived from historical experimental datasets. Firstly, ablation experiments and collection of surface roughness height data of C/Ph composites under different thermal environments were conducted in an electric arc wind tunnel. Then, an ML model based on Ridge regression is developed for surface roughness prediction. The model involves incorporating feature engineering to choose the most concise and pertinent features, as well as developing an ML model. The ML model considers thermal environment parameters and feature screened by feature engineering as inputs, and predicts the surface height as the output. The results demonstrate that the suggested ML framework effectively anticipates the surface shape and associated surface roughness parameters in various heat flow conditions. Compared with the conventional 3D confocal microscope scanning, the method can obtain the surface topography information of the same area in a much shorter time, thus significantly saving time and cost.

Helmets exacerbate head injuries to some degree under blast load, which has been recently researched and referred to as the underwash effect. Various studies indicate that the underwash effect is attributed to either wave interaction or wave-structure interaction. Despite ongoing investigations, there is no consensus on the explanations and verification of proposed mechanisms. This study conducts experiments and numerical simulations to investigate the underwash effect, resulting from the interaction among blast load, helmets, and head models. The analysis of overpressure in experiments and simulations, with the developed simplified models that ignore unimportant geometric details, reveals that the underwash effect arises from the combined action of wave interaction and wave-structure interaction. Initially reflected in front of the head, the blast load converges at the rear after diffraction, forming a high-pressure zone. Decoupling the helmet components demonstrates that the pads alleviate rear overpressure through array hindrance of the load, resulting in a potential reduction of up to 36% in the rear overpressure peak. The helmet shell exacerbates the rear overpressure peak through geometric restriction of the load after diffraction, leading to a remarkable 388% increase in rear overpressure. The prevailing impact of the geometric restriction imposed by the shell of the helmet leads to a significant 57% increase in overpressure when employing a complete helmet.