Design and Driving Performance Study of Soft Actuators for Hand Rehabilitation Training.

Purpose: To address the application requirements of soft actuators in rehabilitation training gloves, and in combination with ergonomic requirements, we designed a segmented soft actuator with bending and elongation modules. This actuator can achieve independent or coupled movements of the finger joints. Methods: A finite element model of the joint actuator was established to compare the driving performance of actuators with different structural forms. Numerical calculations were used to analyze the effects of structural size parameters on the bending characteristics and end output force of the actuator. The design was then refined based on these analyses. Results: The joint actuator designed in this study demonstrated a 71% increase in bending angle compared to the standard fast pneumatic network structure. Key factors affecting the driving performance include the thickness of the constraint layer, the inner wall thickness of the chamber, chamber height, chamber width, chamber spacing, chamber length, and the number of chambers. After improvements, the bending angle of the joint actuator increased by 60.6%, and the output force increased by 145.9%, indicating significant improvement. Conclusion: This study designed and improved a soft actuator for hand rehabilitation training, achieving independent and coupled joint movements. The bending angle, bending shape, and joint driving force of the soft actuator meet the requirements for finger rehabilitation training.

Ultrathin Ionic Diodes with Electrostatically Heterogeneous Hybrid Interfaces of Nanoporous SiO2 Nanofilms and Polymer Layer-by-Layer Multilayers.

Nanofluidic ionic diodes have attracted much attention due to their unique functions as unidirectional ion transportation ability and promising applications from molecular sensing, and energy harvesting to emerging neuromorphic devices. However, it remains a challenge to fabricate diode-like nanofluidic systems with ultrathin film thickness <100 nm. Herein the formation of ultrathin ionic diodes from hybrid nanoassemblies of nanoporous (NP) SiO2 nanofilms and polyelectrolyte layer-by-layer (LbL) multilayers is described. Ultrathin ionic diodes are prepared by integrating polyelectrolyte multilayers onto photo-oxidized NP SiO2 nanofilms obtained from silsesquioxane-containing block copolymer thin films as a template. The obtained ultrathin ionic diodes exhibit ion current rectification (ICR) properties with high ICR factor = ≈20 under low ionic strength and asymmetric pH conditions. It is concluded that this ICR behavior arises from effective ion accumulation and depletion at the interface of NP SiO2 nanofilms and LbL multilayers attributed to high ion selectivity by combining the experimental data and theoretical calculations using finite element methods. These results demonstrate that the hybrid nano assemblies of NP SiO2 nanofilms and polyelectrolyte LbL multilayers have potential applications for (bio)sensing materials and integrated ionic circuits for seamless connection of human-machine interfaces.

Wear simulation of UHMWPE against the different counterface roughness in reciprocating unidirectional sliding motion.

Wear simulations of UHMWPE can economically and conveniently predict the performance of wear resistant bushings used for sealing or other reciprocating unidirectional sliding motion. In this study, pin on plate tribological experiments and microscopic analysis was done to obtained the wear profiles, wear volume and wear mechanism of UHMWPE against the counterface with different surface roughness of which Ra range is 0.03 ~ 2 µm. Meanwhile, the 3D wear simulation model of the pin on plate tribological experiments was established to discuss the adaptability of the energy and Archard wear model by analyzing the difference of wear profiles and wear volume between the experiment and simulation. The results indicate that with an increase in the counterface roughness, the wear simulation of UHMWPE estimated by the energy model were more accurate in reciprocating unidirectional sliding motion.

Finite element analysis of a three-dimensional cervical spine model with muscles based on CT scan data.

BACKGROUND: The incidence of cervical spondylosis is increasing, gradually affecting people's normal lives. Establishing a finite element model of the cervical spine is one of the methods for studying cervical spondylosis. MRI (Magnetic Resonance Imaging) still has certain difficulties in transitioning from human imaging to establishing muscle models suitable for finite element analysis. Medical software provides specific morphologies and can generate muscle finite element models. Additionally, there is little research on the static analysis of cervical spine finite element models with solid muscle. PURPOSE: A new method is proposed for establishing a finite element model of the cervical spine based on CT (Computed Tomography) data and medical software, and the model's effectiveness is validated. Human movement characteristics based on the force distribution in various parts are analyzed and predicted. METHODS: The muscle model is reconstructed in medical software and a three-dimensional finite element model of the entire cervical spine (C0-C7) is established by combining muscle models with CT vertebral data models. 1.5 Nm of load is applied to the finite element model to simulate the cervical spine movement. RESULTS: The finite element model was successfully established, and effectiveness was verified. Stress variations in various parts under six movements were obtained. The effectiveness of the model was basically verified. CONCLUSION: The finite element model of the cervical spine for mechanical analysis can be successfully established by using medical software and CT data. In daily life, the C2-3, C3-4, C4-C5 intervertebral discs, rectus capitis posterior major, longus colli, and obliquus capitis inferior are more prone to injury.

Extended finite element analysis for the 2nd and 3rd metatarsals stress fracture during push-off.

Metatarsal stress fractures (MSF), particularly the 2nd and 3rd MSF, are common injuries among athletes. Although there are several practices to reduce foot and ankle injuries, there is no injury prevention program specifically designed to minimize MSF. This is mainly due to the lack of information about the loadings/postures that cause MSF. Therefore, this study aimed to investigate dangerous loadings/postures potentially causing MSF during push-off (PO). The analysis was conducted with Finite Element Modelling (FEM), calibrated with the three-point bending test, and validated with peak plantar pressure (PPP) and fracture force measurement. Extended Finite Element Method was used for MSF simulation such that ten different foot and ankle configurations were designed, with five for each of the 2nd and 3rd MSF under pure vertical loadings. A more complex loading, ankle eversion/inversion during PO, was also examined for the MSF. The average error percentage for the calibration of the model with the three-point bending test was 3.05%. The average error percentages for the validation of the model with PPP and fracture force measurements were 18% and 30%, respectively. The outcomes of pure vertical loadings indicated the higher potential for the 2nd and 3rd MSF at 30% PO and 70% PO, respectively. The results of ankle eversion/inversion loadings represented that the most dangerous posture for MSF was 30° ankle eversion for the 3rd metatarsal at 70% PO. These results provide a guide, including what postures to avoid for the 2nd and 3rd MSF among people who are at high risk of MSF.

The study of the influence of the geometry of a lateral electric field resonator on its resonant characteristics.

An experimental study of the dependence of the electrical impedance of a lateral electric field resonator on its thickness and the size of the gap between the electrodes was carried out. The resonator was made of PZT-19 piezoceramics in the form of a rectangular parallelepiped with the shear dimensions of 18 × 20 mm2. Two rectangular electrodes with a gap that varied in the range from 4 to 14 mm were applied on one side of the resonator. For each gap width, the frequency dependences of the real and imaginary parts of the electrical impedance were measured using an impedance analyzer. It has been found that increasing the gap width leads to an increase in the resonant frequency and to an increase in the maximum value of the real part of the impedance. Three series of such experiments were carried out for three values of the resonator thickness: 3.02, 2.38 and 1.9 mm. The resonant characteristics of the resonator were also theoretically analyzed by finite element analysis using two models. One resonator model was based on a two-dimensional finite element method. In this case, the vibration modes that existed due to the finite size of the plate in the direction parallel to the gap between the electrodes were not taken into account. The second model of the resonator used a three-dimensional finite element method, which correctly took into account all vibration modes existing in the resonator. Comparison of theory with experiment has shown that the three-dimensional model provides a better agreement between theoretical and experimental results.

Deep Learning-Based Simultaneous Temperature- and Curvature-Sensitive Scatterplot Recognition.

Since light propagation in a multimode fiber (MMF) exhibits visually random and complex scattering patterns due to external interference, this study numerically models temperature and curvature through the finite element method in order to understand the complex interactions between the inputs and outputs of an optical fiber under conditions of temperature and curvature interference. The systematic analysis of the fiber's refractive index and bending loss characteristics determined its critical bending radius to be 15 mm. The temperature speckle atlas is plotted to reflect varying bending radii. An optimal end-to-end residual neural network model capable of automatically extracting highly similar scattering features is proposed and validated for the purpose of identifying temperature and curvature scattering maps of MMFs. The viability of the proposed scheme is tested through numerical simulations and experiments, the results of which demonstrate the effectiveness and robustness of the optimized network model.

On the numerical corroboration of an obstacle problem for linearly elastic flexural shells.

In this article, we study the numerical corroboration of a variational model governed by a fourth-order elliptic operator that describes the deformation of a linearly elastic flexural shell subjected not to cross a prescribed flat obstacle. The problem under consideration is modelled by means of a set of variational inequalities posed over a non-empty, closed and convex subset of a suitable Sobolev space and is known to admit a unique solution. Qualitative and quantitative numerical experiments corroborating the validity of the model and its asymptotic similarity with Koiter's model are also presented.This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

Finite element solution of coupled multiphysics reaction-diffusion equations for fracture healing in hard biological tissues.

This study proposes a computational framework to investigate the multi-stage process of fracture healing in hard tissues, e.g., long bone, based on the mathematical Bailon-Plaza and Van der Meulen formulation. The goal is to explore the influence of critical biological factors by employing the finite element method for more realistic configurations. The model integrates a set of variables, including cell densities, growth factors, and extracellular matrix contents, managed by a coupled system of partial differential equations. A weak finite element formulation is introduced to enhance the numerical robustness for coarser mesh grids, complex geometries, and more accurate boundary conditions. This formulation is less sensitive to mesh quality and converges smoothly with mesh refinement, exhibiting superior numerical stability compared to previously available strong-form solutions. The model accurately reproduces various stages of healing, including soft cartilage callus formation, endochondral and intramembranous ossification, and hard bony callus development for various sizes of fracture gap. Model predictions align with the existing research and are logically coherent with the available experimental data. The developed multiphysics simulation clarifies the coordination of cellular dynamics, extracellular matrix alterations, and signaling growth factors during fracture healing.

Use of Cohesive Approaches for Modelling Critical States in Fibre-Reinforced Structural Materials.

During the operation of structures, stress and deformation fields occur inside the materials used, which often ends in fatal damage of the entire structure. Therefore, the modelling of this damage, including the possible formation and growth of cracks, is at the forefront of numerical and applied mathematics. The finite element method (FEM) and its modification will allow us to predict the behaviour of these structural materials. Furthermore, some practical applications based on cohesive approach are tested. The main effort is devoted to composites with fibres and searching for procedures for their accurate modelling, mainly in the area where damage can be expected to occur. The use of the cohesive approach of elements that represent the physical nature of energy release in front of the crack front has proven to be promising not only in the direct use of cohesive elements, but also in combination with modified methods of standard finite elements.

Numerical Simulation and ANN Prediction of Crack Problems within Corrosion Defects.

Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem.

Nanoindentation Test of Ion-Irradiated Materials: Issues, Modeling and Challenges.

Exposure of metals to neutron irradiation results in an increase in the yield strength and a significant loss of ductility. Irradiation hardening is also closely related to the fracture toughness temperature shift or the ductile-to-brittle transition temperature (DBTT) shift in alloys with a body-centered cubic (bcc) crystal structure. Ion irradiation is an indispensable tool in the study of the radiation effects of materials for nuclear energy systems. Due to the shallow damage depth in ion-irradiated materials, the nanoindentation test is the most commonly used method for characterizing the changes in mechanical properties after ion irradiation. Issues that affect the analysis of irradiation hardening may arise due to changes in the surface morphology and mechanical properties, as well as the inherent complexities in nanoscale indentation. These issues, including changes in surface roughness, carbon contamination, the pile-up effect, and the indentation size effect, with corresponding measures, were reviewed. Modeling using the crystal plasticity finite element method of the nanoindentation of ion-irradiated materials was also reviewed. The challenges in extending the nanoindentation test to high temperatures and to multiscale simulation were addressed.

Numerical Investigation on Thermal Conductivity of Graphene Foam Composite for Thermal Management Applications.

Graphene foam prepared by the chemical vapor deposition method is a promising thermal interfacial material. However, the thermal properties of graphene foam highly depend on the experimental fabrication conditions during the chemical vapor deposition process. Aiming to reveal how to prepare the appropriate graphene foam for the various thermal management scenarios, the influence of experimental conditions on thermal properties of graphene foam was investigated. Furthermore, the contribution of thermal conductivity and thermal radiation to the effective thermal coefficient of graphene foam was carried out for comparison. The research results showed that the porosity and the cross-section shape of the struts of the growth template were two critical factors affecting the thermal transport of graphene foam, especially with the increase of temperature. In addition, the deposition time of graphene determined the wall thickness and affected the thermal conductivity directly. The thermal radiation contributed more than thermal conductivity when the temperature climbed continuously. Comparatively, the effective thermal coefficient of graphene foam composite with high porosity and circular-shape struts was much superior to that of others at high temperature. The research findings provide important guidance for graphene foam fabrication and its applications in the field of thermal management.

Aortic roadmapping during EVAR: a combined FEM-EM tracking feasibility study.

PURPOSE: Currently, the intra-operative visualization of vessels during endovascular aneurysm repair (EVAR) relies on contrast-based imaging modalities. Moreover, traditional image fusion techniques lack a continuous and automatic update of the vessel configuration, which changes due to the insertion of stiff guidewires. The purpose of this work is to develop and evaluate a novel approach to improve image fusion, that takes into account the deformations, combining electromagnetic (EM) tracking technology and finite element modeling (FEM). METHODS: To assess whether EM tracking can improve the prediction of the numerical simulations, a patient-specific model of abdominal aorta was segmented and manufactured. A database of simulations with different insertion angles was created. Then, an ad hoc sensorized tool with three embedded EM sensors was designed, enabling tracking of the sensors' positions during the insertion phase. Finally, the corresponding cone beam computed tomography (CBCT) images were acquired and processed to obtain the ground truth aortic deformations of the manufactured model. RESULTS: Among the simulations in the database, the one minimizing the in silico versus in vitro discrepancy in terms of sensors' positions gave the most accurate aortic displacement results. CONCLUSIONS: The proposed approach suggests that the EM tracking technology could be used not only to follow the tool, but also to minimize the error in the predicted aortic roadmap, thus paving the way for a safer EVAR navigation.

A Novel Finite Element-Based Method for Predicting the Permeability of Heterogeneous and Anisotropic Porous Microstructures.

Permeability is a fundamental property of porous media. It quantifies the ease with which a fluid can flow under the effect of a pressure gradient in a network of connected pores. Porous materials can be natural, such as soil and rocks, or synthetic, such as a densified network of fibres or open-cell foams. The measurement of permeability is difficult and time-consuming in heterogeneous and anisotropic porous media; thus, a numerical approach based on the calculation of the tensor components on a 3D image of the material can be very advantageous. For this type of microstructure, it is important to perform calculations on large samples using boundary conditions that do not suppress the transverse flows that occur when flow is forced out of the principal directions. Since these are not necessarily known in complex media, the permeability determination method must not introduce bias by generating non-physical flows. A new finite element-based method proposed in this study allows us to solve very high-dimensional flow problems while limiting the biases associated with boundary conditions and the small size of the numerical samples addressed. This method includes a new boundary condition, full permeability tensor identification based on the multiscale homogenization approach, and an optimized solver to handle flow problems with a large number of degrees of freedom. The method is first validated against academic test cases and against the results of a recent permeability benchmark exercise. The results underline the suitability of the proposed approach for heterogeneous and anisotropic microstructures.

Numerical and Experimental Analysis of Buckling and Post-Buckling Behaviour of TWCFS Lipped Channel Section Members Subjected to Eccentric Compression.

The paper presents a static analysis of the buckling and post-buckling state of thin-walled cold-formed steel (TWCFS) lipped channel section beam-columns subjected to eccentric compression. Eccentricity is taken into consideration relative to both major and minor principal axes. An analytical-numerical solution to the buckling and post-buckling problems is described. The solution is based on the theory of thin plates. Equations of equilibrium of section walls are derived from the principle of stationary energy. Then, to solve the problem, the finite difference (FDM) and Newton-Raphson methods are applied. Linear (buckling) and nonlinear (post-buckling) analyses are performed. As a result, pre- and post-buckling equilibrium paths are determined. Comparisons of the obtained numerical results, FE simulation results, and experimental test results are carried out and presented in comparative load-shortening diagrams. Additionally, a comparison of the buckling forces and buckling modes obtained from theoretical analysis and experiments is presented.

Loss-Optimized Design of Magnetic Devices.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

Can physics-informed neural networks beat the finite element method?

Partial differential equations (PDEs) play a fundamental role in the mathematical modelling of many processes and systems in physical, biological and other sciences. To simulate such processes and systems, the solutions of PDEs often need to be approximated numerically. The finite element method, for instance, is a usual standard methodology to do so. The recent success of deep neural networks at various approximation tasks has motivated their use in the numerical solution of PDEs. These so-called physics-informed neural networks and their variants have shown to be able to successfully approximate a large range of PDEs. So far, physics-informed neural networks and the finite element method have mainly been studied in isolation of each other. In this work, we compare the methodologies in a systematic computational study. Indeed, we employ both methods to numerically solve various linear and nonlinear PDEs: Poisson in 1D, 2D and 3D, Allen-Cahn in 1D, semilinear Schrödinger in 1D and 2D. We then compare computational costs and approximation accuracies. In terms of solution time and accuracy, physics-informed neural networks have not been able to outperform the finite element method in our study. In some experiments, they were faster at evaluating the solved PDE.

Numerical analysis of potential in electrostatic amplitude caused by paint splashing.

In this research, the behavior of an electric field caused by the mechanism of electrostatic painting has been investigated using the finite element method and FLEXPDE software. The aim of this study is to optimize the electrostatic spraying performance of the paint sprayer by investigating the potential field in the paint nozzle. The results show that the potential and the electric field can be solved at any given point and displayed graphically. Additionally, changing the 2D rectangular covering surface to a circular one increased the potential value reached on the covering surface by 10 percent. The amount of electric potential and electrostatic field in the direction perpendicular to the x-axis is shown to be symmetrical and equal for y > 0 and y < 0. The size of the spray opening/hole is a significant factor in reaching paint particles to the coating surface. Doubling the size of the spray opening increased the potential value on the coating surface by 54.3 percent, while halving it decreased the potential value by 75 percent.

Investigating the Mechanical Performance of Bionic Wings Based on the Flapping Kinematics of Beetle Hindwings.

The beetle, of the order Coleoptera, possesses outstanding flight capabilities. After completing flight, they can fold their hindwings under the elytra and swiftly unfold them again when they take off. This sophisticated hindwing structure is a result of biological evolution, showcasing the strong environmental adaptability of this species. The beetle's hindwings can provide biomimetic inspiration for the design of flapping-wing micro air vehicles (FWMAVs). In this study, the Asian ladybird (Harmonia axyridis Pallas) was chosen as the bionic research object. Various kinematic parameters of its flapping flight were analyzed, including the flight characteristics of the hindwings, wing tip motion trajectories, and aerodynamic characteristics. Based on these results, a flapping kinematic model of the Asian ladybird was established. Then, three bionic deployable wing models were designed and their structural mechanical properties were analyzed. The results show that the structure of wing vein bars determined the mechanical properties of the bionic wing. This study can provide a theoretical basis and technical reference for further bionic wing design.