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In this study, we propose a tetrahedral mesh generation and adaptive refinement method for multi-chamber, multi-facet, multiscale and surface-solid mesh coupling with extremely thin layers, solving the two challenges of mesh generation and refinement in current electromagnetic simulation models.

Utilizing innovative topology transformation techniques, high-precision intersection judgment algorithms and highly reliable boundary recovery algorithms to reduce the number of Steiner locking points. The feasible space for the reposition of Steiner points is determined by using the linear programming. During mesh refinement, an edge-split method based on geometric center and boundary facets node size is devised. Solving the problem of difficult insertion of nodes in narrow geometric spaces, capable of filtering the longest and boundary edges of tetrahedrons, refining the mesh layer by layer through multiple iterations, and achieving collaborative optimization of surface and tetrahedral mesh. Simultaneously, utilizing a surface-facet preserving mesh topology optimization algorithm to improve the fit degree between the mesh and geometry.

Initial mesh generation for electromagnetic models, compared to commercial software, the method proposed in this paper has a higher pass rate and better mesh quality. For the adaptive refinement performance of high-frequency computing, this method can generate an average of 50% fewer meshes compared to commercial software while meeting simulation accuracy.

This paper proposes a complete set of mesh generation and adaptive refinement theories and methods designed for the structural characteristics of electromagnetic simulation models, which meet the needs of real-world industrial applications.

When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

This paper aims to use two different numerical approaches to simulate the induction welding process for a hybrid thermoplastic material, and the results have been validated experimentally.

The first approach used a numerical model that combines electromagnetism, heat transfer and solid mechanics in the same numerical environment using Hexagon Marc software. Simultaneously, a computationally efficient approach combined steady-state electromagnetism results at specific intervals in the Ansys EM suite with heat transfer and solid mechanics in Ansys Workbench.

The results from both numerical approaches showed a strong correlation with the experimental findings.

The current research offers valuable insights into enhancing induction welding procedures within the aerospace industry, as well as across broader industrial applications. The synergistic combination of numerical simulations and experimental validation served as a robust framework for future research endeavors aimed at enhancing the efficiency, reliability and quality of thermoplastic welding techniques.

The effect of spinning has been studied and analysed for different projectile shapes such as ogive, blunt, cylindrical and conical by using numerical simulations.

Projectile shape is one of the important parameters in the penetration mechanism. The present study deals with the failure mechanisms and ballistic evaluation for different nose-shaped projectiles undergoing normal impact with spinning. Materials characterization has been made by Johnson–Cook strength and failure models, and LS-DYNA simulations are used to analyse the impact of steel projectiles on an Al 7075-T651 target at different impact velocities under normal impact conditions. The experimental results from the literature are used to validate the model. Based on the residual velocity values, the Recht-Ipson model has been curve-fitted and approximate ballistic limit velocity has been evaluated. The approximated ballistic limit velocity is found to be 3.4% higher than the experimental results and compared well with the experimental results. Subsequently, the validated model conditions are used to study and analyse the effect of spinning for different nose-shaped projectiles undergoing normal impact conditions.

The ductile hole failure is observed for the ogive nose projectile, petals are formed and fragmented for the conical projectile, and plugging is observed for cylindrical projectiles. A Recht-Ipson curve is presented for each spinning condition for each projectile shape and the ballistic limit has been evaluated for each condition.

The proposed research outputs are original and innovative and, have a lot of importance in defence applications, particularly in arms and ammunition.

This study aims to investigate the influence mechanism of thermal-mechanical deformations on the CO₂ mixture gases dry gas seal (DGS) flow field and compare the deformation characteristics and sealing performance between two-way and one-way thermal-fluid-solid coupling models.

The authors established a two-way thermal-fluid-solid coupling model by using gas film thickness as the transfer parameter between the fluid and solid domain, and the model was solved using the finite difference method and finite element method. The thermal-mechanical deformations of the sealing rings, the influence of face deformation on the flow field and sealing performance were obtained.

Thermal-mechanical deformations cause a convergent gap between the two sealing end faces, resulting in an increase in the gas film thickness, but a decrease in the gas film temperature and sealing ring temperature. The axial relative deformations of rotating and stationary ring end faces caused by mechanical and thermal loads in the two-way coupling model are less than those in the one-way coupling (OWC) model, and the gas film thickness and leakage rate are larger than those in the OWC model, whereas the gas film stiffness is the opposite.

This paper provides a theoretical support and reference for the operational stability and structural optimization design of CO₂ mixture gases DGS under high-pressure and high-speed operation conditions.

The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

The challenges to healthcare caused by the COVID-19 pandemic forced hospital librarians to develop their abilities to cope with change and crises, both on a social level and an organisational level. The aim of this study is to contribute to knowledge about how hospital librarians developed library services during the pandemic and how these changes contributed to building information resilience in the healthcare organisation. This paper also seeks to explore how resilience theory, and specifically the concept information resilience, can be used within library and information science (in LIS) to investigate resilience in the library sector.

Nine semi-structured interviews with librarians were conducted at four different hospital libraries in four different regions in Sweden between March and May 2022. The empirical material was analysed through an interaction between the tzheoretical perspective and the empirical material through a thematic analysis. In each theme, specific resilience resources are identified and analysed as components of the information resilience developed by hospital librarians.

The results show that hospital librarians contribute to several different information resilience resources, which support information resilience in the healthcare organisation. Three aspects characterize the qualities of resilience resources: access, flexibility, and collaboration. The findings suggest that the framework for analysing information resilience used in this study is well suited for studying the resilience of libraries from both organisational and informational aspects.

The analysis of information resilience on an organisational level presents a novel way to study resilience in the library sector.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

The boundary integral method (BIM) is very attractive to practicing engineers as it reduces the dimensionality of the problem by one, thereby making the procedure computationally inexpensive compared to its peers. The principal feature of this technique is the limitation of all its computations to only the boundaries of the domain. Although the procedure is well developed for the Laplace equation, the Poisson equation offers some computational challenges. Nevertheless, the literature provides a couple of solution methods. This paper revisits an alternate approach that has not gained much traction within the community. The purpose of this paper is to address the main bottleneck of that approach in an effort to popularize it and critically evaluate the errors introduced into the solution by that method.

The primary intent in the paper is to work on the particular solution of the Poisson equation by representing the source term through a Fourier series. The evaluation of the Fourier coefficients requires a rectangular domain even though the original domain can be of any arbitrary shape. The boundary conditions for the homogeneous solution gets modified by the projection of the particular solution on the original boundaries. The paper also develops a new Gauss quadrature procedure to compute the integrals appearing in the Fourier coefficients in case they cannot be analytically evaluated.

The current endeavor has developed two different representations of the source terms. A comprehensive set of benchmark exercises has successfully demonstrated the effectiveness of both the methods, especially the second one. A subsequent detailed analysis has identified the errors emanating from an inadequate number of boundary nodes and Fourier modes, a high difference in sizes between the particular solution and the original domains and the used Gauss quadrature integration procedures. Adequate mitigation procedures were successful in suppressing each of the above errors and in improving the solution accuracy to any desired level. A comparative study with the finite difference method revealed that the BIM was as accurate as the FDM but was computationally more efficient for problems of real-life scale. A later exercise minutely analyzed the heat transfer physics for a fin after validating the simulation results with the analytical solution that was separately derived. The final set of simulations demonstrated the applicability of the method to complicated geometries.

First, the newly developed Gauss quadrature integration procedure can efficiently compute the integrals during evaluation of the Fourier coefficients; the current literature lacks such a tool, thereby deterring researchers to adopt this category of methods. Second, to the best of the author’s knowledge, such a comprehensive error analysis of the solution method within the BIM framework for the Poisson equation does not currently exist in the literature. This particular exercise should go a long way in increasing the confidence of the research community to venture into this category of methods for the solution of the Poisson equation.