This research introduces a novel and unified optimization design method for multi-stage non-circular gear transmission (MNCGT) to address the challenges in designing MNCGT for complex motion requirements. The method optimizes non-circular gears comprehensively, reducing design and manufacturing difficulties while ensuring the realization of specified transmission requirements. A unified parameterization method, grounded on periodic B-spline interpolation, is introduced to establish the transmission function of non-circular gears and map it to a finite unified variable space. This innovative approach effectively reduces the difficulty and constraints of MNCGT optimization design. The proposed method takes into account crucial factors such as non-circularity, smoothness, processing conditions, and contact ratio, which significantly impact the transmission performance and manufacturing feasibility of non-circular gears. The effectiveness and superiority of this method are demonstrated through two practical examples and a real-world application in a planetary gear transplant mechanism, highlighting its potential for solving complex engineering problems.

This study presents a simple approach and its associated MATLAB toolbox for 3D block cutting and mesh cutting. The approach is suitable for the meshes of numerical methods including the Key Block Theory (KBT), the Discontinuous Deformation Analysis (DDA), the Numerical Manifold Method (NMM), and the cut Finite Element Method (Cut-FEM). The strategy is based on calculations on convex bodies. It uses two different forms of representation: the geometric representation which includes vertices and faces, and the algebraic representation which consists of inequalities. The cutting was implemented on the algebraic representation, and the resulting inequalities were converted into a geometric representation. The above strategy turned out to be robust and straightforward to execute, at the cost of a general body being regarded as a combination of convex bodies. The efficiency guarantee was considered through pre-checking algorithms. The source code was provided, as well as some simple examples.

Current discrete element parameter calibration methods primarily rely on mechanical tests to analyze particle properties and often overlook the machinery's interaction with the particles. The extensive variation in particle sizes of crushed coal poses challenges in accurately applying parameters derived from mechanical test calibrations to industrial simulations. DEM models of mechanical tests for coal were developed to examine how parameters influence coal's mechanical properties through factor analysis. Simplified engineering test models were developed based on mining equipment, with the equipment responses used as indicators to optimize mechanical test calibration parameters. On this basis, a calibration method of discrete elemental parameters of coal based on the crushing simulation of mining equipment was proposed. This method was validated through mechanical and engineering simplification tests, resulting in a mean error of <10 % in the time-varying response. The research findings enable calibration of discrete element parameters for crushed coal in industrial simulation.

The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven highly effective in several academic cases. However, their lack of physical invariances, coupled with other significant weaknesses, such as an inability to handle complex geometries or their lack of generalization capabilities, make them unable to compete with classical numerical solvers in industrial settings. In this work, a limitation regarding the use of automatic differentiation in the context of physics-informed learning is highlighted. A hybrid approach combining physics-informed graph neural networks with numerical kernels from finite elements is introduced. After studying the theoretical properties of our model, we apply it to complex geometries, in two and three dimensions. Our choices are supported by an ablation study, and we evaluate the generalization capacity of the proposed approach.

A tuned mass damper (TMD) optimization can be performed under various assumptions and objectives. All the variables of the optimization, such as structural model, performance index and load type affect the optimal parameters of the TMD. This paper presents a new optimization method that implements straightforward performance index and allows taking load spectral characteristics into account. Thanks to the usage of modal coordinates, the method allows fast numerical optimization of TMD attached to large or complicated structures with numerous degrees of freedom. One of the complicated tasks while optimizing TMD is the choice of a performance index. In this paper, the mean value of potential energy stored in the elastic deformation of a structure under periodic load serves as a performance index, which leads to a low numerical complexity task if the optimization is performed in the frequency domain. The new method also allows a simple inclusion of load spectral characteristics and permits TMD optimization for any loading spectral range. When applied to a structure with a single degree of freedom, this method leads to H2 optimization in the case of white noise excitation. However, it is applicable to multiple degrees of freedom structures with single or multiple TMDs and any given load. The paper also presents several examples of numerical optimization of the TMD attached to both single and multiple degrees of freedom structures under various loads, including white noise excitation, pedestrian load, and earthquake strong motion.

The identification of boundary conditions in electromagnetic inverse scattering is of importance in various engineering applications, ranging from geophysical exploration to wireless communication. Conventional numerical methods solving this problem often suffer from the iterative process, leading to inefficiencies and non-convergence. This paper introduces a weighted scheme of the stabilized Lagrange interpolation collocation method (weighted SLICM) to resolve this problem. Weighted SLICM efficiently integrates governing equations, boundary conditions, and measurement conditions using a weighted least squares approach, offering a straightforward single-step solution and obviating the need for iterative processes in traditional methods like the finite element method. By incorporating regularization techniques, weighted SLICM decreases measurement errors which are unavoidable in engineering problems, thereby ensuring high efficiency and accuracy. In addition, characterized as a strong-form collocation method that relies solely on point information but not on grid connectivity, the weighted SLICM is readily extendible to complex three-dimensional applications in electromagnetic inverse scattering. Extensive simulations of benchmark problems show its ability to achieve accurate and stable results in boundary condition identification in electromagnetic inverse scattering problems including 1D, 2D, and 3D environments, highlighting the effectiveness of the weighted SLICM in navigating complex engineering challenges and substantially enriching research methodologies in this area.

The settlement of piers and subgrade bending deformation are widely recognized as common issues in the transition zones of high-speed railway bridges. This study aims to investigate the settlement behavior within these transition zones and its impact on the dynamic interaction between trains and the track. To achieve this, a vehicle-track-transition zone mapping relationship model is developed to analyze both the settlement behavior and the resulting dynamic response characteristics. The study employs the finite element method and multi-body dynamics to construct the simulation model. Settlement effects are simulated using the Newton-Raphson iterative method, with the additional rail deformation caused by foundation settlement serving as the excitation for the vehicle-track-transition zone dynamic interaction system. In the numerical analysis, the dynamic effects of three key factors—train speed, transition zone length, and the amplitude of foundation settlement—are examined based on the performance of the vehicle-track-transition zone interaction. The time-frequency technique is utilized to comprehensively reveal and clarify the spatial-frequency characteristics of system responses influenced by settlement excitation. Moreover, the relationship between the safety-based settlement threshold and these three factors is calibrated.

The shift to virtual meetings, online classes, and remote work has established a new norm, leading to a surge in the use of virtual communication platforms such as Zoom and Microsoft Teams. This shift has increased the demand for high-quality headsets and speakerphones, emphasizing the need for clear, superior audio quality. The process of calibrating material properties typically relies on repetitive simulations guided by experts' intuition, presenting challenges in establishing new Finite Element Models (FEMs) of loudspeakers, as it requires the repeated identification of material property values. We present a systematic framework for calibrating the mechanical material properties of loudspeaker drivers, a crucial prerequisite for developing accurate FEMs of loudspeakers. Specifically, we propose a statistically-driven approach to replace the conventional manual calibration process, which typically relies on multiple simulations guided by expert intuition. Efficient Global Optimization (EGO) is applied to address the expensive optimization problems of loudspeaker simulation. To tackle the curse of dimensionality, the objective function is decomposed into several functions based on effective parameter groups using Global Sensitivity Analysis (GSA) results. The parameters of the FEM are then calibrated to the reference data from the Lumped Parameter Model (LPM) using the decomposed-reduced objective function, providing the calibrated parameters for the loudspeaker simulation. By implementing this novel approach, even individuals without prior knowledge or experience in loudspeaker material properties can effectively and reliably obtain the necessary data for finite element modeling.

Directed Energy Deposition (DED) technology is increasingly favored for swiftly fabricating large structural components due to its high printing efficiency. Despite its advantages, challenges persist in achieving satisfactory surface finish and forming precision, hindering its widespread adoption across industries. To address these issues, this paper presents a novel multi-robot collaborative path planning method based on structural primitive partitioning. This method simplifies path planning complexities and seamlessly integrates into process planning software, thereby enhancing overall functionality. This method decomposes complex polygons into tiny primitives (TP), organizing them into TP sets based on bridge and adjacency relations. These sets are then structured into first-level structural TP (F-TP) and second-level structural TP (S-TP), followed by the establishment of monotonic structural TP (M-TP). A minimum rectangular box recalculates the filling path for each M-TP, while the external contour path and internal zigzag path form a complete printing path. Additionally, an optimal printing sequence planning algorithm for multi-robot using a KD-tree-based search algorithm is presented, ensuring the shortest non-productive path and collision avoidance during printing. Experimental verification with four structures of varying geometric features demonstrates a partitioning accuracy of 99.5 % and absence of surface defects in the printed parts. The proposed method presents a viable and effective solution for enhancing the quality of parts produced via DED.

The advancement in high-order computational methods is reshaping the landscape of mesh generation in Computational Fluid Dynamics (CFD), steering the focus towards curvilinear mesh techniques to meet the escalating accuracy demands. Gridder-HO, the software designed to generate high-order curvilinear mesh efficiently and rapidly, has been developed. Gridder-HO supports the elevation of meshes to P2 (quadratic-order) or P3 (cubic-order). It features a layered architecture and utilizes the concurrent hash table and the Alternating Digital Tree (ADT) data structure, supporting thread-level parallelism to convert straight-edge mesh into high-order curvilinear mesh seamlessly. Gridder-HO utilizes the projection method based on a thread pool to precisely preserve geometry, and employs a novel localized RBF method with ADT for volume node interpolation to untangle the mesh, which aims to achieve a satisfactory balance between efficiency and accuracy. Validated through CFD simulations using the GPU-accelerated Python Flux Reconstruction (PyFR) solver, the practicality of Gridder-HO is demonstrated across various Reynolds numbers in typical cases such as sphere, cylinder, and SD7003 airfoil. These results confirm the high-order curvilinear meshes generated by Gridder-HO meet the high-order requirements of emerging computational methods. Moreover, Gridder-HO exemplifies its effectiveness in generating large-scale, high-order curvilinear meshes for the DLR-F6 transport aircraft configuration standard test cases. It elevates a mesh with 5 million elements to P2 in 3 min 39 sec at 68% parallel efficiency on 16 threads, and another with 14 million elements to P3 in 52 min 39 sec at 60% efficiency, illustrating its efficiency and potential in satisfying the demands of complex geometries in engineering applications.