Engineering with Computers最新文献

We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (PDEs). The Galerkin discretized weak formulation is employed to incorporate physics into the loss function, termed finite operator learning (FOL), along with the implicit Euler time integration scheme for temporal discretization. A transient thermal conduction problem is considered to benchmark the performance, where FOL takes a temperature field at the current time step as input and predicts a temperature field at the next time step. Upon training, the network successfully predicts the temperature evolution over time for any initial temperature field at high accuracy compared to the solution by the finite element method (FEM) even with a heterogeneous thermal conductivity and arbitrary geometry. The advantages of FOL can be summarized as follows: First, the training is performed in an unsupervised manner, avoiding the need for large data prepared from costly simulations or experiments. Instead, random temperature patterns generated by the Gaussian random process and the Fourier series, combined with constant temperature fields, are used as training data to cover possible temperature cases. Additionally, shape functions and backward difference approximation are exploited for the domain discretization, resulting in a purely algebraic equation. This enhances training efficiency, as one avoids time-consuming automatic differentiation in optimizing weights and biases while accepting possible discretization errors. Finally, thanks to the interpolation power of FEM, any arbitrary geometry with heterogeneous microstructure can be handled with FOL, which is crucial to addressing various engineering application scenarios.

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh’s fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes: (1) achieve high fidelity, (2) keep the element count reasonably low, and (3) exhibit good element quality.

This work presents a physics-driven machine learning framework for the simulation of acoustic scattering problems. The proposed framework relies on a physics-informed neural network (PINN) architecture that leverages prior knowledge based on the physics of the scattering problem as well as a tailored network structure that embodies the concept of the superposition principle of linear wave interaction. The framework can also simulate the scattered field due to rigid scatterers having arbitrary shape as well as high-frequency problems. Unlike conventional data-driven neural networks, the PINN is trained by directly enforcing the governing equations describing the underlying physics, hence without relying on any labeled training dataset. Remarkably, the network model has significantly lower discretization dependence and offers simulation capabilities akin to parallel computation. This feature is particularly beneficial to address computational challenges typically associated with conventional mesh-dependent simulation methods. The performance of the network is investigated via a comprehensive numerical study that explores different application scenarios based on acoustic scattering.

Meshfree methods, such as the Reproducing Kernel Particle Method, have been proven advantageous in modeling excessive deformation problems involving material separation, fracture, impact, etc. However, the domain integration in RKPM remains challenging due to instability and sub-optimal convergence for high strain rate events. Although some novel developments alleviate the above issue, they are either computationally expensive or require evaluating the contour integral, which is not straightforward to obtain in contact and material separation problems using meshfree discretization. This work develops a simple and stable integration method based on the extension of modified Simpson’s rule. The method is free from conforming subdomains and can straightforwardly be applied to the meshfree formulation with updated configuration. To model penetration into the earth, a standard viscous boundary is introduced to address the issue of reflecting waves from the truncated computational domain for the ground target. The numerical results are validated with experimental data for various geo-materials and experimental setups.

Digital twin, a concept of establishing mapping linkages between physical and digital areas using digital technology to achieve instantaneous information transfer for monitoring, optimization or decision-making. Digital twins has emerged as a crucial instrument for ensuring structural safety. However, achieving real-time prediction in time series for structural safety monitoring is challenging, as is the dynamic synthesis of heterogeneous data from numerous sources. This study presents a shape-performance coupled digital twin (SPC-DT) model that integrates heterogeneous data from various sources. The model combines structural analysis, reduced-order processing, and artificial intelligence techniques to incorporate geometric, performance, and sensor data. The aim is to enable dynamic monitoring of structural performance. Furthermore, the deployment of physical space and digital space was accomplished by constructing the SPC-DT model of the scissor lift platform as an illustrative example. The model's effectiveness was validated by a comparison of the measured results, the finite element calculation results, and the SPC-DT model prediction findings. Correlation and error analyses were conducted as part of this verification process. The time required for doing a performance study of complex heavy machinery is greatly decreased by the SPC-DT model. For instance, the SPC-DT prediction saves over 255 times the time cost in the structural prediction of a scissor lift when compared to finite element calculation. This creates a new opportunity for mechanical structure and system safety monitoring.

Rising bubbles are often encountered in many engineering fields and have diverse applications. A thorough understanding of bubble rising phenomenon is crucial in these engineering applications. In this study, we employ the developed updated Lagrangian particle hydrodynamics (ULPH) multiphase flow model to investigate the dynamic behavior of bubble flow in quiescent liquids, including bubble rise, deformation, fragmentation, and coalescence. First, a comprehensive numerical study of the influences of computational domain dimensions and fluid/bubble density ratios at the multiphase interface on bubble dynamics is conducted. Subsequently, a variety of scenarios featuring single bubble rising in viscous fluid media are examined. The ULPH simulation results are validated against experimental data, the Level-set (LS) method and Lattice Boltzmann Method (LBM) results. Furthermore, results of three calculations are presented, including dynamic characterization of two horizontal coaxial bubbles, three vertical coaxial bubbles and a single bubble in the presence of an obstacle. The results indicate that the established ULPH multiphase flow model is effective in accurately simulating dynamic characteristics of rising bubbles under various conditions, affirming its applicability in engineering analyses.

In this paper we consider a nonlinear poroelasticity model that describes the quasi-static mechanical behaviour of a fluid-saturated porous medium whose permeability depends on the divergence of the displacement. Such nonlinear models are typically used to study biological structures like tissues, organs, cartilage and bones, which are known for a nonlinear dependence of their permeability/hydraulic conductivity on solid dilatation. We formulate (extend to the present situation) one of the most popular splitting schemes, namely the fixed-stress split method for the iterative solution of the coupled problem. The method is proven to converge linearly for sufficiently small time steps under standard assumptions. The error contraction factor then is strictly less than one, independent of the Lamé parameters, Biot and storage coefficients if the hydraulic conductivity is a strictly positive and Lipschitz-continuous function.

Reconstructing microstructures from statistical descriptors is a key enabler of computer-based inverse materials design. In the Yeong–Torquato algorithm and other common methods, the problem is approached by formulating it as an optimization problem in the space of possible microstructures. In this case, the error between the desired microstructure and the current reconstruction is measured in terms of a descriptor. As an alternative, descriptors can be regarded as constraints defining subspaces or regions in the microstructure space. Given a set of descriptors, a valid microstructure can be obtained by sequentially projecting onto these subspaces. This is done in the Portilla–Simoncelli algorithm, which is well known in the field of texture synthesis. Noting the algorithm’s potential, the present work aims at introducing it to microstructure reconstruction. After exploring its capabilities and limitations in 2D, a dimensionality expansion is developed for reconstructing 3D volumes from 2D reference data. The resulting method is extremely efficient, as it allows for high-resolution reconstructions on conventional laptops. Various numerical experiments are conducted to demonstrate its versatility and scalability. Finally, the method is validated by comparing homogenized mechanical properties of original and reconstructed 3D microstructures.

This paper introduces a new explicit-implicit time-marching formulation, presenting a novel hybrid approach for wave propagation analysis. The proposed solution algorithm employs a set of simple, single-step, single-solver, truly self-starting recurrence relationships, which incorporate three time-integration parameters. These parameters are adaptively evaluated for each element of the adopted spatial discretization, taking into account the local characteristics of the model and a user defined parameter. They enable automated extended-explicit/implicit and non-dissipative/dissipative elements to be established, allowing enhanced hybrid analyses to be straightforwardly performed. The proposed formulation is highly accurate, efficient, and very simple to implement and to apply, avoiding complex coupling procedures and interface treatments that are typically considered for mixed explicit/implicit analyses. The new technique is also very versatile, allowing the user to locally control the numerical properties of the adopted time-integration procedure and, consequently, to elaborate very sophisticated solution strategies. Numerical results are presented at the end of the paper, illustrating the good performance and the effectiveness of the proposed novel approach, which combines the best features (such as stability, reduced solver efforts etc.) of both implicit and explicit formulations.

The problem associated with the computational homogenization of composite materials often results in expensive computational cost that prevents engineers from comprehensive study for better understanding of composite behaviors, especially when nonlinear effects are considered. While variation in local fiber arrangements has pronounced effect on damage initiation and failure mechanisms in composite, an attempt to reduce the computational cost for the parametric study of such a problem seems to be absent. This paper demonstrates the capability of a model order reduction (MOR) framework to accelerate the parametric study of the unidirectional composite with a plastic constitutive material model for matrix with the varying fiber distribution in the microstructure as the parameter of interest. The MOR framework used in this work is based on the construction of the reduced order basis (ROB) by proper orthogonal decomposition and then the reduced order model (ROM) by Galerkin projection. The concept of local ROB is incorporated which helps decreasing further the dimension of the ROM and, thus, the computational cost. The results from the RVE-based high-fidelity finite element analysis and from the ROM are compared to assess the efficiency and accuracy of the approach. Notable computational gain is achieved with the potential to improve further in the future work. The error in the global response is less than 10% while the local stress fields in the critical regions can be captured well which paves way for the extension to consider the process of damage initiation and evolution as the source of nonlinearity in the future.