Engineering with Computers最新文献

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.

Forecasting tumor progression and assessing the uncertainty of predictions play a crucial role in clinical settings, especially for determining disease outlook and making informed decisions about treatment approaches. In this work, we propose TGM-ONets, a deep neural operator learning (PI-DeepONet) based computational framework, which combines bioimaging and tumor growth modeling (TGM) for enhanced prediction of tumor growth. Deep neural operators have recently emerged as a powerful tool for learning the solution maps between the function spaces, and they have demonstrated their generalization capability in making predictions based on unseen input instances once trained. Incorporating the physics laws into the loss function of the deep neural operator can significantly reduce the amount of the training data. The novelties of the design of TGM-ONets include the employment of a convolutional block attention module (CBAM) and a gating mechanism (i.e., mixture of experts (MoE)) to extract the features of the input images. Our results show that the TGM-ONets not only can capture the detailed morphological characteristics of the mild and aggressive tumors within and outside the training domain but also can be used to predict the long-term dynamics of both mild and aggressive tumor growth for up to 6 months with a maximum error of less than 6.7 (times 10^{-2}) for unseen input instances with two or three snapshots added. We also systematically study the effects of the number of training snapshots and noisy data on the performance of TGM-ONets as well as quantify the uncertainty of the model predictions. We demonstrate the efficiency and accuracy by comparing the performance of TGM-ONets with three state-of-the-art (SOTA) baseline models. In summary, we propose a new deep learning model capable of integrating the TGM and sequential observations of tumor morphology to improve the current approaches for predicting tumor growth and thus provide an advanced computational tool for patient-specific tumor prognosis.

This study introduces a novel dynamic infinite meshfree method, termed RK-DIMM (reproducing kernel dynamic infinite meshfree method), which is specifically developed for analyzing elastic half-spaces with cavities under the influence of both P-waves and SV-waves. RK-DIMM integrates the principles of reproducing kernel particle methods with dynamic infinite element techniques to enhance computational efficiency and accuracy in wave propagation simulations. The method partitions the infinite domain into near and far domains using artificial boundaries, utilizing RK in the near domain and DIMM in the far domain. Through the application of stabilized conforming nodal integration and naturally stabilized nodal integration, RK-DIMM achieves accurate and stable solutions. Our rigorous benchmark comparisons have confirmed the method’s exceptional ability to simulate wave dissipation and reflections with high accuracy and computational efficiency. RK-DIMM has proven to be highly effective in mimicking soil responses to synthetic earthquake forces, closely aligning with analytical predictions, and has demonstrated robust performance in scenarios involving underground cavities. Furthermore, its application to real earthquake data, particularly the 1999 Chi-Chi earthquake, underscores its practical utility and relevance. The results from this study highlight RK-DIMM’s potential as a transformative tool in computational geomechanics, significantly enhancing the precision and reliability of seismic impact assessments on civil infrastructures.

This work presents the first method for generating tetrahedral-based volume meshes dedicated to the NURBS-enhanced finite element method (NEFEM). Built upon the developed method of generating feature-independent surface meshes tailored for NEFEM, the proposed mesh generation scheme is able to grow volume elements that inherit the feature-independence by using the surface mesh as the initial boundary discretisation. Therefore, the generated tetrahedral elements may contain triangular faces that span across multiple NURBS surfaces whilst maintaining the exact boundary description. The proposed strategy completely eliminates the need for de-featuring complex watertight CAD models. At the same time, it eliminates the uncertainty originated from the simplification of CAD models adopted in industrial practice and the error introduced by traditional isoparametric mesh generators that produce polynomial approximations of the true boundary representation. Thanks to the capability of having element faces traversing multiple geometric surfaces, small geometric features in the CAD model no longer restrict the minimum element size, and the user-required mesh spacing in the generated mesh is better satisfied than in traditional meshes that require local refinement. To demonstrate the ability of the proposed approach, a variety of CAD geometries are meshed with the proposed strategy, including examples relevant to the fluid dynamics, wave propagation and solid mechanics communities.

An adaptive phase-field total Lagrangian material point method (APTLMPM) is proposed in this paper for effectively simulating the dynamic fracture of two-dimensional soft materials with finite deformation. In this method, the governing equations for the fracture of soft materials are derived by integrating the phase-field fracture model with the total Lagrangian material point method (TLMPM), and corresponding discrete equations are then formulated with explicit time integration. To address the significant computational issue in terms of memory and processing time, an adaptive technique for dynamically splitting particles and background grids in the phase-field TLMPM is proposed, based on the phase-field values of the particles. To further maintain continuity of the physical field throughout the computational process and consider the characteristics of the field update, an information remapping strategy is developed. Several representative numerical examples are presented to demonstrate the accuracy and efficiency of the proposed APTLMPM by comparing the simulation results with experimental data and those as obtained with other numerical methods.