Engineering with Computers最新文献

Many thermomechanical processes, such as rolling, turning, grinding, welding or additive manufacturing, involve either a material flowing through a fixed load system or a heat source moving with respect to the material. In many situations, these processes involve a constant speed translational, rotational or helical movement of the loading with respect to the material so that a (quasi-) steady thermo-mechanical state is achieved quickly. Classical Lagrangian steady state finite element simulation of these processes in the material’s frame is a heavy task requiring large meshes refined all along the load path. This article presents a nodal-integration-based finite element method for solving transient and steady-state elastoplastic problems associated with these processes. The simulation is carried out step by step in a frame linked to the loading. As the nodes of the mesh do not represent material points, the computation procedure requires determining the position at the previous time step of the material point associated with each node (anterior point) in order to perform the time-integration of the constitutive equations. The anterior points are located anywhere in the mesh and therefore interpolation techniques are required to get the previous mechanical state there. As all the mechanical variables are calculated at nodes with the method proposed, this approach makes the interpolation more straightforward. Applications to 3D forming and welding are presented to illustrate the efficiency of the proposed method. The results of finite element simulations in the frame tied to the loading are compared to those of Lagrangian calculations simulating the load motion in the material’s frame. The applications demonstrate that the proposed model can significantly accelerate simulations, achieving a maximum acceleration of around 40 in 3D forming and about 4 in welding. These results highlight the substantial efficiency improvements enabled by the proposed method.

Assessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

Laser-directed energy deposition (L-DED) enables the creation of near-net-shape parts with location-specific materials, repair of machine components, and addition of features to existing parts. However, gathering sufficient experimental L-DED data to establish process maps is challenging especially when expensive materials are being investigated. Despite the interest in data-driven modeling for developing such maps, few studies have considered reusing knowledge across multiple materials including uncertainty quantification (UQ). To address this, knowledge transfer methods based on Gaussian process (GP) are proposed. Melt pool data for SS316L and IN718 are used to emulate data-rich and data-scarce conditions, respectively. Three avenues are explored: (i) mixing the data of both materials to train a single GP regression model (the mixed-input model), (ii) relation-based transfer learning (RB-TL) model, and (iii) multi-fidelity GP-based transfer learning (MFGP-TL) model. Results show that the mixed-input model outperforms the baseline or no-transfer model under data-deficient conditions. Compared to the baseline model, the RB-TL model exhibits a general improvement in accuracy and confidence while consuming the least computation time among all proposed models. The MFGP-TL model achieves the best performance, which is only half the error and standard deviation observed for the RB-TL model, albeit resulting in longer computation times. Finally, the proposed transfer learning models, when used on experimental data obtained from the literature, show 22–31% and 24–40% improvement over the baseline model for IN718 and IN625, respectively. This work, therefore, facilitates data- and cost-effective UQ-based knowledge transfer in reconstructing process maps in L-DED.

The stabilized conforming nodal integration (SCNI) is currently widely employed in Galerkin meshfree formulation. A key ingredient of SCNI is the strain or gradient smoothing defined within a set of conforming nodal representative domains, which usually are formed by the auxiliary points in addition to the meshfree nodes. Nonetheless, these auxiliary points may significantly increase the storage requirement and computational cost of SCNI, in comparison with the direct nodal integration. In order to address this issue, a purely node-based consistent non-conforming gradient smoothing (CNGS) scheme is proposed herein to accelerate the Galerkin meshfree computation. In the proposed method, only the meshfree nodes are employed to construct overlapping and non-conforming nodal representative domains, which are then adopted for the nodal gradient smoothing operation. However, unlike the existing non-conforming gradient smoothing algorithms that commonly violate the integration consistency, the proposed method maintains the desirable integration consistency through a proportional separation between the nodal gradient smoothing domains and the nodal integration domains, which essentially ensures the meshfree solution accuracy. Meanwhile, due to the absence of auxiliary points in the gradient smoothing evaluation, the computational efficiency is substantially improved by the proposed method of CNGS compared with SCNI. The effectiveness of the proposed methodology is well demonstrated by numerical results.

A variational approach is developed with a meshless discretization to enable accurate and robust numerical simulation of partial differential equations for meshes that are of poor quality. Traditional finite element methods use the mesh to both discretize the geometric domain and to define the finite element shape functions. The latter creates a dependence between the quality of the mesh and the properties of the finite element basis that may adversely affect the accuracy of the discretized problem. We propose a new approach for defining finite element shape functions that breaks this dependence and separates mesh quality from the discretization quality, which we call discontinuous piecewise polynomial generalized moving least squares (DPP-GMLS). At the core of the approach is a meshless definition of the shape functions, which limits the purpose of the mesh to representing the geometric domain and integrating the basis functions without having any role in their approximation quality. The resulting non-conforming space can be utilized within a standard discontinuous Galerkin framework, providing a rigorous foundation for solving partial differential equations on low-quality meshes. We present a collection of numerical experiments demonstrating our approach in a wide range of settings: strongly coercive elliptic problems, linear elasticity in the compressible regime, and the stationary Stokes problem. We demonstrate convergence for all problems and stability for element pairs for problems which usually require inf-sup compatibility for conforming methods, also referring to a minor modification possible through the symmetric interior penalty Galerkin framework for stabilizing element pairs that would otherwise be traditionally unstable. Mesh robustness is particularly critical for elasticity, and we provide an example that our approach provides a greater than 5(times) improvement in accuracy and allows for taking an 8(times) larger stable timestep for a highly deformed mesh, compared to the continuous Galerkin finite element method.

Extraction of quadrilateral layouts of surfaces is essential for surface rebuilding using splines, semi-structured bilinear quadrilateral mesh extraction, and texture mapping. Layout generation using integer grid based techniques on triangulated meshes have received particular attention for generation of well-structured layouts. In this work, we reiterate a generalization of integer grid parameterizations in which only topological constraints between singularities are necessary to ensure a valid quadrilateral parameterization (and specifically, the integral curves emanating from singularities are of finite length). This generalized representation is motivated by carefully discussing pros and cons of both integer grid and topologically constrained parameterization methods. A computational framework for extracting a quadrilateral layout from a valid input immersion is then presented, which will work for any parameterization that induces a valid quadrilateral layout. Results demonstrate the validity and the potential of the proposed computational framework on a variety of geometries. The proposed extraction framework is ultimately used to reconstruct the body-in-white of a 1996 Dodge Neon as a set of analysis-suitable bicubic B-splines, which are then used in the first known body-in-white crash analysis using boundary-conforming splines, demonstrating that the reconstruction method is viable for industrial use.

An interface-modified reproducing kernel particle method (IM-RKPM) is introduced in this work to allow for a direct model construction from image pixels of heterogeneous polycrystalline Li-ion battery microstructures. The interface-modified reproducing kernel (IM-RK) approximation is constructed through scaling of a kernel function by a regularized distance function in conjunction with strategic placement of interface node locations. This leads to RK shape functions with either weak or strong discontinuities across material interfaces, suitable for modeling various interface mechanics. With the placement of a triple junction node and distance-based scaling of kernel functions, the resulting IM-RK shape function also possesses proper discontinuities at the triple junctions. This IM-RK approximation effectively remedies the well-known Gibb’s oscillation in the smooth approximation of discontinuities. Different from the conventional meshfree approaches for interface discontinuities, this IM-RK approach is done without additional degrees of freedom associated with the enrichment functions, and it is formulated with the standard procedures in the RK shape function construction. This work focuses on identifying the accuracy and convergence properties of IM-RKPM for modeling the coupled electro-chemo-mechanical system. A linear patch test is formulated and numerically tested for the electro-chemo-mechanical coupled problem with a Butler–Volmer boundary condition representing the physical conditions in Li-ion battery microstructures. This is followed by verification of the optimal rates of convergence of IM-RKPM for solving the coupled problem with higher order solutions. The image-based modeling of Li-ion battery microstructures in the numerical examples demonstrates the applicability of the proposed method to realistic Li-ion battery materials modeling.

Computational fluid dynamics (CFD) has widespread application in research and industry. The quality of the mesh, particularly in the boundary layer, significantly influences the CFD accuracy. Despite its importance, the mesh generation process remains manual and time intensive, with the introduction of potential errors and inconsistencies. The limitations of traditional methods have prompted the recent exploration of deep reinforcement learning (DRL) for mesh generation. Although some studies have demonstrated the applicability of DRL in mesh generation, they have limitations in utilizing existing tools, thereby falling short of fully leveraging the potential of DRL. This study proposes a new boundary mesh generation method using DRL, namely an agent-based mesh generator. The nodes on the surface act as agents and optimize the paths into space to create high-quality meshes. Mesh generation is naturally suited to DRL owing to its computational nature and deterministic execution. However, challenges also arise, including training numerous agents simultaneously and managing their interdependencies in a vast state space. In this study, these challenges are addressed along with an investigation of the optimal learning conditions after formulating grid generation as a DRL task: defining states, agents, actions, and rewards. The derived optimal conditions are applied to generate two dimensional airfoil grids to validate the feasibility of the proposed approach.