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  Subjects -> STATISTICS (Total: 130 journals)
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Engineering With Computers
Journal Prestige (SJR): 0.485
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667
Published by Springer-Verlag Homepage  [2468 journals]
  • Elastoplastic peridynamic formulation for materials with isotropic and
           kinematic hardening

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      Abstract: Abstract We present an ordinary state-based peridynamic model in 2D and 3D consistent with rate-independent J2 plasticity with associated flow rule. The new contribution is the capability of the elastoplastic law to describe isotropic, kinematic and mixed hardening. The hardening formulations follow those available in the literature for classical elastoplasticity. The comparison between the results obtained with the peridynamic model and those obtained with a commercial FEM software shows that the two approaches are in good agreement. The extent of the plastic regions and von Mises stress computed with the new model for 2D and 3D examples match well those obtained with FEM-based solutions using ANSYS.
      PubDate: 2024-02-25
       
  • WaveNets: physics-informed neural networks for full-field recovery of
           rotational flow beneath large-amplitude periodic water waves

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      Abstract: Abstract We formulate physics-informed neural networks (PINNs) for full-field reconstruction of rotational flow beneath nonlinear periodic water waves using a small amount of measurement data, coined WaveNets. The WaveNets have two NNs to, respectively, predict the water surface, and velocity/pressure fields. The Euler equation and other prior knowledge of the wave problem are included in WaveNets loss function. We also propose a novel method to dynamically update the sampling points in residual evaluation as the free surface is gradually formed during model training. High-fidelity data sets are obtained using the numerical continuation method which is able to solve nonlinear waves close to the largest height. Model training and validation results in cases of both one-layer and two-layer rotational flows show that WaveNets can reconstruct wave surface and flow field with few data either on the surface or in the flow. Accuracy in vorticity estimate can be improved by adding a redundant physical constraint according to the prior information on the vorticity distribution.
      PubDate: 2024-02-23
       
  • Reinforcement learning for block decomposition of planar CAD models

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      Abstract: Abstract The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler blocks meshable by existing techniques. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks. Even though the simple examples presented here can also be meshed using many conventional methods, we believe this work is proof-of-principle of a AI-based decomposition method that can eventually be generalized to complex 2D and 3D CAD models. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively, thereby holding the promise of a viable method to automate this challenging process for more complex cases.
      PubDate: 2024-02-14
       
  • A new open-source framework for multiscale modeling of fibrous materials
           on heterogeneous supercomputers

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      Abstract: Abstract This article presents MuMFiM, an open-source application for multiscale modeling of fibrous materials on massively parallel computers. MuMFiM uses two scales to represent fibrous materials such as biological network materials (extracellular matrix, connective tissue, etc.). It is designed to make use of multiple levels of parallelism, including distributed parallelism of the macro- and micro-scales as well as GPU-accelerated data-parallelism of the microscale. Scaling results of the GPU accelerated microscale show that solving microscale problems concurrently on the GPU can lead to a 1000x speedup over the solution of a single RVE on the GPU. In addition, we show nearly optimal strong and weak scaling results of MuMFiM on up to 128 nodes of AiMOS (Rensselaer Polytechnic Institute) which is composed of IBM AC922 nodes with 6 Volta V100 GPU and 2 20 core Power 9 CPUs each. We also show how MuMFiM can be used to solve problems of interest to the broader engineering community, in particular providing an example of the facet capsule ligament (FCL) of the human spine undergoing uniaxial extension.
      PubDate: 2024-02-08
       
  • Coupling of bond-based peridynamics and continuous density-based topology
           optimization methods for effective design of three-dimensional structures
           with discontinuities

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      Abstract: This study proposes continuous density-based three-dimensional topology optimization (TO) approaches developed by coupling the peridynamic theory (PD) with optimality criteria (OC) and proportional approach (PROP). These frameworks, abbreviated as PD-OC-TO and PD-PROP-TO, can be practically utilized to enhance the fracture toughness of the structures during the optimization process by taking critical regions into account as pre-defined cracks. Breaking the non-local interactions (bonds) between relevant PD particles enables us to readily model cracks. Utilizing this advantage, we solve several benchmark optimization problems including different numbers, positions, and alignments of the cracks. The major differences between the proposed methods are examined by comparing optimum topologies for various cracked scenarios. Moreover, the mechanical behaviour of the optimized structures is investigated under dynamic loads to prove the significant improvements achieved by the present approach in the final designs. The results of dynamic analyses reveal the viability of both PD-TO methods for increasing the fracture toughness of the structure in the optimization stage. Overall, the proposed approach is confirmed as a superior design and optimization tool for future engineering structures. Graphical abstract
      PubDate: 2024-02-07
       
  • 3D isogeometric indirect BEM solution based on virtual surface sources on
           the boundaries of Helmholtz acoustic problems

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      Abstract: Abstract A solution for 3D Helmholtz acoustic problems is introduced based on an indirect boundary element method (indirect BEM) coupled with isogeometric analysis (IGA). The novelty of this work arises from using virtual surface sources placed directly on the scatterer boundaries, producing robust results. These virtual surface sources are discretized by the same Non-Uniform Rational B-Splines (NURBS) approximating the scatterer CAD model. This allows modeling of general irregular geometries. The proposed solution has the same features of BEM approaches, which do not need any domain discretization or truncation boundaries at the far-field. It shows an additional merit by arranging the linear system of equations directly depending on a single coefficient matrix, consuming less computational time compared to other BEM methods. A Greville abscissae collocation scheme is proposed with offsets at \(C^0\) -continuities. This collocation scheme allows for easy evaluation for both free-terms and normals at the collocation points. The performance of the proposed solution is discussed on 3D numerical exterior problems and compared against other BEM methods. Then, the practical interior muffler problem with internal extended thin tubes is studied and the obtained results are compared against other numerical methods in addition to the available experimental data, showing the capability of the proposed solution in handling thin-walled geometries.
      PubDate: 2024-02-06
       
  • Comprehensive multi-material topology optimization for stress-driven
           design with refined volume constraint subjected to harmonic force
           excitation

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      Abstract: Abstract This paper proposes a comprehensive method for the stress-related multi-material problem of continuum structure sustaining harmonic load excitation using generalized solid isotropic material with the penalization (SIMP) method. To be specific, an upgraded interface stress interpolation model is presented, built upon the moved and regularized Heaviside function (MRHF), significantly enhancing interface stress interpolation’s performance. Employing a filter-driven technique enables precise determination of multi-material interface positions, thus enabling control over the interfacial zone’s breadth and the graded interface’s characteristics. A refined volume formula is also suggested to overcome local solutions caused by the highly non-linear stress behavior of the generalized SIMP-based multi-material problems. Global stress levels are measured using the P-norm stress aggregation scheme integrating the interfacial-region-related model, which is also provided to illustrate the approach’s performance in multi-material frameworks. The proposed optimization problem is solved using the method of moving asymptotes (MMA) optimizer after performing adjoint sensitivity analyses. The approach is demonstrated through several benchmark designs, highlighting its efficiency, robustness, and practicality. Mathematical expressions of the approach are presented in detail, emphasizing its novelty and contribution to the field of multi-material topology optimization.
      PubDate: 2024-02-05
       
  • A novel multi-fidelity surrogate modeling method for non-hierarchical data
           fusion

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      Abstract: Abstract Multi-fidelity (MF) surrogate model has been widely used in simulation-based engineering design processes to reduce the computational cost, with a focus on cases involving hierarchical low-fidelity (LF) data. However, accurately identifying and sorting the fidelity of LF models is challenging when dealing with non-hierarchical cases. In this paper, we propose a novel non-hierarchical MF surrogate framework called weighted multi-bi-fidelity (WMBF) to solve this problem. The proposed WMBF has both the advantage of two non-hierarchical frameworks, the weighted sum (WS) and parallel combination (PC) techniques, leveraging an entropy-based weight to include multiple-moments statistical information. It offers not only a weight with more information but also a more individualized scaling function within the weighted-sum framework, additionally a more individualized discrepancy function compared with existing methods. Moreover, it provides the idea of exploiting Kullback–Leibler (KL) divergence (an entropy-based metric) to characterize uncertainty for calculating weight within the WS framework. To validate the performance of the WMBF, we conduct evaluations using several numerical test functions and one engineering case. The result demonstrates that the WMBF achieves both accurate and robust predictions with minimal computational cost.
      PubDate: 2024-02-04
       
  • A fast boundary node method for transient scalar waves in domains with
           localized inhomogeneities

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      Abstract: Abstract In this paper, we propose an efficient method suitable for 1D/2D/3D plus time wave propagation in homogeneous domains containing localized inhomogeneities. It is well-understood that the numerical solution of such problems, using finite difference and finite element methods, for instance, needs fine discretization depending on the size of the region of the inhomogeneity, making the solution cumbersome. In this study, a time-domain boundary node method, initially devised for homogeneous mediums, is extended to incorporate the inhomogeneities. In addition to the boundary nodes, the so-called sampling points are added to the formulation. The salient feature is that such sampling points are only required in the non-homogeneous regions, making the method efficient and most suitable for scattering problems. Starting from assuming homogeneity, the proposed method considers the effects of the inhomogeneities in an iterative procedure with just a few steps. The efficiency and performance of the method are assessed and compared with the finite element method through some numerical 1D, 2D, and 3D examples.
      PubDate: 2024-02-01
       
  • A general finite deformation hypoelastic-plasticity non-ordinary
           state-based peridynamics model and its applications

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      Abstract: Abstract In this work, we present a general approach to incorporate the rate form finite elastoplasticity theory into the framework of state-based peridynamics. Specifically, we illustrate to how develop a state-based peridynamics of an associative finite deformation elasto-plastic model that explicitly contains isotropic hardening behaviors in mechanical response. Material constitutive models are developed within the framework of the non-ordinary state-based peridynamics theory, and it is employed in formulating explicit numerical integration strategy with operator split technique and classical return mapping strategy of isotropic plasticity. Convergence studies have been conducted to validate the accuracy of the proposed peridynamic formulation in small deformation regime comparing with the results obtained by finite element method. A meshfree particle contact force model is incorporated to the proposed peridynamic model to simulate impact problem. This work develops for the first time a complete rate form of nonlocal hypo-elastoplastic peridynamics formulation, which provides a systematic procedure to incorporate the general inelastic constitutive models in available constitutive libraries into the state-based peridynamics. It lays a foundation for further developing the hyper-elastoplastic peridynamics material model.
      PubDate: 2024-02-01
       
  • Data-driven multiscale finite-element method using deep neural network
           combined with proper orthogonal decomposition

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      Abstract: Abstract In this paper, a data-driven multiscale finite-element method (data-driven FE2) is proposed using a deep neural network (DNN) and proper orthogonal decomposition (POD) to describe nonlinear heterogeneous materials. The concurrent classical FE2 needs the iterative calculations of microscopic boundary-value problem for representative volume element (RVE) at all integration points of the macroscopic structures. These iterative procedures need large computational time. To overcome this limitation, the proposed data-driven FE2 method solves the macroscopic problem by assigning data to all integration points that satisfy microscopic equilibrium by constructing a material genome database in which the microscopic problem of RVE is pre-calculated in online computing. Here, we developed a DNN model that can accurately and efficiently predict microscopic behavior by connecting POD for material genome database construction. Therefore, we improved the data-driven FE2 technique one step further by efficiently generating available material genome database.
      PubDate: 2024-02-01
       
  • An efficient meshless method to approximate semi-linear stochastic
           evolution equations

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      Abstract: Abstract In this article, we are concerned with meshless methods to approximate and simulate the solution of semi-linear stochastic evolution equations. We first study the asymmetric Kansa method and then consider its regularized form. Kansa method is an efficient approach that is easy to implement and adapt and has sufficient accuracy and approximation power. We employ Karhunen–Loéve expansion for having faster and better simulations for the stochastic part. The absolute error, standard deviation, root mean square error, and CPU times for showing the accuracy and speed of our methodology are calculated. From the numerical analysis view, the stability of this methodology for time-dependent problems is investigated by numerical factors in the computational part. Experimentally, the performance of both presented methods is more significant, and proportionally they have better results to previous work in this subject.
      PubDate: 2024-02-01
       
  • An ANN-assisted efficient enriched finite element method via the selective
           enrichment of moment fitting

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      Abstract: Abstract Enrichment techniques that employ nonconforming mesh are effective in modeling structures with discontinuities because numerical issues regarding mesh quality are avoided. However, the accurate integration of the bilinear and linear forms on the discretized domain, which is required in the standard Galerkin-based finite element method, is computationally expensive due to the complexity of the enriched basis function. In this paper, we present a fast and accurate alternative method of numerical integration using nonlinear regression enabled by a multi-perceptron feedforward neural network. The relationship between an implicitly represented geometry and the quadrature rule derived from the moment fitting method is predicted by the neural network; the neural network-based regression model circumvents complex computation and significantly reduces the overall online time by avoiding expensive function evaluations. Through the selected numerical examples, we demonstrate the efficiency and accuracy of the current method, as well as the flexibility of the trained network to be used in different contexts.
      PubDate: 2024-02-01
       
  • An efficient topology optimization method based on adaptive reanalysis
           with projection reduction

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      Abstract: Abstract An efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) method is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM restricts the solution space to avoid large matrix factorization. Second, the PARM is proposed to construct the CARM dynamically to save computational cost. Furthermore, the multi-grid conjugate gradient method is suggested to update PARM adaptively. Finally, several classic numerical examples are tested to show that the proposed method not only significantly improves computational efficiency, but also can solve large-scale problems that are difficult to solve by direct solvers due to the memory limitations.
      PubDate: 2024-02-01
       
  • Uncertainty quantification of mechanical property of piezoelectric
           materials based on isogeometric stochastic FEM with generalized nth-order
           perturbation

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      Abstract: Abstract This paper proposes a novel generalized nth-order perturbation isogeometric finite-element method (GNP-IGA-FEM) for uncertainty quantification of mechanical properties of piezoelectric materials. In this method, the IGA-FEM is used to simulate the linear piezoelectric problem. The statistical characteristics (expected value and standard deviation) of the mechanical property (electric potential) of piezoelectric materials are obtained by Taylor series expansion considering the tiny disturbance parameters \(\varepsilon\) . The two various uncertainty quantification techniques are used to characterize the statistical characteristics of natural frequencies in the dynamics of piezoelectric structures. Numerical examples verify the proposed GNP-IGA-FEM is suitable for low-dimensional random variable problems with small disturbances.
      PubDate: 2024-02-01
       
  • Radial basis function interpolation of fields resulting from nonlinear
           simulations

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      Abstract: Abstract Three approaches for construction of a surrogate model of a result field consisting of multiple physical quantities are presented. The first approach uses direct interpolation of the result space on the input space. In the second and third approaches a Singular Value Decomposition is used to reduce the model size. In the reduced order surrogate models, the amplitudes corresponding to the different basis vectors are interpolated. A quality measure that takes into account different physical parts of the result field is defined. As the quality measure is very cheap to evaluate, it can be used to efficiently optimize hyperparameters of all surrogate models. Based on the quality measure, a criterion is proposed to choose the number of basis vectors for the reduced order models. The performance of the surrogate models resulting from the three different approaches is compared using the quality measure based on a validation set. It is found that the novel criterion can effectively be used to select the number of basis vectors. The choice of construction method significantly influences the quality of the surrogate model.
      PubDate: 2024-02-01
       
  • Adaptive density-based robust topology optimization under uncertain loads
           using parallel computing

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      Abstract: Abstract This work presents an efficient parallel implementation of density-based robust topology optimization (RTO) using adaptive mesh refinement (AMR) schemes permitting us to address the problem with modest computational resources. We use sparse grid stochastic collocation methods (SCMs) for transforming the RTO problem into a weighted multiple-loading deterministic problem at the collocation points. The calculation of these deterministic problems and the functional sensitivity is computationally expensive. We combine distributed-memory parallel computing and AMR techniques to address the problem efficiently. The former allows us to exploit the computational resources available, whereas the latter permits us to increase performance significantly. We propose the parallel incremental calculation of the deterministic problems and the contribution to the functional sensitivity maintaining a similar memory allocation to the one used in the deterministic counterpart. The cumulative computing uses buffers to adapt the evaluation at the collocation points to the parallel computing resources permitting the exploitation of the embarrassing parallelism of SCMs. We evaluate the deterministic problems in a coarse mesh generated for each topology optimization iteration to increase the performance. We perform the regularization and design variable update in a fine mesh to obtain an equivalent design to the one generated in such a mesh. We evaluate the proposal in two- and three-dimensional problems to test its feasibility and scalability. We also check the performance improvement using computational buffers in parallel computing nodes. Finally, we compare the proposal to the same approach using different preconditioners without AMR schemes showing significant performance improvements.
      PubDate: 2024-02-01
       
  • Hybrid modeling of hetero-agglomeration processes: a framework for model
           selection and arrangement

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      Abstract: Abstract Modeling of hetero-agglomeration processes is invaluable for a variety of applications in particle technology. Traditionally, population balance equations (PBE) are employed; however, calculation of kinetic rates is challenging due to heterogeneous surface properties and insufficient material data. This study investigates how the integration of machine learning (ML) techniques—resulting in so-called hybrid models (HM)—can help to integrate experimental data and close this gap. A variety of ML algorithms can either be used to estimate kinetic rates for the PBE (serial HM) or to correct the PBE’s output (parallel HM). As the optimal choice of the HM architecture is highly problem-dependent, we propose a general and objective framework for model selection and arrangement. A repeated nested cross-validation with integrated hyper-parameter optimization ensures a fair and meaningful comparison between different HMs. This framework was subsequently applied to experimental data of magnetic seeded filtration, where prediction errors of the pure PBE were reduced by applying the hybrid modeling approach. The framework helped to identify that for the given data set, serial outperforms parallel arrangement and that more advanced ML algorithms provide better interpolation ability. Additionally, it enables to draw inferences to general properties of the underlying PBE model and a statistical investigation of hyper-parameter optimization that paves the way for further improvements.
      PubDate: 2024-02-01
       
  • An importance sampling method for structural reliability analysis based on
           interpretable deep generative network

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      Abstract: Abstract Importance sampling methods are widely used in structural reliability analysis. However, owing to the complex shape of optimal importance sampling densities, it is usually difficult to fit the optimal importance sampling densities and sample from the fitted distributions using conventional importance sampling methods. In this paper, a novel importance sampling method based on interpretable deep generative network (IDGN-IS) is proposed for structural reliability analysis. The proposed IDGN-IS model can be directly trained using the data from original distribution of random variables and efficiently sampling from an arbitrary importance sampling density. The developed interpretable deep generative network consists of a deep generative network and a monotonic network, which enables the network to fit and sample from the target distributions while being interpretable. Using the interpretability of the deep generative network, the IDGN-IS method can sample from an arbitrary conditional probability distribution of the fitted distributions by choosing an appropriate threshold of the input Gaussian distribution samples. When the threshold of the input Gaussian distribution samples is set to a value close to zero, the IDGN-IS method can efficiently sample from the optimal importance sampling density and provide accurate estimation of the failure probability. The calculation efficiency and estimation accuracy of the proposed IDGN-IS method in structural reliability analysis are demonstrated using four examples.
      PubDate: 2024-02-01
       
  • A fully nonlinear three-dimensional dynamic frictional contact analysis
           method under large deformation with the area regularization

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      Abstract: Abstract This paper presents the NTS-AR (node-to-segment with area regularization) method to analyze the three-dimensional dynamic frictional contact bodies under large deformation and plastic material behavior. The extended NTS-AR method considers the 3D geometric structure of the slave surface and frictional constraint in a convected coordinate system. Despite wide applications of the penalty-based node-to-segment (NTS) method, owing to its light computation cost, the penalty-based NTS algorithm still has limitations in convergence and accuracy. Unlike the original NTS method setting a constant penalty parameter, the NTS-AR method compensates the area so that a proper penalty parameter is applied for each slave node. To the best knowledge of authors, the NTS-AR method has been applied only to 2D frictionless contact problems, although the method maintains the advantages of the fast and straightforward algorithm of the original NTS method and shows an improved accuracy. Following validations with various three-dimensional numerical examples, the effects of friction on the tangential and normal forces and displacements under large deformation are investigated with the proposed method. In particular, a collision event of F-35B and aircraft carrier flight deck is simulated.
      PubDate: 2024-02-01
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
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