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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  [2467 journals]
  • An enhanced hybrid seagull optimization algorithm with its application in
           engineering optimization

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      Abstract: Abstract Aiming at the problems such as slow search speed, low optimization accuracy, and premature convergence of standard seagull optimization algorithm, an enhanced hybrid strategy seagull optimization algorithm was proposed. First, chaos mapping is used to generate the initial population to increase the diversity of the population, which lays the foundation for the global search. Then, a nonlinear convergence parameter and inertia weight are introduced to improve the convergence factor and to balance the global exploration and local development of the algorithm, so as to accelerate the convergence speed. Finally, an imitation crossover mutation strategy is introduced to avoid premature convergence of the algorithm. Comparison and verification between MSSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective and its incomplete algorithms all improve SOA to different degrees in both exploration and exploitation. 25 classic functions and the CEC2014 benchmark functions were tested, and compared with seven well-known meta-heuristic algorithms and its improved algorithm to evaluate the validity of the algorithm. The algorithm can explore different regions of the search space, avoid local optimum and converge to global optimum. Compared with other algorithms, the results of non-parametric statistical analysis and performance index show that the enhanced algorithm in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. At the same time, in order to prove its applicability and feasibility, it is used to solve two constrained mechanical engineering design problems contain the interpolation curve engineering design and the aircraft wing design. The engineering curve shape with minimum energy, minimum curvature, and the smoother shape of airfoil with low drag are obtained. It is proved that enhanced algorithm in this paper can solve practical problems with constrained and unknown search space highly effectively.
      PubDate: 2022-11-21
       
  • Numerical simulations of a vertical-axis hydrokinetic turbine with
           different blade-strut configurations under free-surface effects

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      Abstract: Abstract A numerical study of the free-surface flow over a vertical-axis hydrokinetic turbine with different blade-strut configurations is presented in this paper. The set of equations governing this multi-fluid flow consists of the Navier–Stokes equations and an advection equation of the signed distance function which describes the motion of the air–water interface in the context of the level-set method. For this application which involves domain motion, we adopt an arbitrary Lagrangian–Eulerian (ALE) description of the continuum where domain motion occurs independently of the fluid flow. Moreover, the variational multiscale (VMS) method is used for turbulence modelling resulting in the so-called ALE-VMS formulation. The formulation is used to investigate the performance of the turbine in four different computational settings. First, the quarter-struts and tip-struts configurations are simulated under a deep immersion depth. The results of the deep immersion cases show negligible effect from the free surface on the turbine performance. Next, the quarter-struts and tip-struts configurations are simulated under a shallow immersion depth. The results show significant effects of the turbine wake on the deformation of the air–water interface. A reduction in the performance of the turbine is observed in the shallow immersion cases and discussed. The results show robustness of the numerical formulation and provide opportunities for future studies.
      PubDate: 2022-11-20
       
  • Novel topological and geometrical modelling of N-frequency geodesic
           icosahedron tensegrities

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      Abstract: Abstract We propose a novel graph-theoretical method for efficient generation of the topological structure of N-frequency geodesic icosahedron tensegrities. The method only requires the adjacency list of edges of an N-frequency icosahedron, and using a sophisticated approach, creates the major topological entities of the corresponding geodesic icosahedron tensegrity. The graph theory is used to build a bridge between a regular icosahedron and its dual complex tensegrity. The approach proposed is general and perfectly works on icosahedrons with any degree of frequency. The generation of edges is managed in such a way that enables us to group them in different sets as cables and struts. The spherical geodesic tensegrities generated using our method could remarkably extend the complex data sets and large-scale benchmark models required for researchers in the field of tensegrity structures. The whole process and its parts are described and illustrated step by step. Furthermore, the form-finding of 1 to 5-frequency geodesic icosahedron tensegrities is also performed, and sets of self-equilibrium force densities corresponding to their super-stable geometries are provided. The results clearly demonstrate the effectiveness of the proposed method for automated modelling of the icosahedron tensegrities with a chosen frequency.
      PubDate: 2022-11-18
       
  • Non-probabilistic thermo-elastic reliability-based topology optimization
           (NTE-RBTO) of composite laminates with interval uncertainties

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      Abstract: Abstract This study investigates a non-probabilistic thermo-elastic reliability-based topology optimization (NTE-RBTO) scheme for the lightweight design of composite laminates under thermo-elastic loads with unknown-but-bounded (UBB) parameters. The equivalent constitutive relation of composite laminates is first introduced, and the deterministic topology optimization formulation of composite laminates is derived. In view of the inevitability of multi-source uncertainties during the whole design optimization procedure, the interval model and interval parametric vertex theorem are proposed for the acquisition of the reasonable characterization of uncertain responses in every iterative layout configuration. For reasons of structural safety, an improved non-probabilistic reliability index, the optimization feature distance is adopted, and its design sensitivity with respect to each element pseudo-density under thermal–mechanical coupling loads is calculated. GCMMA, the globally convergent version of MMA (method of moving asymptotes), is employed as the optimization problem solver. The effectiveness and rationality of the proposed method are demonstrated by several numerical examples, eventually.
      PubDate: 2022-11-18
       
  • Leveraging code generation for transparent immersogeometric
           fluid–structure interaction analysis on deforming domains

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      Abstract: Code generation technology has been transformative to the field of numerical partial differential equations (PDEs), allowing domain scientists and engineers to automatically compile high-performance solver routines from abstract mathematical descriptions of PDE systems. However, this often assumes a rigid code structure, which is only appropriate to a subset of applications and numerical methods, such as the traditional finite element methods used by the FEniCS code generation system. The present contribution demonstrates how to productively integrate FEniCS into a custom implementation of immersogeometric analysis (IMGA) of thin shell structures interacting with incompressible fluid flows on deforming domains. IMGA is an emerging paradigm for numerical PDEs with complex domain geometries, where non-watertight geometry descriptions are used directly as computational meshes. In particular, we generalize past related work by leveraging code generation to concisely pull back the deforming-domain Navier–Stokes problem to a stationary reference mesh. We also show how code generation enables rapid implementation of different material models for the structure subproblem. We verify our implementation using several benchmark problems, demonstrate its robustness and flexibility by simulating a prosthetic heart valve immersed in a flexible artery, and distribute the full source code online, to be used and modified by the community. Impact of the last item is amplified by the transparent nature of our code-generation-based implementation.
      PubDate: 2022-11-16
       
  • A cell-based smoothed finite-element method for gradient elasticity

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      Abstract: Abstract In this paper, the cell-based smoothed finite-element method (CS-FEM) is proposed for solving boundary value problems of gradient elasticity in two and three dimensions. The salient features of the CS-FEM are: it does not require an explicit form of the shape functions and alleviates the need for iso-parametric mapping. The main idea is to sub-divide the element into simplicial sub-cells and to use a constant smoothing function in each cell to compute the gradients. This new gradient is then used to compute the bilinear/linear form. The robustness of the method is demonstrated with problems involving smooth and singular solutions in both two and three dimensions. Numerical results show that the proposed framework is able to yield accurate results. The influence of the internal length scale on the stress concentration is studied systematically for a case of a plate with a hole and a plate with an edge crack in two and three dimensions.
      PubDate: 2022-11-16
       
  • A novel enriched degree of freedom method for absorbing boundary
           conditions in the time-domain finite element method

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      Abstract: Abstract This paper proposes an enriched degree of freedom method for absorbing boundary conditions in the time-domain finite element method (TD-FEM). In the proposed method, to reduce the reflection of the elastic waves from the artificial boundary, nodes in the absorbing domain are first enriched by additional degrees of freedom to damp the outgoing elastic waves. Next, based on the motion law of the enriched degree of freedom, a novel damping method is developed to further dampen the oscillation on the enriched degree of freedom. Then, through combination with the modified Newmark algorithm, the proposed method can be employed to efficiently absorb outgoing elastic waves. Finally, numerical tests are conducted to validate the feasibility and application of the proposed method.
      PubDate: 2022-11-11
       
  • Multiphysics model reduction of thermomechanical vibration in a
           state-space formulation

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      Abstract: Abstract The aim of this work is to propose a new multiphysics mode synthesis (MMS) for the thermomechanical vibration problem. The present thermomechanical model is based on a state-space formulation, which consists of displacement, velocity, and temperature shift. The state-space based thermomechanical formulation is symmetric unlike a conventional non-symmetric formulation for the displacement and temperature shift. In the proposed MMS, the structural variables, the displacement and velocity, are first reduced, which is then applied to the coupling term in the thermal parts. A term of the thermal domain is then reduced while preserving the multiphysics coupling effects, resulting in improved accuracy. The proposed two-step MMS with the thermal physics domain update can be implemented with the coupling term derived using the residual flexibility. The proposed MMS strategy can be also applied to accelerate the computational speed using independent parallel solvers. The performance of the proposed MMS method is evaluated through numerical examples.
      PubDate: 2022-11-09
       
  • Special issue: Numerical simulation for additive manufacturing processes
           and products

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      PubDate: 2022-11-08
       
  • An auto-tuned hybrid deep learning approach for predicting fracture
           evolution

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      Abstract: Abstract In this study, a novel auto-tuned hybrid deep learning approach composed of three base deep learning models, namely, long short-term memory, gated recurrent unit, and support vector regression, is developed to predict the fracture evolution process. The novelty of this framework lies in the auto-determined hyperparameter configurations for each base model based on the Bayesian optimization technique, which guarantees the fast and easy implementation in various practical applications. Moreover, the ensemble modeling technique auto consolidates the predictive capability of each base model to generate the final optimized hybrid model, which offers a better prediction of the overall fracture pattern evolution, as demonstrated by a case study. The comparison of the different prediction strategies exhibits that the direct prediction is a better option than the recursive prediction, in particular for a longer prediction distance. The proposed approach may be applied in various sequential data predictions by adopting the adaptive prediction scheme.
      PubDate: 2022-11-08
       
  • The numerical solution of a mathematical model of the Covid-19 pandemic
           utilizing a meshless local discrete Galerkin method

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      Abstract: Abstract It was in early December 2019 that the terrible news of the outbreak of new coronavirus disease (Covid-19) was reported by the world media, which appeared in Wuhan, China, and is rapidly spreading to other parts of China and several overseas countries. In the field of infectious diseases, modeling, evaluating, and predicting the rate of disease transmission are very important for epidemic prevention and control. Several preliminary mathematical models for Covid-19 are formulated by various international study groups. In this article, the SEIHR(D) compartmental model is proposed to study this epidemic and the factors affecting it, including vaccination. The proposed model can be used to compute the trajectory of the spread of the disease in different countries. Most importantly, it can be used to predict the impact of different inhibition strategies on the development of Covid-19. A computational approach is applied to solve the offered model utilizing the Galerkin method based on the moving least squares approximation constructed on a set of scattered points as a locally weighted least square polynomial fitting. As the method does not need any background meshes, its algorithm can be easily implemented on computers. Finally, illustrative examples clearly show the reliability and efficiency of the new technique and the obtained results are in good agreement with the known facts about the Covid-19 pandemic.
      PubDate: 2022-11-07
       
  • Probabilistic risk assessment of earth dams with spatially variable soil
           properties using random adaptive finite element limit analysis

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      Abstract: Abstract Risk assessment of earth dams is concerned not only with the probability of failure but also with the corresponding consequence, which can be more difficult to quantify when the spatial variability of soil properties is involved. This study presents a risk assessment for an earth dam in spatially variable soils using the random adaptive finite element limit analysis. The random field theory, adaptive finite element limit analysis, and Monte Carlo simulation are employed to implement the entire process. Among these methods, the random field theory is first introduced to describe the soil spatial variability. Then the adaptive finite element limit analysis is adopted to obtain the bound solution and consequence. Finally, the failure probability and risk assessment are counted via the Monte Carlo simulation. In contrary to the deterministic analysis that only a factor of safety is given, the stochastic analysis considering the spatial variability can provide statistical characteristics of the stability and assess the risk of the earth dam failure comprehensively, which can be further used for guiding decision-making and mitigation. Besides, the effects of the correlation structure of strength parameters on the stochastic response and risk assessment of the earth dam are investigated through parametric analysis.
      PubDate: 2022-11-07
       
  • Worst case mesh quality in the target matrix optimization paradigm

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      Abstract: Abstract When considering mesh quality improvement and optimization via node movement, a particularly important goal is to attempt to improve the worst quality in the mesh when that quality is unacceptable. Standard mesh optimization methods often do not address worst case quality and can make it worse. Three fundamental methods for addressing worst case quality in mesh optimization by node movement have appeared in the literature: (1) a shifted barrier approach by Barrera et. al. (Math Comput Simul 46(2):87–102, 1998), (2) a pseudo-barrier approach by Escobar et. al. (Comput Methods Appl Mech Eng 192:2775–2787, 2000), and (3) another barrier approach by Garanzha et. al. (In: IMR 2021-29th international meshing roundtable, 2021). The first two result in ‘simultaneous untangle-optimizers’ while the last addresses worst case quality in terms of the maximum value of the optimization metric. In terms of mesh optimization within the Target Matrix Optimization Paradigm (TMOP), worst case quality is defined by two quantities: the maximum value of the optimization metric ( \(\max \mu\) ) and the minimum value of the local volume ( \(\min \tau\) ), both computed over the mesh sample points. In the present paper we show that the methods by the three authors cited above can be applied to the TMOP metrics and used on both linear and high-order element meshes. Unfortunately, the first two methods increase \(\max \tau\) but do not address \(\max \mu\) . The third method addresses \(\max \mu\) , but fails to address \(\min \tau\) . Using a composition of functions approach, the present work creates new compound metrics that simultaneously increase \(\min \tau\) and decrease \(\max \mu\) . This goal can also be accomplished by a two-stage optimization procedure in which the first stage untangles the initial mesh and the second stage decreases \(\max \mu\) . Although none of these methods provide a guarantee that the worst case quality will be improved to the point that the quality becomes acceptable, it is shown by numerical examples that they can be very effective.
      PubDate: 2022-11-05
       
  • Improved river water-stage forecasts by ensemble learning

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      Abstract: Abstract Forecasting water stages is of significance to river and reservoir management. However, conventional models sometimes fail to perform accurately, as water levels are characterized by high nonstationarity. To provide an improved estimation of water stages, this study develops a new prediction framework by coupling stand-alone machine learning models with ensemble algorithms. As base learners, the optimal regression tree (RT) and extreme learning machine (ELM) are integrated into four ensemble strategies, i.e., bagging (BA), boosting (BO), random forest (RF) and random subspace (RS), leading to eight ensemble models. They are then assessed using daily water-stage records at two hydrological stations on the Yangtze River. Their performance is evaluated by statistical criteria: coefficient of determination (CD), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE). The RT and the ELM generate satisfactory predictions with deficiency in capturing extreme values. The ensemble models generally enhance the prediction efficiency, with their mean CD and NSE augment by up to 6.9% and 7.0%, and mean RMSE and MAE reduction by up to 47.9% and 47.0%. The BO-based models, namely BO-RT and BO-ELM, result in the highest accuracy, with a mean absolute relative error (ARE) of 1.0% and 1.4%. Ensemble learning gains even in multi-step-ahead forecasts, which satisfactorily extends the lead time up to 14 days. This study illustrates the capability of ensemble learning for improved water-level forecasts, which provides reference for modeling related issues such as sediment load and rainfall-runoff.
      PubDate: 2022-11-05
       
  • An active learning Kriging model with adaptive parameters for reliability
           analysis

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      Abstract: Abstract The prevalence of highly nonlinear and implicit performance functions in structural reliability analysis has increased the computational effort significantly. To solve this problem, an efficiently active learning function, named parameter adaptive expected feasibility function (PAEFF) is proposed using the prediction variance and joint probability density. The PAEFF function first uses the harmonic mean of prediction variances of Kriging model to judge the iteration degree of the current surrogate model, to realize the scaling of the variance in the expected feasibility function. Second, to improve the prediction accuracy of the Kriging model, the joint probability densities are applied to ensure that the sample points to be updated have a higher probability of occurrence. Finally, a new failure probability-based stopping criterion with wider applicability is proposed. Theoretically, the stopping criterion proposed is applicable to all active learning functions. The effectiveness and accuracy of the proposed PAEFF are verified by two mathematical calculations and three engineering examples.
      PubDate: 2022-11-03
       
  • A novel ensemble model using artificial neural network for predicting
           wave-induced forces on coastal bridge decks

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      Abstract: Abstract Due to the effects of climate change, coastal engineering structures are more vulnerable to the wave forces caused by natural hazards, especially for low-lying bridges. To facilitate the structural design and risk assessment of coastal bridges under extreme events, it is imperative to efficiently predict the wave-induced forces with high accuracy. In this study, a novel predictive model for wave-induced forces is established using the ensemble learning technique. Specifically, four state-of-the-art surrogate models, namely the support vector regression (SVR), Kriging (KRG), polynomial chaos expansion (PCE), and decision tree (DT) are employed to construct a weighted predictive model, where the weights of individual models are implicitly determined by the artificial neural network (ANN). Depending on the architecture of the ANN model, e.g., with or without a hidden layer, these four surrogate models can be ensembled nonlinearly (ANN1) or linearly (ANN2). Four benchmark functions and two ocean engineering cases are utilized to validate the performance of the established ensemble models. The coefficient of determination R2, maximum absolute error (MAE), and root mean square error (RMSE) are used as the error metrics. The results show that the proposed ANN-based ensemble strategy is capable of providing robust and accurate approximation for different force components; it can effectively reduce the adverse effect of poorly behaved candidate surrogates by wisely assigning weights to the individual models, which is beneficial to protect against the use of the worst surrogate model. It is envisioned that the proposed ensemble models can be extended to predict wave forces of unstable wave conditions, thus facilitating the associated risk assessment and structural design of ocean infrastructure assets.
      PubDate: 2022-11-03
       
  • Retraction Note: A new nonlinear formulation‑based prediction approach
           using artificial neural network (ANN) model for rubberized cement
           composite

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      PubDate: 2022-11-02
       
  • A unified scheme for nonlinear dynamic direct time integration methods: a
           comparative study on the application of multi-point methods

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      Abstract: Abstract In this article, we first present a unified scheme to apply nonlinear dynamic time integration methods. The unified scheme covers many existing time integration methods as exceptional cases. This paper has investigated time integration methods, including the Newmark, Wilson, Houbolt, and \(\rho _{\infty }\) -Bathe method. We then implement the multi-point methods as the nonlinear solution schemes along with the direct time integration methods in nonlinear dynamic analysis. Also, a unified scheme for applying single-point and multi-point methods is presented. Finally, we demonstrate with numerical examples that the unified scheme provides a framework for comparing direct time integration methods. We also investigate the performance of multi-point methods as nonlinear solution methods in detail.
      PubDate: 2022-10-14
       
  • Dual-variable-horizon peridynamics and continuum mechanics coupling
           modeling and adaptive fracture simulation in porous materials

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      Abstract: Abstract In this paper, we present a hybrid dual-variable-horizon peridynamics/continuum mechanics modeling approach and a strength-induced adaptive coupling algorithm to simulate brittle fractures in porous materials. Peridynamics theory is promising for fracture simulation since it allows discontinuities in the displacement field. However, they remain computationally expensive. Besides, there exists the surface effect in peridynamics due to the incomplete neighborhoods near the boundaries, including the outer boundaries and the boundaries of inner pores in porous materials. The proposed approach couples continuum mechanics and peridynamics into a closed equation system and an adaptive algorithm is developed to activate the peridynamics according to a strength criterion. In addition, the surface effect is corrected by introducing an improved peridynamic model with dual and variable horizons. We conduct the simulations using the relevant discretization scheme in each domain, i.e., the discontinuous Galerkin finite-element in the peridynamic domain and the continuous finite-element in the continuum mechanics domain. Two-dimensional numerical examples illustrate that successful fracture simulations of random porous materials can be achieved by this approach. In addition, the impact of distribution and size of pores on the fractures of porous materials is also investigated.
      PubDate: 2022-10-12
       
  • Concepts of data collection for the CAD-integrated isogeometric analysis

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      Abstract: Abstract This publication presents required steps for the realization of the pre- and post-processing for the isogeometric analysis and the isogeometric B-Rep analysis, with a focus on the collection of required data. It reveals the essential prerequisites for the preparation and the collection of geometrical information, which are merged with physical information for the creation of numerical models. It addresses both the direct computation on existing CAD drawings and the geometrical design during the preparation of the numerical models. The developments are presented through the example of the open source Rhino plugin Cocodrilo, which shall bring IGA to a larger community, including research and industrial facilities.
      PubDate: 2022-10-10
       
 
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