Efficient Simulation Method Research Articles

A Condition Evaluation Simplified Method for Traction Converter Power Module Based on Operating Interval Segmentation

In the actual operation of urban rail vehicles, it is essential to evaluate the condition of the traction converter IGBT modules. Considering the fixed line and the similarity of operation conditions between adjacent stations, this paper proposes an efficient and accurate simplified simulation method to evaluate IGBT conditions based on operating interval segmentation (OIS). Firstly, this paper proposes the framework for a condition evaluation method by segmenting operating intervals based on the similarity of average power loss between neighboring stations. The framework makes it possible to reduce the number of simulations to shorten the simulation time while ensuring the state trend estimation accuracy. Secondly, this paper proposes a basic interval segmentation model that uses the operating conditions as inputs to implement the segmentation of the line and is able to simplify the operation conditions of entire line. Finally, the simulation and analysis of the temperature and stress fields of IGBT modules based on segmented intervals completes the IGBT module condition evaluation and realizes the combination of lifetime calculation with actual operating conditions and internal stresses. The validity of the method is verified by comparing the interval segmentation simulation with actual test results. The results show that the method can effectively characterize the temperature and stress trends of traction converter IGBT modules in the whole line, which could support the fatigue mechanism and lifetime assessment reliability study of IGBT modules.

A new algorithm for computing path integrals and weak approximation of SDEs inspired by large deviations and Malliavin calculus

The paper gives a novel path integral formula inspired by large deviation theory and Malliavin calculus. The proposed finite-dimensional approximation of integrals on path space will be a new higher-order weak approximation of multidimensional stochastic differential equations where the dominant part of the local expansion is governed by Varadhan's geodesic distance and the correction terms are given as Malliavin weights. An optimal truncation of asymptotic expansion is used to reduce computational effort. Kusuoka's estimate is applied to justify the finite-dimensional approximation of path integrals. An efficient simulation method is provided with the algorithm. Numerical results are shown to verify the effectiveness.

Simulation of a non-Gaussian stochastic process based on a combined distribution of the UHPM and the GBD

To simulate non-Gaussian stochastic processes based on the first four moments, various simulation methods are presented, in which the determination of the transformation model and the calculation of the correlation coefficients between non-Gaussian stochastic processes and Gaussian stochastic processes are the critical procedures in these simulation methods. However, some existing simulation methods are limited to specific ranges. Furthermore, their practical applications are affected negatively due to the expensive cost of determining the transformation model and the correlation coefficients between non-Gaussian and Gaussian stochastic processes. Therefore, an accurate and efficient simulation method of a non-Gaussian stochastic process with a broader range is proposed in this article. Since the simulation of non-Gaussian processes and the Nataf transformation of non-Gaussian variables have some similar characteristics, a new combined distribution is proposed based on the unified Hermite polynomial model (UHPM) and the generalized beta distribution (GBD). Then, the combined distribution is employed in the simulation of non-Gaussian stochastic processes, in which the transformation model is deduced by the combined distribution. The correlation coefficient transformation function (CCTF) between the Gaussian and non-Gaussian stochastic processes can be evaluated through the interpolation method. Furthermore, numerical examples are presented to show the accuracy and effectiveness of the proposed simulation method for non-Gaussian stochastic processes.

A Fully Coupled Hydro-Mechanical Approach for Multi-Fracture Propagation Simulations

Hydraulic fracturing is a complex nonlinear hydro-mechanical coupled process. Accurate numerical simulation is of great significance for reducing fracturing costs and improving reservoir development benefits. The aim of this paper is to propose an efficient numerical simulation method for the fracturing-to-production problem under a unified framework that has good convergence and accuracy. A hydro-mechanical coupled fracturing model (HMFM) is established for poroelastic media saturated with a compressible fluid, and the local characteristics of the physical field are fully considered. Each fracture is explicitly characterized using the discrete fracture model (DFM), which can better reflect the physical characteristics near fractures. Based on the extended finite element method (XFEM) and the Newton–Raphson method, a fully coupled approach named Unified Extended Finite Element (UXFEM) is developed, which can solve the nonlinear system of equations that describe the solution under a unified framework. UXFEM can accurately capture the local physical characteristics of different physical fields on the orthogonal structured grids. It realizes the grid-fracture decoupling, and fractures can propagate in any direction, which shows greater flexibility in simulating fracture propagation. The fully coupled approach can better reflect the essential relationship between pressure, stress, and fracture, which is beneficial to studying hydro-mechanical coupled problems. To validate the UXFEM, UXFEM is compared with the classical KGD model, analytic solution, and COMSOL solution. Finally, based on UXFEM, the interference phenomenon and fracturing-to-production study are carried out to prove the broad practical application prospect of this new fully coupled approach.

Efficient numerical simulation of atmospheric pulsed discharges by introducing deep learning

Plasma simulation is an important but sometimes time-consuming approach to study the discharge behaviors of atmospheric pulsed discharges. In this work, an efficient simulation method is proposed by introducing deep learning to investigate the discharge characteristics driven by very short pulsed voltages. A loss function is designed and optimized to minimize the discrepancy between the Deep Neural Network (DNN) and the verified fluid model. The prediction data obtained via well-trained DNN can accurately and efficiently reveal the key discharge characteristics, such as the waveforms of discharge current and gap voltage, spatial profiles of charged particles density and electric field. The spatial distributions of charged particles density and electric field obtained from DNN are also given to unveil the underlying mechanisms. Additionally, the predictions from deep learning and the formula analysis both highlight that the breakdown voltage and current density can be effectively reduced by increasing repetition frequency, which quantitatively agrees well with the experimental observations. This study provides a great potential promise for vastly improving the simulation efficiency by introducing deep learning in the field of atmospheric plasmas computation.

An efficient non-iterative smoothed particle hydrodynamics fluid simulation method with variable smoothing length

In classical smoothed particle hydrodynamics (SPH) fluid simulation approaches, the smoothing length of Lagrangian particles is typically constant. One major disadvantage is the lack of adaptiveness, which may compromise accuracy in fluid regions such as splashes and surfaces. Attempts to address this problem used variable smoothing lengths. Yet the existing methods are computationally complex and non-efficient, because the smoothing length is typically calculated using iterative optimization. Here, we propose an efficient non-iterative SPH fluid simulation method with variable smoothing length (VSLSPH). VSLSPH correlates the smoothing length to the density change, and adaptively adjusts the smoothing length of particles with high accuracy and low computational cost, enabling large time steps. Our experimental results demonstrate the advantages of the VSLSPH approach in terms of its simulation accuracy and efficiency.

A splitting lattice Boltzmann scheme for (2+1)-dimensional soliton solutions of the Kadomtsev-Petviashvili equation

<abstract> <p>Recently, considerable attention has been given to (2+1)-dimensional Kadomtsev-Petviashvili equations due to their extensive applications in solitons that widely exist in nonlinear science. Therefore, developing a reliable numerical algorithm for the Kadomtsev-Petviashvili equations is crucial. The lattice Boltzmann method, which has been an efficient simulation method in the last three decades, is a promising technique for solving Kadomtsev-Petviashvili equations. However, the traditional higher-order moment lattice Boltzmann model for the Kadomtsev-Petviashvili equations suffers from low accuracy because of error accumulation. To overcome this shortcoming, a splitting lattice Boltzmann scheme for (2+1)-dimensional Kadomtsev-Petviashvili-Ⅰ type equations is proposed in this paper. The variable substitution method is applied to transform the Kadomtsev-Petviashvili-Ⅰ type equation into two macroscopic equations. Two sets of distribution functions are employed to construct these two macroscopic equations. Moreover, three types of soliton solutions are numerically simulated by this algorithm. The numerical results imply that the splitting lattice Boltzmann schemes have an advantage over the traditional high-order moment lattice Boltzmann model in simulating the Kadomtsev-Petviashvili-Ⅰ type equations.</p> </abstract>

ADAPTIVE PHASE-FIELD CONCURRENT MULTISCALE METHOD FOR EFFICIENT SIMULATION OF QUASI-BRITTLE FRACTURE

An adaptive phase-field concurrent multiscale method for efficient simulation of quasi-brittle fracture is presented. In this method, the analysis model is first subjected to coarse mesh discretization and the corresponding damage phase field calculation analysis. Then, adaptive dynamic local mesh refinement is performed for the coarse scale elements exceeding the given damage threshold during the iterative process. The locally refined mesh is defined as the local subdomain, the outermost coarse elements of the refined mesh, and the coarse elements without refinement are defined as the global subdomain. The variant s-version method is used to realize the direct coupling connection of mismatched meshes in different subdomains and the application of the continuity condition of field variables on the global-local subdomain interface. The division of the local subdomain, the global subdomain, and their overlapping domain are dynamically updated with the crack propagation path in the proposed method. The generation of the global-local computing mesh has great flexibility without the need for scale separation, which provides a more concise and flexible implementation for multiscale phase field adaptive simulation. The proposed method overcomes the extra burden and complexity of field variable conversion between coarse and fine scales, imposition of continuity conditions, and program implementation in existing phase field multiscale methods, which can be easily applied to the analysis on damage-fracture behavior of quasi-brittle structures. Typical examples verify the correctness, robustness, and advantages of the method.

Finite element modelling for mitigation of residual stress and distortion in macro-scale powder bed fusion components

Powder bed fusion (PBF) has attracted significant attention in many applications due to its capability of fabricating complex and customized metal parts. However, the potential for high inherent residual stresses that produce distortion in additive manufacturing (AM) components prevents more widespread application of the AM technique. Efficient and accurate prediction of residual stress and distortion at component-level (macro-scale) is a complex task. Although process-level (meso-scale) thermo-mechanical simulations have resulted in accurate predictions for small-scale parts, the computational times (typically weeks) and memory requirements for application of such methods to component-level are prohibitive. The main goal of the current study therefore is to present an efficient and accurate finite element (FE) simulation method with detailed validation for PBF manufacture of a complex 3D Inconel 625 benchmark bridge component (macro-scale). The simulation results are successfully validated against the published benchmark experimental measurements from neutron diffraction, X-ray diffraction (XRD), contour method and coordinate measurement machine (CMM) by the National Institute of Standards and Technology (NIST) laboratory. A key additional novelty of the present work is the investigation of the effects of substrate removal and preheating on mitigation of residual stresses and distortions using the validated model. Ultimately, these results will guide the selection of optimal manufacturing protocols and integration of the FE-based AM modelling for industrial application with complex geometries. The ultimate aim of the present work is to facilitate fatigue life prediction of complex geometry AM components including residual stress effects, for example, conformally-cooled injection moulding dies (for different material than Inconel 625).

Multiphysics simulation of fluid interface shapes in microfluidic systems driven by electrowetting on dielectrics

We present a highly efficient simulation method for the calculation of three-dimensional quasi-static interface shapes under the influence of electric fields. The method is especially useful for the simulation of microfluidic systems driven by electrowetting on dielectrics because it accounts automatically and inherently for the highly non-trivial interface shape in the vicinity of the triple-phase contact. In particular, the voltage independence of the local contact angle predicted based on analytical considerations is correctly reproduced in all our simulations. For the calculation of the shape of the interface, the geometry is triangulated and the mesh nodes are shifted until the system energy becomes minimal. The same mesh is also used to calculate the electric field using the boundary-element method. Therefore, only the surface of the geometry needs to be meshed, and no volume meshes are involved. The method can be used for the simulation of closed systems with a constant volume (e.g., droplet-based microfluidics) while preserving the volume very precisely as well as open systems (e.g., the liquid–air interface within micro-cavities or capillaries). Additional effects, such as the influence of gravitational forces, can easily be taken into account. In contrast to other efficient simulations, such as the volume-of-fluid, level-set, or phase-field methods, ideally, sharp interfaces are obtained. We calculate interface shapes for exemplary systems and compare with analytical as well as experimental results.

Robust Method for Reservoir Simulation History Matching Using Bayesian Inversion and Long-Short-Term Memory Network-Based Proxy

Summary History matching is a critical process used for calibrating simulation models and assessing subsurface uncertainties. This common technique aims to align the reservoir models with the observed data. However, achieving this goal is often challenging due to the nonuniqueness of the solution, underlying subsurface uncertainties, and usually the high computational cost of simulations. The traditional approach is often based on trial and error, which is exhaustive and labor-intensive. Some analytical and numerical proxies combined with Monte Carlo simulations are used to reduce the computational time. However, these approaches suffer from low accuracy and may not fully capture subsurface uncertainties. This study proposes a new robust method using Bayesian Markov chain Monte Carlo (MCMC) to perform assisted history matching under uncertainties. We propose a novel three-step workflow that includes (1) multiresolution low-fidelity models to guarantee high-quality matching; (2) long-short-term memory (LSTM) network as a low-fidelity model to reproduce continuous time response based on the simulation model, combined with Bayesian optimization to obtain the optimum low-fidelity model; and (3) Bayesian MCMC runs to obtain the Bayesian inversion of the uncertainty parameters. We perform sensitivity analysis on the LSTM’s architecture, hyperparameters, training set, number of chains, and chain length to obtain the optimum setup for Bayesian-LSTM history matching. We also compare the performance of predicting the recovery factor (RF) using different surrogate methods, including polynomial chaos expansions (PCE), kriging, and support vector machines for regression (SVR). We demonstrate the proposed method using a water flooding problem for the upper Tarbert formation of the 10th SPE comparative model. This study case represents a highly heterogeneous nearshore environment. Results showed that the Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow ranges of uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and accuracy of the workflow. This approach provides an efficient and practical history-matching method for reservoir simulation and subsurface flow modeling with significant uncertainties.

Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network

Waste heat utilization has been prioritized especially in various industries and sectors. Many researchers have developed heat recovery processes by designing suitable waste heat recovery units (WRU), such as heat exchangers, using water as a coolants to receive heat from the waste heat fluid in the production process. The conventional heat exchanger has limitations such as its equipment size, space for installation, and flexibility. The microchannel heat exchanger is one of many ideas for resolving these limitations. Moreover, the coolant on the cold side can be upgraded by adding nanometer-sized solid particles which is called “Nanofluid”. To reduce the high investigation cost and time, a new efficient and cost-effective simulation method was selected to use for investigating the performance of a microchannel heat exchanger with nanofluids in this study. To analyze the heat recovery at low temperature, i.e. around 100–200 °C, nanofluid property predictive models were developed using an artificial neural network (ANN). Then, the predictive models were embedded and integrated into computational fluid dynamics to design a microchannel heat exchanger. It is found that the use of nanofluids improved the heat transfer efficiency of this heat exchanger. The suitable nanofluid types and concentrations were selected based on the thermal–hydraulic​ performance. Here, the 3% weight TiO2/Water fluid with a 1.03 thermal–hydraulic​ performance ratio was found to be the most promising nanofluid for using in this condition.

A Simple Automatic Hexahedron Mesh Generation and Polyhedral Smoothed Finite Element Method for Mechanics Problems

A new fully automatic pure hexahedron mesh, called lotus mesh, generation technique of an arbitrary 2D/3D geometric domain and polyhedral smoothed finite element method (PSFEM) for efficient numerical simulation are presented. The lotus mesh algorithm is mainly based on the signed distance function (SDF) and the density function to generate nodes and control node density, respectively. We improved the generality of the SDF-based mesh method, realizing the meshing of 2D arbitrary polygons and 3D complex structures, and propose geometry-based adaptive meshing methods. In addition, to improve the stability and accuracy of lotus mesh, PSFEM based on smoothed finite method (SFEM) is proposed. Unlike the existing SFEM, this paper adopts an indirect way to calculate the stiffness matrix, which first calculates the transition matrix on the smoothed domain, and then maps it back to the real stiffness matrix through the transformation matrix, to address stiffness integral operation of the 3D complex polyhedron. Several demonstration examples are presented to verify the lotus mesh and its accuracy. • A fully automatic pure hexahedron mesh, lotus mesh, is proposed, whose algorithm structure is simple and easy to implement. • Mesh generation method based on signed distance function is improved to realize the meshing of 2D arbitrary polygons and 3D complex structures with geometry-based adaptivity. • Polyhedral smoothed finite element method is proposed, which has higher accuracy using lotus mesh than conventional finite element method.

Nanosecond laser high-precision fabrication of microgrooves on TC4 surface: Morphology simulation and drag reduction performance of microgrooves

With the increasing global awareness of environmental protection, higher requirements are also placed on the fuel consumption rate of aircraft. Microgrooves can reduce the near-wall frictional resistance of aircraft to save fuel, which has become the focus of research in the aviation industry. To realize the low-cost and high-precision fabrication of microgrooves with drag reduction effect, an efficient and high-precision numerical simulation method for nanosecond pulsed laser etching TC4 titanium alloy is studied. A laser processing strategy is formulated. Furthermore, an efficient and high-precision computational fluid dynamics simulation method is studied to verify the drag reduction effect of microgrooves. The research results show that the size error of prepared microgrooves is less than 2%, and their drag reduction rate is as high as 9.6%, which verifies the validity and reliability of the research method in this paper. This work can guide the design of high-density functional microstructures and their high-efficiency and high-precision fabrication. The research results can provide technical parameters for aircraft drag reduction, which has essential engineering practical value.

Joint parameters for strain-based geometrically nonlinear beam formulation: Multibody analysis and experiment

• Folding wing aircraft and offshore wind turbine are focused on. • An efficient dynamic simulation method is developed. • Experimental validation using wind tunnel was conducted. • Simulation and experiment were in good agreement. A novel singularity-free, tuning-free, and reduced-order strain-based beam formulation is developed for analyzing slender multibody systems such as folding wing aircraft and offshore wind turbines that perform large rigid body motions and geometrically nonlinear deformations. Conventional linear deformation models are not suitable for the analysis, whereas the strain-based beam formulation has a potential to describe the geometrically nonlinear deformation efficiently using a small number of strain variables and a constant stiffness matrix obtained from a recursive equation. However, it suffers from singularity, penalty-coefficient tuning, and many variables when applied to multibody systems. This study addressed these problems by proposing joint parameters. Singularity-free joint parameters introduced in the recursive equation produce a novel velocity transformation that removes coefficient tuning and achieves model reduction. We demonstrate that the proposed method can perform a more stable multibody analysis compared to that using the conventional method. Further, we measured the strain of the folding wing during deployment in a wind tunnel at the Institute of Fluid Science, Tohoku University to validate the strain-based beam formulation with the proposed joint parameters. The strain of the proposed method is in good agreement with that of the wind tunnel experiment, wherein the folding wing performed multibody dynamic motion with geometrically nonlinear deformation. .

A General Framework to Simulate Diffusions with Discontinuous Coefficients and Local Times

In this article, we propose an efficient general simulation method for diffusions that are solutions to stochastic differential equations with discontinuous coefficients and local time terms. The proposed method is based on sampling from the corresponding continuous-time Markov chain approximation. In contrast to existing time discretization schemes, the Markov chain approximation method corresponds to a spatial discretization scheme and is demonstrated to be particularly suited for simulating diffusion processes with discontinuities in their state space. We establish the theoretical convergence order and also demonstrate the accuracy and robustness of the method in numerical examples by comparing it to the known benchmarks in terms of root mean squared error, runtime, and the parameter sensitivity.

A Fokker–Planck-based Monte Carlo method for electronic transport and avalanche simulation in single-photon avalanche diodes

We present an efficient simulation method for electronic transport and avalanche in single-photon avalanche diodes (SPAD). Carrier transport is simulated in the real space using a particle Monte Carlo approach based on the Fokker–Planck point of view on an advection-diffusion equation, that enables us to reproduce mobility models, including electric fields and doping dependencies. The avalanche process is computed thanks to impact ionization rates implemented using a modified Random Path Length algorithm. Both transport and impact ionization mechanisms are computed concurrently from a statistical point of view, which allows us to achieve a full multi-particle simulation. This method provides accurate simulation of transport and avalanche process suitable for realistic three-dimensional SPADs, including all relevant stochastic aspects of these devices, together with a huge reduction of the computational time required, compared to standard Monte Carlo methods for charge carrier transport. The efficiency of our method empowers the possibility to precisely evaluate SPADs figures of merit and to explore new features that were untrackable by conventional methods. An extensive series of comparisons with experimental data on state-of-the art SPADs shows a very good accuracy of the proposed approach.

Performance analysis of the hypersonic vehicle with dorsal and ventral intake

The integrated design of the airframe and inlet is the favorite choice for the aerodynamic configuration design of the air-breathing hypersonic vehicle. To better understand the integrated configurations with a dorsal intake, this paper utilizes the computational fluid dynamics (CFD) method to comprehensively compare the performance of integrated configurations with the dorsal and traditionally used ventral intake. The performance indexes include aerodynamic performance, such as lift-to-drag ratio, the inlet performance, and thrust-to-drag balance performance. First, under the same geometric constraints, two integrated aerodynamic configurations with dorsal and ventral intake are designed based on a truncated Busemann inlet. Second, the CFD numerical simulation method based on multi-GPU parallel computing is developed and verified. Then, the flow-field characteristics, the aerodynamic, inlet, and thrust-to-drag balance performance of the two types of configurations in a wide range of Mach numbers are comprehensively compared using the GPU-based efficient numerical simulation method. The advantages and disadvantages of the two types of configurations are then analyzed to provide a meaningful reference for the aerodynamic configuration design of air-breathing hypersonic vehicles.

Estimation of power performances and flow characteristics for a Savonius rotor by vortex particle method

AbstractThis study investigates the implementation of the vortex particle method (VPM) with the goal of efficiently and accurately estimating the power performances and flow characteristics for a Savonius rotor. The accuracy and efficiency of simulation methods are critical for the reliable design of Savonius rotors. Among various approaches, VPM is chosen because it can be flexibly incorporated with self‐correction techniques, and the distribution of bound vortex particles can effectively represent complex geometries. In this work, a double‐trailing‐edge‐wake‐modeling vortex particle method (DTVPM) is presented to extend the working range of VPM for dealing with large rotating amplitudes and high tip speed ratios (TSRs). DTVPM addresses asymmetrical torque predictions for a Savonius rotor without gap width. However, DTVPM performs poorly at high TSRs due to the absence of viscous effects near the surface. To capture complex wake structures, such as reverse flow structures, the viscous correction for tip vortices is suggested. The current research focuses on the implementation and validation of DTVPM for predicting torque coefficients and wake patterns, as well as comparisons to OpenFOAM results. Two‐dimensional and incompressible flow is estimated at = 0.2–1.2. For the studied cases, a maximum power coefficient is obtained at , consistent with published experimental data. In addition, the process of trailing‐edge vortices generation and detachment is captured by DTVPM. The comparison results between OpenFOAM and DTVPM show that DTVPM allows to efficiently simulate a Savonius rotor without any empirical parameters. DTVPM will help to improve existing engineering models for wind energy fields.

Analytical and Numerical Treatment of Continuous Ageing in the Voter Model

The conventional voter model is modified so that an agent’s switching rate depends on the ‘age’ of the agent—that is, the time since the agent last switched opinion. In contrast to previous work, age is continuous in the present model. We show how the resulting individual-based system with non-Markovian dynamics and concentration-dependent rates can be handled both computationally and analytically. The thinning algorithm of Lewis and Shedler can be modified in order to provide an efficient simulation method. Analytically, we demonstrate how the asymptotic approach to an absorbing state (consensus) can be deduced. We discuss three special cases of the age-dependent switching rate: one in which the concentration of voters can be approximated by a fractional differential equation, another for which the approach to consensus is exponential in time, and a third case in which the system reaches a frozen state instead of consensus. Finally, we include the effects of a spontaneous change of opinion, i.e., we study a noisy voter model with continuous ageing. We demonstrate that this can give rise to a continuous transition between coexistence and consensus phases. We also show how the stationary probability distribution can be approximated, despite the fact that the system cannot be described by a conventional master equation.