Efficient Simulation Method Research Articles

Investigation on crack propagation of SiCf/SiC composite via PF-CZM method

SiCf/SiC composite materials had become a research hotspot in the fields of aerospace materials and nuclear materials in recent years. This materials have high strength, radiation resistance, high temperature resistance and oxidation resistance. However, SiC is a typical brittle material prone to being damaged due to stress concentration in some cases, leading to cracks and even component failure. Therefore, this study employed an accurate and efficient numerical simulation method, phase field cohesive zone method (PFCZM), to investigate the microscopic damage behavior of SiCf/SiC composite materials. This study aimed to illustrate the influence of the thickness of pyrolytic carbon (PyC) interface layer on the failure behavior of SiCf/SiC composite materials. Firstly, this study conducted relevant verification of PFCZM. Secondly, numerical simulations of crack initiation, crack propagation, and final damage were performed by establishing a representative volume element (RVE). Observing the simulation results, it was noted that at low strain, when PyC was thicker, the SiCf/SiC composite materials exhibited slight damage to both the SiC matrix and PyC interface. Conversely, when PyC was thinner, the SiCf/SiC composite materials displayed only slight damage to PyC interface. As the PyC thickness increases, the damage to PyC interface decreases, while the damage to SiC becomes more prominent. Simultaneously, a competitive relationship in the damage behavior of SiCf/SiC composite materials can be found after the SiC matrix has cracked: When PyC is thinner, the damage primarily manifests as interlayer delamination between the SiC matrix and the SiC fiber. Conversely, when PyC is thicker, it manifests in PyC failure. Finally, this study concluded that, in this investigation, a PyC interface layer with a thickness of 0.1 μm exhibited the most effective protective effect on SiCf/SiC composite material.

A variant of improved discrete velocity method for efficient simulation of flows in entire Knudsen number regimes

In this paper, a variant of the improved discrete velocity method (VIDVM) is proposed for flows in the whole Knudsen number regimes. This method retains the advantage of the improved discrete velocity method (IDVM), which calculates numerical fluxes through a self-adaptive strategy by combining the microscopic reconstruction and the macroscopic reconstruction. Like the IDVM, the microscopic reconstruction for VIDVM is also based on the collisionless Boltzmann solver. However, different from IDVM, the macroscopic reconstruction for VIDVM is established on the Euler solver instead of the Navier–Stokes solver. Considering that the Euler solver merely computes the inviscid fluxes while the Navier–Stokes solver additionally calculates the viscous fluxes, the present method could be more efficient than IDVM. To validate the accuracy and efficiency of the present scheme, some benchmark cases from the continuum regime to the free molecular regime are conducted. Results reveal that the present scheme can predict the flow as well as IDVM, but the present solver is more efficient than IDVM.

A velocity evaluation method for in-pipe metallic flow through inversion of magnetic field perturbation measured surrounding the pipe

It is important to measure the global and/or local velocity of an in-pipe metallic flow to control its running state in applications such as a Tokamak fusion reactor. The magnetic field outside the pipe wall will be perturbed by the motion induced eddy current when the liquid metal flows across an applied static magnetic field. This phenomenon gives a possibility to evaluate the in-pipe velocity from the measured magnetic field perturbation signals. In this paper, a non-intrusive velocity evaluation method is proposed accordingly for measuring the velocity of liquid metal through measurement and inversion of the magnetic field surrounding the pipe. An efficient forward simulation method to calculate the magnetic field near a metallic flow in a static environmental magnetic field is developed at first. An inversion scheme based on the singular value decomposition and the L-curve method is then proposed to reconstruct the velocity distribution at a pipe cross-section with the linear equations correlating the flow velocity and the magnetic field regulated using the Tikhonov method. The reconstruction results of pipe flows of different velocity modes verified the feasibility and efficiency of the proposed velocity measurement method for in-pipe metallic flows.

A method for calculating and simulating phenotype of soybean based on 3D reconstruction

In plant phenotypic research, accurate organ segmentation and crop simulation are crucial for optimizing crop planting and increasing yield. In this study, an efficient method for soybean organ segmentation and phenotypic growth simulation was explored based on 3D reconstruction technology. Taking Dongnong252 soybean as the research object, the soybean phenotype acquisition system based on Kinect sensor was used to achieve high-precision and non-destructive acquisition of soybean canopy images during the whole growth period. First, conditional filtering and statistical filtering were used to remove noise. Then, combined with the Intrinsic Shape Signatures-Coherent Point Drift (ISS-CPD) and the Iterative Closest Point (ICP) algorithms, the multi-view three-dimensional (3D) canopy structure of soybean was reconstructed, which provided a reliable basis for plant stem and leaf segmentation. On this basis, the average accuracy of plant stem and leaf segmentation was 79.99% by using the Distance-field-based segmentation pipeline (DFSP) algorithm. Furthermore, the accurate information was provided for extracting and calculating 3D phenotypic parameters such as plant height, crown width, stem thickness, leaf length and leaf width from three scales of whole plant, stem and leaf. The calculated values were highly consistent with the measured values, with an average coefficient of determination of 0.9654 and an average percentage error of 3.4862%. Meanwhile, by analyzing the quantitative relationship between phenotypic parameters and physiological development time, the data-driven Richards growth simulation model was introduced to accurately predict the growth process of soybean plants. The coefficient of determination R2 values of each phenotypic simulation model reached above 0.9357, which improved the goodness of fit of the model by 0.03 compared with the Logistic model, and its root mean square error (RMSE) ranged from 0.0020 to 0.1112. The research results indicated that this method had high accuracy and reliability in 3D reconstruction of soybean canopy, phenotype calculation, and growth simulation. It could provide quantitative indicators for soybean variety selection, planting, and management, and provide technical support and reference for high-throughput phenotype acquisition and analysis of field crops.

Variational Quantum Eigensolver Boosted by Adiabatic Connection.

In this work, we integrate the variational quantum eigensolver (VQE) with the adiabatic connection (AC) method for efficient simulations of chemical problems on near-term quantum computers. Orbital-optimized VQE methods are employed to capture the strong correlation within an active space, and classical AC corrections recover the dynamical correlation effects comprising electrons outside of the active space. On two challenging strongly correlated problems, namely, the dissociation of N2 and the electronic structure of the tetramethyleneethane biradical, we show that the combined VQE-AC approach enhances the performance of VQE dramatically. Moreover, since the AC corrections do not bring any additional requirements on quantum resources or measurements, they can actually boost the VQE algorithms. Our work paves the way toward quantum simulations of real-life problems on near-term quantum computers.

A Fast Dynamic Simulation Method of an Active Distribution Network with Distributed Generations Based on Decomposition and Coordination

With the penetration of distributed resources into power distribution networks, power distribution networks are transforming into active distribution networks with a high proportion of distributed generations and power electronic equipment. Efficient modeling and simulation methods are essential to perform dynamic response analysis. In order to satisfy the fast/steady/slow multiple time-scale simulation requirements of active distribution networks, a fast/medium/slow time partition model and a network decoupling method for short line characteristic lines is proposed in this paper. Through the decomposition coordination simulation method, the network is decomposed into multiple regions that can be simulated in parallel. Based on the interconnection of fiber optic network cards, a multi-rate parallel simulation and synchronization strategy is proposed, which significantly improves the simulation speed of active distribution networks while ensuring simulation accuracy. The numerical experiments have been conducted based on a modified IEEE 33-bus and a PG&E 69-bus, and simulation results show the feasibility of the proposed method. The verification results of the example show that using adaptive variable-step-size multi-rate parallel simulation technology can increase the subnet computation-time balance rate and simulation acceleration ratio to 119.90% and 121.31% in the same rate-parallel mode.

An Efficient Goal-Oriented Adaptive Finite Element Method for Accurate Simulation of Complex Electromagnetic Radiation Problems

A goal-oriented adaptive frequency domain finite element method for solving electromagnetic radiation problems including complex structures is presented in this paper. Compared with the traditional adaptive finite element method, the goal-oriented method can flexibly control the refined regions according to the parameters of interest; therefore it has better convergence and has made significant progress in scattering problems and eigenvalue problems. To simulate complex antennas, this paper proposes an error indicator with high accuracy and low computational cost, and it uses the adjoint problem to weight element residuals without additional degrees of freedom. Moreover, high-quality mesh refinement algorithms adapted to this indicator are developed using a suitable point insertion strategy for multiscale structures. By simulating two practical antennas, comparisons with the traditional goal-oriented FEM and the well-developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</i> -adaptive finite element method in commercial software demonstrate the accuracy and efficiency of the proposed method.

Simulation of background neuronal activity and noise in human intracranial microwire recordings

BackgroundHuman intracranial microwire recordings allow measurement of neuronal activity in human subjects at a fine temporal and spatial scale. The recorded extracellular potentials represent a mixture of action potentials from nearby neurons, local field potentials, and other noise sources. Signal processing of these recordings is used to separate the activity of putative single neurons from other background and noise. To better understand the separation of single neuron activity, one approach is to simulate the signals produced by neurons firing action potentials combined with background activity and noise. New methodThis paper characterizes the background activity and noise in human intracranial microwire recordings and presents an accurate and efficient method of simulation using an infinite impulse response filter to color white noise. Results and comparisonThis method reproduces the power spectral density of the background activity and noise over a frequency range of 1–5000 Hz and is over 200 times faster than previously used methods. It thus facilitates large scale studies of variation of noise sources, field potentials, and processing parameters. It performs equivalently in terms of spike sorting to simulation using white noise. Another advantage is that the simulated signals are known to arise from a pseudorandom number generator and cannot be the result of detecting simulated background spiking activity. ConclusionsThis approach provides a rapid and accurate method of simulating background noise and neural activity in human intracranial microwire recordings. It is suitable for use in large scale simulations to study spike sorting in this type of recording.

On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach

In the insurance industry, life insurers are required by regulators to meet capital requirements to avoid insolvency caused by, for example, sudden mortality changes due to the COVID-19 pandemic. To prevent any large movements in this required capital, insurance companies are motivated to establish hedging strategies to mitigate the inherent risk exposures they face. Nonetheless, devising and implementing risk mitigation solutions to risk managing capital requirement is frequently impeded by the computational complexities stemming from the extensive simulations required. In this paper, we delve into a simulation quandary concerning the management of solvency capital risk associated with mortality and longevity. More specifically, we introduce a thin-plate regression spline method as a surrogate alternative to the standard nested simulation approach. Using this efficient simulation method, we further investigate hedging strategies that utilize mortality-linked securities coupled with stochastic mortality dynamics. Our simulation results provide a numerical justification to the market-making of mortality-linked securities in the context of mortality and longevity capital risk management.

Transmittance optimization of tunnel shading shed for maximum energy savings and traffic safety: When replacing tunnel lighting in threshold zone

Tunnel shading shed instead of tunnel lighting is one of the ideal ways to reduce the tremendous energy consumption at the entrance (section 2.1), but at present the length of tunnel shading shed and illuminance inside tunnel shading shed rarely meet the relevant technical requirements (section 2.2). Although some literature estimated the energy saving benefits of tunnel shading shed when replacing tunnel lighting in the threshold zone (section 2.2.1), there is no study on the conditions for maximum energy saving, the impact of energy saving on safety and the energy-saving efficiency in all time and all weather. To solve the above problems, suggest a multi-objective optimization condition of transmittance for maximizing energy savings improving traffic safety (section 3.3.2). In addition, propose an efficient sunlight simulation method based on finite element partitioning (section 3.3.1), derive a formula for precise energy-saving efficiency of tunnel shading shed (section 4.2.1), and analyze factors affecting energy-saving efficiency. The results show that: Suitable transmittance can simultaneously maximize the energy savings of tunnel shading shed (section 4.2.2) and improve traffic safety in the tunnel portal area (section 4.3). And the energy-saving efficiency on sunny summer days is not the greatest. It provides the theoretical and methodological support for making full use of sunlight and saving tunnel lighting electric energy.

Viscous coupling effect on hydraulic conductance in a square capillary tube

Viscous coupling effect on pore-scale flow capacity and its impact on macroscopic flow properties in porous media remain subjects of ongoing debate. This study employs the Stokes equation in conjunction with the zero-velocity interface and continuity interface to investigate viscous coupling effect during two-phase flow through a square capillary tube. Our findings substantiate the significance of the viscous coupling effect in angular pore multiphase flows. Enhanced fluid conductance is linked to fluid viscosity ratios, contact angles, and wetting-film lengths at the pore scale. Proposing a novel upscaling approach, we formulate hydraulic conductance in a square capillary tube for multiphase flows by incorporating a viscous coupling term, and its validation is accomplished through comparison with results from lattice Boltzmann method simulations. Our scaling model predicts hydraulic conductance with a mean relative error of less than 1%, outperforming prior viscous coupling models with errors reaching up to 19%. The derived scaling model, incorporating the viscous coupling effect, holds potential for integration within pore-network models, offering an efficient and precise simulation method for characterizing two-phase flow through porous media at a representative scale.

A Widely Applicable Modeling and Efficient Simulation Method for Power Electronics Grids Based on Unit Switching Circuits

As an essential part of modern power systems, power electronics have improved system performance. They also brought new dynamic and transient problems and posed significant challenges to precise and efficient simulation. Traditional modeling and solving methods fail to balance between the simulation accuracy and efficiency, and they are not flexible enough to adapt to converters with various new topologies. This article proposed a widely applicable modeling and simulation method based on the unit switching circuit that greatly promote the simulation efficiency and modeling simplicity by converting switching circuits into constant sub-grid equations with injection functions. Critical issues for engineering applications, such as the blocking state and the triggering deadband of converters, were also discussed and addressed by boundary balance and conduction path estimation. The error and stability of the solving method were qualitatively analyzed, with further discussions on the validity and numerical features. Several cases, including common and general cases and a real project of power electronic microgrid, tested and verified the accuracy, generalization ability, and efficiency of the proposed method.

A conditional extreme value distribution method for dynamic reliability analysis of stochastic structures

An efficient post-processing simulation method is proposed to estimate small failure probabilities of stochastic dynamic structures involving the inherent randomness of structural physical-geometrical parameters and external excitations. To extract a small failure probability, the proposed method introduces an intermediate event to represent the realizations of extreme structural response in the tail of distribution. With the aid of this intermediate event, structural failure probability is reformulated as a product of the event’s occurrence probability and the conditional exceedance probability of extreme response when the event occurs. The latter corresponds to a distribution of extreme response under the condition of the intermediate event, referred to as the conditional extreme value distribution (CEVD). Accordingly, the proposed method is termed the CEVD method. To reconstruct the CEVD, a truncated shifted generalized lognormal distribution model is employed. Bayesian estimation method is utilized to determine the two shape parameters of this model based on the samples of both original extreme value distribution and CEVD, where the CEVD samples are generated by Markov chain Monte Carlo sampling. The efficiency and accuracy of the proposed method are demonstrated through two numerical examples considering seismic reliability analyses of a 10-story nonlinear frame and a soil-foundation-structure interaction system.

Modeling and analysis of voltage gating of gap junction channels at a single-channel level

The advancement of single-channel-level recording via the patch-clamp technique has provided a powerful means of assessing the detailed behaviors of various types of ion channels in native and exogenously expressed cellular environments. However, such recordings of gap junction (GJ) channels are hampered by unique challenges that are related to their unusual intercellular configuration and natural clustering into densely packed plaques. Thus, the methods for reliable cross-correlation of data recorded at macroscopic and single-channel levels are lacking in studies of GJs. To address this issue, we combined our previously published four-state model (4SM) of GJ channel gating by voltage with maximum likelihood estimation (MLE)-based analyses of electrophysiological recordings of GJ channel currents. First, we consider evaluation of single-channel characteristics and the methods for efficient stochastic simulation of single GJ channels from the kinetic scheme described by 4SM using data obtained from macroscopic recordings. We then present an MLE-based methodology for extraction of information about transition rates for GJ channels and, ultimately, gating parameters defined in 4SM from recordings with visible unitary events. The validity of the proposed methodology is illustrated using stochastic simulations of single GJ channels and is extended to electrophysiological data recorded in cells expressing connexin 43 tagged with enhanced green fluorescent protein.

On the Effect of Imperfect Reference Signal Phase Recovery on Performance of PSK System Influenced by TWDP Fading.

We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.

Hamiltonian simulation in the Pauli basis of multi-qubit clusters for condensed matter physics

We propose an efficient method for Hamiltonian simulation of multi-qubit quantum systems with special types of interaction. In our approach, the Hamiltonian of a \(n\)-qubit system should be represented as a linear combination of the standard Pauli basis operators, and then decomposed into a sum of partial Hamiltonians, which are, in general, not Pauli operators and satisfy some anticommutation relations. For three types of Hamiltonians, which are invariant with respect to permutations of qubits, the effectiveness of the main algorithm in the three-qubit cluster model is shown by calculating the operator exponentials for these Hamiltonians in an explicit analytical form. We also calculate the density operator, partition function, entropy, and free energy of the cluster weakly coupled to a thermal environment. In our model, the cluster is in the Gibbs state in the temperature interval \(0.1\!-\!2\:\!\text{K}\), which corresponds to the operating range of modern quantum processors. It follows from our analysis that the thermodynamic properties of such systems strongly depend on the type of internal interaction of qubits in the cluster.

Nonlinear constitutive calculation method of rarefied flow based on deep convolution neural networks

In the field of rarefied gas dynamics, the presence of non-equilibrium flow characteristics poses significant challenges for achieving efficient and accurate numerical simulation methods. These challenges arise from the complex coexistence of these phenomena at multiple scales. The recent advent of intelligent fluid mechanics has introduced the data-driven nonlinear constitutive relation (DNCR) method as a promising approach for expeditious physical modeling of non-equilibrium rarefied flows. To enhance the generalization capabilities of the DNCR method, this study proposes a deep convolutional neural network model (DNCR-CNN) based on data-driven nonlinear constitutive relations, integrated with free-form deformation (FFD). Employing FFD technology, a series of hypersonic geometric shapes are generated for model training, and a multi-task learning-based deep convolutional neural network model is subsequently trained. The prediction of the hypersonic geometric shapes test set is carried out, and the results of the model prediction are substituted in the conservation equation for the iterative solution, thereby enhancing the DNCR method's generalization performance for varying geometric shapes. Upon conducting a comparative analysis of the outcomes obtained from DNCR, Navier–Stokes (NS), and unified gas kinetic scheme (UGKS), it is revealed that the DNCR method can maintain computational resource levels equivalent to those of the NS equation while achieving a level of accuracy comparable to UGKS under diverse geometric shapes and grid resolutions. The enhancements in usability render the DNCR method a potent tool for addressing the challenges posed by rarefied gas, thereby expanding its applicability within the field.

A new pre-conditioned STDP rule and its hardware implementation in neuromorphic crossbar array

This paper proposes a new pre-conditioned spike-timing-dependent plasticity (STDP) learning rule as well as an efficient system-level time-domain circuit modeling and simulation method for the whole crossbar array structure. First, the new pre-conditioned STDP rule is conceived by exercising a slight perturbation to the conventional STDP curve. Software-wise, it can be described in spiking neural network (SNN) codes, while hardware-wise, it is realized by the memristor crossbar array in conjunction with specific spike signals, just as the conventional STDP. The new STDP rule enables the neural network in a neuromorphic chip to achieve better performance both in terms of software and hardware implementation compared with the conventional STDP rule. Second, a time-domain circuit modeling method is proposed for the simulation of the whole crossbar array. Numerical experiments demonstrate that the new pre-conditioned STDP is superior to the conventional STDP both in terms of accuracy and low power consumption. At the software level, a 784 × 100 spiking neural network, trained by the pre-conditioned STDP, for the MNIST handwritten digit recognition achieves an accuracy of 85.90%, which is 3.09% higher than that trained by the conventional STDP. At the hardware level, a downscaled 196 × 10 crossbar array is simulated and trained. The crossbar array trained by the new STDP consumes 21.5% less power than that trained by the conventional STDP. Moreover, it does not compromise the original robustness when subject to non-ideal characteristics such as variation of conductance and resistance of the interconnect in the crossbar array.

Dynamic Analysis and Numerical Simulation of Arresting Hook Engaging Cable in Carried-Based UAV Landing Process

Carrier-based unmanned aerial vehicles (UAVs) require precise evaluation methods for their landing and arresting safety due to their high autonomy and demanding reliability requirements. In this paper, an efficient and accurate simulation method is presented for studying the arresting hook engaging arresting cable process. The finite element method and multibody dynamics (FEM-MBD) approach is employed. By establishing a rigid–flexible coupling model encompassing the UAV and arresting gear system, the simulation model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components. The model considers the material properties, arresting gear system characteristics, and UAV state during engagement. Verification is conducted by comparing simulation results with experimental data from a referenced arresting hook rebound. Finally, simulations are performed under different touchdown points and roll angles of the UAV to analyze the stress distribution of the hook, center of gravity variations, and the tire touch and rollover cable response. The proposed rigid–flexible coupling arresting dynamics model in this paper enables the effective analysis of the dynamic behavior during the arresting hook engaging arresting cable process.

Neural-network-based parameter tuning for multi-agent simulation using deep reinforcement learning

This study proposes a new efficient parameter tuning method for multi-agent simulation (MAS) using deep reinforcement learning. MAS is currently a useful tool for social sciences, but is hard to realize realistic simulations due to its computational burden for parameter tuning. This study proposes efficient parameter tuning to address this issue using deep reinforcement learning methods. To improve compatibility with the tuning task, our proposed method employs actor-critic-based deep reinforcement learning, such as deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). In addition to the customized version of DDPG and SAC for our task, we also propose three additional components to stabilize the learning: an action converter (DDPG only), a redundant full neural network actor, and a seed fixer. For experimental verification, we employ a parameter tuning task in an artificial financial market simulation, comparing our proposed model, its ablations, and the Bayesian estimation-based baseline. The results demonstrate that our model outperforms the baseline in terms of tuning performance, indicating that the additional components of the proposed method are essential. Moreover, the critic of our model works effectively as a surrogate model, that is, as an approximate function of the simulation, which allows the actor to tune the parameters appropriately. We have also found that the SAC-based method exhibits the best and fastest convergence, which we assume is achieved by the high exploration capability of SAC.