Efficient Simulation Research Articles

Three-dimensional peridynamic modeling of damage and penetration in composite plates exposed to localized explosive blasts

Localized failures in composites can lead to catastrophic consequences in some vehicles and systems, such as airplanes and submarines. The paper presents a new three-dimensional (3D) peridynamic model to explore the localized explosive effects on composite plates. The study utilizes the peridynamic method to simulate fractures and deformations in composite plates when exposed to localized explosive blasts. Accurate prediction of composites' performance during explosive blast events is crucial in the design process to avoid the deadly effects of their failures. This study highlights the following novelties: (1) We present, for the first time, a novel 3D mesh-free model to simulate the fracture and damage behavior of composite plates when exposed to explosive blasts; (2) The adopted numerical modeling technique enables highly efficient simulations of fractures and failures in composites when compared with other mesh-based numerical models; (3) We introduce a new mathematical framework to reflect the explosive pressure loads through different composite layers; (4) The study provides an accessible and efficient tool for engineers and researchers to enhance the design of composites in several industries instead relying on limited-access commercial software packages. In addition to the previous novelties, the paper presents a new parametric study that investigates the performance of several composite plates, such as titanium-aramid and aluminum-aramid composites, in explosive blast scenarios. Moreover, the roles of explosive mass and plate thickness in the failure mechanisms of composites are examined.

Fast thermal simulation of WAAM processing: toward manufacturing strategy evaluation

Managing the quality of parts produced by the Wire Arc Additive Manufacturing (WAAM) process presents a significant challenge, particularly due to the complexity of thermal control. Effective thermal management is crucial for minimizing defects, making fast and accurate thermal simulations essential for testing and optimizing various manufacturing strategies. This article proposes a rapid simulation that decouples the calculation of heat conduction from convection and radiation. The proposed simulation is described and validated against experimental data. The influences of spatial and temporal discretization are examined. In conclusion, this developed approach provides a fast and efficient simulation of a manufacturing strategy for improvement.

Generalized Path Integral Energy and Heat Capacity Estimators of Quantum Oscillators and Crystals Using Harmonic Mapping.

Imaginary-time path integral (PI) is a rigorous tool to treat nuclear quantum effects in static properties. However, with its high computational demand, it is crucial to devise precise estimators. We introduce generalized PI estimators for the energy and heat capacity that utilize coordinate mapping. While it can reduce to the standard thermodynamic and centroid virial (CVir) estimators, the formulation can also take advantage of harmonic character of quantum oscillators and crystals to construct a coordinate mapping. The method is not applicable to fluids or systems with imaginary modes such as double-well potentials. This yields harmonically mapped averaging (HMA) estimators, with mappings that decouple (HMAc) or couple (HMAq) the centroid and internal modes. The HMAq is constructed with normal mode coordinates (HMAq-NM) with quadratic scaling of cost or harmonic oscillator staging (HMAq-SG) coordinates with linear scaling. The estimator performance is examined for a 1D anharmonic oscillator and a 3D Lennard-Jones crystal using path integral molecular dynamics (PIMD) simulation. The HMA estimators consistently provide more precise estimates compared to CVir, with the best performance obtained by HMAq-NM, followed by HMAq-SG, and then HMAc. We also examine the effect of anharmonicity (for AO), intrinsic quantumness, and Trotter number. The HMA formulation introduced assumes the availability of forces and Hessian matrix; however, an equally efficient finite difference alternative is possible when these derivatives are inaccessible. The remarkable improvement in precision offered by HMAq estimators provides a framework for efficient PI simulation of more challenging systems, such as those based on ab initio calculations.

Fast high-order linearized exponential methods for efficient simulation of 2D time-fractional Burgers equation in polymer solution dynamics

Fast high-order linearized exponential methods for efficient simulation of 2D time-fractional Burgers equation in polymer solution dynamics

Simulation of spatially varying non-stationary seismic waves using random frequencies and non-uniform fast fourier transform

The simulation of spatially varying non-stationary seismic waves is a crucial consideration for the seismic analysis of engineering structures. The traditional approach often incurs substantial computational expenses and suffers from low efficiency due to the extensive implementation of large-scale matrix factorizations at numerous sampling points in both time and frequency domains. In this study, an enhanced approach is developed for the efficient simulation of non-stationary seismic waves. The core of this approach lies in the utilization of random frequency sampling coupled with time-domain interpolation to reduce matrix factorizations, and non-uniform fast Fourier transform for expediting the summation of trigonometric functions. A parametric analysis is conducted to investigate the effect of vital parameters, including the decoupling strategy of time-frequency spectrum, the number of random frequencies, and the number of interpolation knots over time, on both the efficiency and accuracy of the enhanced approach. Consequently, the preferred values for these parameters are determined. By considering the simulation of seismic waves acting on a group of bridges, the enhanced approach is validated in the view of correlation functions. The results demonstrate a high level of fidelity of the simulated seismic waves and proves the effectiveness of the enhanced approach in the efficient simulation of spatially varying non-stationary earthquake ground motions. The developed approach can be used for the simulation of non-stationary seismic waves for a wide range of engineering structures.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT

Objective.This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors.Approach.The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3×3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations.Main results.Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%and 31%across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%.Significance.The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Efficient Simulation of Inhomogeneously Correlated Systems Using Block Interaction Product States.

The strength of the density matrix renormalization group (DMRG) in handling strongly correlated systems lies in its unbiased and simultaneous treatment of identical sites that are both energetically degenerate and spatially similar, as typically encountered in physical models. However, this very feature becomes a drawback when DMRG is applied to quantum chemistry calculations for large, realistic correlated systems. This is because entangled orbitals often span broad ranges in both energy and space, with their interactions being notably inhomogeneous. In this study, we suggest addressing the strong intrafragment correlations and weak interfragment correlations separately, utilizing a large-scale multiconfigurational calculation framework grounded in the block interaction product state formulation. The strong intrafragment correlation can be encapsulated in several electronic states located on fragments, which are obtained by considering the entanglement between fragments and their environments. Moreover, we incorporate non-Abelian spin-SU(2) symmetry in our work to target the desired states we interested with well-defined particle number and spin, providing deeper insights into the corresponding chemical processes. The described method has been examined in various chemical systems and demonstrates high efficiency in addressing the inhomogeneous effects in strong correlation quantum chemistry.

Efficient simulations of Hartree-Fock equations by an accelerated gradient descent method

Efficient simulations of Hartree-Fock equations by an accelerated gradient descent method

An affine formulation of eigenstrain-based homogenization method and its application to polycrystal plasticity

Abstract Reduced order models (ROMs) are typically incorporated into concurrent multiscale approaches to allow for efficient nonlinear multiscale simulations and to alleviate high cost of direct nonlinear computational homogenization schemes. ROMs based on the ideas of transformation field analysis are among the most popular in the literature since they only require linear elastic simulations for model construction and typically have low number of degrees of freedom. However, these models have been shown to deliver overly stiff response in simulating wide range of materials. The present study focuses on mitigating this problem in the context of eigenstrain-based homogenization method (EHM) using instantaneous moduli information for polycrystal elastoviscoplasticity. For this purpose, a new EHM model is developed with the intention of using affine moduli for recomputation of the instantaneous localization tensors. The accuracy of the method is compared to the original EHM and direct crystal plasticity finite element simulations for several synthetic polycrystal microstructures, loading conditions and varying phase contrast. We show that the affine model delivers consistently softer response compared to the original EHM model. In particular, the affine model delivers notably more accurate response in the presence of high phase contrast. The affine EHM is able to capture local load redistribution through recomputation of the localization tensors.

A new approach for spatio-temporal interface treatment in fluid–solid interaction using artificial neural networks employing coupled partitioned fluid–solid solvers

Partitioned fluid–solid interaction (FSI) problems involving non-conforming grids pose formidable challenge in interface treatment, especially for information exchange, interface tracking, and field variable interpolation between solvers in both space and time. These demand special considerations for accurate and efficient simulations. This paper presents an application of artificial neural networks (ANN) for the interface treatment in a coupled FSI problem employing partitioned solvers. A shallow time-series ANN (nonlinear auto-regressive model with exogenous inputs, NARX) scheme is proposed to handle the exchange of Neumann/Dirichlet information at the coupling interface. This scheme involves two interface treatment models that were developed and analysed. The proposed models interpolate and transfer loads from the fluid to the solid domains, and conversely, displacements from the solid to the fluid domains between non-collocated grids. To validate this approach, we tested it on a 3D FSI problem, which involved damped oscillations of a flexible flap submerged in a fluid cavity. Adequately trained NARX interface models demonstrate reliable input–output mapping and accurate prediction of transient behaviour at the interface. Additionally, we explored the concept of reduced-order modelling (ROM) in the time domain. This allowed us to reduce the model’s complexity by half. Different training algorithms were evaluated to enhance the efficiency and performance of the proposed scheme. The study demonstrates that NARX networks trained with Bayesian Regularization (BR) and Levenberg–Marquardt (LM) algorithms exhibit the best accuracy, while the scaled conjugate gradient (SCG)-based training method provides better computational efficiency with acceptable accuracy. Overall, the NARX interface models provide precise performance and offer a viable potential for applications in FSI problems requiring accurate and faster computations.

Experimental prototype of mesh antenna based on digital twin technology

The ring mesh antenna plays an important role in the field of aerospace communication owing to its excellent performance characteristics, but faces the challenge of decreasing profile accuracy due to environmental and structural failures during in-orbit operation. Digital twin, a promising technology, can effectively monitor and adjust the antenna surface accuracy. In other words, by creating a digital model of the physical entity, real-time monitoring and adjustment can be made to maintain the electrical performance of the antenna. In this paper, a digital twin system for an experimental prototype of a ring mesh antenna is proposed. Firstly, we introduce the preliminaries of digital twin technology. Secondly, we propose a system framework based on a five-dimensional digital twin model. Then, a digitization software and a visualization interface are developed. Finally, according to efficiency analysis and simulation results, the feasibility of the system was verified. We demonstrate its effectiveness in terms of real-time monitoring and automatic mesh adjustment functions, and significantly improve the efficiency of the adjustment process. It provides a reference with significant value for the application of digital twin technology in the field of aerospace communication, which significantly advances the development of this field.

Propagation of Hydraulic Fractures and Natural Fractures: The Bypassing Behavior in 3D Space

Summary The interaction between natural fractures (NFs) and hydraulic fractures (HFs) in 3D space poses significant challenges to numerical simulation. The interaction behaviors between HFs and NFs in 3D space include crossing, offset, stopping, and bypassing. Many existing numerical simulators are 2D, which inherently limits their ability to account for the vertical growth of fractures. Consequently, they are unable to model the bypassing behavior effectively. In this paper, a mathematical model for the propagation of HFs and NFs in a naturally fractured reservoir is established. The displacement discontinuity method (DDM) is used to solve the rock deformation, while the finite difference method (FDM) is utilized to solve the fluid flow within fractures. The undirected graph structure is used to represent the complex fracture network, and a dynamic adjustment of grid connectivity method is implemented to describe the process by which HFs bypass NFs. By integrating with graphics processing unit (GPU) computing, an efficient 3D simulator for HFs and NFs propagation is developed. The accuracy is verified against analytical solutions and reference solutions. Subsequently, a series of numerical studies on the bypassing behavior are conducted. The primary findings are as follows: (1) The simulator can accurately capture the interactions between HFs and NFs in 3D space, including crossing, arresting, and bypassing behaviors. (2) The bypassing behavior is characterized by three distinct processes—intersection, height growth of HF, and bypassing. (3) During the intersection stage, both the injection pressure and maximum NF width increase. During the height growth stage, the pressure is relatively stable, while the maximum NF width continues to increase. These two stages together result in the “storage” phenomenon. Once the bypassing behavior occurs, both the pressure and the maximum NF width decrease sharply, leading to a “release” phenomenon. Additionally, the “storage” and “release” phenomena may impact proppant transport. (4) Given the presence of bypassing behavior, it is essential to consider the NFs in 3D fracture simulators. The simulator and its findings can provide valuable insights for field design.

DeepPipe: A Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network for Hydraulic Transient Simulation of Multi-Product Pipeline

In the chemical pipelining industry, owing to the high-pressure transportation process, an accurate hydraulic transient simulation tool plays a central role in preventing the slack line flow and overpressure from causing pipeline operation treacherous. Nevertheless, the current model-driven method often faces challenges in balancing computational efficiency with accuracy, and the existing data-driven models struggle to produce explainable results from the physics perspectives since insufficient theoretical principles are incorporated into the model training. Additionally, the existing physics-informed learning architecture fails to achieve a gradient-balanced training, resulting from the significant magnitude difference in outputs and multiple loss terms. Consequently, a Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network (MS-KE-PINN) is proposed for the hydraulic transient simulation of multi-product pipelines. To enforce the neural network producing simulation results with high consistency to physical laws, the governing equations, boundary, and initial condition are incorporated into the training process for an efficient mesh-free simulation. Then, considering that the significant magnitude difference between outputs can easily lead to deficient performance in the gradient descent, the magnitude conversion on the outputs and the equivalent conversion of the governing equations are implemented to enhance the training effect of the neural network. Subsequently, to tackle the imbalanced gradient of multiple loss terms with fixed weights, a multi-stage hierarchical training strategy is designed to improve the approximation capacity of the neural network. Numerical simulation cases demonstrate a better approximation function of the proposed model than the state-of-art models, while the mean absolute percentage errors yielded by MS-KE-PINN are reduced by 77.4 %, 88.7 %, and 87.8 % in three simulation operation conditions for pressure prediction. Furthermore, experimental investigations from a real-world multi-product pipeline suggest that the proposed model can still draw accurate simulation results even under complex and dynamic hydraulic transient scenarios in practice, with root mean squared errors reduced by 94.8 % and 80 % than that of the physics-informed neural network. To this end, the proposed model can conduct accurate and effective hydraulic transient analysis, thus ensuring the safe operation of the pipeline.

A novel method for rapidly simulating X-ray pulsar signals at a spacecraft

This paper addresses the challenges posed by the low photon flux and limited atmospheric penetration capabilities of X-ray pulsar signals, which are further exacerbated by technical, economic, and temporal constraints during in-orbit observations. These challenges result in a lack of continuous and high-fidelity data support for ground-based X-ray pulsar navigation (XPNAV) research and validation. To overcome these obstacles, we propose an innovative method for the efficient and rapid simulation of X-ray pulsar signals as received by spacecraft. Building on the physical process that, although the arrival times of the same pulsar signal differ between the spacecraft and the Solar System Barycenter (SSB), these times can be calibrated to the same phase at the SSB using a timing model, we leverage the relationship between the pulsar signal’s arrival rate at the SSB and its arrival phase. Using the Order Statistical Method (OSM), we directly and effectively generate the arrival phase series of pulsar signals received by the spacecraft, which follow a non-homogeneous Poisson process (NHPP). Subsequently, from these phase series, we derive the arrival time series of spacecraft-received pulsar signals, incorporating multiple physical effects based on a relationship model between arrival time series and phases. This method significantly reduces the required simulation time while maintaining high accuracy, thereby greatly enhancing simulation efficiency. Validation against data from the Rossi X-ray Timing Explorer (RXTE) and the Neutron Star Interior Composition Explorer (NICER) shows that the correlation coefficients between the accumulated profiles of simulated and measured data exceed 0.99. Furthermore, our method reduces the simulation time of high-flux pulsar signals by 25.7%, high spin frequency signals by 40.8%, and long observation duration signals by 25.38% compared to the latest fast simulation techniques, highlighting its advantages in accuracy, speed, and versatility for simulating pulsar signals across varying flux rates, spin frequencies, and observation durations. This approach provides continuous and effective data support for XPNAV research, significantly advancing theoretical and practical developments in the field.

The spectral representation method: A framework for simulation of stochastic processes, fields, and waves

The Spectral Representation Method (SRM) was developed in the 1970s to simulate Gaussian stochastic processes and fields from a Fourier series expansion according to the Spectral Representation Theorem. Since those early developments, the SRM has continuously evolved into a comprehensive framework for the simulation of stochastic processes, fields, and waves with a rigorous theoretical foundation. Its major advantages are conceptual simplicity and computational efficiency. In the 1990s, much of the theory for simulation of Gaussian stochastic processes, fields, and waves was firmly established and early methods for simulation of non-Gaussian processes, fields, and waves were introduced. In the 2000s and 2010s, methods that coupled the SRM with Translation Process Theory were improved to enable efficient and accurate simulations of stochastic processes, fields, and waves with strongly non-Gaussian marginal probability distributions. More recently, the SRM was extended for higher-order non-Gaussian processes, fields, and waves by extending the Fourier stochastic expansion to include non-linear wave interactions derived from higher-order spectra. This paper reviews the key theoretical developments related with the SRM and provides the relevant algorithms necessary for its practical implementation for the simulation of stochastic processes, fields, and waves that can be either stationary or non-stationary, homogeneous or non-homogeneous, one-dimensional or multi-dimensional, scalar or multi-variate, Gaussian or non-Gaussian, or any combination thereof. The paper concludes with some brief remarks addressing the open research challenges in SRM-based theory and simulations.

Surface Flux Transport Modeling Using Physics-informed Neural Networks

Studying the magnetic field properties on the solar surface is crucial for understanding the solar and heliospheric activities, which in turn shape space weather in the solar system. Surface flux transport (SFT) modeling helps us to simulate and analyze the transport and evolution of magnetic flux on the solar surface, providing valuable insights into the mechanisms responsible for solar activity. In this work, we demonstrate the use of machine learning techniques in solving magnetic flux transport, making it accurate. We have developed a novel physics-informed neural network (PINN)-based model to study the evolution of bipolar magnetic regions using SFT in one-dimensional azimuthally averaged and also in two dimensions. We demonstrate the efficiency and computational feasibility of our PINN-based model by comparing its performance and accuracy with that of a numerical model implemented using the Runge–Kutta implicit–explicit scheme. The mesh-independent PINN method can be used to reproduce the observed polar magnetic field with better flux conservation. This advancement is important for accurately reproducing observed polar magnetic fields, thereby providing insights into the strength of future solar cycles. This work paves the way for more efficient and accurate simulations of solar magnetic flux transport and showcases the applicability of PINNs in solving advection–diffusion equations with a particular focus on heliophysics.

Exploring Free Energy Landscapes for Protein Partitioning into Membrane Domains in All-Atom and Coarse-Grained Simulations.

It is known that membrane environment can impact the structure and function of integral membrane proteins. As such, elucidation of the thermodynamic driving forces governing protein partitioning between membrane domains of varying lipid composition is a fundamental topic in membrane biophysics. Molecular dynamics simulations provide valuable tools for quantitatively characterizing the free energy landscapes governing protein partitioning at the molecular level. In this study, we propose an efficient simulation methodology for the calculation of free energies for the partitioning of transmembrane proteins between liquid-disorder (Ld) and liquid-ordered (Lo) domains in all-atom (AA) phase-separated lipid bilayers. The computed potential of mean force defining the equilibrium partition coefficients is compared for AA and coarse-grained systems. Energy decomposition is used to identify differences in the underlying thermodynamics. Our findings highlight the importance of employing AA models to accurately estimate relevant free energy changes during protein translation between membrane domains.

Reduced-order peridynamics for efficient simulation of fracture in a turbine blade root

This paper introduces an adaptive partitioned reduced-order model (ROM) of peridynamics for the efficient simulation of fracture in a gas turbine blade root. The computational domain is divided into an ROM subdomain and a full-order model (FOM) subdomain. The subdomains adaptively evolve to ensure that the FOM domain covers the fractured area. The proposed model is used to investigate the failure of a gas turbine blade root under static and fatigue loads, respectively. The simulation results coincide well with the actual fracture morphology. In addition, in the three-dimensional case, the computational efficiency can be improved by approximately 300 times

An object-oriented model of the invasion and eradication of Poa annua L. on King George Island, Antarctica

We implemented a custom object-oriented model to predict the spread of annual bluegrass population at Point Thomas Oasis and to evaluate potential eradication outcome at different eradication success rates. We modeled unobstructed population growth without eradication, sole elimination of plants and removal of plants together with soil within 10cm around each specimen. Both eradication scenarios considered removal efficiency of 50 – 100%. The population with no elimination campaign applied, was still in the exponential phase of growth after 45 simulated years and almost fulfilled the study area. The removal efficiency of 50% sufficed to control the species, maintaining the population number at a stable and low level. Once the removal action ceased, the population number increased dramatically. 100% elimination efficiency for 20 years did not guarantee eradication, as leaving even five or less seeds in the soil could initiate population reestablishment. Soil removal within 10cm around each plant speeded up the elimination. The predicted duration of eradication process to reach 30 or less seeds in the soil, was around 20 years. The removal of annual bluegrass is obstructed by the soil seed bank. The most important parameter driving eradication success is eradication efficiency. Low removal efficiency simulation indicates that poor management, even for a few years, may be amended if the eradication efficiency increases. Our model is transferable for other potential invasive species at Point Thomas Oasis by changing dispersal and germination parameters. Its transferability for other locations would require changing the modeled space.

A physics knowledge-based surrogate model framework for time-dependent slope deformation: Considering water effect and sliding states

The surrogate model serves as an efficient simulation tool during the slope parameter inversion process. However, the creep constitutive model integrated with dynamic damage evolution poses challenges in development of the required surrogate model. In this study, a novel physics knowledge-based surrogate model framework is proposed. In this framework, a Transformer module is employed to capture strain-driven softening-hardening physical mechanisms. Positional encoding and self-attention are utilized to transform the constitutive parameters associated with shear strain, which are not directly time-related, into intermediate latent features for physical loss calculation. Next, a multi-layer stacked GRU (gated recurrent unit) network is built to provide input interfaces for time-dependent intermediate latent features, hydraulic boundary conditions, and water-rock interaction degradation equations, with static parameters introduced via external fully-connected layers. Finally, a combined loss function is constructed to facilitate the collaborative training of physical and data loss, introducing time-dependent weight adjustments to focus the surrogate model on accurate deformation predictions during critical phases. Based on the deformation of a reservoir bank landslide triggered by impoundment and subsequent restabilization, an elasto-viscoplastic constitutive model that considers water effect and sliding state dependencies is developed to validate the proposed surrogate model framework. The results indicate that the framework exhibits good performance in capturing physical mechanisms and predicting creep behavior, reducing errors by about 30 times compared to baseline models such as GRU and LSTM (long short-term memory), meeting the precision requirements for parameter inversion. Ablation experiments also confirmed the effectiveness of the framework. This framework can also serve as a reference for constructing other creep surrogate models that involve non-time-related across dimensions.