Current Simulation Research Articles

Research status of simulation modeling technology for the 3D braiding process

Three-dimensional (3D) braiding has excellent mechanical properties due to its unique spatial organization, in which simulation modeling of the braiding process is a key technology to study the complex spatial structure of 3D braided fabrics. The paper first summarizes the research methods of 3D braiding simulation modeling technology in recent years; second, it focuses on the analysis of numerical method modeling based on geometric dynamics, geometric modeling based on the braiding process, and modeling based on the finite-element method; then, based on the existing technology and application status quo, it sums up the advantages and shortcomings of the current 3D braiding process simulation modeling technology; and, finally, it describes the challenges and opportunities of the 3D braiding simulation modeling technology. The future development direction of the three modeling methods and the new idea of mutual coupling of the three methods are proposed and future development prospects are described. The paper provides technical support for the simulation modeling of the braiding process in 3D braiding technology.

Real-time scattering in the lattice Schwinger model

Tensor network methods have demonstrated their suitability for the study of equilibrium properties of lattice gauge theories, even close to the continuum limit. We use them in an out-of-equilibrium scenario, much less explored so far, by simulating the real-time collisions of composite mesons in the lattice Schwinger model. Constructing wave-packets of vector mesons at different incoming momenta, we observe the opening of the inelastic channel in which two heavier mesons are produced and identify the momentum threshold. To detect the products of the collision in the strong coupling regime we propose local quantitites that could be measured in current quantum simulation platforms. Published by the American Physical Society 2025

Numerical Modeling and Analysis of Shadow Flicker Using Solar Path Functions for Enhanced Predictive Accuracy

Shadow flicker caused by wind turbine blades passing through sunlight can significantly affect nearby residential buildings, raising environmental and regulatory concerns in wind farm development. The accurate assessment of shadow flicker exposure is critical for compliance and minimizing community impacts. We present a novel method for accurately determining the exposure of shadow flicker from wind turbines on residential buildings, addressing a key regulatory concern in wind farm planning. Current simulation techniques rely on discrete sampling of solar positions, resulting in potential inaccuracies tied to sampling resolution. Our proposed approach models shadow flicker as a continuous function and applies numerical minimization and numerical root finding to compute the duration of exposure. Our evaluation proves that this method achieves a superior balance between precision and computational efficiency, significantly improving existing techniques.

Persistent Current Simulation for CCT Testing Magnet Used in EIC

Persistent Current Simulation for CCT Testing Magnet Used in EIC

Origins of whole-building energy simulations for high-performance commercial buildings: Contributions of the 1968 and 1971 ASHRAE algorithms for weighting factor-based hourly heating and cooling load calculations

This study analyzes the 1968 and 1971 ASHRAE LOADS algorithms and clarifies their significance to the field of building energy simulation programs that first provided publicly available weighting factor-based hourly heating and cooling load calculation procedures and first proposed the concept of precalculated WFs. In addition, this study also finds their significant contributions to developments of many of the first generation of weighting factor-based building energy simulation programs in the 1970’s, which became the forerunners to current whole-building energy simulation programs, such as the eQUEST, DOE-2.1E, TRACE 3D Plus, and HAP programs. Therefore, this study fills a gap in the literature regarding the origins of today’s whole-building energy simulation programs. In the related work (Ahn and Haberl 2024), contributions of the 1975 ASHRAE LOADS algorithms for heat balance-based hourly heating and cooling load calculations are discussed. These two studies extend the previous research (Ahn and Haberl 2023) that analyzed the origins of today’s whole-building energy simulation programs.

WIMS : A Modern Web-Based MIPS Simulator for Improved Learning in Computer Architecture and Operating Systems

The MIPS processor is well-regarded in teaching Computer Architecture and Operating Systems due to its simpler architecture and efficient design. Simulators are a highly effective method for learning MIPS. Current simulators, like MARS, face issues such as lack of data path visualization, outdated interfaces, and installation requirements, which impede student learning. This work aims to develop an accessible online MIPS simulator featuring a modern code editor, data path visualization, step-by-step execution, and memory visualization. The simulator runs in a web browser, eliminating installation barriers and enhancing usability. This tool aims to improve the educational experience and deepen students’ understanding of MIPS architecture and assembly programming.

Steering spin fluctuations in lattice systems via two-tone Floquet engineering

Abstract We report on the controlled creation and destruction of antiferromagnetic dimers using two-tone Floquet engineering. We consider a one-dimensional spin-1/2 lattice with periodically modulated bonds using parametric resonances. The stroboscopic dynamics generated from distributed bond modulations lead to pair correlation between spins. Consequently, subharmonic response in local observables breaks discrete time translational symmetry and leads to the emergence of Floquet dynamical dimerisation. We present a protocol allowing the control of local spin-correlated pairs driven by one-period evolution operators, providing significant insight into new nonequilibrium states of matter that can be feasibly implemented in current quantum simulator platforms.

Simulation of High-Voltage Cable Sheath Current with a Panoramic Digital Tunnel Model

Monitoring cable sheath current is crucial for guaranteeing the secure and efficient operation of high-voltage cable tunnels. In this study, a three-dimensional (3D) panoramic digital model of a tunnel in Beijing is modeled, a parallel cable sheath current simulation model is proposed, and a 3D panoramic visualization system with which the cable sheath current can be simulated online is developed in order to generate early warnings on the sheath current situation. The accuracy of the simulation model is verified and the results are as follows: The difference between the simulated sheath current and the actual measurement is within a maximum margin of 3.71%. A phase sequence optimization strategy can be obtained after calculations based on the simulation model, and an average sheath current reduction of 47.2% can be achieved following the optimization strategy. This study introduces a new approach by which cable sheath current dynamics and real-time warning against anomalies are visualized on a 3D tunnel model. In addition, a sheath current simulation model is integrated in the 3D visualization system developed in this study, and phase sequence optimization strategies for cables with abnormal sheath currents are automatically suggested, which may provide scientific support for auxiliary decision-making in real-time cable operation.

An efficient q-procedures to solve q-generalized quintic complex Ginzburg-Landau equations

Abstract In this work, q-Residual power series method (q-RPSM) is extended for the first time to solve q-generalized quintic complex Ginzburg-Landau (q-GCGL) equations. In 2019, O. A. Arqub [39], has investigated the solutions of time fractional Schrödinger equations without q-derivative by using RPSM. However, in this article, the RPSM procedure is extended to non-linear q-fractional partial differential
equations and thus some useful results are obtained under the definition of q-derivative, q-gamma func tion, q-Caputo derivative and q-factorial function. The solutions to few numerical examples under the q-Caputo operator are presented. In the current procedure, the targeted q-problems are first converted into system of two non-linear q-fractional partial differential equations and then solved by using q-RPSM. In particular, we considered two illustrative examples of fractional q-GCGL for analytical solutions using q-RPSM. The numerical simulations for the suggested numerical problems are done successfully. Various graphs such as Figures 1-12 are provided to compare the q-RPSM results with the exact solutions of the proposed problems. In this connection, Figures 1 and 4 show the 3D plot comparison between q-RPSM and exact solution of the 1st numerical example for œ and q=1. Figures 2 and 5 illustrate the q-graph
for various values of q and τ . In Figures 3 and 6, we discussed the fractional-solution graphs for different values of œ = 1, 0.7 and 0.5 at fix values of q=0.5, τ = 1 and 0.5. It is also investigated that higher terms series solution have the higher degree of accuracy. In the current q-RPSM simulations, the higher accuracy is achieved by taking sixth terms series solution. The fractional-order solutions are convergent toward integer-order solution as the fractional-order approaches to integer-order. The solutions curve at different q-values is also calculated and obtain the useful dynamics of the targeted problems. The q-analogy can be used effectively in the field of quantum physics and thus keep the higher norm for researchers working in the field.

The hot circumgalactic medium in the eROSITA All-Sky Survey. III. Star-forming and quiescent galaxies

The galaxy population shows a characteristic bimodal distribution based on the star formation activity and is sorted into star-forming or quiescent. These two subpopulations have a tendency to be located in different mass halos. The circumgalactic medium (CGM), as the gas repository for star formation, might contain the answer to the mystery of the formation of such bimodality. Here we consider the bimodality of the galaxy population and study the difference between the properties of the hot CGM around star-forming and quiescent galaxies. We used the X-ray data from the first four SRG/eROSITA all-sky surveys (eRASS:4). We selected central star-forming and quiescent galaxies from the Sloan Digital Sky Survey DR7 with stellar mass $10.0< or halo mass $11.5< 200m /M_ within spectroscopic redshift spec <0.2$, and we built approximately volume-limited galaxy samples. We stacked the X-ray emission around star-forming and quiescent galaxies, respectively. We masked detected point sources and carefully modeled the X-ray emission from unresolved active galaxy nuclei (AGN) and X-ray binaries (XRB) to detect the X-ray emission from the hot CGM. We measured the X-ray surface brightness ($S_ X, CGM $) profiles and integrated the X-ray emission from hot CGM within $R_ 500c $ ($L_ X, CGM $) to provide the scaling relations between $L_ X, CGM $ and galaxies' stellar or halo mass. We detect extended X-ray emission from the hot CGM around star-forming galaxies with $ and quiescent galaxies with $ extending out to 500c $. The $S_ X, CGM $ profile of quiescent galaxies follows a beta model with $ 0.4$, where beta quantifies the slope of the profile. Star-forming galaxies with median stellar masses $ *,med /M_ = 10.7, 11.1, 11.3$ have X, CGM erg/s$, while for quiescent galaxies with $ *,med /M_ X, CGM erg/s$. Notably, quiescent galaxies with $ *,med /M_ > 11.0$ exhibit brighter hot CGM than their star-forming counterparts. In halo mass bins, we detect similar X-ray emission around star-forming and quiescent galaxies with $ 200m /M_ > 12.5$, suggesting that galaxies in the same mass dark matter halos host equally bright hot CGM. We emphasize that the observed X, CGM - M_ 500c $ relations of star-forming and quiescent galaxies are sensitive to the stellar-to-halo mass relation (SHMR). A comparison with cosmological hydrodynamical simulations (EAGLE, TNG100, and SIMBA) reveals varying degrees of agreement, contingent on the simulation and the specific stellar or halo mass ranges considered. Either selected in stellar mass or halo mass, the star-forming galaxies do not host brighter stacked X-ray emission from the hot CGM than their quiescent counterparts at the same mass range. The result provides useful constraints on the extent of feedback's impacts as a mechanism for quenching star formation as implemented in current cosmological simulations.

Improving accuracy of parametric surrogate model for turbidity currents using diffusion-based super-resolution

Turbidity currents are a sort of density-driven flow carrying particles that are generated between fluids with small density differences. They also are a mechanism responsible for the deposition of sediments on a seabed. A deep understanding of this phenomenon may help geologists on strategic knowledge in oil exploration. We simulate such currents using a stabilized finite element formulation in a Eulerian-Eulerian framework.This brings the challenge of a high computational cost for the evaluation of the high-fidelity model. We thus use a surrogate model composed of one linear (Proper Orthogonal Decomposition) and one non-linear (Autoencoder) reduction [1,2]. Once the surrogate is trained for a set of parameters with data generated by the high-fidelity model, one can use such a surrogate model for predicting the dynamics for unseen values of the parameter.Denoising Diffusion Probabilistic Models (DDPM) [3] is the method behind SOTA generative AI algorithms with very good performance in tasks like super-resolution. Based on such usage of diffusion for generative AI, the authors in [4] developed a framework for flow field reconstruction using a super-resolution task. Such a diffusion model is trained only using high-resolution data, without a pairing between low and high-resolution data, common in other techniques in the area.The present work aims to use the dataset of high-fidelity parametric simulations of turbidity currents for training both the surrogate and the diffusion model for the super-resolution task. The goal is: using the trained super-resolution model, improve the fields predicted by the reduced model for unseen points of the parametric space.[1] - M. Cracco et al., “Deep learning-based reduced-order methods for fast transient dynamics”, Arxiv Preprint 2212.07737, 2022.[2] - S. Fresca and A. Manzoni, “POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition”, Comput. Methods Appl. Mech. Engrg., 2022.[3] - J. Ho, A. Jain and P. Abbeel, “Denoising Diffusion Probabilistic Models”, arXiv: 2006.11239, 2020.[4] - D. Shu, Z. Li, A. Barati Farimani, “A physics-informed diffusion model for high-fidelity flow field reconstruction”, Journal of Computational Physics, Volume 478, 2023

Eliminating Inrush Current in Three-Phase Transformer using Artificial Neural Network

Transformers are important parts of an electrical power system. When a power transformer is connected to the grid, usually inrush current increases substantially with a high value of harmonic components with a duration up to many cycles. The amount of flux in the core increases causing the magnetic circuit to saturate due to the increasing in the load. This paper describes a technique to accurately predict the inrush current and third harmonic of three phase transformer. A shallow neural network was created. The input parameters of the artificial neural network were the magnetization resistance Rm, the initial flux of phase A and the switching angle q. The number of neurons has been changed in the code to see the best performance value. The best validation performance was at epoch 71 with a value of 5.3641e-05. A good prediction results were obtained using this ANN. The simulation of the inrush current was done using the MATLAB Simulink software.

Insights of Dynamic Forcing Effects of MJO on ENSO from a Shallow Water Model

Abstract The Madden–Julian oscillation (MJO) is believed to play a significant role in triggering El Niño–Southern Oscillation (ENSO) events and affect the dynamics of ENSO. In this study, the dynamic forcing effects of MJO on the equatorial oceanic dynamic fields and the onsets of different types of ENSO events are investigated through sensitive experiments using spatiotemporally filtered forcing based on an anomalous shallow water model. The comparisons between observations and model responses provide meaningful insights into the extent of MJO’s impacts on sea surface dynamics relative to other atmospheric variabilities. The following conclusions are made. First, the MJO-forced perturbations on zonal currents are stronger and more significant than those on sea surface heights. Second, MJO is essential for improving zonal current simulation in the western-central Pacific and generating activity centers of zonal currents in the eastern Pacific in the model. Third, MJO tends to contribute to the onset of El Niño events rather than La Niña events. Strong intraseasonal oceanic Kelvin waves forced by MJO are confirmed in simulations during the onset stages of the 1997/98 and 2004/05 events. The 120-day running standard deviations of zonal current and sea surface height anomaly series forced by MJO exhibit positive skewness similar to those of the 20–100-day band-passed observational series. Yet, not all the onsets of historical ENSO events are in company with strong MJO-related perturbations. Additionally, the wind stress formula can amplify the responses of zonal current and sea surface height anomalies to synoptic forcings with periods shorter than 20 days through entraining lower-frequency variabilities. Significance Statement The Madden–Julian oscillation (MJO) is believed to be able to trigger El Niño–Southern Oscillation (ENSO) events and influence our understanding of the fundamental nature of ENSO. In this study, spatiotemporally filtered forcing experiments are implemented on an anomalous shallow water model. The results show that MJO is more important for improving the simulation of surface zonal currents rather than the sea surface heights and tends to contribute to the onset of El Niño events rather than La Niña events through triggering strong intraseasonal oceanic Kelvin waves.

Capacity for Alzheimer’s Disease confirmatory testing in the US ‐ current situation and simulations for future increase

Capacity for Alzheimer’s Disease confirmatory testing in the US ‐ current situation and simulations for future increase

Analytical solutions for lined noncircular tunnels in deep ground considering hydromechanical coupling

This study provides a general analytical approach to predict the stresses and displacements of deep noncircular tunnels excavated in saturated elastic ground, considering the hydro-mechanical coupling, the interaction between rock and liner, and arbitrary tunnel shapes. The complex variable theory is innovatively extended to solve the seepage fields for noncircular supported tunnels in both surrounding rocks and liner fields. After that, coupled hydro-mechanical solutions of stresses and displacements can be determined by considering the influence of seepage flow on the mechanical responses. Furthermore, stability analyses of supported tunnels are conducted in the hydro-mechanical coupled framework, incorporating the effect of seepage flow. As a verification, a good agreement is observed between stress and displacement fields obtained from the current analytical solutions and numerical simulations. Then, parametric analyses are performed to assess how variations in tunnel shape, liner permeability, and liner thickness influence the mechanical behaviours of supported tunnels. Meanwhile, by employing the Mohr–Coulomb strength criterion, an expression of equivalent stress is proposed to assess the potential failure behaviour of supported tunnels in the entire process of excavation and operation stages. This study provides high potential of application to a number of relevant case studies, such as tunnelling below the water table.

Rheology of polyacrylamide-based fluids and its impact on proppant transport in hydraulic fractures

Rheological experiments and measurements of particle settling velocity under static conditions were conducted for two types of polyacrylamide (PAA-based) fluids. The flow behavior of the viscoelastic fluids is described by a simplified, three-parameter model that integrates a constant viscosity at low shear rates with a power-law viscosity dependency at higher shear rates. The estimated dependencies of the rheological parameters and particle settling velocity on PAA concentration align with the classical power-law scaling for semi-dilute polymer solutions. The identified scaling offers valuable insight for optimizing the chemical composition of fracturing fluids, thereby improving the efficiency of hydraulic fracturing technology. By applying the lubrication approximation to the Navier–Stokes equations, a set of equations for the suspension flow in a vertical plane channel, characterized by the three-parameter rheology model, was derived. In typical fracturing conditions, the conventional power-law viscosity model used in current fracturing simulators significantly overestimates the gap-averaged apparent fluid viscosity compared to the proposed model. The novel model of the suspension flow aims to enhance the accuracy of proppant transport models applied in hydraulic fracturing simulators. Based on the three-parameter rheology model, a new model of particle settling in the studied PAA-based fluids is developed. It is based on smart Stokes' law, where viscosity is calculated according to either the plateau or power-law region, depending on the self-induced shear rate. The new model more accurately approximates experimentally determined settling velocity of proppant under static conditions across a broad range of PAA concentrations than existing models.

Process-Based Evaluation of Intraseasonal Oceanic Kelvin Waves in the Pacific Ocean in CMIP6 Models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models. Significance Statement Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.

Study on predicting the stability of penetrating projectile charges via machine learning methods

Abstract Due to the limitations of current theoretical research, numerical simulation, and experimental analysis for predicting the stability of penetrating projectile charge, in this work, we propose a method to predict the stability of charges by using machine learning methods. Three models are selected, including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost). A practical sample dataset with inputs of the charge type, temperature and speed, and outputs of whether the charge is stabilized or not is constructed to train the model. The results indicate that using machine learning methods to predict the stability of charges is a feasible research direction.

Hydrodynamic characteristics of cavity fluctuation behind a cone-rod assembly entering water

This study explores the phenomenon of cavity fluctuation occurring behind a cone entering water at a constant velocity. The current simulations reveal that cavity fluctuations arise following deep pinch-off, leading to pronounced pressure oscillations in both the water and air regions. Concurrently, ripples form along the cavity surface, extending from the nose to the tail, resulting in a wavy cylindrical cavity. Notably, when the water entry Froude number is below 10, the load on the cone is predominantly due to pressure oscillations induced by cavity fluctuations, which exceed the slamming load experienced during initial water impact. The study also identifies a significant impact of an attached rod on cavity evolution. Specifically, the frequency of cavity rippling increases with the rod's radius; however, when the rod-to-cone radius ratio is less than 20%, the rod's impact on the cavity dynamics becomes negligible. A theoretical analysis, modeling the cavity as a hollow cylindrical structure, is developed to elucidate the relationship between rippling frequency and rod size. The research results demonstrate that the cavity fluctuation frequency is inversely proportional to the difference in the squared radii of the cone and rod. Furthermore, when the scaling length of the cavity at the pinch-off moment exceeds a ratio of Lp/Rc > 6, the water entry cavity can be accurately modeled as a long cylindrical cavity. The numerical results confirm that the proposed theoretical model provides reliable predictions of the impact of a solid rod on the fluctuation characteristics of the cavity.

A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model

Current simulation models considerably underestimate local-scale, short-duration extreme precipitation induced by tropical cyclones (TCs). This problem needs to be addressed to establish active response policies for TC-induced disasters. Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. The TCPM-ML was applied for multiple temporal scale hazard assessment. The results show that: (1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations, but also preserved the physical meaning of the results, contrary to ML methods; (2) Machine learning algorithms enhanced the TCPM ability to reproduce observations, although the hourly extreme precipitations remained slightly underestimated; (3) Best performance was obtained with the XGBoost or EL algorithms. Combining the strengths of both XGBoost and RF, the EL algorithm yielded the best overall performance. This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.