Simulation Framework Research Articles

Influence of Ice Growth Mode on the Ice Thickness and Shape Prediction of Two-Dimensional Airfoil

Computational results of aircraft icing and predictions of ice shape are not only determined by the solutions of air-supercooled droplet two-phase flow and icing thermodynamic models of surface water film, but are also influenced by the growth mode of the ice layer. Two ice growth modes were established in a two-dimensional (2D) icing process simulation framework to calculate the ice thickness and ice shape, depending on whether surface deformation of the icing process was considered. Ice accretion simulations were performed with the two ice growth modes for an NACA0012 airfoil under rime ice and mixed ice conditions, and the results of ice amount, ice thickness, and ice shape were compared and analyzed. Under the same amount of ice formation, the ice thickness and ice shape obtained using different ice growth modes vary. The ice thickness and the ice shape size are relatively large without considering surface deformation, whereas the results with growth correction show a certain degree of reduction, which is more noticeable around the leading edge and the ice horns. However, the degrees of difference in ice thickness and ice shape are not the same, and the deviation in ice thickness is more obvious. Furthermore, the ice thickness and ice shape obtained using the ice growth correction mode are more consistent with experimental data and commercial software results, verifying the accuracy of the ice simulation method and the necessity of considering ice surface deformation. This paper is an essential guide for understanding the icing mechanism and accurately predicting two-dimensional ice shape.

A Maneuver Coordination Analysis Using Artery V2X Simulation Framework

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS).

Shale pore pressure seismic prediction based on the Hydrogen generation and compaction-based rock physics model and Bayesian Hamiltonian Monte Carlo inversion method

The seismic prediction of formation pore pressure is critically important for the exploration and appraisal of shale oil and gas reservoirs, as well as for identifying optimal drilling targets within these formations. In particular, the complex pressure regimes of deep shale reservoirs introduce substantial challenges to traditional seismic prediction methodologies. A Bayesian Hamiltonian Monte Carlo pre-stack seismic inversion approach is proposed to enhance the accuracy of pore pressure estimations based on an advanced rock physics model that integrates compaction and hydrocarbon generation effects. Initially, a comprehensive petrophysical model is developed to account for the effects of hydrocarbon generation and compaction, establishing a robust framework for the normal compaction trend simulation. Subsequently, a novel formation pressure prediction model is derived based on integrating the Eaton model and the Bower stress hypothesis. Then, the Bayesian HMC seismic inversion method is proposed to predict formation pore pressure in shale reservoirs with elastic impedance data set. The proposed methodology is validated through application to actual well log and seismic data from the Fuling shale oil and gas reservoirs in the Sichuan Basin, which demonstrates significant potential of this method for enhancing the prediction of pore pressure in complex geological settings.

An intelligent simulation result validation method based on graph neural network

With the rapid development of modeling and simulation, there has been a growing concern about the credibility of complex simulations. To validate complex simulation models or systems with dynamic and correlated outputs, an intelligent simulation result validation method based on graph neural network (GNN) is presented. A framework for simulation result validation is proposed, illustrating the process divided into three parts: graph structure modeling for validation data, feature extraction based on graph representation learning (GRL), and bayes factor (BF)-based model’s credibility assessment. A graph structure modeling method is introduced to provide a predefined graph structure for subsequent GRL primarily. Next, the interdependencies and dynamic evolutionary patterns among variables are captured by a Multi-level Feature Extraction-based Graph Representation Learning (MFEGRL) model. The similarity of the graph representations is then compared based on the BF to determine the model’s credibility. Finally, the effectiveness of this method is demonstrated through a case study focusing on validating simulation models about the terminal guidance stage of a flight vehicle.

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

Simulating atherosclerotic plaque mechanics using polyvinyl alcohol (PVA) cryogel artery phantoms, ultrasound imaging and inverse finite element analysis.

The aim of this study is to demonstrate the potential of polyvinyl alcohol phantoms to simulate atherosclerotic features. In addition, a testing and simulation framework is developed for full PVA vessel material characterization using ring tensile testing and inflation testing combined with non-invasive ultrasound imaging and computational modelling. Strain stiffening behavior was observed in PVA through ring tensile tests, particularly at high (n=6) freeze-thaw cycles. Inflation testing of bi-layered phantoms featuring lipid pool inclusions demonstrated high strains at shoulder regions. The application of an inverse finite element framework successfully recovered boundaries and determined the shear moduli for the PVA wall to lie within the range 27 kPa to 53 kPa. The imaging-modelling framework presented facilitates the use and characterization of arterial mimicking phantoms to further explore plaque rupture. It also shows translational potential for non-invasive mechanical characterization of atherosclerotic plaques to improve the identification of clinically relevant metrics of plaque vulnerability.

Research on deformation prediction of CFETR multi-purpose overloaded robot based on real-time structural simulator

The CFETR Multi-Purpose Overload Robot (CMOR) is a key subsystem of the China Fusion Engineering Test Reactor (CFETR), which can perform maintenance tasks of the internal components. However, the large slenderness ratio of the structure results in low control accuracy of the CMOR end-effector. This paper proposes a CMOR deformation prediction method based on the real-time structural simulator. The overall deformation model of CMOR is analyzed based on the single joint deformation mechanism. Based on the principle of layered control, the control framework of the CMOR structural simulator is constructed, and the deformation data of CMOR in different positions are calculated based on the finite element method. A hybrid neural network containing a multilayer perceptron, transformer, and attention mechanism is designed to train the CMOR deformation prediction model. The training results show that the deformation prediction model converges quickly and fits the deformation of the CMOR structure well with small prediction errors. Finally, the real-time structure simulator is developed based on the deformation prediction model, and the deformation of CMOR is reconstructed with the update frequency of 2 Hz and the absolute error at any point within ±10 mm, which verifies the correctness of the CMOR structural deformation prediction method.

A Smoothed Particle Hydrodynamics framework for fluid simulation in robotics

A Smoothed Particle Hydrodynamics framework for fluid simulation in robotics

An investigation on the similarity theory framework for CFD-DEM simulation in a stirred liquid bath containing particles

An investigation on the similarity theory framework for CFD-DEM simulation in a stirred liquid bath containing particles

Simulating hydrogen-controlled crack growth kinetics in Al-alloys using a coupled chemo-mechanical phase-field damage model

Environmentally Assisted Cracking (EAC) of 7xxx series aluminium alloys involves interactions between multiple physical phenomena, which ultimately influence the in-service life of critical components. In this work, we present a new model to study EAC in 7xxx series alloys, which is implemented in the multiphysics simulation framework, DAMASK. The chemo-mechanical model couples crack tip hydrogen generation, resulting from surface oxidation, and transport, with crystal-plasticity-governed intergranular crack propagation, through the microstructural trapping of hydrogen at dislocations, grain boundaries (GB), and crack tip stress fields. Large-scale simulations with realistic grain structures have been performed to provide novel insight into the dominant rate-controlling processes associated with intergranular EAC in 7xxx series aluminium alloys. The model was able to reproduce experimentally measured crack velocities under different loading conditions. Parametric studies indicate that, in addition to the GB network morphology, the crack growth rate was controlled by hydrogen generation at the crack tip with long-range diffusion having negligible influence. Additionally, the total hydrogen generated through crack tip oxidation appears to be more significant than the peak generation rate.

Performance Analysis of a Water-Injected Twin-Screw Compressor in a High-Temperature R718 Heat Pump

Abstract High-temperature heat pumps with twin-screw compressors and water (R718) as the working fluid show promising potential for providing industrial heat and process steam at temperature levels of up to 200 °C. This paper presents a simulation approach for a two-layered simulation structure of a water-based high-temperature heat pump system including a twin-screw compressor with water-injection. The heat pump process with two-phase steam compression is investigated with respect to various aspects. In addition to controlling the compressor outlet temperature, the evaporation of the injected liquid leads to an increase in the displaced mass flow rate on the sink side of the heat pump. This effect is analyzed in detail for various system operating points using a thermodynamic simulation framework for the R718-based heat pump cycle and a comprehensive compressor model. The main target of the presented paper is the analysis of system performance and the detailed investigation of the interdependencies. Performance of the twin-screw compressor is determined by means of two-phase chamber model simulations. In the chamber model method thermodynamic properties of both injected liquid and steam are calculated based on the conservation equations of mass and energy for the compression process. Adequate sub-models for internal leakage and heat and mass transfer between vapor and liquid phase are applied. Integral results of the compressor simulations are embedded in the heat pump system simulation using a characteristic map. In conclusion, this paper contributes to the understanding of advanced compression technologies in the field of high-temperature heat pumps, offering a detailed examination of a water-injected steam compressor’s applicability and efficiency in a practical system setting.

A downscaling framework with WRF-UCM and LES/RANS models for urban microclimate simulation strategy: Validation through both measurement and mechanism model

Microscale numerical simulation models are widely applied to explore potential factors and adaptive strategies for localized high temperatures in urban surface or near-surface environments. However, few studies address the limited availability of meteorological input data and the use of multiple meteorological outputs to investigate the mechanisms between factors as a theoretical verification for simulation. This study used the WRF-UCM model outputs in Tianjin, China, as the basic background meteorological field for microclimate simulation and compared the improvement in simulation accuracy of LES-based scheme (PALM-4U) and RANS-based software (ENVI-met) in predicting pedestrian-level air temperature and relative humidity during the downscaling simulation. Subsequently, attribution analysis of land surface temperature imbalance is performed using the two-resistance model (TRM) based on surface and atmospheric simulation outputs which also aids in verifying the applicability of the one-way downscaling simulation framework. It is found that the WRF-UCM-RANS framework exhibits superior overall performance, reducing the error in 2-m height relative humidity by approximately 50 % at the same location compared to mesoscale results. The attribution results indicate that localized high temperature on impervious surfaces within urban neighborhood are primarily driven by surface resistance (rs) during the daytime heating process and ground heat storage (G) during nighttime cooling. However, surface resistance (rs) remains the dominant driving factor influencing land surface temperature throughout both daytime and nighttime. The framework reduces the challenge of obtaining initial meteorological data and provides technical support for expanding microclimate research to multi-site simulations and future scenario predictions in complex urban environment.

A Comprehensive Analysis of the Impact of an Increase in User Devices on the Long-Term Energy Efficiency of 5G Networks

The global deployment of fifth-generation (5G) mobile networks, especially in urban cities, is dedicated to accommodating the demand for high data rates and reliable wireless communications. While the latest 5G networks improve service quality, the support for a simultaneous serving of more user devices (UDs) with higher data rates than previous mobile network generations will require a massive installation of different 5G base station (BS) types dominantly in urban cities. Besides contributing to the smart city service improvements, this massive installation of heterogeneous 5G BSs will also contribute to the increase in 5G network energy consumption (EC) and carbon dioxide emissions. Since this increase in installed 5G BSs imposes environmental and economic challenges, this paper analyzes the impact of the continuously rising number of 5G UDs on the energy efficiency (EE) of the radio part of Croatian and Dutch 5G networks as example cases in the period of 2020s. Analyses consider the countries’ rural, suburban, urban, and dense urban UD density areas by utilizing the proposed simulation framework for the EE evaluation of 5G heterogeneous networks (HetNet) valued through standardized mobile networks EE metrics. The study examines four proposed BS installation and operation scenarios for reducing energy costs of 5G networks that differ in optimizing energy consumption via different BS installations, sleep modes, and transmission power scaling techniques. The obtained results indicate that dynamic adaptation of BS deployments and radio resource management during operation according to the increase in the number of UDs and corresponding DVs can enhance 5G HetNet EE. The findings provide valuable insights for mobile network operators looking to optimize 5G network EE in the upcoming decade.

ECG analysis of ventricular fibrillation dynamics reflects ischaemic progression subject to variability in patient anatomy and electrode location

BackgroundVentricular fibrillation (VF) is the deadliest arrhythmia, often caused by myocardial ischaemia. VF patients require urgent intervention planned quickly and non-invasively. However, the accuracy with which electrocardiographic (ECG) markers reflect the underlying arrhythmic substrate is unknown.MethodsWe analysed how ECG metrics reflect the fibrillatory dynamics of electrical excitation and ischaemic substrate. For this, we developed a human-based computational modelling and simulation framework for the quantification of ECG metrics, namely, frequency, slope, and amplitude spectrum area (AMSA) during VF in acute ischaemia for several electrode configurations. Simulations reproduced experimental and clinical findings in 21 scenarios presenting variability in the location and transmural extent of regional ischaemia, and severity of ischaemia in the remote myocardium secondary to VF.ResultsRegional acute myocardial ischaemia facilitated re-entries, potentially breaking up into VF. Ischaemia in the remote myocardium modulated fibrillation dynamics. Cases presenting a mildly ischaemic remote myocardium yielded sustained VF, enabled by the high proliferation of phase singularities (PS, 11–22) causing remarkably disorganised activation patterns. Conversely, global acute ischaemia induced stable rotors (3–12 PS). Changes in frequency and morphology of the ECG during VF reproduced clinical findings but did not show a direct correlation with the underlying wave dynamics. AMSA allowed the precise stratification of VF according to ischaemic severity in the remote myocardium (healthy: 23.62–24.45 mV Hz; mild ischaemia: 10.58–21.47 mV Hz; moderate ischaemia: 4.82–11.12 mV Hz). Within the context of clinical reference values, apex-anterior and apex-posterior electrode configurations were the most discriminatory in stratifying VF based on the underlying ischaemic substrate.ConclusionThis in silico study provides further insights into non-invasive patient-specific strategies for assessing acute ventricular arrhythmias. The use of reliable ECG markers to characterise VF is critical for developing tailored resuscitation strategies.

Supernova electron-neutrino interactions with xenon in the nEXO detector

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-β decay detector (∼5 metric ton, 90% Xe136, 10% Xe134) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae. Published by the American Physical Society 2024

A comparative analysis of car fleet efficiency evolution in Europe and Australia insights on policy influence

Regulators worldwide introduce measures to improve energy consumption and reduce greenhouse emissions of road vehicles. The present study focuses on the impacts of passenger car carbon dioxide (CO2) and fuel efficiency measures, attempting to evaluate their effectiveness using vehicle and fleet modelling. To obtain a robust counterfactual result, two regions are compared as a case study: the European Union (EU), where CO2 emissions have been regulated for over fifteen years, and Australia, a region that recently introduced such a policy element. The average difference in the certified CO2 emissions of new cars registered in the two regions increased from 50 g/km in 2018 to 60 g/km in 2021 due to accelerated electrification in the EU. To demonstrate the importance of mandatory targets, a sensitivity analysis of the 2020 EU registrations showed that lower by one-third sales of zero and low-emission vehicles (Z-L-EVs) would have resulted in seven out of ten manufacturer pools failing to meet the 2020 target of 95 g/km. The simulation framework was validated against certified values of 2021 registrations and was subsequently used to calculate real-world fleet performance to quantify the actual emissions savings. The real-world CO2 emissions in the registered fleets for 2021 were estimated to be 143 g/km and 204 g/km, for the EU and Australia, respectively. With these results as a reference, the share of ZEVs required to meet the targets set in both regions is calculated: 51% of the new registrations in 2030 in the EU for achieving 49.5 g/km, and 60% in Australia for recently set target of 58 g/km for 2029. The respective calculated average fleet-wide real-world tailpipe CO2 emissions were estimated to be 66 g/km (EU) and 77 g/km (Australia). As a last step, the study discusses the application of similar tools and analysis in the design of future policies.

Single-Scene SAR Image Data Augmentation Based on SBR and GAN for Target Recognition

High-performance neural networks for synthetic aperture radar (SAR) automatic target recognition (ATR) often encounter the challenge of data scarcity. The lack of sufficient labeled SAR image datasets leads to the consideration of using simulated data to supplement the dataset. On the one hand, electromagnetic computation simulations provide high amplitude accuracy but are inefficient for large-scale datasets due to their complex computations and physical models. On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. Furthermore, the emergence of generative adversarial networks (GANs) provides a way to generate simulated datasets, trying to balance computational efficiency with image quality. Nevertheless, the simulated SAR images generated based on random noise lack constraints, and it is also difficult to generate images that exceed the parameter conditions of the real image’s training set. Hence, it is essential to integrate physics-based simulation techniques into GANs to enhance the generalization ability of the imaging parameters. In this paper, we present the SingleScene-SAR Simulator, an efficient framework for SAR image simulation that operates under limited real SAR data. This simulator integrates rasterized shooting and bouncing rays (SBR) with cycle GAN, effectively achieving both amplitude correctness and geometric accuracy. The simulated images are appropriate for augmenting datasets in target recognition networks. Firstly, the SingleScene-SAR Simulator employs a rasterized SBR algorithm to generate radar cross section (RCS) images of target models. Secondly, a specific training pattern for cycle GAN is established to translate noisy RCS images into simulated SAR images that closely resemble real ones. Finally, these simulated images are utilized for data augmentation. Experimental results based on the constructed dataset show that with only one scene SAR image containing 30 target chips, the SingleScene-SAR Simulator can efficiently produce simulated SAR images that exhibit high similarity in both spatial and statistical distributions compared with real images. By employing simulated SAR images for data augmentation, the accuracy of target recognition networks can be consistently and significantly enhanced.

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system

In the face of increasing global disruptions, resilient manufacturing systems are critical to ensuring operational stability, a concept central to the Industry 5.0 vision. The Matrix-Structured Manufacturing System (MMS) is a key example of such resilience, designed to maintain functionality and recover quickly under major disruptions. This paper introduces a novel System Dynamics (SD)-based modelling and simulation framework to quantitively assess the performance indicators and resilience of MMS. By simulating key disruption scenarios, including partial equipment failures and order fluctuations with resource constraints, this study evaluates the system’s response and recovery capabilities using a Comprehensive Resilience Index (CRI). Unlike conventional approaches, the proposed SD model captures the dynamic interactions across various production configurations and simulates the operational logic governing them, which can also be applied to other production systems. The simulation results demonstrate the potential of SD to support decision-making by providing insights into the effectiveness of reconfiguration strategies and resource management. This work aims at offering robust methods for quantitative resilience assessment and strategic planning towards the practical implementation of resilient manufacturing in Industry 5.0 envisions.

Predicting the evolution of texture and grain size during deformation and recrystallization of polycrystals using field fluctuations viscoplastic self-consistent crystal plasticity

This paper advances a recent formulation of a field fluctuations viscoplastic self-consistent (FF-VPSC) crystal plasticity model to predict not only the evolution of texture in polycrystalline metals undergoing deformation and recrystallization but also the evolution of grain size. The model considers stress and lattice rotation rate fluctuations inside grains to calculate intragranular misorientation spreads. The spreads are used for modeling of grain fragmentation during deformation and grain nucleation during recrystallization enabling the predictions of concomitant evolution of texture and grain size distributions. The evolutions of textures and grain size distributions are first simulated for commercially pure Cu undergoing severe plastic deformation (SPD) in high pressure torsion to agree well with corresponding experimental data. Next, the evolution of texture and grain size in an aluminum alloy (AA) 5182-O after simple tension and static recrystallization are predicted and compared with experiments. Finally, the predictions of texture and grain size distributions in a magnesium alloy WE43 undergoing a thermo-mechanical loading in the dynamic recrystallization regime are presented and compared with experiments. After validation, the model is coupled with the implicit finite element method (FEM) via a user-material subroutine (UMAT) in Abaqus to facilitate modeling of complex boundary conditions and geometries. The multilevel approach is referred to as FE-FF-VPSC in which every integration point embeds the FF-VPSC constitutive law, considering texture evolution and the directionality of deformation mechanisms operating at the single crystal level. FE-FF-VPSC is applied to simulate a sequence of rolling, recrystallization, and deep drawing of a circular cup of AA6022-T4. The simulated processing sequence demonstrates the versatility of the simulation framework developed in the present paper in not only predicting texture and grain size evolution and phenomena pertaining to behavior of materials but also geometrical shape changes important for the optimization of metal forming processes. To this end, the effects of initial texture and underlying anisotropy on the formation of earing profiles during deep drawing are simulated and discussed.

A refined model of lithium-ion battery considering agglomerated active material and non-uniform graphite particle

This study presents a refined lithium-ion battery model based on the Newman model, incorporating agglomerate structures in the positive electrode and non-uniform particle size distribution in the negative electrode. The agglomerates in the positive region consist of nano-scale primary particles, pore spaces, and binders, while the negative region features a random distribution of particle sizes. The model employs the volume averaging method and integrates a thermal model to provide a comprehensive simulation framework. The results of the numerical calculations and experimental measurements show that the model is in good agreement with the experimental data. The predicted discharge capacity of the proposed model is significantly higher than that of the traditional P2D model. This improvement is primarily due to the increased diffusion distance in agglomerates and the non-uniform particle sizes, which result in substantial changes in overpotential, electrolyte concentration, and solid-phase lithium-ion distribution. The model effectively captures the complex electrochemical behavior during discharge, underscoring the importance of considering structural heterogeneities in battery modeling.