Simulation Framework Research Articles

Ion–surface interactions in plasma-facing material design

A multi-scale simulation framework for ion–solid interactions in plasma-exposed materials provides crucial insight into advancing fusion energy and space electric propulsion. Leveraging binary-collision approximation (BCA) simulations, the framework uniquely predicts sputter yields and analyzes material transport within volumetrically complex materials. This approach, grounded in the validated BCA code TRI3DYN, addresses key limitations in existing models by accurately capturing ion–solid interaction physics. A case study is presented, highlighting the framework’s ability to replicate experimental sputter yield results, underscoring its reliability and potential for designing durable materials in harsh plasma environments. Insights into sputtering transport phenomenology mark a significant advancement in material optimization for improved resilience in plasma-facing applications.

Computational study of diffraction image formation from XFEL irradiated single ribosome molecule

Single particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation.

Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.

CoopeRIS: A framework for the simulation of reconfigurable intelligent surfaces in cooperative driving environments

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

A linear response framework for quantum simulation of bosonic and fermionic correlation functions

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green’s functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

Development of 6-way CFD-DEM-FEM momentum coupling interface using partitioned coupling approach

Fluid-particle-structure interactions (FPSI) govern a wide range of natural and engineering phenomena, from landslides to erosion in abrasive water jet cutting nozzles. Despite the importance of studying FPSI, existing numerical frameworks often simplify or neglect certain physics, limiting their applicability. This work introduces a novel 6-way CFD-DEM-FEM momentum coupling for FPSI using a partitioned coupling approach, providing a flexible and adaptable solution.Our prototype uses the preCICE coupling library to couple three numerical solvers: OpenFOAM for fluid dynamics, eXtended Discrete Element Method (XDEM) for particle motion, and CalculiX for structural mechanics. The coupling approach extends existing adapters and introduces a novel XDEM preCICE adapter, allowing data exchange over surface and volumetric meshes.Numerical experiments successfully demonstrate the 6-way coupling, showcasing fluid-structure interactions and particle dynamics. The versatility of the partitioned coupling approach is highlighted, allowing the interchangeability of different single-physics solvers and facilitating the study of complex FPSI phenomena.This article offers a thorough description of the methodology, coupling strategies, and detailed results, offering insights into the advantages and disadvantages of the proposed approach. This work lays the groundwork for a scalable and customizable FPSI simulation framework with a wide range of applications.

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

A Modular Simulation Framework for Organ Allocation

BackgroundWe describe and validate a new simulation framework addressing important limitations of the Simulated Allocation Models (SAMs) long used to project population effects of transplant policy changes. MethodsWe developed the Computational Open-source Model for Evaluating Transplantation (COMET), an agent-based model simulating interactions of individual donors and candidates over time to project population outcomes. COMET functionality is organized into interacting modules. Donors and candidates are synthetically generated using data-driven probability models which are adaptable to account for ongoing or hypothetical donor/candidate population trends and evolving disease management. To validate the first implementation of COMET, COMET-Lung, we attempted to reproduce lung transplant outcomes for U.S. adults from 2018-2019 and in the six months following adoption of the Composite Allocation Score (CAS) for lung transplant. ResultsSimulated (median [Interquartile Range, IQR]) versus observed outcomes for 2018-2019 were: 0.162 [0.157, 0.167] versus 0.170 waitlist deaths per waitlist year; 1.25 [1.23, 1.28] versus 1.26 transplants per waitlist year; 0.115 [0.112, 0.118] versus 0.113 post-transplant deaths per patient year; 202 [102, 377] versus 165 nautical miles travel distance. The model accurately predicted the observed precipitous decrease in transplants received by type O lung candidates in the six months following CAS implementation. ConclusionsCOMET-Lung closely reproduced most observed outcomes. The use of synthetic populations in the COMET framework paves the way for examining possible transplant policy and clinical practice changes in populations reflecting realistic future states. Its flexible, modular nature can accelerate development of features to address specific research or policy questions across multiple organs.

Large-Eddy Simulations with remeshed Vortex methods: An assessment and calibration of subgrid-scale models

This study assesses various subgrid-scale models within the framework of Large Eddy Simulation (LES) using a remeshed Vortex method (RVM). RVM is a semi-Lagrangian method discretizing the vorticity-velocity Navier–Stokes equations that has proven to be a stable and less dissipative alternative to more classical Eulerian methods. The subgrid-scale models are first tested on the well-known Taylor–Green Vortex case at Re=5000. Notably, the Variational Multiscale (VMS) variant of the Smagorinsky model and the Spectral Vanishing Viscosity (SVV) approaches emerge as the best-suited to the RVM, as they add diffusion to only the smallest resolved vorticity scales. Then, a stochastic uncertainty quantification analysis is conducted for both selected models, and the model coefficients are calibrated against direct numerical simulation. These coefficients are then applied to additional cases (different regimes, grid resolutions and test cases), showing the robustness of the calibration within the RVM-LES framework.

A Multifidelity Framework for Aerothermodynamic Modeling and Simulation of Destructive Atmospheric Entry

The aerothermodynamic environment surrounding bodies undergoing destructive atmospheric entry is characterized by multiple interacting shock waves generated by the presence of several fragments moving close to one another at high speed. Shock interaction and impingement produce highly localized surface loads, influencing the overall dynamics of the fragment, its temperature, and in turn its demise. To account for the relevant physics during the reentry process of fragmenting bodies, a multifidelity framework is presented that allows for selecting the most appropriate level of fidelity, between high-fidelity solutions or low-fidelity aerothermodynamic metamodels. A physics-informed approach is implemented to automatically choose between the two fidelity levels on the basis of the intersection of shock wave envelopes. The complete framework is validated on relevant test cases from the literature and its performance is demonstrated for destructive atmospheric reentry cases, such as the case of the Attitude Vernier Upper Module and the Automated Transfer Vehicle from the European Space Agency.

New curved boundary scheme in lattice Boltzmann framework for simulation of dissolution through nonlinear heterogeneous reactions in general form

Nonlinear heterogeneous reactions are important for simulating dissolution as they involve reactant adsorption, reaction, and product desorption, leading to nonlinear behavior. This study proposes a new curved reaction boundary condition in general form in the lattice Boltzmann framework. This method calculates the unknown distribution functions and the interface concentration using extrapolated distribution functions on actual interface position. Various analytical benchmarks were used to compare this method's accuracy with two available schemes, including Kashani et al. and Huber et al. methods. According to the results, in the simulation of reactant transport on straight and curved surfaces with and without dissolution, errors obtained by the proposed method did not exceed 1.7% in different conditions, while errors of the two other methods were up to 50%. The convergence rate of different methods was determined, and based on the results, the convergence rate of the proposed method was second-order, while the corresponding values for the two other methods were only first-order. The results of different root-finding methods in the proposed method including Bisection, Newton-Raphson, and linear approximation were compared to determine the interface concentration. The results showed that Bisection errors did not exceed 1%. At the same time, using Newton-Raphson and linear approximation led to errors of 12.9% and 25.3%, respectively. The effect of reaction orders on an obstacle dissolved under reactive flows in a channel was investigated. According to the results, in each Damköhler number, increasing the reaction order decreased the dissolution rate; however, increasing the Damköhler number significantly restricted the effect of orders.

Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees

Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges – incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.

Optimizing bikeshare service to connect affordable housing units with transit service

Affordable housing and transit accessibility have long been focal points for housing and urban development agencies at all levels, from national to regional. However, these elements are often considered and planned for independently, leading over time to an expanding spatial gap between affordable housing and transit services. This separation creates issues of mobility equity. Fortunately, bikeshare, an emerging mobility solution, provides the potential to bridge this spatial gap between affordable housing communities and transit services. To leverage this, we designed a bikeshare station (BSS) location optimization tool that aims to alleviate the impacts of this spatial divide through bikeshare services. Specifically, we developed a multimodal agent-based modeling (ABM) and simulation framework aimed at enhancing accessibility to a variety of destinations. To solve this optimization problem, we developed a genetic algorithm to determine the optimal locations for BSS. There are three main conclusions from this research. First, the strategic positioning of BSS will enhance accessibility for affordable housing community (AHC) residents by reducing average transit travel time and walk distance, or increasing the number of destinations accessible by transit. This underscores the advantages of combining bikeshare and transit systems to cater to the mobility requirements of AHC residents, especially for non-work-related trips in suburban areas. Second, the relationship between transit and bikeshare is twofold: complementation and substitution. In instances where a lengthy trip involves multiple transit transfers and significant waiting time, bikeshare could replace some transit trips. Lastly, but most importantly, this study advises a forward-looking approach for governments and operators to avoid the waste of public resources when planning bikeshare systems. The tool developed by this research can facilitate this forward-looking approach in practice.

Phenomenological sintering model and experimental validation of gravity-induced distortions in binder-jetted stainless steel components

Sintering is an important consolidation step employed in sinter-based metal additive manufacturing processes. Binder Jetting (BJT) starts with green components with low green density (40-60%) that results in large sintering shrinkages and geometrical shape distortions caused by external forces (e.g. gravity). Consequently, the prediction of the final sintered geometry is crucial during the design process. In this work, a novel sintering simulation framework for gravity-affected sintering of stainless-steel components is presented, including the Rios-Olevsky-Hryha sintering model and the methodology for the identification of the required material parameters. The constitutive law includes material constants to account for the powder packing effects and the delta-ferrite transformation occurring at high temperatures. The material shear viscosity was explicitly related to the equilibrium phase fraction of austenite and delta-ferrite during sintering temperatures. Dilatometry experiments were conducted and followed by the data postprocessing for the model calibration. The calibrated model was incorporated in a FEM code and validated against experimental data from BJT sintered components, showing the remarkably accuracy of the numerical simulations, with small geometric deviations (0.56 mm) related to the assumption of isotropic shrinkage in the model proposed. In parallel, other alternative models were implemented based on different normalized bulk viscosity formulations, which underestimate/overestimate the sintered distortions.

Multi-scale simulation of typhoon wind field at building scale utilizing mesoscale model with nested large eddy simulation

Based on the mesoscale WRF (Weather Research Forecast) and LES (Large Eddy Simulation), an integrated numerical simulation framework aiming to resolve typhoon wind fields around urban building blocks has been developed. The holistic multi-scale simulation spans four scales: macroscale (∼1000 km), mesoscale (∼100 km), microscale (∼1 km), and building scale (0.1–100m). By interactively sharing the meteorological data among different scales, this simulation framework addressed the challenge of downscaling typhoon effects in urban environments. Typhoon weather reanalysis and urban wind field simulation were conducted by the WRF model with nested computational domains incorporating high-resolution topography data. For the downscaled LES model with urban buildings, the wind profiles produced by the WRF model were used at the interface with provided turbulent fluctuations in the flow. K11 building is a 270-m-tall building located in a densely built-up area of Hong Kong. Wind velocity and pressure fields surrounding the K11 building in Hong Kong during Typhoon Kammuri were obtained by the proposed framework, and the simulation results were validated by comparing with the meteorological data and full-scale measurements at the top of the K11 building. The interactions of the wind fields and building clusters resulting in vortex shedding, separation, and channeling effects have been resolved at high resolutions. Probabilistic distributions and higher-order statistics of wind pressure, i.e., skewness and kurtosis, were presented to highlight non-Gaussian characteristics of the simulated local wind pressure on the K11 building. Based on the simulated wind pressure, wind-induced vibration of the K11 building during the typhoon was analyzed and compared to the measurements.

Analysis of P-N Junction Length of Drain and Source in MOSFET Transistor Through TCAD Simulation

This paper delves into the convergence of mathematical and computer science principles within the realm of electronics engineering, particularly focusing on device-level transistor restructuring. A pressing challenge confronting the semiconductor sector is the persistent escalation of leakage current alongside technological advancements. At the heart of any chip or semiconductor device lies the transistor, serving as its fundamental building block. Among the various forms of leakage current, subthreshold leakage stands out as a dominant component in transistors. Within this study, a systematic exploration of the numerical association governing the physical configuration of MOSFET and leakage amount specifically the subthreshold one is undertaken. One innovative algorithm is crafted to facilitate the automated tracking of transistor structures, laying the groundwork for subsequent analyses. The simulation framework is meticulously constructed by leveraging mathematical formulations derived from the algorithmic outputs. Remarkably, the results obtained from the simulation conducted through TCAD software exhibit a remarkable proximity to established mathematical models. Given the ubiquity of Complementary Metal Oxide Semiconductor (CMOS) technology in contemporary semiconductor fabrication, it serves as the cornerstone for the simulation endeavours herein.

A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain

This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 105. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.