Simulation Speed Research Articles

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.

Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility, and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use an LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

While Experiencing a Forest Trail, Variation in Landscape Is Just as Important as Content: A Virtual Reality Experiment of Cross-Country Skiing in Estonia

It has long been understood that diversity is a key aspect of what makes a landscape attractive but to what degree of diversity and how is it experienced? Many forest landscapes are generally monotonous in character or are broken up by forest management activities such as clear cutting, which may negatively impact their potential for recreation and psychological well-being benefits. We conducted a virtual reality experiment where people were taken on a trip along a simulated cross country skiing track in an Estonian forest. Participants followed a route at simulated speeds typical of cross-country skiing. The route was long enough to experience several minutes passing through one type of forest landscape with a series of small variations in character followed by several minutes passing through a notably different forest landscape. The restorative experience obtained by the visit was measured periodically in each version of the landscape. Univariate general linear modelling analysis was statistically significant (r2 = 0.651, F(198, 965) = 9.108, p < 0.001) and showed that while respondents preferred less-dense forest in general (B = 0.189, p = 0.001)—an expected result—a comparable amount of positive restorative response could also be attributed to prominent changes in forest character, regardless of the type of forest (B = 0.401, p < 0.001). We hypothesise that respondents were reacting favourably to sudden changes in forest appearance after prolonged exposure to one forest type—that diversity is important in maintaining interest, reducing boredom, and in providing a restorative experience. The implications are, firstly, that a virtual experience can detect restorative effects and, secondly, that recreational trails should be designed to pass through varied landscapes offering continually changing diverse experiences—the impact of which can be tested in the virtual setting because there is control of all variables.

Deep learning-based urban morphology for city-scale environmental modeling.

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

Coarse-Grained Modeling of the SARS-CoV-2 Spike Glycoprotein by Physics-Informed Machine Learning

Coarse-grained (CG) modeling has defined a well-established approach to accessing greater space and time scales inaccessible to the computationally expensive all-atomic (AA) molecular dynamics (MD) simulations. Popular methods of CG follow a bottom-up architecture to match properties of fine-grained or experimental data whose development is a daunting challenge for requiring the derivation of a new set of parameters in potential calculation. We proposed a novel physics-informed machine learning (PIML) framework for a CG model and applied it, as a verification, for modeling the SARS-CoV-2 spike glycoprotein. The PIML in the proposed framework employs a force-matching scheme with which we determined the force-field parameters. Our PIML framework defines its trainable parameters as the CG force-field parameters and predicts the instantaneous forces on each CG bead, learning the force field parameters to best match the predicted forces with the reference forces. Using the learned interaction parameters, CGMD validation simulations reach the microsecond time scale with stability, at a simulation speed 40,000 times faster than the conventional AAMD. Compared with the traditional iterative approach, our framework matches the AA reference structure with better accuracy. The improved efficiency enhances the timeliness of research and development in producing long-term simulations of SARS-CoV-2 and opens avenues to help illuminate protein mechanisms and predict its environmental changes.

A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems

In this research, a hierarchical met-ocean data selection model is proposed to reduce the computational cost in stochastic simulation of operation and maintenance (O&M) and enable rapid evaluation of offshore renewable energy systems. The proposed model identifies the most representative data for each calendar month from the long-term historical met-ocean data in two steps, namely the preselection and the refined selection. The preselection incorporates three distinct metrics to evaluate the characteristics of statistical distributions, including the Jensen–Shannon divergence, the encapsulation of extreme met-ocean conditions, as well as the overall vessel accessibility. For the refined selection, a component of temporal synchrony is devised to emulate dynamic changes of met-ocean conditions. As such, a met-ocean reference year comprising twelve representative historical months is subsequently produced and deployed as the input for O&M stochastic simulation. While this research focuses on the development of a generalised methodology for selecting representative met-ocean data, the proposed statistical method is validated empirically using a case study inspired by real-life floating offshore wind installations in Scotland, e.g., Hywind and Kincardine projects. According to the O&M simulation results with five capacity scenarios, the proposed data selection model reduces the computational cost by up to 97.65% while emulating the original results with minor deviations, i.e., within ±5%. The simulation speed is therefore 43 times quicker. Overall, the proposed met-ocean data selection model attains an excellent trade off between computational efficiency and accuracy in O&M stochastic simulation.

3-D EM Modeling of Medical Microwave Imaging Scenarios With Controllable Accuracy

Medical microwave imaging (MMWI) is a multidisciplinary task that requires precise electromagnetic modeling of heterogeneous human tissues, solving non-linear and ill-posed inverse scattering problems with weak contrasts, and design of compact antenna arrays well-matched to adjacent body parts. If any of the activities listed above does not perform well, the resulting imaging will be poor. In this article, we devote a special effort to achieving high accuracy in electromagnetic simulations of MMWI scenarios. This complex task requires developing a suitable meshing procedure, which balances model fidelity and computational bottlenecks such as a large number of unknowns in the electromagnetic model. Thus, instead of a simple conversion from triangles to quads, we offer a technique that generates high-quality mesh (in terms of both simulation speed and geometrical accuracy) with controllable deviation from the initial model. In addition, we developed a strategy for self-convergence tests that prove the reliability of the simulation results in the MMWI framework. Moreover, an additional validity test has been proposed, based on the inverse scattering operator, commonly used in many microwave imaging algorithms. The effectiveness of these novel techniques is demonstrated with the example of a 21-antenna system placed around the 5-tissue head phantom with and without stroke.

A simplified new multigrid algorithm of lattice Boltzmann method for steady states

In the present paper, a new strategy of multigrid method is introduced to accelerate the convergence speed of numerical simulations via lattice Boltzmann method. Based on the popular V-cycle multigrid algorithm, a simplified multigrid algorithm is presented and validated through the simulations of the classic lid-driven cavity flow for steady states. The novelty of the present algorithm resides in the construction of the information transferring process, in which, for a full cycle, the numerical simulation starts on the coarse mesh, transferring to medium mesh and then streams to the fine mesh through a prolongation operator. Afterwards, instead of using a restriction operator, the fluid information jumps back directly from the fine mesh to the coarse mesh via an assignment operator. The accuracy and efficiency of the simplified new algorithm are validated by comparing the results obtained when employing the classic V-cycle multigrid algorithm and the traditional lattice Boltzmann method with uniform Cartesian grid.

Improving extreme offshore wind speed prediction by using deconvolution

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

Research on simplified modeling of large-scale wind farms based on equivalent transfer function and aggregate equivalent

Introduction: In grid-connected research of large-scale wind farms, the problem of long simulation time exists in the research based on detailed model. In order to realize the fast simulation and grid-connected transient research of wind farm, this paper presents a simplified equivalent model of wind farm based on equivalent transfer function and improved capacity weighting method.Methods: This paper analyzes the mathematical model of wind turbine, corresponding to the strategy of design simplification, and the simplified model is built according to the equivalent transfer function. In addition, this paper considers the equivalent line loss model of collector line, classifies and aggregates the simplified wind turbines, and calculates the equivalent value of the converged wind turbines by using the improved capacity weighting method, the grid-connected simplified modeling of large-scale wind farm is realized.Results: A model of wind farm-MMC-HVDC flexible grid-connected wind power system is built. The simulation results show that the simplified equivalent model improves the simulation speed greatly while guaranteeing the dynamic characteristics.Discussion: The simplified equivalent strategy of wind farm can keep the same dynamic performance with the detailed model, and improve the simulation speed, and realize the fast simulation and grid-connected transient research of wind farm.

Traffic safety assessment and prediction under different lighting service states in road tunnels

The lighting service state of road tunnels has an important impact on traffic safety. To assess and predict traffic safety in road tunnels under different lighting service states, a traffic safety assessment system of road tunnels was innovatively established by using visual recognition clarity data from 300 field experiments, which uses the proposed traffic safety factor to assess the traffic safety of road tunnels under different lighting service states. Then, the Particle Swarm Optimization (PSO) algorithm is used to optimize the Back Propagation (BP) neural network, and a new traffic safety intelligent prediction method in road tunnels was constructed to predict the traffic safety factor under different lighting service states. The results of the study show that there is a negative correlation between simulated vehicle speed and visual clarity and a positive correlation between lighting attenuation and visual clarity. There is a negative correlation between the number of luminaire failures and the visual recognition clarity. When the lighting attenuation is below 70% or four and above luminaires fail, the lighting service states of road tunnels can seriously threaten traffic safety. The PSO-BP neural network model can accurately predict the traffic safety factor. The critical value of the traffic safety factor is 0.5965. This means that the lighting service state poses a threat to traffic safety in road tunnels when the traffic safety factor is lower than this value. The results of the study can provide the basis for developing a safe and low-carbon tunnel lighting maintenance program.

A novel methodology to construct compartment models for a circulating fluidized bed riser

During the integrated modeling of a chemical process, there always exists a conflict between model accuracy and computational speed for non-ideal reactors. In view of this, a novel, easy to use, methodology to construct compartment or ideal reactor network models is proposed for a circulating fluidized bed (CFB) riser based on time-series. The pressure drop time-series is sampled during the Multi-Phase Particle-in-Cell (MP-PIC) simulation of the riser. Detailed procedures for mapping the time-series to a directed weighted complex network and the determination of model parameters are presented. Effects of the sampling frequency and division scheme for the time-series on compartment models are also investigated. Findings indicate that the MP-PIC simulation is reliable to record the time-series of pressure drop in the CFB riser. The non-ideal flow in the riser can be equivalently reproduced by a compartment model consisting of eight CSTRs with various sizes and backflow rates. The appropriate sampling frequency for the time-series should be greater than 200 Hz. The optimum division scheme for the time-series is with the predefined threshold value of K = [4025, 4150, 4275, 4400, 4525, 4650, 4775]. It is believed that the proposed methodology based on the time-series can be generalized to develop compartment models for non-ideal flow. It is also expected that compartment models can be readily integrated into a process simulator, e.g., Aspen Plus, to resolve the conflict between model accuracy and computational speed in process simulation, on-line analysis and real-time optimization.

Prediction of Linear Energy Transfer Based on Geant4 Simulation and Neural Networks

AbstractMost of the traditional studies based on single event effects (SEEs) favor the analysis of electrical mechanisms of semiconductor devices. Professional simulation software in microelectronics requires researchers to have a solid knowledge of microelectronics theory, and the modeling threshold of the software is relatively high, the simulation speed is slow, and accurate simulation of inter‐particle and particle–material interactions is lacking. SEEs are related to linear energy transfer (LET), in this paper, a method is proposed to obtain LET datas to predict SEEs of particles incident on silicon materials by using the Geant4 Monte Carlo toolkit in combination with a dense convolutional network to accurately and rapidly estimate the energy deposition characteristics of the particles. The proposed network structure has a high prediction accuracy with a mean square error （MSE） of only 1.77 × 10−4. Compared with Geant4, which takes 1 min to compute a set of data, the proposed network structure takes only 0.0817 s. The method explores the feasibility of using Geant4 to model semiconductor devices combined with deep learning algorithms, providing a new research perspective for the prediction of microelectronic devices and making it possible to explore the influence of integrated circuits by SEEs.

Fully Atomistic Molecular Dynamics Simulation of a TIPS-Pentacene:Polystyrene Mixed Film Obtained via the Solution Process

Organic thin-film transistors using small-molecule semiconductor materials such as 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-P) have been recently studied for the production of flexible and printed electronic devices. Blending a semiconductor with an insulating polymer, such as polystyrene, is known to improve the device performance; however, its molecular-level structure remains unknown. In this study, we performed molecular dynamics (MD) simulations on a mixed system of TIPS-P and atactic polystyrene (aPS) with fully atomistic models to understand the structure of the mixed thin film at the molecular level and the influence on the device properties. To reproduce the deposition from the solution, we gradually reduced the number of toluene molecules in the simulation. The dynamic characteristics of the system, mean squared displacement, diffusion coefficient, density profile, and P2 order parameter were analyzed. Some of the simulated systems reached the equilibrium state. In these systems, the simulated structures suggested the presence of more TIPS-P molecules on the surface than inside the bulk, even at the low molecular weight of aPS, where phase separation was not observed experimentally. The results of the fully atomistic MD simulations are also a basis for the coarse-grained model to increase the speed of the MD simulation.

Parameter Optimization and Tuning Methodology for a Scalable E-Bus Fleet Simulation Framework: Verification Using Real-World Data from Case Studies

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated.

Numerical simulation of fluidization: Driven by challenges

In the century-long development of fluidization technology, simulation methods have evolved in response to scientific and engineering demands, which in turn have produced advances in technology. Faced with the profound global challenges of climate change and sustainable development, fluidization has found new applications in carbon capture and utilization, non-energy uses of fossil fuels, production of functional materials, and the continuous production of drugs and fine chemicals. Revolutionary research and development tools, such as digital virtual reactors and even factories, are highly desirable for the effective and efficient conceptualization, scaling-up, and optimization of the processes and equipment in these sophisticated applications, and require unprecedented advances in the accuracy and speed of simulations. After a short review of the existing simulation methods for fluidization, this article focuses on how such advances could be achieved through the organic integration of multiscale modeling, scalable algorithms, and supercomputing. Gallery of representative simulation studies on fluidization. • Inherent logic and driving force behind the development of modeling and simulation. • Importance of multi-scale modeling, scalable algorithms and supercomputing for future. • Integration of simulation with AI, VR, big data and robot scientists for new applications.

Разработка методов автоматизации высокоуровневого моделирования сетей на кристалле

The paper analyzes the existing methods to optimize the time costs and increase the accuracy of calculations in the high-level simulation of networks-on-chip. The description of parameters and characteristics of networks-on-chip calculated by different models is given, and their influence on the speed of high-level simulation is analyzed. Adaptation of existing methods of modeling optimization for implementation in the system of automation of networks-on-chip design is carried out.

Validation of Soave–Redlich–Kwong equation of state coupled with a classical mixing rule for sound speed of non-ideal gas mixture of oxygen-hydrogen as liquid rocket propellants

Numerical simulations of inlet jets flow of propellants in the main combustion chamber equipped in a liquid rocket engine have been conducted by compressible CFD technics employing a van der Waals-type equation of state (EOS) and chemical reaction models. Various simulations have been already conducted both for combustion and non-combustion state in the chamber; however, the accuracy of EOS for the mixture of propellant employing a classical mixing rule (CMR), particularly for the sound speed, has not been understood well. Therefore, we have validated the applicability of Soave–Redlich–Kwong (SRK) EOS as one of the van der Waals-type EOSs coupled with the CMR for the sound speed using molecular dynamics (MD) simulations against an imaginary model of oxygen-hydrogen mixture system. We found that SRK EOS coupled with a well-employed CMR can reproduce the sound speed in the MD simulations within a relative error of several percent and that the CMR hardly increases the deviation of the SRK EOS in the single component. The present results suggest that the SRK EOS with the CMR cannot be the main factor to cause critical errors in the CFD based on RANS, which will give reliability to current CFD simulations for internal flows in a combustion chamber of a liquid rocket engine.

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator.

Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "bypass all physics laws" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.