High-performance Simulation Research Articles

Mathematical Modeling of Electrical Power Systems for Fault Detection

Control networks are critical offices that have to be exceptionally solid and productive. Finding issues in these frameworks is exceptionally imperative for keeping them steady, keeping gear from breaking, and maintaining a strategic distance from long power blackouts. It has gotten to be clear that scientific modeling may be a awesome way to think about and discover issues in control frameworks. Taking after this strategy requires making a scientific show of the control framework, which incorporates motors, transformers, transmission lines, and loads. A few physical laws, like Ohm's Law and Kirchhoff's laws, are frequently utilized within the models to accurately show how electrical circuits work when they are working legitimately and when they aren't. On the off chance that you need to discover issues, you'll utilize numerical models to form flaws like brief circuits, open circuits, and ground deficiencies appear genuine. Through examining how the framework responds to these fake issues, engineers can make programs that can discover genuine flaws and tell where they are when they happen. These calculations frequently utilize strategies like evaluating the state, evaluating the parameters, and recognizing designs. Utilizing real-time information to figure the current state of the framework is what state estimation is all approximately. On the other hand, parameter estimation is all almost finding changes in framework parameters that might cruel there is a problem. Design acknowledgment employments past information and machine learning to discover peculiarities and sort diverse sorts of flaws. Differential conditions, framework polynomial math, and numerical strategies are fair some of the progressed science instruments that are frequently utilized to unravel the complicated conditions that depict how the power framework works. Putting these models beside unused computer innovations like high-performance computing and real-time simulations makes issue location strategies more exact and quicker. As green vitality sources and shrewd network advances are included to control frameworks, they will continue to alter. Mathematical modeling will become more and more important to make sure they work safely and efficiently. It talks about how to mathematically model electrical power systems to find faults, what they can be used for, and what the future holds for this field.

Non-smooth Bayesian optimization in tuning scientific applications

Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.

High-performance simulation of disease outbreaks in growing-finishing pig herds raised by the precision feeding method

Perturbations always affect livestock during the breeding process, including harmful diseases. Researching the impact of disease outbreaks on pig herds is extremely important so that disease control measures can be applied early. However, conducting practical experiments on disease outbreaks is extremely expensive. Precision feeding systems (PFS) for pigs use data on the animal’s own feed intake to calculate the appropriate amount of feed for each individual. This helps increase productivity and product quality while contributing to reducing waste generation in the environment. Daily feed intake (DFI) and cumulative feed intake (CFI) data can be automatically collected and estimated from the PFS, which can help detect or predict disease outbreaks. In this article, we introduce an advanced simulation model of the PFS for pigs and the integration of disease outbreak models into this system. A disease outbreak simulation application within the pig herd raised by the precision feeding method is also developed for running high-performance experimental simulations. The results of the simulation scenarios are analyzed and compared with data from a real-world experiment to assess the accuracy of the application. The correlation coefficient values of DFI in all scenarios fall within the range of 0.25 to 0.5, suggesting almost no correlation between simulated DFI and actual DFI. The overall average correlation coefficient of CFI for all scenarios is 0.764, falling within the strong correlation range. It can be concluded that the simulation accurately represents CFI values compared to reality.

The Evolution of Flow Structures and Coolant Coverage in Double-Row Film Cooling with Upstream Forward Jets and Downstream Backward Jets

The spatiotemporal evolution of the flow structures and coolant coverage of double-row film cooling with upstream forward jets and downstream backward jets, having a significant impact on film-cooling performance, is studied using the simplified thermal lattice Boltzmann method (STLBM). Moreover, the effect of the inclination angle of downstream backward jets is considered. The high-performance simulations of film cooling have been conducted by using our verified in-house solver. Results show that special flow structures, such as a sand dune-shaped protrusion, appear in double-row film cooling with upstream forward jets and downstream backward jets, which is mainly because of the blockage effect resulting from the coolant jet with backward injection. The interaction among structures results in the generation of an anti-counterrotating vortex pair (anti-CVP). The anti-CVP with the downwash motion can result in the attachment of coolant to the bottom wall, which promotes the stability and lateral coverage of coolant film. The momentum and heat transport are strengthened as the backward jet is injected into the boundary layer of the mainstream. Although the downstream evolution of the backward jet is not very smooth, its core attaches closely to the bottom wall due to the downwash motion of anti-CVP. Moreover, there is an obvious backflow zone shown in the trailing edge of the downstream backward jet with a large inclination angle. The obvious backflow makes the coolant attach to the bottom wall well. Therefore, the film cooling effectiveness is improved as the inclination angle of the downstream backward jet varies from αdown=135° to αdown=155°, with a constant blowing ratio of BR=0.5. In addition, the fluctuation of the bottom wall’s temperature is weak due to the stable coverage of the coolant layer under αdown=155°. The film-cooling performance with an inclination angle of αdown=155° is the best among all the cases studied in this work. This work provides essential insights into film cooling with backward coolant injection and contributes to obtaining a complete understanding of film cooling with backward coolant injection.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

BosonSampling.jl: A Julia package for quantum multi-photon interferometry

We present a free open source package for high performance simulation and numerical investigation of boson samplers and, more generally, multi-photon interferometry. Our package is written in Julia, allowing C-like performance with easy notations and fast, high-level coding. Underlying building blocks can easily be modified without complicated low-level language modifications. We present a great variety of routines for tasks related to boson sampling, such as statistical tools, optimization methods and classical samplers. Special emphasis is put on validation of experiments, where we present novel algorithms. This package goes beyond the boson sampling paradigm, allowing for the investigation of new interferometric behaviours such as bosonic bunching.

LTspice Modeling for GaN-GIT HEMT Including Cryogenic Temperature

Highly efficient electrically driven avionics have led to a renewed interest in cryogenic propulsion systems with the goal of reducing carbon emission footprint. Although cryogenic converters promise better efficiency and improved power density, the successful design is incumbent upon the appropriate switching device selection and simulation-based analyses prior to initial prototyping. In this work, a datasheet-driven compact model for a gallium nitride (GaN) Gate Injection Transistor (GIT) has been proposed and implemented in LTspice, a versatile, high performance, and free circuit simulator in order to investigate the merit of the chosen device in a power electronic system.

Large post-liquefaction deformation of sand: Mechanisms and modeling considering water absorption in shearing and seismic wave conditions

Large deformation of sand due to soil liquefaction is a major cause for seismic damage. In this study, the mechanisms and modeling of large post-liquefaction deformation of sand considering the significant influence of water absorption in shearing and seismic wave conditions. Assessment of case histories from past earthquakes and review of existing studies highlight the importance of the two factors. Based on the micro and macro scale mechanisms for post-liquefaction shear deformation, the mechanism for water absorption in shearing after initial liquefaction is revealed. This is aided by novel designed constant water-absorption-rate shear tests. Water absorption in shearing can be classified into three types, including partial water absorption, complete water absorption, and compulsory water absorption. Under the influence of water absorption in shearing, even a strongly dilative sand under naturally drained conditions could experience instability and large shear deformation. The mechanism for amplification of post-liquefaction deformation under surface wave load is also explained via element tests and theoretical analysis. This shows that surface wave–shear wave coupling can induce asymmetrical force and resistance in sand, resulting in asymmetrical accumulation of deformation, which is amplified by liquefaction. A constitutive model, referred to as CycLiq, is formulated to capture the large deformation of sand considering water absorption in shearing and seismic wave conditions, along with its numerical implementation algorithm. The model is comprehensively calibrated based on various types of element tests and validated against centrifuge shaking table tests in the liquefaction experiments and analysis projects (LEAP). The model, along with various numerical analysis methods, is adopted in the successful simulation of water absorption in shearing and Rayleigh wave-shear wave coupling induced large liquefaction deformation. Furthermore, the model is applied to high-performance simulation for large-scale soil-structure interaction in liquefiable ground, including underground structures, dams, quay walls, and offshore wind turbines.

High-order thread-safe lattice Boltzmann model for high performance computing turbulent flow simulations

We present a highly optimized thread-safe lattice Boltzmann model in which the non-equilibrium part of the distribution function is locally reconstructed via recursivity of Hermite polynomials. Such a procedure allows the explicit incorporation of non-equilibrium moments of the distribution up to the order supported by the lattice. Thus, the proposed approach increases accuracy and stability at low viscosities without compromising performance and amenability to parallelization with respect to standard lattice Boltzmann models. The high-order thread-safe lattice Boltzmann is tested on two types of turbulent flows, namely, the turbulent channel flow at Reτ=180 and the axisymmetric turbulent jet at Re = 7000; it delivers results in excellent agreement with reference data [direct numerical simulations (DNS), theory, and experiments] and (a) achieves peak performance [∼5×1012 floating point operations (FLOP) per second and an arithmetic intensity of ∼7 FLOP/byte on a single graphic processing unit] by significantly reducing the memory footprint, (b) retains the algorithmic simplicity of standard lattice Boltzmann computing, and (c) allows to perform stable simulations at vanishingly low viscosities. Our findings open attractive prospects for high-performance simulations of realistic turbulent flows on GPU-based architectures. Such expectations are confirmed by excellent agreement among lattice Boltzmann, experimental, and DNS reference data.

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

AbstractWhile wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.

A web of sticky strands: how localized stress controls spatio-temporal fluctuations in viscoelastic flows through a lattice of obstacles

Recent microfluidic experiments have evidenced complex spatio-temporal fluctuations in low-Reynolds-number flows of polymer solutions through lattices of obstacles. However, understanding the nonlinear physics of such systems remains a challenge. Here, we use high performance simulations to study viscoelastic flows through a hexagonal lattice of cylindrical obstacles. We find that structures of localized polymer stress – in particular birefringent strands – control the stability and the dynamics. We first show that, at steady state, strands act as a web of sticky flow barriers that induce channelization, multistability and hysteresis. We then demonstrate that a spontaneous destabilization of the strands drives the transition to unsteady flow with regimes of self-sustained oscillations, travelling waves and strand pulsations. We further show that these pulsations, which result from the destabilization of envelope patterns of stress with strands wrapped around multiple obstacles, are integral to the transition towards elastic turbulence in our two-dimensional simulations. Our study provides a new perspective on the role of birefringent strands and a framework for understanding experimental observations. We anticipate that it is an important step towards unifying existing interpretations of the nonlinear physics of viscoelastic flows through complex structures.

Function phononic crystals

We propose a novel type of phononic crystal for which the materials parameters are continuous functions of space coordinates without discontinuities corresponding to a seamless fusion of the constituent materials within the crystal lattice. With the help of an adaptation of this fundamental approach, we extend the well-established concept of phononic crystals, allowing an investigation of the transition from conventional phononic crystals with a regulated step-like parameter function to the realm of so-called function phononic crystals. Our study is based on a first-principle theory assisted by high-performance computer simulations and focuses on an understanding of the effects of a deviation from the typical parameter step function on the phononic density of states (DOS). Our exploration of the DOS reveals a characteristic rapid convergence: even a slight deviation from an ideal step function has the potential to induce radical changes in the band structure leading to the emergence of desirable features, especially multiple complete phononic band gaps.

Machine learning techniques for sequential learning engineering design optimisation

When designing a fusion power plant, many first-of-a-kind components are required. This presents a large potential design space across as many dimensions as the component’s parameters. In addition, multiphysics, multiscale, high-fidelity simulations are required to reliably capture a component’s performance under given boundary conditions. Even with high performance computing (HPC) resources, it is not possible to fully explore a component’s design space. Thus, effective interpolation between data points via machine learning (ML) techniques is essential. With sequential learning engineering optimisation, ML techniques inform the selection of simulation parameters which give the highest expected improvement for the model: balancing exploitation of the current best design with exploration of uncertain areas in the design space. In this paper, the application of an ML-driven design of experiment procedure for the sequential learning engineering design optimisation of a fusion component is shown. A parameterised divertor monoblock is taken as a typical example of a fusion component requiring HPC simulation to model. The component’s geometry is then optimised using Bayesian optimisation, seeking the design which minimises the stress experienced by the component under operational conditions.

A robotic AI-Chemist system for multi-modal AI-ready database.

By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.

Kinetic simulation of ion thruster plume neutralization in a vacuum chamber

The electrical environment of a ground vacuum testing chamber creates facility effects for gridded ion thrusters. For example, it is well known that the plume from the thruster generates current paths that are very different from what occurs in space, and the neutralization of this plume is also different. For reasons such as this, it is important to clarify how the experimental testing environment affects plasma flows, but understanding this effect solely through ground experiments is difficult. To that end, this study utilizes particle-in-cell and direct simulation Monte Carlo methods to simulate xenon beam ions and electrons emitted from a neutralizer. First, we compare simulations conducted within the chamber to those conducted in space, demonstrating that grounded chamber walls increase the electric potential and electron temperature. Next, we investigate the impact of the neutralizer’s position and the background pressure on the plume in the vacuum chamber. We find that as the neutralizer position moves closer to the location of maximum potential, more electrons are extracted, resulting in increased neutralization of the plume. We also observe that high background pressure generates slow charge-exchange ions, creating ion sheaths on the side walls that alter ion current paths. Finally, we discuss how the potential at the thruster and neutralizer exits affects the plume. The relative potential of the neutralizer to the vacuum chamber wall is observed to significantly influence the behavior of the electrons, thereby altering the degree of plume neutralization. These findings are shown to be consistent with experimental results in the literature and demonstrate the promise of high-performance simulation.

Thread-safe lattice Boltzmann for high-performance computing on GPUs

We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids the onset of memory dependencies, which may arise during the propagation step, with no need to resort to more complex streaming strategies. The thread-safe lattice Boltzmann achieves peak performances, both in two and three dimensions and it allows to reduce significantly the memory footprint (tens of GigaBytes for order billions lattice nodes simulations) by retaining the algorithmic simplicity of standard LB computing. Our findings open attractive prospects for high-performance simulations of complex flows on GPU-based architectures.

Catalyst: Fast and flexible modeling of reaction networks.

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst's broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

Molecular Dynamics and Other HPC Simulations for Drug Discovery.

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enablesthe study of protein-protein interactions (PPI) in healthy and disease states, thus helpingthe identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

SimQN: A Network-Layer Simulator for the Quantum Network Investigation

Along with the rapid development of quantum information technologies, quantum networks that transmit quantum states have gradually shifted from laboratory to reality. However, the high cost of building a quantum network testbed seriously hinders the research progress of quantum networks, and the existing simulators have been developed with less exposure to network models. To overcome those deficiencies, this article proposes a new discrete-event driven network-layer simulation framework named SimQN, available at https://github.com/ertuil/SimQN, to provide full functionalities of the quantum networking simulation. The design goal of SimQN is to make it easy-to-configure while providing high performance and accurate simulation capability. We present multiple simulation physical backends to enable different functionalities and reduce computational overhead. The modularized SimQN also provides reusable network protocols and utilities to release the large-scale quantum networking evaluation burden. We present several demonstrations and analyses to illustrate the protocol design and the performance of SimQN.

Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging

Medical simulations have proven to be highly valuable in the education of healthcare professionals. This significance was particularly evident during the COVID-19 pandemic, where simulators provided a safe and effective means of training healthcare practitioners in the principles of lung ultrasonography without exposing them to the risk of infection. This further emphasizes another important advantage of medical simulation in the field of medical education. This paper presents the principles of ultrasound simulation in the context of inflammatory lung conditions. The propagation of sound waves in this environment is discussed, with a specific focus on key diagnostic artifacts in lung imaging. The simulated medium was modeled by assigning appropriate acoustic characteristics to the tissue components present in the simulated study. A simulation engine was developed, taking into consideration the requirements of easy accessibility through a web browser and high-performance simulation through GPU-based computing. The obtained images were compared with real-world examples. An analysis of simulation parameter selection was conducted to achieve real-time simulations while maintaining excellent visual quality. The research findings demonstrate the feasibility of real-time, high-quality visualization in ultrasound simulation, providing valuable insights for the development of educational tools and diagnostic training in the field of medical imaging.