Simulation Of Physical Processes Research Articles

PLC Honeypots: Enhancing Interaction-Level Assessment

The motivation for this work arose when noticing that definitions of honeypots’ interaction level are mainly based on the information technology environment and do not reflect operational technology even if several honeypot projects approach this field. Within operational technology, programmable logic controllers (PLCs) have a main role, resulting in several honeypot researchers choosing to mimic this device at a certain interaction level. However, searching for an interaction level definition that approaches PLCs results in few studies. In this context, this work aims to explore how to adapt the information technology definition of the interaction level in order to encompass PLCs and their specific features. The method chosen to obtain inputs was a literature review where, in attempting to keep the connection with information technology, the features were based in terms of honey system, honey service, and honey token. The findings of this review provide a means to translate these terms when developing a PLC honeypot for a desired interaction level, resulting in a metrics proposal for low and high interaction. Summarizing the proposed metrics, the system of a PLC can be considered as the vendor specific firmware, its unique device banner, and a realistic network topology. For services, a PLC honeypot reflects the tasks performed by the real device, thus resulting in industrial communication protocols, network management protocols, appropriate response times, code-related interactions, dynamic input and output data processing, physical process simulation, and web interface. Lastly, a PLC honey token can be approached with the PLC program file, MIB file, and software license, among other elements. Based on these metrics, researchers can better evaluate how to design a programmable logic controller honeypot or select tools that match their target interaction level.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRIsimulation. Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equationsusing the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulationdevelopment. The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement betweenmodels. A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRIsimulation.

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model.

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm-1, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra.

Simulation and Diagnosis of Physical Precipitation Process of Local Severe Convective Rainstorm in Ningbo

Simulation and Diagnosis of Physical Precipitation Process of Local Severe Convective Rainstorm in Ningbo

Universal enzymatic numerical P systems with small number of enzymatic rules

Enzymatic Numerical P Systems (ENPSs) are a model of membrane computing that is well-suited for the simulation of physical processes and that has been used for the design and the implementation of motion controllers for wheeled robots and flying drones. The ENPSs model has been proven to be Turing universal and the theoretical effort was focused on minimizing various descriptional complexity parameters. In this paper, we explore the minimum number of enzymatic rules needed to achieve universality in ENPSs, specifically focusing on the all-parallel derivation mode where all applicable rules are applied at the same time. We show that in the case of a linear restriction for production functions, the universality can be obtained using 21 enzymatic rules, substantially improving previously known results. If production functions are allowed to be polynomials of degree 2, we show that a single enzymatic rule is sufficient to achieve universality. To obtain these results, a new proof method is introduced based on the translation of ENPSs to systems of conditional recurrences.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Simulation of physical processes based on visual basic medium

The simulation of physical processes using the example of the movement of a body on dependence of the angle to the horizon using the graphical capabilities of the Visual Basic programming language. A new expression has been proposed, that allows one determine the throwing angle of a body at a given initial speed and target coordinates. The created program consists of three parts. The first part is designed to simulate movement based on the entered initial velocity and throwing angle. The second part allows determine the throwing angle for given v0, x, y. In the third part is possible to observe the body (projectile) hitting the target. A specific aspect of the proposed program is that the user can independently perform, observe, compare and draw conclusions in a short time. The program can be used for independent studying and teaching of physics.

Coupling the COST reference plasma jet to a microfluidic device: a computational study

The use of microfluidic devices in the field of plasma-liquid interaction can unlock unique possibilities to investigate the effects of plasma-generated reactive species for environmental and biomedical applications. So far, very little simulation work has been performed on microfluidic devices in contact with a plasma source. We report on the modelling and computational simulation of physical and chemical processes taking place in a novel plasma-microfluidic platform. The main production and transport pathways of reactive species both in plasma and liquid are modelled by a novel modelling approach that combines 0D chemical kinetics and 2D transport mechanisms. This combined approach, applicable to systems where the transport of chemical species occurs in unidirectional flows at high Péclet numbers, decreases calculation times considerably compared to regular 2D simulations. It takes advantage of the low computational time of the 0D reaction models while providing spatial information through multiple plug-flow simulations to yield a quasi-2D model. The gas and liquid flow profiles are simulated entirely in 2D, together with the chemical reactions and transport of key chemical species. The model correctly predicts increased transport of hydrogen peroxide into the liquid when the microfluidic opening is placed inside the plasma effluent region, as opposed to inside the plasma region itself. Furthermore, the modelled hydrogen peroxide production and transport in the microfluidic liquid differs by less than 50% compared with experimental results. To explain this discrepancy, the limits of the 0D–2D combined approach are discussed.

Original Methods and Approaches to Numerical Simulation of Physical Processes in Fast-Response Technical Systems

Original Methods and Approaches to Numerical Simulation of Physical Processes in Fast-Response Technical Systems

EUSO-Offline: A comprehensive simulation and analysis framework

The complexity of modern cosmic ray observatories and the rich data sets they capture often require a sophisticated software framework to support the simulation of physical processes, detector response, as well as reconstruction and analysis of real and simulated data. Here we present the EUSO-Offline framework. The code base was originally developed by the Pierre Auger Collaboration, and portions of it have been adopted by other collaborations to suit their needs. We have extended this software to fulfill the requirements of Ultra-High Energy Cosmic Ray detectors and very high energy neutrino detectors developed for the Joint Exploratory Missions for an Extreme Universe Observatory (JEM-EUSO). These path-finder instruments constitute a program to chart the path to a future space-based mission like POEMMA. For completeness, we describe the overall structure of the framework developed by the Auger collaboration and continue with a description of the JEM-EUSO simulation and reconstruction capabilities. The framework is written predominantly in modern C++ (compliled against C++17) and incorporates third-party libraries chosen based on functionality and our best judgment regarding support and longevity. Modularity is a central notion in the framework design, a requirement for large collaborations in which many individuals contribute to a common code base and often want to compare different approaches to a given problem. For the same reason, the framework is designed to be highly configurable, which allows us to contend with a variety of JEM-EUSO missions and observation scenarios. We also discuss how we incorporate broad, industry-standard testing coverage which is necessary to ensure quality and maintainability of a relatively large code base, and the tools we employ to support a multitude of computing platforms and enable fast, reliable installation of external packages. Finally, we provide a few examples of simulation and reconstruction applications using EUSO-Offline.

Automated parameter tuning with accuracy control for efficient reservoir simulations

Computer simulations of complex physical processes typically require sophisticated numerical schemes that internally involve many parameters. Different choices of such internal numerical parameters may lead to considerably different levels of computational efficiency, some may even result in wrong simulation results. The task of finding an optimal set of the numerical parameters (e.g. for the purpose of minimising the simulation time), while ensuring an accepted level of numerical accuracy, is therefore extremely important but challenging. In this paper, we propose a new automated search algorithm that is based on constrained stochastic searches within the parameter space. This iterative search scheme is also equipped with an accuracy check, which adopts several complementary measures for quantifying the similarities between time series from different simulations, such that parameter choices that lead to insufficiently accurate results will be automatically rejected. As a concrete scenario of usage, we have applied the automated parameter search scheme to the open-source reservoir simulation framework OPM. An empirical study shows that a suitable design of the optimisation objective function, together with an appropriate choice of the number of trials per search iteration and the perturbation scale per trial, can produce fast and convergent improvements with respect to the optimisation objective. For example, for a set of 12 numerical parameters, 30 trials from five search iterations are sufficient for reducing the objective function by 30% for the open Norne black-oil reservoir model. The robustness of the automated search scheme is also demonstrated for two other open reservoir models. Moreover, it is found that the parameter values automatically identified for the Norne model can also greatly improve the simulation efficiency of another proprietary reservoir model that has drastically different scale, resolution and geological properties.

Property evaluation by numerical modelling based on voxelized images – Accuracy versus resolution

3D imaging techniques such as Micro-CT and MRI provide insights into the sample inner structure, therefore attract increasing attention. It is common practice to conduct numerical simulations for physical properties based on 3D voxelized images. Although errors caused by voxelization in the 3D structure are inevitable, voxel scale mechanisms have not been well explained or explored. This study assesses such errors by simulations of physical processes in an ideal porous media made of simple cubic packing spheres, with known analytical solutions or well-understood properties, and its voxelized counterpart. Physical properties of the porous media including thermal conductivity, stiffness, electrical conductivity, and permeability are extracted, meanwhile considering different conditions such as cementation, freezing, and partial water saturation. Results reveal that the error for most physical properties is solely determined by the relative voxel size defined as voxel size divided by particle or pore size, rather than actual voxel size or resolution. The error does not always decrease with the increase in resolution. Errors of different properties vary several orders of magnitude and are dominated by different geometry features of representative units. Suggestions whether the evaluation of a certain physical property could be credibly conducted are given based on the relative error.

Applications of MATLAB in Natural Sciences: A Comprehensive Review

In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines.

Modelling and simulation of fast pyrolysis of pomace from three-phase olive mill targeting optimal yields of pyrolysis products

Biomass can be transformed into useful bioproducts (biofuels, biomaterials) through its thermochemical conversion. This study concerns modeling and simulation of fast pyrolysis of olive pomace from three-phase olive mill using Super Pro Designer (SPD) software, which was selected because of its vast databank of specific chemical compounds as well as specific unit operations specifically designed for modeling and simulation of biological, physical and chemical processes. The simulation was carried out at pyrolysis temperatures ranging from 400 to 675 °C and residence times varying between 0.1 and 15s. Simulation results indicate that fast pyrolysis yielded maximum bio-oil yield of 23.72% at 650 °C and a residence time of 0.1s, and maximum syngas yield of 41.17% at 675 °C and a residence time of 15s. Predicted product yields were in accordance with experimental data collected from the literature, with relative errors in the range of 9%, which may be due to variable feedstock properties. The developed model provides very useful information on olive pomace fast pyrolysis conditions.

Geant4 simulation of energy deposition ratio and physical processes of CdZnTe crystals irradiated by high energy particles

Geant4 simulation of energy deposition ratio and physical processes of CdZnTe crystals irradiated by high energy particles

Development Results of a Cross-Platform Positioning System for a Robotics Feed System at a Dairy Cattle Complex

Practical experience demonstrates that the development of agriculture is following the path of automating and robotizing operational processes. The operation of feed pushing in the feeding alley is an integral part of the feeding process and significantly impacts dairy cattle productivity. The aim of this research is to develop an algorithm for automatic positioning and a mobile remote-control system for a wheeled robot on a dairy farm. The kinematic and dynamic motion characteristics of the wheeled robot were obtained using software that allows simulation of physical processes in an artificial environment. The mobile application was developed using Swift tools, with the preliminary visualization of interfaces and graphic design. The system uses technical vision based on RGB cameras and programmed color filters and is responsible for the automatic positioning of the feed-pusher robot. This system made it possible to eliminate the inductive sensors from the system and suspend the labor effort required for assembling the contour wire of the feed alley. By assessing the interaction between the mobile app and the feed pusher via the base station connected to the Internet and located on the farm, the efficiency and accuracy of the feedback was measured. Furthermore, remote changes in the operating regime of the robot (start date) were proven to be achievable, and the productiveness of the food supplement dispenser also became manageable.

Assessment of Wave–Current-Induced Liquefaction under Twin Pipelines Using the Coupling Model

Although twin pipelines in series have been used to transport hydrocarbons in engineering practice, most previous studies focused on the dynamic response of the seabed around a single pipeline. A two-way coupling model of fluid–structure–seabed interaction (FSSI) is proposed for the study of the soil response and liquefaction caused by waves and currents around twin pipelines. The present model integrates the flow model and the seabed model by introducing a boundary condition of velocity continuity in addition to the continuity of pressures at the seabed surface. Then, the inconsistency between the physical process and numerical simulation can be overcome in the one-way coupling model. Through a series of numerical simulations, the influence of different flow characteristics, soil properties, and pipeline configurations on the seabed response under the two-way coupling process were explored, and compared with the results of the single pipeline. The numerical results indicate that the twin pipeline configuration significantly alters the relevant responses compared to the single pipeline configuration, including the after-consolidation state, amplitude of velocity at the seabed surface, and distribution of pore pressure in the seabed. The parametric studies show that the amplitudes of the wave and current have significant impacts on the distribution of pore pressure in the seabed. The pore pressure in the seabed increases with the increase of forward wave current, while the results of reverse wave current are the opposite. In addition, the liquefaction range around the pipeline increases with the increase of Hw and Tw, and increases with the decrease of Sr and ks. At the same time, the gaps (G) and the ratio of pipe radius (R1/R2) between the twin pipelines also significantly affect the seabed response and liquefaction distribution around the pipeline.

A Study on the Dynamic Evolution Mechanism of the Steady Magnetic Field on the Internal Flow Behavior of a Laser Melting Pool

A 2D model of laser melting consisting of heat transfer, hydrodynamic flow, surface tension, and a free surface motion was established. A physical field simulation of the laser melting process was performed, and the effect of steady magnetic field parameters on the internal flow and temperature fields of the melt pool was analyzed and validated by experiments. The results show that the steady magnetic field can suppress the melt pool flow rate, but slightly influences its temperature field, and with an increase in the magnetic field strength, the ripples on the melting surface decrease with increasing magnetic flux density. Compared with the molten pool depth experiment results, the simulation molten pool depth was 792 μm, representing a difference in value of 13.5%. The surface ripples of the molten pool fluctuated greatly in the absence of a magnetic field, while the surface ripples were suppressed when the magnetic flux density was 2T. This is consistent with the simulation results, thus effectively demonstrating the simulation model’s accuracy.

Shielding Capability Research on Composite Base Materials in Hybrid Neutron-Gamma Mixed Radiation Fields

The 16N monitoring system operates in a mixed neutron-gamma radiation field and is subject to high background radiation, thus triggering instability in the 16N monitoring system measurement data. Due to its property of actual physical process simulation, the Monte Carlo method was adopted to establish the model of the 16N monitoring system and design a structure-functionally integrated shield to realize neutron-gamma mixed radiation shielding. First, the optimal shielding layer with a thickness of 4 cm was determined in this working environment, which had a significant shielding effect on the background radiation and improved the measurement of the characteristic energy spectrum and the shielding effect on neutrons was better than gamma shielding with the increase in the shield thickness. Then, functional fillers such as B, Gd, W, and Pb were added to the matrix to compare the shielding rates of three matrix materials of polyethylene, epoxy resin, and 6061 aluminum alloy at 1 MeV neutron and gamma energy. The shielding performance of epoxy resin as the matrix material was better than that of the aluminum alloy and polyethylene, and the shielding rate of boron-containing epoxy resin was 44.8%. The γ-ray mass attenuation coefficients of lead and tungsten in the three matrix materials were simulated to determine the best material for the gamma shielding performance. Finally, the optimal materials for neutron shielding and gamma shielding were combined, and the shielding performance of single-layer shielding and double-layer shielding in mixed radiation field was compared. The optimal shielding material-boron-containing epoxy resin was determined as the shielding layer of the 16N monitoring system to realize the integration of structure and function, which provides a theoretical basis for the selection of shielding materials in a special working environment.

Study on lightning protection of collector line in wind farm by multi-fracture active arc extinguishing device

Wind farms are mostly concentrated in complex mountain terrain, and lightning accidents occur frequently. In order to ensure the safe and stable operation of the wind farm, based on the gas arc extinguishing technology and taking the collector line as the research object, this paper proposes a multi-fracture active arc extinguishing device for arc extinguishing protection. Firstly, the arc extinguishing principle of the device is analyzed, and then the physical field simulation of the arc extinguishing process is carried out by COMSOL Multiphysics simulation software. The actual operation results show that the device can effectively extinguish the arc generated on the insulator of the collector line.