Complete Simulation Research Articles

Influence on Sample Determination for Deep Learning Electromagnetic Tomography.

Deep learning (DL) has been frequently applied in the image reconstruction of electromagnetic tomography (EMT) in recent years. It offers the potential to achieve higher-quality image reconstruction. Among these, research on samples is relatively scarce. Samples are the cornerstone for both large and small models, which is easy to ignore. In this paper, a deep learning electromagnetic tomography (DL-EMT) model with nine elements is established. Complete simulation and experimental samples are obtained based on this model. On the sample sets, the reconstruction quality is observed by adjusting the size and configuration of the training set. The Mann-Whitney U test shows that beyond a certain point, the addition of more samples to the training data fed into the deep learning network does not result in an obvious improvement statistically in the quality of the reconstructed images. This paper proposes a CC-building method for optimizing a sample set. This method is based on the Pearson correlation coefficient calculation, aiming to establish a more effective sample base for DL-EMT image reconstruction. The statistical analysis shows that the CC-building method can significantly improve the image reconstruction effect in a small and moderate sample size. This method is also validated by experiments.

Modular Finite Element Modeling of Heavy Plate Rolling Processes Using Customized Model Reduction Approaches

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for.

Characterizing sub-glacial hydrology using radar simulations

Abstract. The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing or enhancing basal shear stress and accelerating grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne radar echo sounding (RES) survey. Basic RES analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model RES reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulation cases modeled water structures from 5 to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB, while equivalent Röthlisberger channels show only modest reflectivity gains of 8–13 dB. Changes in substrate roughness may also alter observed reflectivity by 3–6 dB. All of these results indicate that a sophisticated approach to RES interpretation can be useful in constraining the size and shape of sub-glacial water features. However, a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single RES flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations comparing various water system geometries, such as canals and sub-glacial lakes, we demonstrated the important role that topography and water geometry can play in observed RES reflectivity. From the scenarios that we tested, we concluded the bright reflector from our RES flight line cannot be a Röthlisberger channel but could be consistent with a series of flat canals or a sub-glacial lake. However, we note our simulations were not exhaustive of all possible sub-glacial water configurations. The approach outlined here has broad applicability for studying the basal environment of large glaciers. We expect to apply this technique when constraining the geometry and extent of many sub-glacial hydrologic structures in the future. Further research may also include comprehensive investigations of the impact of sub-glacial roughness, substrate heterogeneity, and computational efficiencies enabling more complex and complete simulations.

CORHEL-CME: An Interactive Tool For Modeling Solar Eruptions

Coronal Mass Ejections (CMEs) are immense eruptions of plasma and magnetic fields that are propelled outward from the Sun, sometimes with velocities greater than 2000 km/s. They are responsible for some of the most severe space weather at Earth, including geomagnetic storms and solar energetic particle (SEP) events. We have developed CORHEL-CME, an interactive tool that allows non-expert users to routinely model multiple CMEs in a realistic coronal and heliospheric environment. The tool features a web-based user interface that allows the user to select a time period of interest, and employs Regularized Biot-Savart Law (RBSL) flux ropes to create stable and unstable pre-eruptive configurations within a background global magnetic field. The properties of these configurations can first be explored in a zero-beta magnetohydrodynamic (MHD) model, followed by complete CME simulations in thermodynamic MHD, with propagation out to 1 AU. We describe design features of the interface and computations, including the innovations required to efficiently compute results on practical timescales with moderate computational resources. CORHEL-CME is now implemented at NASA's Community Coordinated Modeling Center (CCMC) using NASA Amazon Web Services (AWS). It will be available to the public in early 2024.

Modeling gamma detectors in OpenMC: Validation of a newly implemented pulse-height tally

Gamma spectroscopy measurements can be simulated using a pulse-height tally functionality of Monte Carlo particle transport codes. Such a functionality must account for the complete simulation history of a particle’s energy deposited in a particular volume. OpenMC, an open-source application for neutron and photon particle transport which is widely used in the nuclear engineering community, previously lacked adequate simulation capabilities for gamma spectroscopy. In this paper, we outline the implementation of the photon pulse-height tally for OpenMC. Additionally, we validate the new function by comparing results to analytical calculations, other software, and experimental data.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling.

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

A Large-Scale Three-Dimensional Apparatus to Study Failure Mechanisms of Rockfalls in Underground Engineering Contexts.

Rockfalls are an important factor affecting underground engineering safety. However, there has been limited progress in understanding and predicting these disasters in the past few years. Therefore, a large-scale three-dimensional experimental simulation apparatus to study failure mechanisms of rockfalls occurring during underground engineering was developed. This apparatus, measuring 4 m × 4 m × 3.3 m in size, can achieve vertical and horizontal symmetric loading. It not only simulates the structure and stress environment of a rock mass but also simulates the stepwise excavation processes involved in underground engineering. A complete simulation experiment of rockfalls in an underground engineering context was performed using this apparatus. Dynamic evolution characteristics of block displacement, temperature, natural vibration frequency, and acoustic emissions occurring during rockfalls were studied during the simulation. These data indicate there are several indicators that could be used to predict rockfalls in underground engineering contexts, leading to better prevention and control.

OMNeT++ Simulation Framework for Avionics Full-Duplex Switched Ethernet

Avionics Full-Duplex Switched Ethernet (AFDX) is a popular network technology for integrated modular avionics (IMA). When designing an AFDX network, it is important to fulfill the real-time communication requirements of avionic systems. To this end, simulation tools offer a low-cost solution for the performance evaluation of a given AFDX network and message set configuration at the design stage. In this paper, we develop an improved AFDX model for the widely used OMNeT++ (extensible, modular, component-based C++ simulation library and framework) network simulator. Our model exhibits high fidelity to the standard, adds detailed parameter configuration and data logging capabilities, and is compatible with the most recent OMNeT++ version. Our model is accepted by the OMNeT++ community and published as the current AFDX simulation model. Moreover, we propose a complete AFDX simulation framework by augmenting our improved OMNeT++ models with a new network configuration and analysis software tool that we call ANCAT. ANCAT enables users who lack familiarity with the OMNeT++ interface and specific file formats to provide simulation parameters in generic file formats such as.xlsx. Subsequently, ANCAT runs the simulation and generates reports for the results. We first systematically verify the correctness of our AFDX simulation framework by custom-designed experiments. Then, we investigate the practicability of our proposed framework with the performance analysis of a realistic AFDX network topology and message set. In particular, we demonstrate that our proposed framework can be efficiently used to explore the real-time capabilities and relevant features of AFDX networks, as well as provide basic guidelines for network configuration.

The violent collapse of vapor bubbles in cryogenic liquids

Vapor bubbles in cryogenic fluids may collapse violently under subcooled and pressurized conditions. Despite important implications for engineering applications such as cavitation erosion in liquid propellant rocket engines, these intense phenomena are still largely unexplored. In this paper, we systematically investigate the ambient conditions leading to the occurrence of violent collapses in liquid nitrogen and analyze their thermodynamic characteristics. Using Brenner’s time ratio χ, the regime of violent collapse is identified in the ambient pressure–temperature parameter space. Complete numerical simulations further refine the prediction and illustrate two classes of collapses. At 1 < χ < 10, the collapse is impacted by significant thermal effects and attains only moderate wall velocity. Only when χ > 10 does the collapse show more inertial features. A mechanism analysis pinpoints a critical time when the surrounding liquid enters supercritical state. The ultimate collapse intensity is shown to be closely associated with the dynamics at this moment. Our study provides a fresh perspective to the treatment of cavitation in cryogenic fluids. The findings can be instrumental in engineering design to mitigate adverse effects arising from intense cavitational activities.

Resolved photons in Sherpa

We present the first complete simulation framework, in the event generator, for resolved photon interactions at next-to leading order accuracy. It includes photon spectra obtained through the equivalent-photon approximation, parton distribution functions to parametrize the hadronic structure of quasi-real photons, the matching of the parton shower to next-to leading order QCD calculations for resolved photon cross sections, and the modelling of multiple-parton interactions. We validate our framework against a wide range of photo-production data from and experiments, observing good overall agreement. We identify important future steps relevant for high-quality simulations at the planned Electron–Ion Collider.

The adsorption effect on chemical kinetics at the reaction surface in a microfluidic channel of a biosensor for the SARS-Cov-2 detection

The global impact of the Severe Acute Respiratory Syndrome coronavirus has brought about significant changes in the lives of people worldwide, affecting societies and economies. Consequently, there is a growing need to explore the field of research focused on developing wearable sensors capable of continuously monitoring the presence of viruses in the environment. In this particular study, we simulated the binding reaction of the SARS-CoV-2 protein within a microchannel biosensor. One challenge encountered in this setup is the transportation of the analyte to the biosensor's reaction surface. The limited mass transport leads to the creation of a diffusion boundary layer, impeding the overall kinetic reaction. To improve the biosensor performance by enhancing transport, we thoroughly examined the impact of adsorption phenomena on the chemical kinetics. We compared the kinetic response of the biosensor in both cases, i.e. taking into account the adsorption and neglecting it in the numerical model. A parametric study concerning the trapping coefficient was carried out such as the quantity adsorbed at the saturation of a layer, the concentration at half saturation, and the relaxation time. In addition, we investigated the effect of the length of the reaction surface and the inlet velocity of the fluid. The main novelty in this work is to highlight the importance of adsorption on the kinetics of the binding reaction of the SARS-CoV-2-S protein which results in obtaining a complete simulation of the entire process of SARS-Cov-2 binding reaction. Therefore, the adsorption mechanism was described qualitatively and quantitatively using a kinetic model containing a trapping or source term. The obtained results demonstrate the successful integration of adsorption into the kinetic reaction process. The best biosensor performance is achieved in the first case. This theoretical analysis shows that the studied parameters significantly enhance the biosensor performance, especially the sensitivity as well as the response time.

Computational Characterization of Quantum‐Dot Light‐Emitting Diodes by Combinatorial Exciton Recombination Parameters and Photon Extraction Efficiency

AbstractQuantum‐dot light‐emitting diodes (QD‐LEDs) have gained significant attention for next‐generation display and lighting systems owing to their superior color selectivity and color purity. To maximize the efficiency of QD‐LED devices, it is of great importance to identify the key factors that govern their electro–optical properties. The efficiency of QD‐LED devices is strongly influenced by combinatorial processes, represented by the Shockley‐Read‐Hall rate A, Langevin strength B, and Auger probability C (ABC parameters) of quantum‐dots (QDs), along with photon extraction efficiency of QD‐LED devices. In this study, an integrated computational framework is proposed to accurately analyze the electro–optical properties of QD‐LED devices. The experimental device properties are characterized by ABC and photon extraction efficiency parameters through an innovative numerical data‐fitting procedure. Utilizing these parameters, a parametric analysis is performed based on a complete computational charge transport simulation model to explore the influence of the combinatorial exciton recombination processes. This computational framework aligns excellently with experimental results, showcasing its remarkable reliability and effectiveness in both quantitatively characterizing QD nanoparticles and in the detailed analysis of the electro–optical properties of QD‐LED devices.

Analysis of the Operating Characteristics of a Photothermal Storage Coupled Power Station Based on the Life-Cycle-Extending Renovation of Retired Thermal Power Units

Analysis of the Operating Characteristics of a Photothermal Storage Coupled Power Station Based on the Life-Cycle-Extending Renovation of Retired Thermal Power Units

A smart and intelligence based controlling algorithms for maximum power extraction and effective battery management in EV systems

AbstractAn economically appealing alternative to the use of fossil fuels in many nations is the electric vehicle (EV). Commercial EVs have a battery that powers the motor while also being charged by solar photovoltaic (PV). The original contribution of this research work is to implement a smart and advanced methodologies for accomplishing the objectives of MPPT control and battery management in EVs. Here, a novel spotted hyena optimized (SHO)‐MPPT controlling algorithm is applied to get the most out of the electricity generated by PV panels to meet EV energy requirements. A modified high gain super‐lift Luo converter has been implemented to improve and regulate the output voltage of PV for optimal battery charging and motor control. Then, an adaptive dynamic encoded network controller is designed to improve the voltage regulation and boosting performance of the DC‐DC converter. Consequently, a Henry gas solubility optimization is deployed to perform an effective battery management in EVs by optimally estimating the state of charge, which helps to increase the charging efficiency of EVs. Moreover, a three‐phase voltage source inverter has also been used to lower harmonic levels and deliver high‐quality output power to the EV motor. The results of the proposed MPPT controlling, converter controlling, and battery management strategies are verified in this study using a complete simulation and comparison analysis, where the parameters such as IV‐PV characteristics, motor torque, speed, total harmonic distortions (THDs), and so forth are taken into account for performance validation. By using the proposed controlling methodology, the THD is reduced to 1.09% with the improved vehicle speed of 3000 rpm, and overall performance efficiency of 99%.

1D simulation analysis and optimization design of engine cooling system

The engine cooling system takes away the heat absorbed by high-temperature parts during the operation of the car engine in a timely manner, keeping it working within the normal temperature range. This article applies Flowmaster software to establish a one-dimensional cooling system model for a certain model of National Five diesel engine. Relevant boundary conditions are inputted to complete steady-state simulation calculations of the system under different operating conditions, obtaining the flow rate, pressure distribution, and temperature rise of each component. At the same time, three optimization schemes are designed based on the original cooling system to predict and evaluate the working capacity of the cooling system, Based on the calculation and analysis results, improvement suggestions are proposed to guide the structural design and testing of the cooling system, greatly reducing research and development time and reducing research and development costs. The calculation results show that the flow rate of each component in the rated point, heating test condition, and idle condition of the original plan has decreased compared to the flow rate of a certain four machine on the same platform under the same working condition (the air compressor flow rate is higher than the limit value), and the temperature difference of the radiator and heating flow rate do not meet the specification requirements. Suggest using Plan 3 to increase the water pump speed ratio to 1.25, with a warm air flow rate of 5.33L/min under idle conditions, which is 72% higher than the original plan and meets the vehicle limit requirements. In addition, to ensure the accuracy and reliability of the model and calculations, this project conducted cooling system tests on an engine bench. The comparison between the simulation calculation data and the experimental data showed that the simulation calculation results were consistent with the experimental results.

Three-phase four wire shunt active power filter based on Simplified Backstepping technique for DC voltage control

This work presents a simulation of Three-Phase Four-Wire Shunt Active Power Filter (SAPF) destined to suppress harmonic currents generated by nonlinear loads. The SAPF guarantee also the compensation for unbalanced nonlinear load currents, reactive power and harmonic neutral current. The identification method is based on the synchronous reference frame. In order to have good performances of DC voltage control, simplified Backstepping control for three-Phase four-Wire (SAPF) system is proposed. Complete simulation of the resultant active filtering system validates the efficiency of the proposed simplified Backstepping control law over to the traditional control with PI controller.

Super-resolution image reconstruction of a neutron thick pinhole imaging system using image denoiser prior based on half quadratic splitting method

Thick pinhole imaging system is a powerful tool to diagnose intense pulsed radiation sources. However, high spatial resolution of a thick pinhole imaging system cannot be achieved due to the limitation of field of view (FOV) and detection efficiency. In this paper, we propose a novel super-resolution image reconstruction algorithm to improve the spatial resolution of a practical thick pinhole imaging system designed for 14.1 MeV neutrons with 100 mm FOV. Point spread function (PSF) of the imaging system is obtained through complete simulation methods, indicating that the spatial resolution of the imaging system is about 500 μ m. A 4 × super-resolution image reconstruction algorithm using image denoiser as implicit image prior, named image denoiser prior, based on half quadratic splitting (HQS) method is firstly applied to restore more details of neutron sources. The results show that the proposed algorithm performs better than the Richardson-Lucy (RL) algorithm with fewer artifacts and sharper boundaries in reconstructed images. Notably, at a low noise level, the proposed algorithm can surpass the Nyquist sampling limit of 200 μ m, which is determined by the equivalent camera pixel size, to achieve a spatial resolution of 150 μ m.

50-Channel Ionoacoustic Sensor for 60 MeV Proton Beam Characterization in Hadron Therapy Applications

This paper presents the design of a piezoelectric multichannel sensor optimized for sensing weak ionoacoustic signals generated at the Bragg peak (BP) of pulsed proton beams, with interesting possible applications in real-time monitoring of oncological hadron therapy treatments. To overcome current single-channel detector limitations and acquire the weak acoustic signals of clinical scenarios (60–200 MeV proton energy and few mGy dose deposition), the hereby presented detector overcomes the state-of-the-art approach (based on time-domain correlation i.e., averaging different beam pulses) by using spatial correlation (i.e., averaging signals from different detector channels) to increase the SNR without increasing the delivered dose. The detector design is tailored around the experimental environment characteristics (signal amplitude, signal frequency, relative BP-detector position) of a clinical proton beam (60 MeV, 2 mGy/pulse dose deposition). The detector design was characterized by a complete cross-domain simulation of the physical (proton beam), acoustic (wave propagation) and electrical (sensor and electronics frequency response and noise) environments. It achieves a clear 10 dB single-pulse SNR (2 mGy total dose) and allows to locate the BP with 125 μm precision (< 3% w.r.t. the particle range). Finally, the detector was experimentally validated by a piezoelectric acoustic testbench and has shown the capability to localize an acoustic source in 2D with sub-millimeter accuracy by using a multilateration-based BP detection algorithm.

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

METODOLOGÍA COMBINADA DE SIMULACIÓN PARA UNA CARACTERIZACIÓN COMPLETA DE LOS EFECTOS DE LA RADIACIÓN IONIZANTE EN DISPOSITIVOS DE DETECCIÓN

Ionizing radiation detection devices have been widely used in recent years in various applications and experimental fields, such as high-energy physics, nuclear physics, and medical imaging. Detailed description of their operational properties and their characterization by means of numerical modelling, as simulations, are key issue to understand the characteristics of radiation detectors in terms of efficiency, resolution and signal-to-noise ratio, since they allow optimising parameters that will be further used. The modelling processes, as the simulations, are routinely carried out using various tools, like Monte Carlo approaches, for instance: PENELOPE, FLUKA or GEANT4 are used to study the interaction of the radiation with the detector accounting for the whole physical processes. However, transporting electron/hole pairs, as generated through the device sensor to conform the corresponding electronic signals uses tools based on the finite element method, such as TCAD (Technology Computer Aided Design), which are developed mainly to help the microelectronics industry to design products. Usually, modelling approaches for radiation transport and electronic signal production are not integrated, thus performing the simulation process by splitting the problem into successive independent phases. Within this framework, the present study proposes, implements, and reports on a novel methodology that allows combining the two approaches aimed at integrating the complete simulation, thus achieving astep-by-step integrated modelling flow to describe the interaction of ionizing radiation with the sensor along with thetransport of the generated charge to the photodiodes and, finally, to the generation of electronic signals.