Simulation Tool Research Articles

The Uncertainty of Reusable Building Components/Materials A Subsidiary of Circular Economy in the Africa Construction Sector: The Issue of Affordable Housing in Kenya

There is growing interest in the adaptation of circular economy approaches in the construction sector, for developing African countries like Kenya with an increase in construction activities leading to more generation of construction wastes. The approach of circularity presents an alternative to reuse these construction or demolition wastes thereby lowering environmental risks and providing for a lower price for building components/materials required for affordable building projects. However, there is little, or no information on these reusable building components/materials, which lowers the confidence by construction stakeholders in using them. No information on its possible new life longevity (durability), its possible maintenance budget, and no means of enabling a design team to plan for its acquisition (accessibility) in a design project. This paper adopted a review of previous literature on these topics to search for possible solutions to the research questions, of which some literature provided only a list of requirements needed to give a level of information to reusable building elements. The paper went further to provide proposals for an information catalogue that would enable the insertion of reusable building elements’ Indicators of service (availability, reliability, and maintainability) obtainable from experience with the elements. Also, a format for a maintenance budget plan would be required during the new service life of the reused element. And a means of simulation using the Monte Carlo simulation tool to obtain reliable viability of access to reusable building elements in a transitional building in its design stage. These proposals would have to be put into practice to ascertain their effectiveness, meanwhile, other areas of further studies are provided in the paper.

ACABLoop simulation tool: Improving the activation prediction of flowing PbLi alloy in support of DEMO fusion reactor design

ACABLoop simulation tool: Improving the activation prediction of flowing PbLi alloy in support of DEMO fusion reactor design

Simulation Games as an Alternative Formative Assessment Tool for Experiential Learning in VET Marketing Education: A Desk-Research Review

Simulation games continue to be widely used in marketing education. Based upon a review of simulation tools from the past decade, this study gathers insights on how simulations can be used as an effective assessment tool to enhance learning in marketing and digital marketing vocational subjects. In particular, this research looks more closely at the opportunities afforded by simulation games for experiential learning in relation to formative assessments as well as the challenges presented by the application and validity of the games. This study made use of the desk-based approach as a line of research study while making use of secondary data that is available in competent scholarly literature related to simulation games as an alternative formative assessment tool for experiential learning in VET marketing education. The methodological flow followed in this research used the five phases implementation of industry 4.0 technologies as suggested by Garrido-Lova (2021). This research is original as, through desk-research, the study gave an overview of how simulation games can contribute towards experiential learning. The study indicated that a plethora of evidence points towards the number of pedagogical effects students benefit from when assessments take a formative approach and are carried out through a simulation game. This research should be of keen interest to current and future users of marketing simulation games who might have questions about how to use them effectively in a vocational setting and for assessment purposes.

ETHOS.PeNALPS: A Tool for the Load Profile Simulation of Industrial Processes Based on a Material Flow Simulation

ETHOS.PeNALPS: A Tool for the Load Profile Simulation of Industrial Processes Based on a Material Flow Simulation

A versatilely high fidelity electric machines emulator for rapid testing of motor controller.

Electric machines emulators (EMEs) based on hardware-in-the-loop (HIL), which effectively act as emulators to mimic the actual motor behavior of Interior Permanent Magnet (IPM) machines. EME is frequently used to evaluate motor controller and motor control methodologies prior to development. The inverse magnetization motor model, which is used as the basis for real-time simulation in this paper's proposal for an electric machine emulator system based on HIL, uses FEA to create the motor model data. The nonlinear features of the motor may be successfully replicated with this motor model, and the accuracy of the electric machine emulator can be enhanced by using a straightforward and trustworthy motor controller. The real-time simulation tool typhoon HIL is used in the study to develop a hardware-in-the-loop simulation platform for an IPM electric machines emulator.

The concept of an intelligent mobile application with elements of augmented reality and PLM software for value stream mapping

AbstractGrowing demands for variability, reducing losses and increasing production efficiency bring difficult tasks for business management. In order to cope with increasingly demanding tasks, a good knowledge of effective management methods is required, as well as orientation in a wide range of application programs. One of the approaches dealing with reducing losses and streamlining selected processes is the introduction of the Lean philosophy. This philosophy is firmly embedded in the concept of Industry 4.0 and its principles are proving to be highly effective. In the presented contribution, we will focus on the possibilities of reducing losses by implementing selected Lean principles in conjunction with selected program resources and elements of mixed reality. The goal is to create an intelligent mobile application that, based on a simulation model, would enable an in-depth analysis of selected processes from the aspect of material value stream mapping analysis. The model created in this way could serve as a relevant basis for top management in the search for solutions to optimize processes. In the article, we evaluate selected Lean methods, create a workplace simulation model using the PLM simulation tool Tecnomatix Plant Simulation, and the obtained data are presented in an intelligent mobile application based on augmented reality. After the online update, the created application conveys all required relevant information to managers and simplifies their decision-making process.

Topology optimization of non-linear electromagnetic actuator based on Reluctance Network Analysis

Topology Optimization (TO) enables unrestricted exploration within a design domain and is typically based on a spatial discretization that is also used as the mesh for simulation. In this article, we propose to use for the simulation a mesh-based equivalent circuit method termed the Reluctance Network Analysis (RNA), with nonlinear magnetic properties of the material. The simulation tool is then used in a topology optimization process solved with generalized optimality criteria (GOC) method. During the optimization, the sensitivity matrix is needed, and we describe here how to derive the matrix in the case of a nonlinear RNA using Adjoint Variable Method (AVM). Finally, we implemented this topology optimization through a case study of an electromagnetic actuator.

Roadmap for the increase of beam brilliance from ECRIS and Microwave Discharge Ion Sources

The requirements for future accelerator chains need to increase the injected beam brilliance significantly, still keeping high the beam quality in terms of reliability, reproducibility and stability. A roadmap for ion source development may consist of several steps: plasma simulation, multiphysics simulation of each system component, high-level control system, plasma characterization, beam characterization, data analysis and, again, plasma simulation. The cycle starts and ends with plasma simulation because it is the instrument that shows how different phenomena take part in the plasma and beam formation and because, in such a way, the accuracy grows with each cycle. Commercial multiphysics simulation tools are essential for adequately designing all ion source equipment: magnets, intense electrostatic field regions, microwave propagation and coupling, thermal dissipation and vacuum. The dependence of source performances from source parameters (magnetic field profile, gas pressure, microwave power) has been widely investigated using a high-level control system[1] able to test tens of thousands of source configurations without human interaction. This characterization technique allowed us to identify a new magnetic configuration, High Stability Microwave Discharge Ion Sources[2], that produces a beam with high stability, intensity and brilliance. The plasma simulation tool we developed discloses the role of two types of electrostatic waves in plasma formation and their correlation to stability. The simulation provides a complete view of ions and electrons energy and density distributions, the formation of the plasma meniscus and the beam extraction. The paper will present the results obtained with this development procedure on Microwave Discharge Ion Sources and how we started to apply it to the Electron Cyclotron Resonance Ion Sources development.

Mechanically Controlled Landslide Deformation

AbstractMany landslide models assume a fully deformable body without any resistance against deformation. However, in reality, landslide bodies can display negligible to large deformation during motion. Examples for limited deformation include the prehistoric giant landslides of Flims and Köfels, or the Vajont landslide of 1963, where the structure of rock largely remained intact and the slides did not evolve into rock avalanches. Here, we propose a novel mechanical model for the controlled deformation of landslides. The model is based on the principle of material strength or resistance and includes a user‐specified function that reflects the mechanisms (internal friction, cohesion, viscosity, and yield strength) that act against the deformation induced by the free‐surface or the hydraulic pressure gradient of the landslide. This controls the landslide deformation and, in turn, also the motion and run‐out, and offers a unique possibility to describe the landslide motion ranging from a fully non‐deformable body sliding along the mountain slope to a completely fluidized motion without any resistance against the force associated with the free‐surface pressure gradient. The latter is the situation often considered for the motion of granular flows such as avalanches of snow or rock, or debris flows. The former can play a substantial role in the dynamics, however, has not yet been considered in mass flow simulations, severely limiting the applicability of those models. We demonstrate the performance of the new model and its applicability, also with the advanced open‐source computational mass flow simulation tool r.avaflow.

Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines

The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture, then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision, using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result, calculation time, and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally, based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380, a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 %, both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems, firefighting systems, simulation, and prototyping tools.

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, transcriptome or read sequences and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols; noise-free 'ideal' data; or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Relation between characteristic temperature and elution temperature in temperature programmed gas chromatography – Part II: Influence of column properties

The method development process in gas chromatography can be accelerated by suitable computer simulation tools using knowledge about the solute-column interactions described by thermodynamic retention parameters. Since retention parameters usually are determined under isothermal conditions, the presented work offers a step to estimate one of the most important retention parameters, the characteristic temperature Tchar by less laborious temperature programmed measurements.In the first part an empirical multivariate model was introduced describing the correlation between the elution temperature Telu of a solute and its characteristic temperature Tchar. Now in the second part a simulation model of GC and available retention data from a retention database was used to investigate the correlation between Telu and Tchar for an expanded range of heating rates and initial temperatures. In addition to part I, the simulation is used to investigate the influences of different properties of the separation column such as different phase ratios and column geometries like length and diameter or various stationary phases including SLB-5 ms, SPB-50, Stabilwax, Rtx-Dioxin2, Rxi-17Sil MS, Rxi-5Sil MS, ZB-PAH-CT, DB-5ms, Rxi-5ms, Rtx5 and FS5ms. The fit model is valid for all investigated stationary phases. The influence of the phase ratio to the correlation could be determined. Therefore, the model was expanded to this parameter.The expanded range of heating rates and the normalization for the system independent dimensionless heating rate required a further modification of the previously presented correlation model. The model now fits also under isothermal conditions. The results were used for estimation of the Tchar of an analyte from the elution temperature in the temperature program. The prediction performance was investigated and evaluated for 20 different temperature program conditions and at two phase ratios (β=125 and β=250). Under bes conditions the estimated and the measured Tchar values show relative differences <0.5%. With this novel model estimations for Tchar are possible at 20°C above the initial temperature, which expands the prediction range compared even for low and medium retained analytes compared to earlier approaches.

Optimizing the ITER NBI ion source by dedicated RF driver test stand

The experimental fusion reactor ITER will feature two (or three) heating neutral beam injectors (NBI) capable of delivering 33(or 50) MW of power into the plasma. A NBI consists of a plasma source for production of negative ions (extracted negative ion current up to 329 A/m2 in H and 285 A/m2 in D) then accelerated up to 1 MeV for one hour. The negative ion beam is neutralized, and the residual ions are electrostatically removed before injection. The beamline was designed for a beam divergence between 3 and 7 mrad.The ion source in ITER NBIs relies on RF-driven, Inductively-Coupled Plasmas (ICP), based on the prototypes developed at IPP Garching; RF-driven negative-ion beam sources have never been employed in fusion devices up to now. The recent results of SPIDER, the full size ITER NBI ion source operating at NBTF in Consorzio RFX, Padova, measure a beamlet divergence minimum of 12mrad and highlighted beam spatial non-uniformity. SPIDER results confirmed the experimental divergence found in smaller prototype sources, which is larger compared to filament-arc ion sources. Although prototype experiments have shown that the extracted current requirement can be achieved with minor design improvements, the beamlet divergence is expected to marginally achieve the design value of 7 mrad, which in multi-grid long accelerators results in unexpected heat loads over the accelerator grids. A contributor to the beam divergence is the energy/temperature of the extracted negative ions, so it is believed that plasma differences between the two source types play a role. Research is focused on the plasma parameters in the ion source.One RF driver, identical to the ones used in SPIDER, installed in a relatively small-scale experimental set-up, inherently more flexible than large devices, is starting operations devoted to the investigation of the properties of RF-generated plasmas, so as to contribute to the assessment of negative ion precursors, and of their relationship with the plasma parameters, particularly when enhancing plasma confinement. The scientific questions, that have arisen from the preliminary results of SPIDER, guided the design of the test stand, which are described in this contribution, together with the diagnostic systems and related simulation tools. The test stand, which shares with the larger experiment all the geometrical features and constraints, will allow technological developments and optimized engineering solutions related to the ICP design for the ITER NBIs.

Request Dispatching Over Distributed SDN Control Plane: A Multiagent Approach.

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request distribution. Existing policies are designed under certain assumptions, including a single centralized agent, global network knowledge, and a fixed number of controllers, which often cannot be satisfied in practice. This article proposes MADRina, Multiagent Deep Reinforcement Learning for request dispatching, to design policies with high dispatching adaptability and performance. First, we design a multiagent system to address the limitation of using a centralized agent with global network knowledge. Second, we propose a Deep Neural Network-based adaptive policy to enable request dispatching over an elastic set of controllers. Third, we develop a new algorithm to train the adaptive policies in a multiagent context. We prototype MADRina and build a simulation tool to evaluate its performance using real-world network data and topology. The results show that MADRina can significantly reduce response time by up to 30% compared to existing approaches.

Strategic CO2 management in the energy and petroleum sector for the production of low-carbon LNG: A Qatar Case Study

The imperative to mitigate industrial CO2 emissions amidst global climate change has led to the development of innovative strategies that align economic growth with environmental sustainability. This study introduces a comprehensive CO2 allocation and utilisation framework, designed to reduce the environmental impact on the production of Liquefied Natural Gas (LNG). Demploying a multi-objective Linear Programming (LP) approach, the study introduces a system that dynamically allocates CO2 from a major source to several sinks, each with unique characteristics and requirements. Focusing on the State of Qatar, a major player in the global LNG market, advanced simulation tools, such as Aspen HYSYS and QASR Simulator are utilised to define the parameters of these sinks, ensuring precise and efficient CO2 allocation. An integral component of the model is the incorporation of a carbon tax, considered for both the source and the sinks. The results demonstrate that the profit maximisation objective generated a $23.6 billion annual profit, although with high emissions of 23.37 Mt/year. In contrast, the emission minimisation objective curbed emissions to 19.01 Mt/year at a profit of $1.4 billion. The multi-objective approach garnered $18.6 billion annually with emissions at 22.37 Mt/year. The CO2/LNG metric was used to gauge LNG's environmental footprint. Objective 1 yielded a ratio of 0.3033, while objective 2 achieved the best score at 0.2463. The multi-objective approach balanced both, with a ratio of 0.2905. These findings illuminate the feasibility of optimising industrial practices for producing low-carbon LNG through deliberate CO2 allocation and within a circular economy framework.

Real-time Finite Element Analysis based on surrogate model

In the field of engineering, Finite Element Analysis (FEA) serves as a pivotal numerical simulation tool. Nevertheless, its inherent offline nature and dependency on real-time data pose limitations on its versatility. To confront this challenge, a real-time FEA approach centered on surrogate modeling has been developed. This method enables the instantaneous prediction of critical performance parameters, thereby providing crucial support for real-time decision-making and process monitoring. In the context of this study, the focus is on the glass compression molding process, a process that is not easily observable. The research commences with preliminary thermal conduction simulations conducted using ABAQUS software. Subsequently, a surrogate model is constructed to facilitate real-time FEA for the temperature field of the glass preform. Finally, Unity 3D software is harnessed for visualization, offering an effective tool for the real-time monitoring and control of the glass-forming process.

A Novel Mechano-Synthesized Zeolitic Tetrazolate Framework for a High-Performance Triboelectric Nanogenerator and Self-Powered Selective Neurochemical Detection.

Designing a high-performing triboelectric novel material with eco-friendly, rapid, and cost-effective synthesis is the future of material research in triboelectric nanogenerators (TENG). We report a mechanochemical ball mill synthesis of a zeolitic tetrazolate framework (ZTF-8) that is isostructural with the well-known zeolitic imidazolate framework ZIF-8. ZTF-8 is extremely stable in water, 0.1 M aqueous acid/base solutions for 75 days at 25 °C, and boiling water (100 °C) for 7 days. Kelvin probe force microscopy and molecular electrostatic surface potential computational analysis exhibited that ZTF-8 has a very high positive surface potential. Atomic force microscopy and three-dimensional digital microscopy studies reveal the high roughness profile in the ZTF-8 film. The unique structure, exceptional acid/base stability, good dielectric property, and high roughness profile combined with the extremely electropositive nature of ZTF-8 make it a suitable candidate as a polymer-free triboelectric positive material in TENG with outstanding performance (power density of 720 mW/m2). High triboelectric output was further validated using the COMSOL Multiphysics simulation tool. Simple mechanical hand tapping of the ZTF-based TENG (ZTF-TENG) device generates high electric output, which was practically used to power numerous low-powered devices like tally counter, clinical thermometer, and digital clock and also illuminates 125 light-emitting diodes. In addition, the efficiency of ZTF-TENG was utilized as a self-powered device for a selective dopamine (DA) sensor with good sensitivity (377.76 mV/μM/cm2), wide range linearity (5-120 μM), and excellent limit of detection (0.42 μM).

Investigation into the impact of bulk defects in the drift layer on the electrical properties of GaN-based trench Schottky barrier diodes

Using numerical simulation tools, this work systematically investigates the impact of bulk defects in the drift layer on GaN-based trench metal–insulator–semiconductor barrier-controlled Schottky rectifiers. Investigations show that in forward conduction, the acceptor-type defects significantly increase the on-resistance (R on.sp). When the device is in reverse blocking mode, donor-type defects tend to weaken the charge-coupling effect, leading to early breakdown of the device, while acceptor-type defects show the opposite feature. In addition, our report identifies that the reverse blocking effect is significantly impacted when the defects are located in the region with maximum electric field magnitude. We also find that the acceptor-type traps generate a remarkable charging/discharging effect, which will destabilize the dynamic forward conduction process. Hence, we numerically prove that bulk defects should be avoided in actual power diodes.

Prediction of the in-plane permeability and air evacuation time of fiber-placed thermoplastic composite preforms with engineered intertape channels

The two main void removal mechanisms during vacuum-bag-only (VBO) consolidation of thermoplastic composites are through-thickness diffusion and in-plane air evacuation. The automated fiber placement (AFP) process allows for the creation of preforms with an engineered intertape channel network by deliberately introducing spacing between the tapes that can facilitate air evacuation during the VBO consolidation. However, it is unclear what dimensions of the intertape channels allow for effective in-plane air evacuation. The current research presents a predictive simulation tool to optimize the intertape channel dimensions for effective in-plane air evacuation. An analytical method is developed to calculate the in-plane permeability tensors of the composite preform with uniform intertape channel dimensions. In addition, a more elaborate mesoscale method is developed that estimates the in-plane permeability tensor of the engineered intertape channel network based on the distributions in intertape channel dimensions. Finally, a finite difference model is implemented to calculate the time required for air evacuation as a function of the in-plane permeability tensors of the preform and its in-plane dimensions. The results from the models indicated that in-plane air evacuation through the engineered intertape channel network is quick, and it takes only a few minutes to evacuate 99% of the air from large preforms like fuselage panels.

Particle content of very inclined air showers for radio signal modeling

The reconstruction of very inclined air showers is a new challenge for next-generation radio experiments such as the AugerPrime radio upgrade, BEACON, and GRAND, which focus on the detection of ultra high energy particles. To tackle this, we study the electromagnetic particle content of very inclined air showers, which has scarcely been studied so far. Using the simulation tools CORSIKA and CoREAS, and analytical modeling, we explore the energy range of the particles that contribute most to the radio emission, quantify their lateral extent, and estimate the atmospheric depth at which the radio emission is strongest. We find that the spatial distribution of the electromagnetic component in very inclined air showers has characteristic features that could lead to clear signatures in the radio signal, and hence impact the reconstruction strategies of next-generation radio-detection experiments.