Simulation Programming Research Articles

Output Characteristics of Graphene Field Effect Transistors

The use of graphene, which has high mobility of charge carriers, high thermal conductivity and a number of other positive properties, is promising for the creation of new semiconductor devices with good output characteristics. The aim was to simulate the output characteristics of field effect transistors containing graphene using the Monte-Carlo method and the Poisson equation.Two semiconductor structures in which a single layer (or monolayer) of graphene is placed on a substrate formed from 6H-SiC silicon carbide material are considered. The peculiarity of the first of them is that the contact areas of drain and source were completely located on the graphene layer, the length of which along the longitudinal coordinate was equal to the length of the substrate. The second structure differed in that the length of the graphene layer was shortened and the drain and source areas were partly located on the graphene layer and partly on the substrate.The main output characteristics of field-effect transistors based on the two semiconductor structures considered were obtained by modeling. The modeling was performed using the statistical Monte Carlo method. To perform the simulation, a computational algorithm was developed and a program of numerical simulation using the Monte-Carlo method in three-dimensional space using the Poisson equation was compiled and debugged.The results of the studies show that the development of field-effect transistors using graphene layers can improve the output characteristics – to increase the output current and transconductance, as well as the limit frequency of semiconductor structures in high frequency ranges.

Molecular dynamics study of confined water in the periclase-brucite system under conditions of reaction-induced fracturing

The volume-increase associated with hydration reactions in rocks may lead to reaction-induced fracturing, but requires a stable water film to be present at reactive grain boundaries even when subject to compressive stress. Hydration of periclase to brucite is associated with a solid volume increase of ca. 110%. Recent experiments on the periclase-brucite system observed that when the effective mean stress exceeds 30 MPa, the reaction rate slows down dramatically. We hypothesize that for the brucite forming reaction to progress, the fluid film between grains must remain stable. If the applied pressure becomes larger than the hydration force, the fluid film will collapse and be squeezed out of the grain contacts. To quantify this effect, we study the behavior of a water film confined between periclase or brucite surfaces subject to compressive stress, by performing molecular dynamics simulations. The simulations are carried out using the ClayFF force field and the single point charge (SPC) water model in the molecular dynamics simulations program LAMMPS. The setup consists of two interfaces of either periclase or brucite surrounded by water. Our simulations show that when the pressure reaches a few tens of MPa, the water film collapses and reduces the water film to one or two water layers, while the self-diffusion coefficient of water molecules by a factor of eight. A water film thickness below two water layers is thinner than the size of the hydration shell around Mg2+-ions, which will limit ion-transport. The observed collapse of the water film to a single layer at a normal pressure of 25–30 MPa might explain the observed slow-down of reaction-induced fracturing in the periclase-brucite system.

Quantifying the impact of water availability on China's energy system under uncertainties: A perceptive of energy-water nexus

Energy and water are interdependent and interwoven, forming energy-water nexus. The chronic of water shortage in China can make a negative impact on energy system and further hinder the socioeconomic sustainable development. The coal-dominated energy structure consumes a large amount of water, which further intensities the dilemma between increasing energy demand and shrinking water resources. In this study, a China's non-deterministic energy-water nexus (CNEWN) model is established through incorporating techniques of Monte Carlo simulation (MCS) and interval-parameter programming (IPP). The CNEWN model is capable of simulating probability distributions of water availability, reflecting uncertainties derived from economic development and technology choice, and providing optimal scheme for China's energy system management over a long-term (2021–2050) horizon. Multiple scenarios are set up to quantify the impact of water availability on the national-scale energy system and sensitivity analysis is performed to assess the influence of uncertain parameters on modeling outputs. Results indicate that a variety of uncertainties existed in the energy system's parameters can affect the modeling outputs and the making decisions. Strict water availability constraints can stimulate the development of renewable energies and promote the transition of energy structure to clean and low-carbon pattern. Results also reveal that water scarcity can have a significant influence on the national energy system assocaited with water resources utilization, pollutant and carbon dioxide emissions, as well as system cost. The results provide a solid scientific basis for coordinately optimizing energy and water as well as effectively supporting the sustainable development of national energy system.

The Development and Effectiveness of Combining Case-Based Online Lecture and Simulation Programs to Facilitate Interprofessional Function Care Training in Nursing Homes.

The aim of this study was to (1) develop a case-based combination of online lectures and simulations for function-focused care by nursing home interdisciplinary caregivers and (2) evaluate participants' increasing shared team and task knowledge by surrogate measures of team process and team performance. The study comprised the following steps: (1) the design of a learning module using a shared understanding of function-focused care as a shared mental model; (2) the establishment of two computer programs for online lectures and simulations using cases involving older adults with disabilities; and (3) testing the educational programs using a repeated-measures design. Participants were 61 interdisciplinary caregivers from seven nursing homes in four regions. Data from participants' team process and team performance were collected pre test, post online lecture, and post simulation. Repeated-measures analysis of variance showed that the mean scores for scales of "transactive memory system," "knowledge sharing and utilization," "team outcomes," "motivation for learning transfer," "self-efficacy," "interpersonal understanding," "proactivity in problem solving," and "function-focused care knowledge" differed significantly between the time points. The results indicate that a case-based program of a combination of online lectures and simulations elicits significant improvements in participants' team process and team performance.

Resilient distribution network planning under the severe windstorms using a risk-based approach

This paper proposes a risk-based approach for planning resilient radial distribution networks to severe windstorms. The proposed approach is based on the concept of interval analysis, Relative Distance Measure (RDM) interval arithmetic, mixed integer linear programming, and risk analysis. It enables obtaining a number of different resilient network plans taking into account dependence among the following uncertain inputs: maximum wind speed, storm duration and its annual frequency of occurrence, fragility of network components, repair duration, forecasted load and generation from renewable generators. In order to improve the computational efficiency of the proposed approach a hybrid simulated annealing and mixed integer linear programming algorithm is introduced. The best resilient plan is selected by employing the Minimal risk (Minimax) criterion for measuring and managing risk. In this way, the proposed approach provides a decision-maker with a means of determining the resilient network plan that minimizes the risk of significant costs due to severe windstorms.

Correlation between air and sound propagation to determine air permeability of buildings for single/double wood pane windows

Building air leakage is a very important criterion on which one building could be differentiated from another one. It can lead to a huge impact on several important parameters inside a building, like energy consumption or indoor air quality. There are several possibilities for its determination, and it could be highlighted three major categories: experimental determinations, predicting models or simulations programs. As the experimental approach is difficult to be conducted being weather dependent or time consuming and the predicting models could lead to errors up to 100%, in this paper we aim to present a novel experimental method for determining the air permeability of a building, by measuring the sound pressure level difference between outdoor-indoor of the analyzed façade. An experimental campaign was conducted using the Blower Door method and an acoustic approach determining the impact of the window’s type on the airtightness of a façade. In extension, the correlation between the two transfer phenomena was highlighted throughout a mathematical model. Multiple acoustical parameters that could influence the air leakage determinations were analyzed. The importance of the reverberation time of the analyzed chamber in the acoustical transfer phenomenon was determined and a practical nomogram was developed.

Simulated laughter, perceived stress, and discourse in adults with aphasia

ABSTRACT Background Several studies have revealed an association between stress and language in nonclinical and aphasia groups. Laughter is considered to be an important contributor to physical and mental health including physiological and self-reported perceived stress. Simulated laughter programs, such as Laughter Yoga (LY), are a way to engage a person in the act of laughter without a humorous stimulus. The importance of laughter in aphasia therapy has been underexplored. Aims The current study investigated the effects of a simulated laughter program based in LY techniques on discourse measures and perceived stress for adults with aphasia. Qualitative feedback regarding the LY program was also collected from participants to assess their perception of the program. Additionally, the convergent validity of the Simple Aphasia Stress Scale (SASS) was explored. SASS measures were correlated with discourse measures in post hoc analyses to contribute to the growing literature examining the relation between perceived stress and language performance. Methods A waitlist crossover design was conducted across nine participants all with stroke and mild to moderate aphasia. All participants participated in a 3-day intervention period in which they engaged in a 20-minute simulated laughter program and a 3-day comparison condition in which they engaged in a 20-minute sitting session. Pre- and post- measures of self-reported perceived stress and discourse were taken on days 1 and 3 of each condition. Qualitative feedback on the simulated laughter program was also gathered. Outcomes & results No significant changes were noted in discourse measures or perceived stress in relation to the LY intervention. Responses regarding enjoyment of the simulated laughter program were primarily positive, however, some responses revealed an unfavorable opinion of the experience. The SASS demonstrated good convergent validity by significantly correlating with the Single-Item Stress Measurement (SISM) and Self-Assessment Manikin (SAM)-Arousal. Post-hoc analysis revealed associations between the SASS and certain measures of discourse dysfluency and productivity. Conclusions The simulated laughter program proved ineffective at differentially changing discourse dysfluency, productivity, or perceived stress in adults with aphasia when compared to a sitting condition. The SASS’ convergent validity is encouraging for its use as a measure of perceived stress by clinicians and researchers. Perceived stress correlated with certain discourse dysfluency and productivity measures adding to a growing body of literature in the area of stress and aphasia.

Advisor Based Modelling of Regenerative Braking Performance of Electric Vehicles at Different Road Slopes

In this study, an electrical vehicle model, which has 75kW AC asynchronous engine and 25kW NiCad batteries, has been composed by means of (ADVISOR-Advanced Vehicle Simulator) programme. During the driving cycle formed for the designed electrical vehicle, charging state of batteries, braking losses, battery temperatures and fuel consumption have been analysed at different road slopes. The study has shown that power to batteries is provided by regenerative braking at all slopes and if the slope is downside, more energy is stored into batteries due to regenerative braking. In simulation of the modelled device, maximum brake power loss of 4.43 kW has decreased at road slope of ∝=-%1.5 by means of regenerative recovery. At road slope of α=-4.5, the highest charging level has been obtained as 99.1%.

Improvement of warpage prediction through integrative simulation approach for thermoplastic material

In the injection-molded parts, prediction of accurate warpage at initial level becomes mandatory to avoid iterative work of mold modifications. Simulation teams of many organizations are using existing commercial programs for process simulations. Material models in existing simulation technologies are having certain limitations and assumptions, which can regularly result in up to 50% variation of warpage results as compared to the actual physical warpage measurement. The commonly used Moldflow simulation model, for example, ignores temperature-dependent mechanical properties and the stress relaxation spectrum for viscoelastic materials. These assumptions affect the accuracy of the warpage prediction results significantly. To decrease these kinds of variations, BASF extended its Ultrasim® tool which is based on integrative simulation technology. Recently, a newly developed thermomechanical material model with temperature-dependent nonlinear mechanical properties and stress relaxation behavior was added in the Ultrasim. This model has been used in this work to consider the complete transient description of the warpage, which starts at packing phase of the part inside the mold, followed by actual cooling and ejection. In this article, unreinforced semicrystalline polybutylene terephthalate polymer material (Ultradur® B4520) is considered for warpage correlational study. The accuracy of the warpage prediction is compared between the integrative simulation approach, existing warpage simulation method, and the actual experimental inspection results. The result exhibits that the accuracy of the integrative simulation (Ultrasim)-based warpage simulation is relatively better than existing simulation technologies and closer to the actual measurement.

Speed-guided intelligent transportation system helps achieve low-carbon and green traffic: Evidence from real-world measurements

Intelligent transportation systems (ITS) have emerged as essential measures to manage traffic congestion and to reduce vehicular emission and energy consumption, while their effectiveness has not been directly quantified under real-world driving conditions. By conducting real-world driving and emission measurements with a portable emission measurement system, we found that the usage of the speed guided-ITS (SG-ITS) significantly reduces fuel consumption and pollutant emissions (i.e., carbon monoxide, nitrogen monoxide, nitrogen dioxide, and particle number) of both tested vehicles (a light-duty gasoline car and a heavy-duty diesel truck) through decreasing the frequency of starts and stops, the strength of acceleration, and the time proportions of acceleration and idle. These reductions obtained from real-world measurements are higher than those predicted by vehicle emission models such as Motor Vehicle Emission Simulator (MOVES) and Computer Programme to calculate Emissions from Road Transport (COPERT). Reductions in fuel consumption and pollutant emissions due to the usage of the SG-ITS are most efficient on urban roads (low traffic) among all the three road types, while the most time saving can be obtained on urban roads (high traffic). By combining a high-resolution vehicle emission inventory, we estimate that the implementation of the SG-ITS may significantly reduce the total vehicular exhaust emissions of nitrogen oxides, carbon monoxide, total hydrocarbons, and particulate matter in Beijing by 15.9%, 20.5%, 23.9%, and 22.5%, respectively. Our study directly evaluates the benefits of using SG-ITS based on real-world measurements, highlighting that implementation of the SG-ITS can be a practical policy option in terms of efficiently reducing energy consumption, mitigating urban air pollution, and alleviating urban traffic congestion.

Modeling viscosity of light and intermediate dead oil systems using advanced computational frameworks and artificial neural networks

The dead oil viscosity is a key parameter to numerous reservoir engineering problems such as modeling of (viscously-unstable) flow and transport in hydrocarbon reservoirs, sweep efficiency of enhanced oil recovery scenarios as well as the breakthrough times of the injected fluid. Prediction of this thermos-physical parameter, however, is of challenge due to nonlinear dependence on reservoir conditions as well as the crude oil characteristics. Previous studies have attempted to develop predictive empirical correlations or other intelligent models for dead oil viscosity; however, they often suffer from the lack of generality and required accuracy. In this work, based on a comprehensive databank from diverse geological sources, we develop three intelligent models –upon various schemes including simulated annealing programming, artificial neural network, and decision tree– for estimating dead oil viscosity. The latter may be used further for prediction of saturated and under-saturated oil viscosity as well. Our models function in wide range of temperatures and oil API gravity; hence, they can be employed as unified, general-in-purpose frameworks for universal prediction of dead oil viscosity. We compare the resulting novel frameworks with the pre-existing models available in the literature, and demonstrate the superiority of the decision tree-based model over others in terms of statistical (and graphical) error estimates as well as the (physical) validity of the model. The findings of this study can help for better understanding and more accurate management, simulation and prediction in different oil fields.

A modified finite difference model to the reverse recovery of silicon PIN diodes

In this paper, Silvaco Atlas TCAD device-circuit mixed simulation and MATLAB programming are used to compute the reverse recovery processes of silicon PIN diodes. The latter is based on solving the ambipolar diffusion equation (ADE) with the moving boundaries. The results of the ADE-based Fourier expansion (FE) and finite difference (FD) method are first compared with that from the Atlas simulation. It is found that the result from the FE method agrees very well with that from the Atlas simulation, while the result from the FD method is much worse. The reason is attributed to approximating the second-order partial space differentiation by a FD form with a time-dependent constant space step in the FD method. One clear phenomenon is that the voltage in a FD simulation shows a very steeper drop followed by a very steeper rise. To solve this problem, we propose a modified finite difference (MFD) method in which the space discretization step is fixed when solving the ADE by a single-step back-Euler method and the new coordinates of two moving boundaries of the un-depleted N− region are iterated through the zero-value of the boundary carrier density, current and voltage requirement. Then a new grid is set up based on the new boundary coordinates and a cubic spline interpolation is used to transfer p(x, t) from the old grid to the new one. The result from our MFD method agrees very well with those from Atlas and FE simulation. In addition two sets (a slow set and a fast one) of carrier concentration dependent Shockley-Read-Hall recombination life time parameters are used to study the validity of the usual boundary conditions at two boundaries of the un-depleted N− region in the ambipolar diffusion approximation. Our results in some cases the boundary conditions may fail.

Optimized breeding strategies to harness genetic resources with different performance levels

BackgroundThe narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy.ResultsWe compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.ConclusionResults of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.

NLN/Jeffries Simulation Framework for Simulated Participant Methodology

The integration of human role players into simulation education is at an all-time high. In 2017, the Association of Standardized Patient Educators released the Standards of Best Practice with the intention of complementing the International Nurses Association for Clinical Simulation and Learning Standards of Best Practicesm: Simulation. As the popularity of human role players in simulation rises, it is imperative to wrap the andragogy with a theoretical framework to ensure simulation-based education (SBE) is maximized for safety, psychological fidelity, and educational outcomes.This article suggests a modification of the NLN Jeffries Theory into SBE, where human role players and simulated participants are integrated. The identified components of Jeffries Theory, context, background, design, educational practices, simulation experience, and outcomes remain unchanged from the original. The suggested adaptations within each component are also discussed. This modification can be referenced to establish, evaluate, and/or modify simulated participant education programs.

Evaluation of solar-assisted absorption refrigeration cycle by using a multi-ejector

The theoretical analysis for the absorption cooling system of the multi-ejectors flash tank has been investigated. In this research, the analysis of the energy balance is conducted to predict the possibility of improvement in the thermal loads components of the absorption cooling system with multi-ejectors -flash tank. The computer program of simulation has been used to assess the single, double and triple ejectors for the absorption cooling system performance by utilizing a solution of the water ammonia as a working liquid, which operated under stable conditions. The results indicate that the cycle with three ejectors has the lower generator and evaporator thermal loads than the other cycles. It is also showed that the generator load for a cycle using double ejectors is higher than other cycles, while the evaporator thermal load is found permanently less than the basic and single cycle and slightly higher than the cycle which has three ejectors. As well as the improvement in overall COPs of the combined cycle using double ejectors under different operating conditions is higher than that of the other combined cycles. It was found that the total COPs for the double ejector cycle are higher by ratios (9.2% and 5%) at higher and lower operation conditions, respectively, than the combined triple ejectors’ cycle.

Self-Regulating Artificial-Free Linear Programming Solver Using a Jump and Simplex Method

An enthusiastic artificial-free linear programming method based on a sequence of jumps and the simplex method is proposed in this paper. It performs in three phases. Starting with phase 1, it guarantees the existence of a feasible point by relaxing all non-acute constraints. With this initial starting feasible point, in phase 2, it sequentially jumps to the improved objective feasible points. The last phase reinstates the rest of the non-acute constraints and uses the dual simplex method to find the optimal point. The computation results show that this method is more efficient than the standard simplex method and the artificial-free simplex algorithm based on the non-acute constraint relaxation for 41 netlib problems and 280 simulated linear programs.

A choice behavior experiment with circular business models using machine learning and simulation modeling

BackgroundTransitions from a linear (take-make-dispose) to a circular product system (considering reuse/remanufacturing/recycling) require changes of business models through new value propositions. Therefore, the focus for industrial businesses shifts from selling physical products to, for example, providing access to functionality through business innovation. In this context it is particularly challenging to understand what complexity a new concept like circular economy (CE) brings to established businesses where the success and the failure of the business is dependent on customer’s acceptance of new value propositions. ObjectiveThe objective of this paper is to develop an algorithm based on gathered survey data to “learn” choice behavior of a small customer group and then replicate that choice behavior on a larger population level. PurposeThis paper explores the opportunities of different circular business offers in the city of Stockholm by embedding support vector machine classifiers, which are trained on CE survey data, in a simulation model to quantify and study choice behavior on city level. MethodStated choices from CE surveys including unique demographic data from the respondents, i.e. age, income, gender and education, are used for algorithm training. Based on the survey data, support vector machine algorithms are trained to replicate the decision-making process of a small sample of respondents. The example of a washing machine is used as a case study with the attributes price and payment scheme, environmental friendliness as well as service level. The trained support vector machines are then implemented in a simulation model to simulate choice behavior on population level (Stockholm city). OriginalityThis paper is the first of its kind to use both machine learning and simulation approaches in a CE market acceptance context. FindingsBased on the washing machine-specific survey and Stockholm-specific customer data, results indicate that larger share of the Stockholm population would be willing to opt for circular washing machine offers compared to the existing linear sales model. Given the data-driven nature of machine learning algorithms and the process-oriented structure of simulations programs allows for generating large amounts of data from small samples. This supports exploration of new emerging areas like CE in addition to saving time and expenses.

Gamma ray shielding characteristics of Sn-20Bi and Sn-20Bi-0.4Cu lead-free alloys

As a result of the expansion in the different uses of gamma ray sources and as a result of the harmful effects of radiation exposure, scientific research remains in need of finding and developing materials used as radiation shields. In the present study, shielding parameters of Sn-20Bi (SB) and Sn-20Bi-0.4Cu (SBC) lead-free alloys solidified with and without the effect of a rotating magnetic field (RMF) have been studied. Gamma ray mass attenuation coefficient (μt/ρ) was measured experimentally for the studied alloys at photon energy of 356, 511, 662, 1173, 1274, and 1332 KeV. The experimental μt/ρ values were compared with the calculated values by using the WinXCom and MCNP5 programs for alloys solidified without RMF. Besides, WinXCom and MCNP5 were used to calculate μt/ρ for gamma ray photons of energy range of 10−3-102 MeV. The atomic and electronic cross sections (σa and σe) and hence the effective atomic number (Zeff) and effective electron density (Neff) were calculated. The mean free path (MFP) of photons was calculated and introduced to MCNP5 input file to calculate the gamma ray energy buildup factor (EBF). The results of μt/ρ are shown to be highly compatible between the theoretical WinXCom and simulated MCNP5 programs and with the experimental values. The results of the shielding parameters confirm that the alloys under study are good shielding materials against gamma rays.

Numerical simulation of combustion in a biomass briquette chain boiler

A bed and furnace integrated model of biomass briquette chain boilers was built on basis of theories of computational fluid mechanics, heat transfer, thermodynamics, and chemical reaction kinetics. The combustion in the biomass briquette chain boilers was characterized under five working parameters (air-preheating temperature, fuel bed thickness, grate advancing speed, particle diameter, air supply mode) through experimental design, numerical simulation, and mathematical programming. Finally, these parameters were optimized with the ant colony algorithm. It was found that the combustion efficiency and economic benefits were both optimized at the air-preheating temperature of 553.22 K, fuel bed thickness of 0.16 m, particle diameter of 0.02 m, and uniform centered air supply. This study offers some theoretical basis and method for the optimized design and operation of biomass briquette chain boilers.

Optimization Simulation programming for NoC

The article presents the concept of networks-on-chip (NoCs) as a promising alternative to communication subsystem for multiprocessor systems with bus architecture. The networks simulator developed as important software tool to estimate NoC performance parameters. The results of approbation of the developed simulator are reliance of the number of hops on the NoC dimension for mesh and torus topologies, as well as the dependences of communication links workload on the frequency, with which IP blocks generate messages. Its possibilities are considered and the accepted results are given.