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Journal articles on the topic 'Simulation software'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

Harwood, Keith. "Simulation software." New Scientist 193, no. 2590 (February 2007): 19. http://dx.doi.org/10.1016/s0262-4079(07)60336-4.

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2

Lepley, Cyndi J. "Simulation Software." JONA: The Journal of Nursing Administration 31, no. 7/8 (July 2001): 377–85. http://dx.doi.org/10.1097/00005110-200107000-00009.

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3

Sadeghi, Payman, and Michael D. Utzinger. "Simulation Software Application." International Journal of Environmental Sustainability 8, no. 1 (2012): 131–46. http://dx.doi.org/10.18848/2325-1077/cgp/v08i01/55040.

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4

Banerjee, S. "CMS Simulation Software." Journal of Physics: Conference Series 396, no. 2 (December 13, 2012): 022003. http://dx.doi.org/10.1088/1742-6596/396/2/022003.

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5

Holder, Karen. "Selecting simulation software." OR Insight 3, no. 4 (October 1990): 19–24. http://dx.doi.org/10.1057/ori.1990.32.

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6

Galić, Mario, Ralf Thronicke, Benjamin Michael Schreck, Immo Feine, and Hans-Joachim Bargstädt. "PROCESS MODELING AND SCENARIO SIMULATION IN CONSTRUCTION USING ENTERPRISE DYNAMICS SIMULATION SOFTWARE." Elektronički časopis građevinskog fakulteta Osijek 6, no. 10 (July 2, 2015): 22–29. http://dx.doi.org/10.13167/2015.10.3.

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7

Corti, Gloria, Adam Davis, Michał Kreps, Michal Mazurek, Dmitry Popov, and Benedetto Gianluca Siddi. "New software technologies in the LHCb Simulation." Journal of Physics: Conference Series 2438, no. 1 (February 1, 2023): 012108. http://dx.doi.org/10.1088/1742-6596/2438/1/012108.

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Abstract Monte Carlo simulations are key to the design and commissioning of new detectors as well as the interpretation of physics measurements. The amount of simulated samples required for the Run 3 physics program of the LHCb experiment will increase significantly from 2022 onward to match the increase in the amount of data collected with respect to Run 1 and 2 operation. A new version of the LHCb Gauss simulation framework has been developed to better accommodate new simulation techniques and software technologies to produce the necessary samples within the computing resources allocated for the next few years. It provides the LHCb specific functionality while the generic simulation infrastructure has been encapsulated in an experiment-independent framework, GAUSSINO. The latter combines the GAUDI core software framework and the GEANT4 simulation toolkit and fully exploits their multi-threading capabilities. Fast simulation interface is the latest feature being developed in GAUSSINO to interact with GEANT4 giving the possibility of replacing its detailed description of physics processes with an extensive palette of fast simulation models for a specific LHCb sub-detector. A facility to ease the production of training datasets for fast simulations models has also been introduced.

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8

Wang, Shihao. "Software Simulation for Hardware/Software Co-Verification." Journal of Computer Research and Development 42, no. 3 (2005): 514. http://dx.doi.org/10.1360/crad20050322.

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9

Shaikh, Habib, Rishabh Mehra, Sagar Mhatre, and Deepali Vora. "Psychoanalysis using Software Simulation." International Journal of Computer Applications 182, no. 47 (April 11, 2019): 6–9. http://dx.doi.org/10.5120/ijca2019918700.

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10

Hutagalung, Cerlinto. "Banker Algorithm Simulation Software." Instal : Jurnal Komputer 12, no. 02 (October 26, 2021): 61–68. http://dx.doi.org/10.54209/jurnalkomputer.v12i02.22.

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Banker's algorithm is an algorithm that models a bank in a small town dealing with a set of customers. This banker algorithm is used to deal with queuing problems in banking. In this case, one of them is how to simulate queues in banking. In this study, the design and manufacture of simulation software is used to help simulate whether a system is in a safe state or an unsafe state, in a safe condition the process is continued but if the process is unsafe the process is delayed until the system is in a safe state. The result of this research is a banker algorithm simulation software that models a banker who is dealing with a group of customers in a bank.

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11

Lutfullin, A. A., I. I. Girfanov, I. T. Usmanov, and O. S. Sotnikov. "Software for geomechanical simulation." Neftyanoe khozyaystvo - Oil Industry, no. 7 (2021): 49–52. http://dx.doi.org/10.24887/0028-2448-2021-7-49-52.

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12

Ören, Tuncer I. "Software Agents and Simulation." SIMULATION 76, no. 6 (June 2001): 328. http://dx.doi.org/10.1177/003754970107600601.

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13

Zhu, Xuliang. "DarkSHINE Simulation software framework." Nuclear and Particle Physics Proceedings 346 (October 2024): 57. http://dx.doi.org/10.1016/j.nuclphysbps.2024.07.003.

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14

Kane, A. J., and D. J. Evans. "Neural network software simulation." International Journal of Computer Mathematics 71, no. 4 (January 1999): 475–94. http://dx.doi.org/10.1080/00207169908804823.

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15

Hlupic, Vlatka. "Simulation software: Users' requirements." Computers & Industrial Engineering 37, no. 1-2 (October 1999): 185–88. http://dx.doi.org/10.1016/s0360-8352(99)00051-0.

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16

Marshall, Z. "The ATLAS Simulation Software." Nuclear Physics B - Proceedings Supplements 197, no. 1 (December 2009): 254–58. http://dx.doi.org/10.1016/j.nuclphysbps.2009.10.079.

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17

Takahashi, Isamu, and Yo Yamagata. "CASTING SIMULATION SOFTWARE ADSTEFAN." Proceedings of The Computational Mechanics Conference 2002.15 (2002): 93–94. http://dx.doi.org/10.1299/jsmecmd.2002.15.93.

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18

Raffo, David, and Paul Wernick. "Software Process Simulation Modelling." Journal of Systems and Software 59, no. 3 (December 2001): 223–25. http://dx.doi.org/10.1016/s0164-1212(01)00063-2.

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19

Tseng, Ampere A. "Software for robotic simulation." Advances in Engineering Software (1978) 11, no. 1 (January 1989): 26–36. http://dx.doi.org/10.1016/0141-1195(89)90033-8.

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20

Bader, Michael, Miriam Mehl, Ulrich Rüde, and Gerhard Wellein. "Simulation software for supercomputers." Journal of Computational Science 2, no. 2 (May 2011): 93–94. http://dx.doi.org/10.1016/j.jocs.2011.05.003.

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21

Pidd, Mike. "Choosing discrete simulation software." OR Insight 2, no. 3 (July 1989): 22–23. http://dx.doi.org/10.1057/ori.1989.27.

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22

Quarrell, Peter. "“Choosing discrete simulation software”." OR Insight 2, no. 4 (October 1989): 26. http://dx.doi.org/10.1057/ori.1989.42.

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23

Sithebe, Thembelani. "Automated Assembly Simulation Using Arena Software." Advanced Materials Research 740 (August 2013): 27–38. http://dx.doi.org/10.4028/www.scientific.net/amr.740.27.

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Most research has been concentrating on getting input from and simulating specific assembly processes. The most advanced simulation research is based on data driven methods. The rest of simulation articles are case study based. This work envisages establishing a generic simulation process, which will be based on the generic algorithm and generic assumptions to be used to simulate an automated assembly process. The simulation model is based on proposed configuration and operational information. It is used to verify that the proposed system will meet required production rates and, to predict the relative performance of alternative configurations. It can be used for what-if analysis to investigate different operation scenarios and optimize production systems. Validation of the result was done through the use of different scenarios. It is only by using the model to answer specific questions about ways of changing the system that realizable improvements are identified.

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24

Chávez, José Antonio Manco Chávez, Joel Núñez Mejía, Haydeé Verónica Túllume Huayanay, Daniel Enrique Terrones Rojas, Rolando Juan Borja Torres, and Carlos Héctor Cerna Gonzales. "Simulation of magnetic field produced by induction in toroid and solenoid using GeoGebra software." Journal of Posthumanism 5, no. 2 (April 4, 2025): 85–104. https://doi.org/10.63332/joph.v5i2.406.

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In today’s era of modernity and the appearance of new knowledge-construction approaches supported by technological tools such as A.I., various instruments contribute to enhancing educational quality like GeoGebra, an open-source software with extensive capabilities for simulations. This research established three objectives, all of which are answered in its conclusions. The study focused on simulating magnetic fields with predefined geometric shapes, analyzed using mathematical principles. Computational simulation was the primary methodology, involving the implementation of Ampere’s law, Biot-Savart law, and electromagnetism, as well as their applications in solenoids and toroids. The simulations were developed using GeoGebra’s virtual simulation tools and Java Script application. As a result, a functional simulation was created to model the behavior of a normally closed solenoid valve, allowing manipulation of parameters such as radius, length, number of turns, diameter and current intensity. Similarly, a toroidal transformer simulation was developed, enabling adjustments to coil count, toroidal surface area, voltage, primary and secondary toroids to control each parameter in the respective model. The discussion highlights that similar applications have been successfully developed by other researchers, demonstrating their effectiveness in supporting university students’ learning. The study concludes that simulations significantly strengthen foundational physics knowledge in engineering education.

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25

Asmi, Ade, Jouvan Chandra Pratama Putra, and Ismail Abdul Rahman. "Simulation of Room Airflow Using Comsol Multiphysics Software." Applied Mechanics and Materials 465-466 (December 2013): 571–77. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.571.

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Airflow in a room can be supplied both through natural mean and also by the helps of mechanical ventilation. Natural ventilation is more sustainable compared to mechanical system; however natural ventilation, it may not be sufficient to fulfil the need of ventilation for a specific room. This study presents simulation works carried out regarding to the airflow movement in a room due to mechanical ventilation. The measurement of air velocity was taken using Davis anemometer at random point in the room. The measured air velocity then used as an input in simulation work which used Comsol Multiphysics software. The simulation process begins by building up geometry of the room, assigning constant parameters, meshing the geometry of the room, and finally run the solver analysis. The results from simulations indicate that the air distributions in the room are below ASHRAE standard. This is due to the airflow distribution from the airflow injection of air-conditioning system is not well distributed. The simulations results are validated with the measured value and found that the percentage differences between the simulated and measured values are within the range of 3 - 10 %. Keywords: Simulation, Airflow movement, Mechanical ventilation, Comsol Multiphysics software

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26

Cavanaugh, S. Austin, Ciji A. Heiser, Karen B. Hoeve, Eren Halil Ozberk, Elizabeth A. Patton, John C. Sessoms, Myrah R. Stockdale, Elif Bengi Unsal-Ozberk, and Claire Wood. "Software Review of flexMIRT Version 3.5." Applied Psychological Measurement 42, no. 3 (October 26, 2017): 240–55. http://dx.doi.org/10.1177/0146621617726792.

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flexMIRT is a versatile program for unidimensional and multidimensional item response theory (IRT) calibrations, scoring analyses, and model-based simulations. With an adaptable syntax that allows for various combinations of model specifications, estimation constraints, and estimation choices, flexMIRT can handle almost all of the most popular IRT models for dichotomous and polytomous data. The software package also supports diagnostic classification models and multigroup and multilevel analyses. This review evaluates the software from a user’s perspective as well as some of its calibration, scoring, and simulation capabilities. Two simulation studies are included: one demonstrates some basic simulation capabilities and the other provides some direct comparisons with BILOG-MG. The review suggests that flexMIRT is a very good product that is only likely to get better as new features and suggestions for improvement are implemented.

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27

Kovbasiuk, Kateryna, Kamil Židek, Michal Balog, and Liudmyla Dobrovolska. "ANALYSIS OF THE SELECTED SIMULATION SOFTWARE PACKAGES: A STUDY." Acta Tecnología 7, no. 4 (December 31, 2021): 111–20. http://dx.doi.org/10.22306/atec.v7i4.120.

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The simulation software market is becoming more complex and universal. Computer simulations are thus more accessible and are becoming a modern tool that has a wide application in industry. Their potential and benefits can be used in small and large projects. A simulation model can take into account inventory, assembly, production and human resources, leading to decisions that can maintain or improve efficiency at the lowest possible cost. The data obtained through the simulation allow to test different combinations and scenarios in the virtual world. The benefits of manufacturing simulation include reducing investment risk, minimizing waste, improving efficiency, reducing energy consumption and even increasing worker health. The question arises as to which of the possible simulation packages is the most suitable for a given company, so that the investments made are the best possible. In the first part of the paper the theoretical basis of simulation in Industry 4.0 is presented, including the description of the possible simulation modelling tools. The second part of the paper offers comparative and descriptive analysis of six selected discrete-event simulation software packages – AnyLogic, Arena, FlexSim, SIMUL8, Tecnomatix Plant Simulation and WITNESS. The given simulation tools are compared based on their main characteristics, simulation features, application areas and popularity among the companies which use simulation software packages.

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28

Aryan, Raunak, and Bhavinay Shelly. "Prosper Software for Gas Lift System Design and Simulation." International Journal of Research Publication and Reviews 4, no. 8 (August 2023): 2874–84. http://dx.doi.org/10.55248/gengpi.4.823.51837.

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29

Antonov, Anton. "SIMULATION SOFTWARE FOR MODELING THE MOVEMENT OF MATERIAL FLOWS." Journal Scientific and Applied Research 14, no. 1 (December 1, 2018): 17–22. http://dx.doi.org/10.46687/jsar.v14i1.244.

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The Computer modeling is one of the best tools for the development of a real automated warehouse system. It aims to explore and define the behavior of the system and make the evaluation of its performance. In this paper is analyzed and simulated a software for the design of logistic systems.

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30

Kopec, Ján, Laura Lachvajderová, Marek Kliment, and Peter Trebuňa. "SIMULATION PROCESSES IN COMPANIES USING PLM AND TECNOMATIX PLANT SIMULATION SOFTWARE." Acta Simulatio 7, no. 3 (September 30, 2021): 13–18. http://dx.doi.org/10.22306/asim.v7i3.61.

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This paper aims to demonstrate the use of simulations in the process of improving production processes. With the help of simulations, it is possible to test the efficiency of production in the virtual world, various variants of simulations. The advantage of simulations is the fact that it is possible to make simple and especially cost-effective changes to the production process and thus make it more efficient to the required level. 3D modeling of production halls is already an integral part of improvement. At the same time, this article points out that PLM positively affects the improvement process itself. The final simulation is aimed at improving the production process.

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31

Zheltovа, I. S., A. A. Filippov, A. V. Pestrikov, D. Yu Kholodov, A. G. Klimentiev, V. A. Kononenko, and K. N. Baydyukov. "Coiled tubing simulation software development." Neftyanoe khozyaystvo - Oil Industry 7 (2020): 120–26. http://dx.doi.org/10.24887/0028-2448-2020-7-120-126.

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32

Banks, P. S., K. A. Irons, and M. R. Woodman. "Interoperability of Process Simulation Software." Oil & Gas Science and Technology 60, no. 4 (July 2005): 607–16. http://dx.doi.org/10.2516/ogst:2005043.

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33

YU, Zhi-Bin, Hai JIN, and Nan-Hai ZOU. "Computer Architecture Software-Based Simulation." Journal of Software 19, no. 4 (March 25, 2010): 1051–68. http://dx.doi.org/10.3724/sp.j.1001.2008.01051.

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34

BOGEN, D. K. "Simulation Software for the Macintosh." Science 246, no. 4926 (October 6, 1989): 138–42. http://dx.doi.org/10.1126/science.246.4926.138.

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35

Tinker, N. A., and D. E. Mather. "GREGOR: Software for Genetic Simulation." Journal of Heredity 84, no. 3 (May 1993): 237. http://dx.doi.org/10.1093/oxfordjournals.jhered.a111329.

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36

Ryan, Robert R. "ADAMS Mechanical System Simulation Software." Vehicle System Dynamics 22, sup1 (January 1993): 144–48. http://dx.doi.org/10.1080/00423119308969488.

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37

Seidel, Jawor. "Turbochargers — Role of Software Simulation." Auto Tech Review 3, no. 4 (April 2014): 52–55. http://dx.doi.org/10.1365/s40112-014-0599-5.

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38

Hlupic, Vlatka. "Simulation Software Selection Using SimSelect." SIMULATION 69, no. 4 (October 1997): 231–39. http://dx.doi.org/10.1177/003754979706900405.

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39

Molares, Alfonso R., and Manuel A. Sobreira‐Seoane. "Benchmarking for acoustic simulation software." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3515. http://dx.doi.org/10.1121/1.2934429.

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40

Deng, Ziyan. "Status of JUNO Simulation Software." EPJ Web of Conferences 245 (2020): 02022. http://dx.doi.org/10.1051/epjconf/202024502022.

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The JUNO (Jiangmen Underground Neutrino Observatory) experiment is a multi-purpose neutrino experiment designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. It will be composed of a 20k ton liquid scintillator (LS) central detector equipped with about 18000 20-inch photon-multipliers (PMTs) and 25000 3-inch PMTs, a water Cherenkov detector with about 2000 20-inch PMTs, and a top tracker. Monte-Carlo simulation is a fundamental tool for optimizing the detector design, tuning reconstruction algorithms, and performing physics study. The status of JUNO simulation software will be presented, including generator interface, detector geometry, physics processes, MC truth, pull-mode electronic simulation.

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41

Jones, Ian. "Simulation software helps tidal power." Renewable Energy Focus 13, no. 2 (March 2012): 24–25. http://dx.doi.org/10.1016/s1755-0084(12)70035-2.

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42

CAO, Y., and K. TANAKA. "DEVELOPMENT OF FRASTA SIMULATION SOFTWARE." Acta Metallurgica Sinica (English Letters) 19, no. 3 (June 2006): 165–70. http://dx.doi.org/10.1016/s1006-7191(06)60039-2.

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43

Breedam, Alex Van, Jan Raes, and Karel Van de Velde. "Segmenting the simulation software market." OR Insight 3, no. 2 (April 1990): 9–13. http://dx.doi.org/10.1057/ori.1990.12.

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44

Fumagalli, Luca, Adalberto Polenghi, Elisa Negri, and Irene Roda. "Framework for simulation software selection." Journal of Simulation 13, no. 4 (April 16, 2019): 286–303. http://dx.doi.org/10.1080/17477778.2019.1598782.

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45

Taylor, S. J. E., and S. Robinson. "Simulation software: evolution or revolution?" Journal of Simulation 3, no. 1 (March 2009): 1–2. http://dx.doi.org/10.1057/jos.2008.25.

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46

Merks, J. W. "Process simulation with spreadsheet software." Mining, Metallurgy & Exploration 16, no. 2 (May 1999): 29–36. http://dx.doi.org/10.1007/bf03402805.

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47

Glattfelder, A. H., X. Qiu, W. Schaufelberger, and K. Reimann. "Educational Simulation Software in Oberon." IFAC Proceedings Volumes 27, no. 9 (August 1994): 107–10. http://dx.doi.org/10.1016/s1474-6670(17)45906-4.

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48

Narasimhan, S. V., and D. Narayana Dutt. "Software simulation of the EEG." Journal of Biomedical Engineering 7, no. 4 (October 1985): 275–81. http://dx.doi.org/10.1016/0141-5425(85)90054-8.

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49

Hlupic, V., Z. Irani, and R. J. Paul. "Evaluation Framework for Simulation Software." International Journal of Advanced Manufacturing Technology 15, no. 5 (May 18, 1999): 366–82. http://dx.doi.org/10.1007/s001700050079.

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50

Saravanos, Antonios, and Matthew X. Curinga. "Simulating the Software Development Lifecycle: The Waterfall Model." Applied System Innovation 6, no. 6 (November 14, 2023): 108. http://dx.doi.org/10.3390/asi6060108.

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This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.

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