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Artykuły w czasopismach na temat „Simulation software”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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

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

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

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Holder, Karen. "Selecting simulation software." OR Insight 3, no. 4 (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 (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 (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

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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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Shaikh, Habib, Rishabh Mehra, Sagar Mhatre, and Deepali Vora. "Psychoanalysis using Software Simulation." International Journal of Computer Applications 182, no. 47 (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 (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

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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 (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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Kane, A. J., and D. J. Evans. "Neural network software simulation." International Journal of Computer Mathematics 71, no. 4 (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 (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 (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 (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 (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 (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 (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 (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

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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 (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-

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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 Multiphy

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26

Cavanaugh, S. Austin, Ciji A. Heiser, Karen B. Hoeve, et al. "Software Review of flexMIRT Version 3.5." Applied Psychological Measurement 42, no. 3 (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, s

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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 (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 in

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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 (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 (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 (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 pr

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31

Zheltovа, I. S., A. A. Filippov, A. V. Pestrikov, et al. "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 (2005): 607–16. http://dx.doi.org/10.2516/ogst:2005043.

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

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

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

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

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

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

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Molares, Alfonso R., and Manuel A. Sobreira‐Seoane. "Benchmarking for acoustic simulation software." Journal of the Acoustical Society of America 123, no. 5 (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 sim

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41

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

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

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

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

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

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

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

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

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

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Saravanos, Antonios, and Matthew X. Curinga. "Simulating the Software Development Lifecycle: The Waterfall Model." Applied System Innovation 6, no. 6 (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 bot

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