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Artykuły w czasopismach na temat „Simulation software”

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Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 6 lutego 2022

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1

Harwood, Keith. "Simulation software". New Scientist 193, nr 2590 (luty 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, nr 7/8 (lipiec 2001): 377–85. http://dx.doi.org/10.1097/00005110-200107000-00009.

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3

Galić, Mario, Ralf Thronicke, Benjamin Michael Schreck, Immo Feine i Hans-Joachim Bargstädt. "PROCESS MODELING AND SCENARIO SIMULATION IN CONSTRUCTION USING ENTERPRISE DYNAMICS SIMULATION SOFTWARE". Elektronički časopis građevinskog fakulteta Osijek 6, nr 10 (2.07.2015): 22–29. http://dx.doi.org/10.13167/2015.10.3.

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Sadeghi, Payman, i Michael D. Utzinger. "Simulation Software Application". International Journal of Environmental Sustainability 8, nr 1 (2012): 131–46. http://dx.doi.org/10.18848/2325-1077/cgp/v08i01/55040.

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5

Holder, Karen. "Selecting simulation software". OR Insight 3, nr 4 (październik 1990): 19–24. http://dx.doi.org/10.1057/ori.1990.32.

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6

Banerjee, S. "CMS Simulation Software". Journal of Physics: Conference Series 396, nr 2 (13.12.2012): 022003. http://dx.doi.org/10.1088/1742-6596/396/2/022003.

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7

Wang, Shihao. "Software Simulation for Hardware/Software Co-Verification". Journal of Computer Research and Development 42, nr 3 (2005): 514. http://dx.doi.org/10.1360/crad20050322.

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8

Shaikh, Habib, Rishabh Mehra, Sagar Mhatre i Deepali Vora. "Psychoanalysis using Software Simulation". International Journal of Computer Applications 182, nr 47 (11.04.2019): 6–9. http://dx.doi.org/10.5120/ijca2019918700.

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9

Raffo, David, i Paul Wernick. "Software Process Simulation Modelling". Journal of Systems and Software 59, nr 3 (grudzień 2001): 223–25. http://dx.doi.org/10.1016/s0164-1212(01)00063-2.

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Marshall, Z. "The ATLAS Simulation Software". Nuclear Physics B - Proceedings Supplements 197, nr 1 (grudzień 2009): 254–58. http://dx.doi.org/10.1016/j.nuclphysbps.2009.10.079.

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11

Hlupic, Vlatka. "Simulation software: Users' requirements". Computers & Industrial Engineering 37, nr 1-2 (październik 1999): 185–88. http://dx.doi.org/10.1016/s0360-8352(99)00051-0.

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12

Ören, Tuncer I. "Software Agents and Simulation". SIMULATION 76, nr 6 (czerwiec 2001): 328. http://dx.doi.org/10.1177/003754970107600601.

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13

Bader, Michael, Miriam Mehl, Ulrich Rüde i Gerhard Wellein. "Simulation software for supercomputers". Journal of Computational Science 2, nr 2 (maj 2011): 93–94. http://dx.doi.org/10.1016/j.jocs.2011.05.003.

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14

Takahashi, Isamu, i 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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15

Kane, A. J., i D. J. Evans. "Neural network software simulation". International Journal of Computer Mathematics 71, nr 4 (styczeń 1999): 475–94. http://dx.doi.org/10.1080/00207169908804823.

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16

Pidd, Mike. "Choosing discrete simulation software". OR Insight 2, nr 3 (lipiec 1989): 22–23. http://dx.doi.org/10.1057/ori.1989.27.

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17

Quarrell, Peter. "“Choosing discrete simulation software”". OR Insight 2, nr 4 (październik 1989): 26. http://dx.doi.org/10.1057/ori.1989.42.

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18

Tseng, Ampere A. "Software for robotic simulation". Advances in Engineering Software (1978) 11, nr 1 (styczeń 1989): 26–36. http://dx.doi.org/10.1016/0141-1195(89)90033-8.

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19

Lutfullin, A. A., I. I. Girfanov, I. T. Usmanov i O. S. Sotnikov. "Software for geomechanical simulation". Neftyanoe khozyaystvo - Oil Industry, nr 7 (2021): 49–52. http://dx.doi.org/10.24887/0028-2448-2021-7-49-52.

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20

Sithebe, Thembelani. "Automated Assembly Simulation Using Arena Software". Advanced Materials Research 740 (sierpień 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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21

Asmi, Ade, Jouvan Chandra Pratama Putra i Ismail Abdul Rahman. "Simulation of Room Airflow Using Comsol Multiphysics Software". Applied Mechanics and Materials 465-466 (grudzień 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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22

YU, Zhi-Bin, Hai JIN i Nan-Hai ZOU. "Computer Architecture Software-Based Simulation". Journal of Software 19, nr 4 (25.03.2010): 1051–68. http://dx.doi.org/10.3724/sp.j.1001.2008.01051.

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23

Banks, P. S., K. A. Irons i M. R. Woodman. "Interoperability of Process Simulation Software". Oil & Gas Science and Technology 60, nr 4 (lipiec 2005): 607–16. http://dx.doi.org/10.2516/ogst:2005043.

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24

Jones, Ian. "Simulation software helps tidal power". Renewable Energy Focus 13, nr 2 (marzec 2012): 24–25. http://dx.doi.org/10.1016/s1755-0084(12)70035-2.

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25

Zheltovа, I. S., A. A. Filippov, A. V. Pestrikov, D. Yu Kholodov, A. G. Klimentiev, V. A. Kononenko i 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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26

Glattfelder, A. H., X. Qiu, W. Schaufelberger i K. Reimann. "Educational Simulation Software in Oberon". IFAC Proceedings Volumes 27, nr 9 (sierpień 1994): 107–10. http://dx.doi.org/10.1016/s1474-6670(17)45906-4.

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27

CAO, Y., i K. TANAKA. "DEVELOPMENT OF FRASTA SIMULATION SOFTWARE". Acta Metallurgica Sinica (English Letters) 19, nr 3 (czerwiec 2006): 165–70. http://dx.doi.org/10.1016/s1006-7191(06)60039-2.

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28

Seidel, Jawor. "Turbochargers — Role of Software Simulation". Auto Tech Review 3, nr 4 (kwiecień 2014): 52–55. http://dx.doi.org/10.1365/s40112-014-0599-5.

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29

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

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30

Tinker, N. A., i D. E. Mather. "GREGOR: Software for Genetic Simulation". Journal of Heredity 84, nr 3 (maj 1993): 237. http://dx.doi.org/10.1093/oxfordjournals.jhered.a111329.

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31

Fumagalli, Luca, Adalberto Polenghi, Elisa Negri i Irene Roda. "Framework for simulation software selection". Journal of Simulation 13, nr 4 (16.04.2019): 286–303. http://dx.doi.org/10.1080/17477778.2019.1598782.

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32

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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33

Taylor, S. J. E., i S. Robinson. "Simulation software: evolution or revolution?" Journal of Simulation 3, nr 1 (marzec 2009): 1–2. http://dx.doi.org/10.1057/jos.2008.25.

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34

Breedam, Alex Van, Jan Raes i Karel Van de Velde. "Segmenting the simulation software market". OR Insight 3, nr 2 (kwiecień 1990): 9–13. http://dx.doi.org/10.1057/ori.1990.12.

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35

BOGEN, D. K. "Simulation Software for the Macintosh". Science 246, nr 4926 (6.10.1989): 138–42. http://dx.doi.org/10.1126/science.246.4926.138.

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36

Molares, Alfonso R., i Manuel A. Sobreira‐Seoane. "Benchmarking for acoustic simulation software". Journal of the Acoustical Society of America 123, nr 5 (maj 2008): 3515. http://dx.doi.org/10.1121/1.2934429.

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37

Hlupic, Vlatka. "Simulation Software Selection Using SimSelect". SIMULATION 69, nr 4 (październik 1997): 231–39. http://dx.doi.org/10.1177/003754979706900405.

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38

Hlupic, V., Z. Irani i R. J. Paul. "Evaluation Framework for Simulation Software". International Journal of Advanced Manufacturing Technology 15, nr 5 (18.05.1999): 366–82. http://dx.doi.org/10.1007/s001700050079.

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39

Merks, J. W. "Process simulation with spreadsheet software". Mining, Metallurgy & Exploration 16, nr 2 (maj 1999): 29–36. http://dx.doi.org/10.1007/bf03402805.

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40

Narasimhan, S. V., i D. Narayana Dutt. "Software simulation of the EEG". Journal of Biomedical Engineering 7, nr 4 (październik 1985): 275–81. http://dx.doi.org/10.1016/0141-5425(85)90054-8.

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41

Campbel, S. L., J. T. Betts, A. Engelsone, R. Nikoukhah i M. Najafi. "Some software aspects of DAE simulation & optimization software". PAMM 7, nr 1 (grudzień 2007): 1023103–4. http://dx.doi.org/10.1002/pamm.200700495.

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42

Siniksaran, Enis, i M. Hakan Satman. "WURS: a simulation software for university rankings—software review". Scientometrics 122, nr 1 (14.11.2019): 701–17. http://dx.doi.org/10.1007/s11192-019-03269-8.

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43

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 i Claire Wood. "Software Review of flexMIRT Version 3.5". Applied Psychological Measurement 42, nr 3 (26.10.2017): 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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44

Cioch, Frank A., i Scott Lohrer. "Reusing a Simulation Facility Software Architecture for Embedded Simulation". SIMULATION 79, nr 4 (kwiecień 2003): 183–96. http://dx.doi.org/10.1177/0037549703038885.

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Teuben, Peter. "Simulation Software: Then, Now and Virtual Observatory". Symposium - International Astronomical Union 208 (2003): 359–68. http://dx.doi.org/10.1017/s0074180900207316.

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Like hardware, evolution of software has had a major impact on the field of particle simulations. This paper illustrates how simulation software has evolved, and where it can go. In addition, with the various ongoing Virtual Observatory efforts, producers of data should think more about sharing their data! Some examples are given of what we can do with our data and how to share it with our colleagues and observers. In the Appendix we summarize the findings of an informal data and software usage survey that we took during this conference.
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46

Liu, Dalong, Xiaowei Jia i Wenqin Wang. "Comparative analysis of simulation of urban radiation field". MATEC Web of Conferences 282 (2019): 02026. http://dx.doi.org/10.1051/matecconf/201928202026.

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In order to compare the differences between various outdoor environmental simulation software in the urban radiation field, ENVI-met, RayMan and SOLWEIG were selected as the research objects. The multiple types of urban radiation intensities that can be obtained by each software are compared, and the calculation method of types of these radiations was analyzed. Effects of underlying surface on the radiation field were simulated separately. The differences in radiation field performance simulations of three softwares were compared from several aspects such as short wave, direct radiation and reflected radiation. It is found that Rayman has the high sensitivity and fast simulation speed, but the output radiation parameters are less; ENVI-met can calculate the most radiation parameters and calculate the long-short wave radiation heat transfer process between building surfaces; the SOLWEIG can be used for simulation in large outdoor space, and has more calculated radiation parameters, but has large error. All three softwares can calculate the effect of space enclosure on direct and scattered radiation.
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47

Sharp, Helen C., i Pat Hall. "An Interactive Multimedia Software House Simulation for Postgraduate Software Engineers". Journal of Interactive Media in Education 2001, nr 1 (4.06.2001): 1. http://dx.doi.org/10.5334/2001-3.

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48

Navarro, Emily Oh, i André van der Hoek. "Software process modeling for an educational software engineering simulation game". Software Process: Improvement and Practice 10, nr 3 (2005): 311–25. http://dx.doi.org/10.1002/spip.232.

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49

Tuohineva, Terho, Ilkka Väisänen, Antti Mäntylä, Teemu Kuivaniemi, Mauri Haataja i Tero Frondelius. "Benchmarking of two flexible multibody dynamic simulation software in engine simulations". Rakenteiden Mekaniikka 51, nr 2 (8.12.2018): 32–48. http://dx.doi.org/10.23998/rm.69961.

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In this paper, two different commercial multibody dynamic (MBD) simulation software cases are studied. Due to the restrictions determined in the conditions of contract, the names of the software are not revealed, instead being called Software S and Software E. The central purpose of this research was to investigate the abilities of Software S in the simulation of a large engine, as a part of the strength analysis process. The abilities were studied by comparing the program with another, here called Software E, which is designed primarily for engine simulations. The capabilities of Software E have been proven after years of usage at Wärtsilä, resulting in its essential role in the strength analysis process today. The aim was to find the shortcomings and restrictions of Software S but also advantages it could bring to the strength analysis process for Wärtsilä. Similar simulation models were also built using both programs during this research. A 16-cylinder V-engine was selected as the subject because of its size in order to obtain further information about the behavior of the program when working with extensive model files. The components of the engine were flexible and were reduced FE models, also called super elements. The forces and contact situations that occur inside the engine were modeled using elements provided by the MBD programs. Different levels of detail of the modeling elements were used to obtain information about the flexibility of the program. The results obtained from time integrations were compared to ensure the similarity of both modeling elements used. Also, this paper reports the calculation times. In addition, a small-scale study was performed for Software S to clarify the effect of the modes used in time integrations towards results accuracy and calculation times. Simulation models were built successfully in both programs, and the results obtained correlated with each other on an adequate level. Significant differences appeared in the features and usability of the programs in general. The GUI of Software S is advanced and user-friendly, whereas Software E is not focused on these features. On the other hand, the modeling element library of Software E covers all of the required features related to large engine simulations, some of which Software S is lacking. This work can be used in assistance when considering buying new software for a company as well as when investigating new development areas that could be improved with new software.
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Hlubeňová, Jana, i Daniel Hlubeň. "Algorithm for Selection of Simulation Software". Advanced Materials Research 463-464 (luty 2012): 1077–80. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1077.

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A lot of performance products designed for the simulation of manufacturing systems is currently available on the market. Some of them are designed exclusively for logistics, others are designed to simulate the robotic workplace and some of them allow simulation the entire virtual enterprise from its logistics to the entire workplace connected to the real system. The article describes the algorithm for choosing the most suitable simulation program.
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