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Journal articles on the topic 'Simulation e modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

HD, Sunitha, and Keshaveni N. "Modeling and Simulation of LDMOS Device." International Journal of Engineering Research 4, no. 6 (2015): 291–95. http://dx.doi.org/10.17950/ijer/v4s6/604.

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2

Wang, Haosheng, and Hongen Zhong. "Modeling and Simulation of Spacecraft Power System Based on Modelica." E3S Web of Conferences 233 (2021): 04033. http://dx.doi.org/10.1051/e3sconf/202123304033.

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Spacecraft power system simulation involves the coupling of electrical, thermal and control domains. At present, the modeling and simulation of multi-domain physical system mainly uses the single-domain software to establish a single-domain model, and solves the unified multi-domain modeling and simulation through the interface between the software or using HLA. But it cannot fully support the modeling and simulation of multi-domain physical system, and the model has poor reusability and extensibility. As a multi-domain modeling language, Modelica language supports acausal modelling, unified m

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3

Khan, Irfan, and Ameen Uddin Ahmad. "Modeling and Simulation of Solar Photovoltaic System." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (2017): 1276–80. http://dx.doi.org/10.31142/ijtsrd5743.

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4

Whitlock, B. K., J. J. Morikuni, E. Conforti, and Sung-Mo Kang. "Simulation and modeling: simulating optical interconnects." IEEE Circuits and Devices Magazine 11, no. 3 (1995): 12–18. http://dx.doi.org/10.1109/101.385721.

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5

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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6

Gradella Villalva, Marcelo, Jonas Rafael Gazoli, and Ernesto Ruppert Filho. "Modeling And Circuit-based Simulation Of Photovoltaic Arrays." Eletrônica de Potência 14, no. 1 (2009): 35–45. http://dx.doi.org/10.18618/rep.2009.1.035045.

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7

Sandeep, Potnuru, and Rajesh C. V. S. "Modeling and Simulation of Bevel Gearboxes in AutoCAD." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 1937–39. http://dx.doi.org/10.31142/ijtsrd18200.

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8

Penev, Valentine. "Simulation Modeling in Military Affairs: Status and Perspectives." Information & Security: An International Journal 1, no. 1 (1998): 91–102. http://dx.doi.org/10.11610/isij.0107.

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9

Greif, Hajo. "Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence." Perspectives on Science 29, no. 4 (2021): 409–35. http://dx.doi.org/10.1162/posc_a_00377.

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Abstract The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the traditional dichotomies between symbolic and

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10

Opara, L. U. "Modeling and simulation." Computers and Electronics in Agriculture 18, no. 1 (1997): 63–64. http://dx.doi.org/10.1016/s0168-1699(97)01318-5.

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11

Lewis, Richard A. "Applied Simulation Modeling." Technometrics 46, no. 3 (2004): 360–61. http://dx.doi.org/10.1198/tech.2004.s194.

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12

Neumann, Peter G. "Modeling and simulation." Communications of the ACM 36, no. 6 (1993): 124. http://dx.doi.org/10.1145/153571.163293.

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13

Rhodes, M. L. "Simulation And Modeling." IEEE Computer Graphics and Applications 17, no. 5 (1997): 15. http://dx.doi.org/10.1109/mcg.1997.610197.

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14

Simacek, T. K. "Simulation and modeling." IEEE Circuits and Devices Magazine 8, no. 3 (1992): 7–8. http://dx.doi.org/10.1109/101.136778.

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15

Aoyagi, Takeshi. "Modeling and Simulation." Seikei-Kakou 18, no. 9 (2006): 674. http://dx.doi.org/10.4325/seikeikakou.18.674.

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16

Tian, Guang Shu, and Li Chen Zhang. "Multi-Domain Modeling and Co-Simulation Based on Modelica and Simulink." Applied Mechanics and Materials 596 (July 2014): 927–30. http://dx.doi.org/10.4028/www.scientific.net/amm.596.927.

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A co-simulation solution based on multi-domain modeling with Modelica is proposed to achieve the co-simulation of multi-domain modeling and simulation environment with other simulation environment . Based on the connection mechanism of multi-domain Modelica models the co-simulation under S-function co-simulation framework is implemented using the converting principle between Modelica models and Simulink modules. A co-simulation example between MWorks which is a multi-domain physical system modeling and simulation tool based on Modelica and AMESim indicates that the method can extend the applic

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17

Chen, Jia Xin, Hong Jun Wang, Chang Yu Liu, and Xiang Jun Zou. "Modeling and Performance Analyzing of Helix Transmission Base on Modelica." Key Engineering Materials 455 (December 2010): 511–15. http://dx.doi.org/10.4028/www.scientific.net/kem.455.511.

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Picking manipulator ontology structure used rotation and translational motion pairs to join the components together, and then a multi-joint motion mechanism was built which meet the picking space requirement. According to the characters of growth space of litchi fruit, here used helix transmission as the first axes of the picking manipulator, to achieve motion in vertical direction. This study introduces the application of multi-domain modeling and simulation when constructing and simulating a helix transmission. Use object-oriented technology, multi-domain modeling and simulation language Mod

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18

Kozlov, Petr Alexeyevich, Vitaliy Sergeyevich Kolokolnikov, and Ekaterina Vitalyevna Kopylova. "About simulation modeling and simulation systems." Transport of the Urals, no. 1 (2019): 3–6. http://dx.doi.org/10.20291/1815-9400-2019-1-3-6.

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19

FJELDLY, TOR A., and MICHAEL S. SHUR. "SIMULATION AND MODELING OF COMPOUND SEMICONDUCTOR DEVICES." International Journal of High Speed Electronics and Systems 06, no. 01 (1995): 237–84. http://dx.doi.org/10.1142/s0129156495000079.

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We review the simulation and modeling techniques used for popular compound semiconductor devices such as the Heterostructure Field Effect Transistor (HFET), the Metal Semiconductor Field Effect Transistor (MESFET), and the Heterostructure Bipolar Transistor (HBT). Starting with the basic transport theory and the numerical simulation techniques based on this theory, we proceed to give examples of Monte Carlo simulations and of 2D balance equation simulations for investigating fundamental device properties and for exploring new design concepts. Next, we present analytical HFET and MESFET models

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20

Parsanejad, Mohammadreza, Ebrahim Teimoury, and Abozar Parsanejad. "Supply Chain Simulation and Modeling with Theory of Control." International Journal of Modeling and Optimization 4, no. 2 (2014): 167–70. http://dx.doi.org/10.7763/ijmo.2014.v4.367.

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21

El-Hady, Abeer A. M., Maged N. F. Nashed, Shawky H. Arafa, and Salah G. Ramadan*. "Modeling and Simulation for Hybrid of PV-Wind system." International Journal of Engineering Research 4, no. 4 (2015): 178–83. http://dx.doi.org/10.17950/ijer/v4s4/403.

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22

Amine Souissi, Mohamed, Khalid Bensaid, and Rachid Ellaia. "Multi-agent modeling and simulation of a stock market." Investment Management and Financial Innovations 15, no. 4 (2018): 123–34. http://dx.doi.org/10.21511/imfi.15(4).2018.10.

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The stock market represents complex systems where multiple agents interact. The complexity of the environment in the financial markets in general has encouraged the use of modeling by multi-agent platforms and particularly in the case of the stock market.In this paper, an agent-based simulation model is proposed to study the behavior of the volume of market transactions. The model is based on the case of a single asset and three types of investor agents. Each investor can be a zero intelligent trader, fundamentalist trader or traders using historical information in the decision making process.

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23

Ku, Namkug, Sol Ha, and Myoung-Il Roh. "Crane Modeling and Simulation in Offshore Structure Building Industry." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 278–84. http://dx.doi.org/10.7763/ijcte.2014.v6.875.

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24

Thompson, Bradley, and Hwan-Sik Yoon. "Internal Combustion Engine Modeling Framework in Simulink: Gas Dynamics Modeling." Modelling and Simulation in Engineering 2020 (September 3, 2020): 1–16. http://dx.doi.org/10.1155/2020/6787408.

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With advancements in computer-aided design, simulation of internal combustion engines has become a vital tool for product development and design innovation. Among the simulation software packages currently available, MATLAB/Simulink is widely used for automotive system simulations, but does not contain a comprehensive engine modeling toolbox. To leverage MATLAB/Simulink’s capabilities, a Simulink-based 1D flow engine modeling framework has been developed. The framework allows engine component blocks to be connected in a physically representative manner in the Simulink environment, reducing mod

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25

Yang, Qi Rui, Lin Hua Piao, and Wen Jie Tian. "Modeling and Simulation of Airflow Omnibearing Posture Sensor." Advanced Materials Research 889-890 (February 2014): 817–20. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.817.

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In order to improve the performance of sensor, Using ANSYS software, the three-dimensional finite element modelling method and simulation results of micromachined airflow omnibearing level posture sensor are presented. The results show that: (1)Using the sense organ’s symmetry characteristic, a half model is built first, which is convenient for modelling and observing the simulation results;(2) Gridding should be meshed more densely where fluid grads is large, which is helpful of saving simulation time and improving precision. Compared to two-dimensional modeling, the simulation results of thr

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26

Anikeyev, S. H., S. M. Bahriy, and B. B. Hablovskiy. "SIMULATION MODELLING IN THE STRUCTURAL GRAVITY PROSPECTING." Prospecting and Development of Oil and Gas Fields, no. 2(71) (June 25, 2019): 38–48. http://dx.doi.org/10.31471/1993-9973-2019-2(71)-38-48.

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In accordance with the purpose of geophysical exploration, the gravity data interpretation is aimed at prospecting mineral resources which is based on the study of the geological cross-section structure. The task of quantitative interpretation, which uses methods of gravity modeling and gravity inversion, is the modelling of a gravity field (gravity modeling) and of a density structure of geological environments (gravity inversion). The article presents the definition and steps of the gravity data modelling technique. This technique is based on the construction of an informal sequence of equiv

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27

Herbert, D. B. "Simulation and modeling-simulating differential equations with SPICE2." IEEE Circuits and Devices Magazine 8, no. 1 (1992): 11–14. http://dx.doi.org/10.1109/101.121308.

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28

Deniz, Fatih, M. Nedim Alpdemir, Ahmet Kara, and Halit Oğuztüzün. "Supporting dynamic simulations with Simulation Modeling Architecture (SiMA): a Discrete Event System Specification-based modeling and simulation framework." SIMULATION 88, no. 6 (2012): 707–30. http://dx.doi.org/10.1177/0037549711428233.

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29

Li, Zhi Hua, Hong Guang Yang, Jun Yu, and You Ping Gong. "Modeling and Simulation of the PMSM-Precision Reducer System with Modelica." Applied Mechanics and Materials 201-202 (October 2012): 202–7. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.202.

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There is still lack of effective modeling and simulation method for complex electromechanical coupling system. Modelica is a multi-domain unified modeling language to solve the modeling and simulation problems of the complex and heterogeneous physical systems. Dymola is a Modelica-based modeling and simulation platform for the complex physical systems. In this paper, the dynamics model of the permanent magnet synchronous motor (PMSM)-precision reducer system is established using Lagrange-Maxwell equation. The simulation model of this system is set up with Modelica language. The simulation of t

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30

Daneshjo, Naqib. "Computers Modeling and Simulation." Advanced Materials Research 463-464 (February 2012): 1102–5. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1102.

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Modeling and Simulation is a discipline for developing a level of understanding of the interaction of the parts of a system, and of the system as a whole. The level of understanding which may be developed via this discipline is seldom achievable via any other discipline. A simulation is the manipulation of a model in such a way that it operates on time or space to compress it, thus enabling one to perceive the interactions that would not otherwise be apparent because of their separation in time or space. A model is a simplified representation of a system at some particular point in time or spa

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31

Deal, R. B., Averill M. Law, and W. David Kelton. "Simulation Modeling and Analysis." Technometrics 36, no. 4 (1994): 429. http://dx.doi.org/10.2307/1269971.

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32

Ziegel, Eric R., and Wayne Winston. "Simulation Modeling Using @Risk." Technometrics 39, no. 3 (1997): 345. http://dx.doi.org/10.2307/1271163.

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33

KATO, Toshiji. "Overview : Modeling and Simulation." Journal of The Institute of Electrical Engineers of Japan 129, no. 6 (2009): 344–47. http://dx.doi.org/10.1541/ieejjournal.129.344.

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34

Takahashi, Takehiro, and Noboru Schibuya. "EMC Simulation and Modeling." IEEJ Transactions on Electronics, Information and Systems 123, no. 7 (2003): 1192–95. http://dx.doi.org/10.1541/ieejeiss.123.1192.

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35

Ryu, Seol, and George C. Schatz. "Nanografting: Modeling and Simulation." Journal of the American Chemical Society 128, no. 35 (2006): 11563–73. http://dx.doi.org/10.1021/ja063138b.

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36

Sanchez, Paul. "Fundamentals of simulation modeling." IEEE Engineering Management Review 37, no. 2 (2009): 23. http://dx.doi.org/10.1109/emr.2009.5235453.

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37

Isbăşoiu, Eliza Consuela. "Numerical Modeling and Simulation." Advanced Science Letters 19, no. 1 (2013): 166–69. http://dx.doi.org/10.1166/asl.2013.4663.

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38

Schruben, Lee. "Simulation modeling for analysis." ACM Transactions on Modeling and Computer Simulation 20, no. 1 (2010): 1–22. http://dx.doi.org/10.1145/1667072.1667074.

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39

Zhang, Zhijun, Ireneusz Zbicinski, Lixin Huang, Tsutomu Nozaki, and Zhonghua Wu. "Drying Modeling and Simulation." Mathematical Problems in Engineering 2012 (2012): 1–3. http://dx.doi.org/10.1155/2012/398130.

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40

Yang, Ping. "Circuit simulation and modeling." IEEE Circuits and Devices Magazine 3, no. 5 (1987): 36–44. http://dx.doi.org/10.1109/mcd.1987.6323156.

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41

Yang, Ping. "Circuit simulation and modeling." IEEE Circuits and Devices Magazine 3, no. 6 (1987): 34–38. http://dx.doi.org/10.1109/mcd.1987.6323179.

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42

Yang, Ping. "Circuit simulation and modeling." IEEE Circuits and Devices Magazine 3, no. 1 (1987): 35. http://dx.doi.org/10.1109/mcd.1987.6323207.

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43

Berndt, E. R., A. S. Kydes, A. K. Agrawal, S. Rahman, R. Vichnevetsky, and W. F. Ames. "Energy Modeling and Simulation." Journal of the American Statistical Association 80, no. 392 (1985): 1063. http://dx.doi.org/10.2307/2288578.

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44

Giannini, Paul J., Fritz H. Grupe, and Robert M. Saholsky. "Reengineering Through Simulation Modeling." Information Systems Management 14, no. 3 (1997): 61–66. http://dx.doi.org/10.1080/10580539708907061.

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45

Campbell, Randall B. "Global Modeling and Simulation." SIMULATION 52, no. 1 (1989): 33–35. http://dx.doi.org/10.1177/003754978905200106.

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46

Green, David G. "Syntactic modeling and simulation." SIMULATION 54, no. 6 (1990): 281–86. http://dx.doi.org/10.1177/003754979005400605.

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47

Paoli, Roberto, and Karim Shariff. "Contrail Modeling and Simulation." Annual Review of Fluid Mechanics 48, no. 1 (2016): 393–427. http://dx.doi.org/10.1146/annurev-fluid-010814-013619.

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48

Ellwein, Leon B., and Samuel M. Cohen. "Simulation modeling of carcinogenesis." Toxicology and Applied Pharmacology 113, no. 1 (1992): 98–108. http://dx.doi.org/10.1016/0041-008x(92)90013-i.

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49

Ryan, John, and Cathal Heavey. "Process modeling for simulation." Computers in Industry 57, no. 5 (2006): 437–50. http://dx.doi.org/10.1016/j.compind.2006.02.002.

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50

Johnson, Mark E., and Mansooreh Mollaghasemi. "Simulation input data modeling." Annals of Operations Research 53, no. 1 (1994): 47–75. http://dx.doi.org/10.1007/bf02136826.

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