Статті в журналах: "Models and simulation"

1

Dixion-Gough, Robert. "Theoretical Models and Simulation Models." Géographes associés 18, no. 1 (1996): 51–53. http://dx.doi.org/10.3406/geoas.1996.2006.

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2

Glenn, Floyd. "The Case for Micro-models." Proceedings of the Human Factors Society Annual Meeting 33, no. 18 (October 1989): 1228–32. http://dx.doi.org/10.1177/154193128903301813.

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This paper examines the appropriate role of human performance micro-models in simulations of human-machine system operations. Requirements for general human micro-models are considered relative to the objectives of simulation studies, the conditions under which simulations are constructed and used, the status of human performance data bases and models, and the features provided with general-purpose simulation software. This investigation focuses particularly on a new simulation tool for simulating human-machine systems; it is known as the Human Operator Simulator – Version V (HOS-V). A general design principle of HOS-V has been to provide embedded human performance micro-models for the basic performance processes that seem most pervasive and interactive with other processes. These include representations for processes of body movement, cognition, and attention. Key to these representations are the substructures in each area. Body movement models describe locations of body parts and constraints on their movement. Cognition models describe how the human processes information through perception, memory, decision-making, and action initiation. The attention model describes how a limited attentional resource is allocated to the various body movement and cognition processes, each of which has a defined attentional requirement. Plans for implementation of micro-model components of HOS-V are discussed.

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3

Bot, G. P. A. "GREENHOUSE SIMULATION MODELS." Acta Horticulturae, no. 245 (August 1989): 315–25. http://dx.doi.org/10.17660/actahortic.1989.245.42.

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4

Kelly, Drew, and Betsy M. Nolan. "Models and Simulation." Journal of Bone and Joint Surgery 98, no. 5 (March 2016): e21. http://dx.doi.org/10.2106/jbjs.15.01178.

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5

Choucri, Nazli, and Christopher Heye. "3.5. Simulation models." Energy 15, no. 3-4 (March 1990): 363–78. http://dx.doi.org/10.1016/0360-5442(90)90096-k.

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6

Gray, Jeff, and Bernhard Rumpe. "Models in simulation." Software & Systems Modeling 15, no. 3 (July 2016): 605–7. http://dx.doi.org/10.1007/s10270-016-0544-y.

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7

Rozinat, A., R. S. Mans, M. Song, and W. M. P. van der Aalst. "Discovering simulation models." Information Systems 34, no. 3 (May 2009): 305–27. http://dx.doi.org/10.1016/j.is.2008.09.002.

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8

Lankford, Philip M. "Testing Simulation Models." Geographical Analysis 6, no. 3 (September 3, 2010): 295–302. http://dx.doi.org/10.1111/j.1538-4632.1974.tb00514.x.

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9

Thompson, James. "Forward simulation models." WIREs Computational Statistics 2, no. 1 (January 2010): 61–68. http://dx.doi.org/10.1002/wics.68.

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10

Ritchie, Nicholas W. M. "Spectrum Simulation in DTSA-II." Microscopy and Microanalysis 15, no. 5 (September 16, 2009): 454–68. http://dx.doi.org/10.1017/s1431927609990407.

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AbstractSpectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on ϕ(ρz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of arbitrarily complex samples. DTSA-II provides a broad range of simulation tools in a framework with many different interchangeable physical models. In addition, DTSA-II provides tools for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.

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11

Tan, Xiao-ming, Peng-peng Xie, Zhi-gang Yang, and Jian-yong Gao. "Adaptability of Turbulence Models for Pantograph Aerodynamic Noise Simulation." Shock and Vibration 2019 (March 12, 2019): 1–20. http://dx.doi.org/10.1155/2019/6405809.

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This study was targeted at CX-PG-type Faiveley pantograph of high-speed train and cylinders and analysed the fluctuating flow field around these objects by using the large eddy simulation (LES) model, the scale adaptive simulation (SAS) model, the improved delayed detached eddy simulation with shear-stress transport-kω (IDDES sst-kω) model, the delayed detached eddy simulation with shear-stress transport-kω (DDES sst-kω) model, and the delayed detached eddy simulation with realizable-kε (DDES R-kε) model. The space distributions of velocity, vorticity, and vortex structures were compared to investigate their performances on simulating fluctuating flow fields and computing aeroacoustic sources through Fourier transformation based on the surface fluctuating pressures. Furthermore, the far-field radiated noise was calculated based on the Ffowcs Williams–Hawkings equation. Based on the computation precision of the five models, a feasible turbulent model was selected for simulating aerodynamic noise. The relative errors to the results from wind-tunnel experiments of the sound pressure level (SPL) were obtained as 0.7%, 1.6%, 7.8%, 3.8%, and 12.1%, respectively, and the peak Strouhal numbers were obtained as 2.0%, 8.5%, 5.5%, 11.5%, and 51.0% for cylinder simulation. Moreover, the relative errors of SAS, IDDES sst-kω, DDES sst-kω, and DDES R-kε models to the result from LES of SPL were respectively obtained as 2.3%, 4.5%, 5.6%, and 10.8% for pantograph. Thus, it is conclusive that none of the aforementioned models are comparable with the LES model with respect to the precision in the aeroacoustic simulation. However, SAS, IDDES sst-kω, and DDES sst-kω are practically competent with the LES model considering the numerical simulations with respect to the engineering computation precision. The numerical computation model was verified using the wind-tunnel test results.

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12

MacNeil, Michael D., and Dewey L. Harris. "Highly Aggregated Simulation Models." Journal of Animal Science 66, no. 10 (1988): 2517. http://dx.doi.org/10.2527/jas1988.66102517x.

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13

Cianfanelli, Elisabetta, Lorenzo Corbetta, Gabriele Goretti, Lorenzo Pelosini, and Maria Luisa Malpelo. "SAM - Simulation Airways Models." Design Journal 20, sup1 (July 28, 2017): S2451—S2462. http://dx.doi.org/10.1080/14606925.2017.1352758.

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14

Molnár, István, and Pham Quang Huy. "Multimedia using simulation models." Mathematics and Computers in Simulation 46, no. 1 (April 1998): 23–33. http://dx.doi.org/10.1016/s0378-4754(97)00155-9.

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15

Hoffman, Maximilian, Morgan Krey, Margaret Iwanicki, Jordon Cooper, Sasha Jones, Peggy Ochoa, Pam Aitchison, Jin-cheng Zhao, Morris Kharasch, and Ernest E. Wang. "Innovative Simulation Training Models." Disease-a-Month 57, no. 12 (December 2011): 807–26. http://dx.doi.org/10.1016/j.disamonth.2011.08.013.

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16

Saseendran, S. A., L. R. Ahuja, and L. MA. "Crop–Soil Simulation Models." Journal of Environment Quality 32, no. 6 (2003): 2445—a. http://dx.doi.org/10.2134/jeq2003.2445a.

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17

Ibbett, Roland N., J. C. Diaz y Carballo, and D. A. W. Dolman. "Computer architecture simulation models." ACM SIGCSE Bulletin 38, no. 3 (September 26, 2006): 353. http://dx.doi.org/10.1145/1140123.1140263.

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18

Rehman, Muniza, and Stig Andur Pedersen. "Validation of simulation models." Journal of Experimental & Theoretical Artificial Intelligence 24, no. 3 (September 2012): 351–63. http://dx.doi.org/10.1080/0952813x.2012.695459.

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19

Pos, Anita, Pim Borst, Jan Top, and Hans Akkermans. "Reusability of simulation models." Knowledge-Based Systems 9, no. 2 (April 1996): 119–25. http://dx.doi.org/10.1016/0950-7051(95)01023-8.

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20

Rutter, Carolyn M., Amy B. Knudsen, and Pari V. Pandharipande. "Computer Disease Simulation Models." Academic Radiology 18, no. 9 (September 2011): 1077–86. http://dx.doi.org/10.1016/j.acra.2011.02.004.

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21

Baaser, Herbert. "Simulation models for elastomers." ATZ worldwide 112, no. 5 (May 2010): 44–48. http://dx.doi.org/10.1007/bf03225245.

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22

Deuringer, Stefan, Raphael Gerdes, Jan Eilers, and Rainer Müller. "Simulationsgerechte Maschinenmodelle/Machine models for simulation – Preparation of CAD machine models for the simulation of production processes." wt Werkstattstechnik online 110, no. 10 (2020): 716–21. http://dx.doi.org/10.37544/1436-4980-2020-10-72.

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Mit zunehmender Produktvielfalt steigen die Anforderungen an die Planung von Montagelinien. Um diese Komplexität zu bewältigen, werden Montageprozesse virtuell simuliert. Der Aufwand zur Aufbereitung bestehender CAD-Anlagenmodelle zu Simulationsmodellen ist aber sehr hoch. Daher werden mit der hier vorgestellten Methodik CAD-Modelle automatisiert für die Simulation aufbereitet. Ziel ist, die benötigte Zeit, Expertise und das Fehlerpotenzial der Modellaufbereitung deutlich zu reduzieren und die Wirtschaftlichkeit des Simulationseinsatzes in der Montage zu steigern.   Machine-oriented simulation helps to cope with complex planning processes for assembly systems. However, the effort to prepare existing CAD models for machine-oriented simulations is very high. Therefore, a method is presented for the automated preparation of CAD models for assembly simulation. Applying this method reduces significantly the required time, expertise and potential errors in model preparation while increasing the economic efficiency of simulation use in assembly.

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23

Greif, Hajo. "Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence." Perspectives on Science 29, no. 4 (July 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 embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them.

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24

Zhao, Hu, and Julia Kowalski. "Topographic uncertainty quantification for flow-like landslide models via stochastic simulations." Natural Hazards and Earth System Sciences 20, no. 5 (May 26, 2020): 1441–61. http://dx.doi.org/10.5194/nhess-20-1441-2020.

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Abstract. Digital elevation models (DEMs) representing topography are an essential input for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as on further topographic characteristics and the DEM error's variability. We further find that, in the absence of systematic bias in the DEM, a performant root-mean-square-error-based unconditional stochastic simulation yields similar results to a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability in the DEM error, which leads to an increase in the potential hazard area as well as extreme values of dynamic flow properties.

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25

Durán, Juan M. "What is a Simulation Model?" Minds and Machines 30, no. 3 (March 7, 2020): 301–23. http://dx.doi.org/10.1007/s11023-020-09520-z.

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Abstract Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology—what I call recasting—and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation.

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26

M. Al-Ismaili, Abdulrahim, and Tahani Bait Suwailam. "Simulation Models of the Seawater Greenhouse." International Journal of Engineering & Technology 7, no. 3.16 (July 26, 2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.4.16190.

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In arid climates, extremely high temperatures in the summer and the chronic water scarcity put a firm barrier against agricultural development and sustainability. The SWGH technology is an engineering phenomenon that came to overcome both the constraints particularly in areas where seawater is accessible and/or brackish groundwater is available. It is a greenhouse used to cultivate crops and at the same time produce its own freshwater need. This study aimed to highlight the models that were carried out to simulate the SWGH as a whole or only the dehumidification rate of the SWGH condenser. Four types of simulation models were identified, namely, analytical, numerical, empirical and artificial neural network simulations. The factors affecting the dehumidification rate were also discussed taking into consideration the results from the simulation models.

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27

Shi, Peng, Fei Liu, and Ming Yang. "Critical Problems in Validation Process of Simulation Models." Advanced Materials Research 187 (February 2011): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amr.187.422.

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Simulation models are increasingly being used to solve more and more complex problems and to aid in decision-making. To provide a realistic and confident simulation environment for users, simulation models have become key components in military simulations. This paper discusses the modeling nature of simulation models, and then the modified validation criteria for measuring the agreements between Subject Matter Experts and simulation models are presented. Furthermore, validation methods such as graphical comparison, feature analysis, face validation, confidence interval and hypothesis tests of three types errors, are discussed according to the validation metrics of simulation models. Simulation models could be validated based on the proposed validation process effectively. The proposed process could be applied to the simulation systems and solve many VV&A difficulties. Example of the mass moment missile, illustrates the validity of the proposed process.

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28

Sterman, John D. "Systems Simulation. Expectation formation in behavioral simulation models." Behavioral Science 32, no. 3 (1987): 190–211. http://dx.doi.org/10.1002/bs.3830320304.

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29

BERNASCHI, MASSIMO, and FILIPPO CASTIGLIONE. "COMPUTATIONAL FEATURES OF AGENT-BASED MODELS." International Journal of Computational Methods 02, no. 01 (March 2005): 33–48. http://dx.doi.org/10.1142/s0219876205000399.

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Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

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30

Abraham, Chris Joseph, Arnold Rix, and Marthinus Johannes Booysen. "Aligned Simulation Models for Simulating Africa’s Electric Minibus Taxis." World Electric Vehicle Journal 14, no. 8 (August 19, 2023): 230. http://dx.doi.org/10.3390/wevj14080230.

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Planning for the eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis—vehicles extensively used in sub-Saharan Africa’s public transport systems—vary greatly in their results. This paper analyses, compares and corrects the only two existing studies that project energy consumption of electric minibus taxis in the region. One of the studies projected an energy consumption of 0.39 kWh/km, while the other projected 0.93 kWh/km. This paper carefully analyses the simulation tools and models and cumulatively applies corrections from the literature and scientific analyses. As a result, the discrepancy between the two simulation tools is eliminated for a given data input and a final energy consumption is estimated in the range of 0.49–0.52 kWh/km, depending on the input data.

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31

Arnaldi, Bruno, Rémi Cozot, Stéphane Donikian, and Michel Parent. "Simulation Models for French Praxitele Project." Transportation Research Record: Journal of the Transportation Research Board 1521, no. 1 (January 1996): 118–25. http://dx.doi.org/10.1177/0361198196152100116.

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The Praxitele project was charged with designing a new kind of transportation in an urban environment, which consisted of a fleet of electric public cars. These public cars are capable of autonomous motion on certain displacements between stations. The realization of such a project requires experimentation regarding the behaviors of autonomous vehicles in the urban environment. Because of the danger connected with these kinds of experiments at a real site, it was necessary to design a virtual urban environment in which simulations could be done. To perform an authentic simulation of a real environment composed of a large set of vehicles (some of which are autonomous and others of which are controlled by the user or by some specific control law), different models need to be implemented: geometric modeling of the environment, mechanical simulation, motion control models, driver models, sensor models, and visualization algorithms. To implement these different models into a unique system, a new simulator system was designed. This simulator takes into account real-time synchronization and communication between cooperative processes implementing the models mentioned earlier. First, the aims and goals of the Praxitele project are presented. The motion control algorithm for automatic platooning of autonomous vehicles is then briefly presented. The focus is on the simulation of a virtual urban environment that includes Praxitele vehicles. The implementation of all of these models is described. Finally, results of a simulation of cooperative driving of the Praxitele vehicles in a virtual urban environment are given.

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32

Mueller, Patrick, Matthias Lehmann, and Alexander Braun. "Simulating tests to test simulation." Electronic Imaging 2020, no. 16 (January 26, 2020): 149–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-148.

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Simulation is an established tool to develop and validate camera systems. The goal of autonomous driving is pushing simulation into a more important and fundamental role for safety, validation and coverage of billions of miles. Realistic camera models are moving more and more into focus, as simulations need to be more then photo-realistic, they need to be physical-realistic, representing the actual camera system onboard the self-driving vehicle in all relevant physical aspects – and this is not only true for cameras, but also for radar and lidar. But when the camera simulations are becoming more and more realistic, how is this realism tested? Actual, physical camera samples are tested in laboratories following norms like ISO12233, EMVA1288 or the developing P2020, with test charts like dead leaves, slanted edge or OECF-charts. In this article we propose to validate the realism of camera simulations by simulating the physical test bench setup, and then comparing the synthetical simulation result with physical results from the real-world test bench using the established normative metrics and KPIs. While this procedure is used sporadically in industrial settings we are not aware of a rigorous presentation of these ideas in the context of realistic camera models for autonomous driving. After the description of the process we give concrete examples for several different measurement setups using MTF and SFR, and show how these can be used to characterize the quality of different camera models.

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33

Durst, Phillip J., Derek T. Anderson, and Cindy L. Bethel. "A historical review of the development of verification and validation theories for simulation models." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 02 (January 9, 2017): 1730001. http://dx.doi.org/10.1142/s1793962317300011.

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Modeling and simulation (M&S) play a critical role in both engineering and basic research processes. Computer-based models have existed since the 1950s, and those early models have given way to the more complex computational and physics-based simulations used today. As such, a great deal of research has been done to establish what level of trust should be given to simulation outputs and how to verify and validate the models used in these simulations. This paper presents an overview of the theoretical work done to date defining formal definitions for, and methods of, verification and validation (V&V) of computer models. Simulation models are broken down into three broad categories: analytical and simulation models, computational and physics-based models, and simulations of autonomous systems, and the unique theories and methods developed to address V&V of these models are presented. This paper also presents the current problems in the theoretical field of V&V for models as simulations move from single system models and simulations to more complex simulation tools. In particular, this paper highlights the lack of agreed-upon methods for V&V of simulations of autonomous systems, such as an autonomous unmanned vehicles, and proposes some next steps needed to address this problem.

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34

Macdonald, Stuart Elaine, George Rabinowitz, and Ola Listhaug. "Simulating Models of Issue Voting." Political Analysis 15, no. 4 (2007): 406–27. http://dx.doi.org/10.1093/pan/mpm016.

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How should one analyze data when the underlying models being tested are statistically intractable? In this article, we offer a simulation approach that involves creating sets of artificial data with fully known generating models that can be meaningfully compared to real data. The strategy depends on constructing simulations that are well matched to the data against which they will be compared. Our particular concern is to consider concurrently how voters place parties on issue scales and how they evaluate parties based on issues. We reconsider the Lewis and King (2000) analysis of issue voting in Norway. The simulation findings resolve the ambiguity that Lewis and King report, as voters appear to assimilate and contrast party placements and to evaluate parties directionally. The simulations also provide a strong caveat against the use of individually perceived party placements in analyses of issue voting.

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35

CAUSSIGNAC, PH, J. DESCLOUX, and A. YAMNAHAKKI. "SIMULATION OF SOME QUANTUM MODELS FOR SEMICONDUCTORS." Mathematical Models and Methods in Applied Sciences 12, no. 08 (August 2002): 1049–74. http://dx.doi.org/10.1142/s0218202502002033.

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Three different existing steady-state models with quantum correction for simulating the resonant tunnelling diode are summarized. Numerical methods and a theoretical argument for one of the models are briefly described. Results of simulation are focused on the capability of reproducing the negative differential resistivity.

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36

Gaidarski, Ivan, and Pavlin Kutinchev. "Transformation of UML Design Models of Information Security System into Agent-based Simulation Models." Information & Security: An International Journal 53 (2022): 65–77. http://dx.doi.org/10.11610/isij.5305.

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37

Rauter, Matthias, and Anselm Köhler. "Constraints on Entrainment and Deposition Models in Avalanche Simulations from High-Resolution Radar Data." Geosciences 10, no. 1 (December 25, 2019): 9. http://dx.doi.org/10.3390/geosciences10010009.

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Depth-integrated simulations of snow avalanches have become a central part of risk analysis and mitigation. However, the common practice of applying different model parameters to mimic different avalanches is unsatisfying. In here, we analyse this issue in terms of two differently sized avalanches from the full-scale avalanche test-site Vallée de la Sionne, Switzerland. We perform depth-integrated simulations with the toolkit OpenFOAM, simulating both events with the same set of model parameters. Simulation results are validated with high-resolution position data from the GEODAR radar. Rather than conducting extensive post-processing to match radar data to the output of the simulations, we generate synthetic flow signatures inside the flow model. The synthetic radar data can be directly compared with the GEODAR measurements. The comparison reveals weaknesses of the model, generally at the tail and specifically by overestimating the runout of the smaller event. Both issues are addressed by explicitly considering deposition processes in the depth-integrated model. The new deposition model significantly improves the simulation of the small avalanche, making it starve in the steep middle part of the slope. Furthermore, the deposition model enables more accurate simulations of deposition patterns and volumes and the simulation of avalanche series that are influenced by previous deposits.

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38

Wu, Yongxing, Fei Peng, Yang Peng, Xiaoyang Kong, Heming Liang, and Qi Li. "Dynamic 3D Simulation of Flood Risk Based on the Integration of Spatio-Temporal GIS and Hydrodynamic Models." ISPRS International Journal of Geo-Information 8, no. 11 (November 18, 2019): 520. http://dx.doi.org/10.3390/ijgi8110520.

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Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional (2D) hydrodynamic models is achieved for data flow, calculation processes, operation flow, and system functions. Oblique photography-based three-dimensional (3D) modeling technology is used to quickly build a 3D model of the study area (including the hydraulic engineering facilities). A multisource spatio-temporal data platform for dynamically simulating flood risk was built based on the digital earth platform. Using the spatio-temporal computation framework, a dynamic visual simulation and decision support system for flood risk management was developed for the Xiashan Reservoir. The integration method proposed here was verified using flood simulation calculations, dynamic visual simulations, and downstream river channel and dam-break flood simulations. The results show that the proposed methods greatly improve the efficiency of flood risk simulation and decision support. The methods and system put forward in this study can be applied to flood risk simulations and practical management.

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Guia, Sana Sahar, Abdelkader Laouid, Mohammad Hammoudeh, Ahcène Bounceur, Mai Alfawair, and Amna Eleyan. "Co-Simulation of Multiple Vehicle Routing Problem Models." Future Internet 14, no. 5 (April 29, 2022): 137. http://dx.doi.org/10.3390/fi14050137.

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Complex systems are often designed in a decentralized and open way so that they can operate on heterogeneous entities that communicate with each other. Numerous studies consider the process of components simulation in a complex system as a proven approach to realistically predict the behavior of a complex system or to effectively manage its complexity. The simulation of different complex system components can be coupled via co-simulation to reproduce the behavior emerging from their interaction. On the other hand, multi-agent simulations have been largely implemented in complex system modeling and simulation. Each multi-agent simulator’s role is to solve one of the VRP objectives. These simulators interact within a co-simulation platform called MECSYCO, to ensure the integration of the various proposed VRP models. This paper presents the Vehicle Routing Problem (VRP) simulation results in several aspects, where the main goal is to satisfy several client demands. The experiments show the performance of the proposed VRP multi-model and carry out its improvement in terms of computational complexity.

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Storli, Pål-Tore, and Torbjørn K. Nielsen. "Simulation and Discussion of Models for Hydraulic Francis Turbine Simulations." IFAC-PapersOnLine 51, no. 2 (2018): 109–14. http://dx.doi.org/10.1016/j.ifacol.2018.03.019.

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Chen, Shi Hong, and Shang Guan Dayan. "Integration of Tree Models into a Real-Time Pollution Dispersion Simulation." Applied Mechanics and Materials 385-386 (August 2013): 378–81. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.378.

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For exploring the effect of particle dispersion and wind simulation in an urban surrounding. The tree was constructed with smaller numbers of polygons for saving the rendering time in real-time virtual environment simulation system. It was integrated into a real time pollution dispersion simulation system. Also a way was used for simulating the wind tree. The results show that this method is proper and efficient for simulating tree animation in real-time virtual environment simulation system.

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42

Filippi, Jean-Baptiste, Frédéric Bosseur, Xavier Pialat, Paul-Antoine Santoni, Susanna Strada, and Céline Mari. "Simulation of Coupled Fire/Atmosphere Interaction with the MesoNH-ForeFire Models." Journal of Combustion 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/540390.

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Simulating interaction between forest fire and atmospheric processes requires a highly detailed and computationally intensive model. Processing this type of simulations in wildland fires forbids combustion-based models due to the large amount of fuels to be simulated in terms of quantity and diversity. In this paper, we propose an approach that couples a fire area simulator to a mesoscale weather numerical model in order to simulate local fire/atmosphere interaction. Five idealized simulation cases are analysed showing strong interaction between topography and the fire front induced wind, interactions that could not be simulated in noncoupled simulations. The same approach applied to a real-case scenario also shows results that are qualitatively comparable to the observed case. All these results were obtained in less than a day of calculation on a dual processor computer, leaving room for improvement in grid resolution that is currently limited to fifty meter.

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Zhao, Yaodi, and De-Zheng Sun. "ENSO Asymmetry in CMIP6 Models." Journal of Climate 35, no. 17 (September 1, 2022): 5555–72. http://dx.doi.org/10.1175/jcli-d-21-0835.1.

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Abstract An interesting aspect of the El Niño–Southern Oscillation (ENSO) phenomenon is the asymmetry between its two phases. This paper evaluates the simulations of this property of ENSO by the Coupled Model Intercomparison Project phase 6 (CMIP6) models. Both the surface and subsurface signals of ENSO are examined for this purpose. The results show that the models still underestimate ENSO asymmetry as shown in the SST field, but do a better job in the subsurface. A much weaker negative feedback from the net surface heat flux during La Niña in the models is identified as a factor causing the degradation of the ENSO asymmetry at the surface. The simulated asymmetry in the subsurface is still weaker than the observations owing to a weaker dynamic coupling between the atmosphere and ocean. Consistent with the finding of a weaker dynamic coupling strength, the precipitation response to the SST changes is also found to be weaker in the models. The results underscore that a more objective assessment of the simulation of ENSO by climate models may have to involve the examination of the subsurface signals. Future improvements in simulating ENSO will likely require a better simulation of the surface heat flux feedback from the atmosphere as well as the dynamical coupling strength between the atmosphere and ocean. Significance Statement The ENSO phenomenon affects weather and climate worldwide. An interesting aspect of this phenomenon is the asymmetry between its two phases. Previous studies have reported a weaker asymmetry in the simulations by climate models. But these studies have focused on the ENSO asymmetry at the surface. Here by examining the ENSO asymmetry at the surface and the subsurface, we have found that ENSO asymmetry is better simulated in the subsurface than at the surface. We have also identified factors that are responsible for the degradation of the ENSO asymmetry at the surface as well as the remaining weakness in the subsurface, pointing out specific pathways to take to further improve ENSO simulations by coupled climate models.

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King, M. J., and Mark Mansfield. "Flow Simulation of Geologic Models." SPE Reservoir Evaluation & Engineering 2, no. 04 (August 1, 1999): 351–67. http://dx.doi.org/10.2118/57469-pa.

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Summary We report upon our experience in performing flow simulation within detailed three dimensional geologic models. Such models have the potential to significantly improve mechanistic understanding of fluid flow through our reservoirs, especially those influenced by strong contrasts in permeability at multiple scales. We have found that utilizing these static models in a dynamic sense places new requirements upon the models, and forces a reevaluation of the theoretical foundations behind our flow simulators. We review a range of physical and numerical concepts, and provide the required conceptual extensions and new derivations. Introduction The last several years have seen an explosive growth in the ability of the petroleum industry to develop flow simulation models based upon detailed three dimensional geologic descriptions.1–9 As "Shared Earth" models, they provide an integrated repository of static data and a means of visualizing and integrating well and reservoir data at multiple scales.10–12 These models tend to be large and complex, as they include the reservoir structural framework, reservoir zonation and flow units, trends in quality, and local reservoir heterogeneity. Such an integrated data set offers a significant opportunity to improve our mechanistic understanding of fluid flow through our reservoirs, especially those impacted by strong contrasts in permeability and the interaction of complexity at these many scales.13–15 Until recently, such modeling activities have required access to research codes, and specialists to utilize them. But, with a wide range of vendor tools now available, it is possible to build such models within asset teams without the direct involvement of the specialists. However, this does not imply that the underlying technology has matured to the point where it can be used routinely. In the course of an asset study of bypassed oil within the Magnus reservoir, we have repeatedly run into limitations of the available industry tools. This is especially true in the treatment of the geologic model as a flow simulator, e.g., in performing fluid flow simulation on the geologic grid, with that model providing the initialization data. We do not believe that this is a shortcoming of the specific tools, but is instead due to the additional requirements being placed upon our theoretical foundations by these new reservoir modeling applications. In some instances the theoretical foundations seem to be well developed; we need only to generalize the derivations. This is the case when evaluating well connection factors for wells at arbitrary orientations to the computational grid, now with a (symmetric) full tensor permeability.16–22 Some concepts need only a slight reexamination. How does the definition of tensor permeability differ between the geologic model and a flow simulator? Other concepts and algorithms have relied heavily upon the existence of a computational grid with an (I, J, K) "shoe box" topology. These are the most in need of reexamination. The most important is the definition of transmissibility if the principle directions of permeability are not aligned with the computational grid. The most "obvious" generalization of the ECLIPSE NEWTRAN equation is not guaranteed to provide positive transmissibilities.23–25 What formulation (or formulations) should we use instead? Similarly, the usual equations for the upscaling of effective permeability from a fine to a coarse grid is strongly dependent upon the shoe box topology of the grid.26,27 How do we formulate the upscaling calculation when the fine grid is not a simple refinement of the coarse grid, or where upscaling regions are irregular in shape? There are also new requirements placed upon the geologic model. A computational grid and static model which is adequate for defining volumes may be far from optimal for modeling flow. This is demonstrated within the Magnus geologic model at reservoir zone boundaries, where the erosion of one zone by another generates nonconformities across much of the grid. A new gridding algorithm has been implemented, improving our ability to model flow at these boundaries. A simple treatment of transmissibility across the pinched out cells at the zone boundaries is also derived, which greatly simplifies the finite difference formulation at the boundary. Work was performed as much as possible using standard industry tools. As a result the issues that arise, and the solutions we present, will reflect a compromise between the "correct" or "ultimate" solutions and those that are practical today. Nonetheless, we believe that the conceptual developments and many of the particular solutions we present will be of general applicability. This paper will first discuss the management of the computational grid at zone boundaries and the transmissibility across pinched-out cells. It will be necessary to include a brief introduction to the modeling requirements posed by the remaining oil study of the Magnus reservoir. Permeability will be discussed next. How does its representation differ between the geologic and simulation models? Transmissibility will be reviewed and two different formulations will be derived. The applicability of each will be discussed. An extension of Peaceman's expression for the well connection factor to full tensor permeability will be provided, although with minimal details of the derivation. The final section of the paper provides two formulations of the upscaling of effective permeability: one for each of the two definitions of transmissibility. Key result equations are listed at the end of each section. Smedvig's IRAP Reservoir Modeling System (IRAP/RMS version 4.0.7) was used to construct the geologic models.28 TSC's streamline based FRONTSIM simulator was used for the flow modeling.29 BP's TIME OF FLIGHT (TOF) streamline code30–32 was used to visualize the flow solutions. (At the time of this project, industry codes were unable to provide this capability.) A research upscaling code was used for the demonstration examples cited in the last section of this paper, but not for any of the production work. Managing the 3D Geologic Grid The issues that we discuss have all arisen in an evaluation of the habitat of the remaining oil of the Magnus sand member (MSM) of the Magnus reservoir.33 The Magnus MSM is a large Upper Jurassic, sand dominated, turbidite reservoir. It was discovered in 1974, commenced production in August of 1983, and was on plateau until January of 1995. Since then, decline has been managed with an active infill drilling program.34

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Prokopenko, Olha, Vitaliy Omelyanenko, T. Ponomarenko, and O. Olshanska. "Innovation networks effects simulation models." Periodicals of Engineering and Natural Sciences (PEN) 7, no. 2 (July 25, 2019): 752. http://dx.doi.org/10.21533/pen.v7i2.574.

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46

Bell, Peter C. "Stochastic Visual Interactive Simulation Models." Journal of the Operational Research Society 40, no. 7 (July 1989): 615. http://dx.doi.org/10.2307/2582970.

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Lin, Qi, and Zhi Li. "Credibility Evaluation of Simulation Models." Advanced Materials Research 765-767 (September 2013): 713–16. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.713.

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This paper discusses an evaluation methodology that can be used to assess the credibility of simulation models. The goal of verification and validation of simulation models will be described, together with a description of evaluation methodology and evaluation procedures. The credibility of simulation models can be evaluated using the method proposed here, which will be much meaningful to the simulation systems development. Taking a space Tether-net simulation system as an instance, the credibility evaluation result is provided.

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郑, 子泽. "Overview of Crowd Simulation Models." Modeling and Simulation 11, no. 03 (2022): 725–33. http://dx.doi.org/10.12677/mos.2022.113068.

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Sharma, Bharti, Neeraj Sharma, and Neeshu Sharma. "Simulation of Different SPI Models." International Journal of Computer Applications 1, no. 10 (February 25, 2010): 31–36. http://dx.doi.org/10.5120/227-378.

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Botaschanjan, Jewgenij, Thomas Hensel, Benjamin Hummel, Alexander Lindworsky, and Michael F. Zäh. "Simulation models for virtual commissioning." ATZproduktion worldwide 2, no. 5-6 (November 2009): 8–11. http://dx.doi.org/10.1007/bf03224204.

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