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Статті в журналах з теми "Computer simulation"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 серпня 2024

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1

Zheng, Lei, Ying Huang, Dong Liu, and Wei Yan Xing. "A Reliability Simulation Method for On-Board Computer." Applied Mechanics and Materials 380-384 (August 2013): 3350–53. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3350.

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Анотація:

As high reliable equipment, on-board computer often has difficulties to evaluate its reliability. The paper put forward a simulation method to compute on-board computers reliability. The method uses a forced transition (FT) based reliability simulation model to deal with the on-board computers that have complex structure and diversiform characteristic parameters. The model is particularly suitable for the on-board computers which are composed of the components whose failure processes obey tub life curve. As an example, a prototype on-board computer was put forward and simulated using the model. The presented reliability simulation model can be adopted for the on-board computer probability risk assessment where analytic methods or exact solutions cannot be easily reached.

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2

Pias, Claus. "On the Epistemology of Computer Simulation." ZMK Zeitschrift für Medien- und Kulturforschung 2/1/2011: Offene Objekte 2, no. 1 (2011): 29–54. http://dx.doi.org/10.28937/1000107521.

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"Der Aufsatz plädiert dafür, die Geschichte der wissenschaftlichen Computersimulation auf eine spezifisch medienhistorische Weise zu untersuchen. Nach einigen Vorschlägen zur Charakterisierung der Besonderheiten von Computersimulationen werden zwei Beispiele interpretiert (Management-Simulationen der 1960er und verkehrstechnische bzw. epidemiologische Simulationen der 1990er). Daraus leiten sich Fragen nach dem veränderten Status wissenschaftlichen Wissens, nach der Genese wissenschaftstheoretischer Konzepte und nach wissenschaftskritischen Optionen ab. </br></br>The paper suggests to analyze the history of scientific computer simulations with respect to the history of media. After presenting some ideas concerning the peculiarities of computer simulation, two examples (management simulations of the 1960s; traffic-related and epistemological simulations of the 1990s) are interpreted. From them, further questions concerning the status of scientific knowledge, the genesis of epistemological concepts and their critique are derived. "

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3

PREVE, NIKOLAOS P., and EMMANUEL N. PROTONOTARIOS. "MONTE CARLO SIMULATION ON COMPUTATIONAL FINANCE FOR GRID COMPUTING." International Journal of Modeling, Simulation, and Scientific Computing 03, no. 03 (May 17, 2012): 1250010. http://dx.doi.org/10.1142/s1793962312500109.

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Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating complex systems. Because of their reliance on repeated computation of random or pseudo-random numbers, these methods are most suited to calculation by a computer and tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm. In finance, Monte Carlo simulation method is used to calculate the value of companies, to evaluate economic investments and financial derivatives. On the other hand, Grid Computing applies heterogeneous computer resources of many geographically disperse computers in a network in order to solve a single problem that requires a great number of computer processing cycles or access to large amounts of data. In this paper, we have developed a simulation based on Monte Carlo method which is applied on grid computing in order to predict through complex calculations the future trends in stock prices.

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4

Wu, Qing, Maksym Spiryagin, Ingemar Persson, Chris Bosomworth, and Colin Cole. "Parallel computing of wheel-rail contact." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 10 (October 8, 2019): 1109–16. http://dx.doi.org/10.1177/0954409719880737.

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Railway wheel–rail contact simulations are the most important and time-consuming tasks when simulating the system dynamics of vehicles. Parallel computing is a good approach for improving the numerical computing speed. This paper reports the advances in parallel computing of the wheel–rail contact simulations. The proposed method uses OpenMP to parallelise the multiple contact points of all the wheel–rail interfaces of a locomotive model. The method has been implemented in the vehicle system dynamics simulation package GENSYS. Simulations were conducted using two numerical solvers (4th Runge-Kutta and HeunC) and a maximum of four computer cores. Simulation cases have shown exactly the same numerical results using serial computing and parallel computing, which prove the effectiveness of the parallel computing method. The HeunC solver achieved the same simulation results and is 3.5 times faster than the 4th Runge-Kutta method. Simulation results obtained from both numerical solvers show that parallel computing using 2, 3 and 4 computer cores can improve the simulation speeds by roughly 29, 39 and 41%, respectively. There is an apparent diminishing of the rate of improvement due to the increase of the communication resource overhead when more computer cores are used. Using up to four computer cores does not require revision of the GENSYS code, and simulations can be executed using personal computers.

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5

Matsuoka, Takaaki. "Computer Simulation." Nihon Reoroji Gakkaishi 31, no. 1 (2003): 51–57. http://dx.doi.org/10.1678/rheology.31.51.

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6

Fishwick, P. A. "Computer simulation." IEEE Potentials 15, no. 1 (1996): 24–27. http://dx.doi.org/10.1109/45.481372.

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7

Nance, Richard E., and C. Michael Overstreet. "Computer simulation." ACM SIGSIM Simulation Digest 24, no. 3 (January 1995): 40–50. http://dx.doi.org/10.1145/219271.219277.

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8

Klaassens, Elizabeth. "Computer Simulation." Nurse Educator 13, no. 2 (March 1988): 7. http://dx.doi.org/10.1097/00006223-198803000-00004.

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9

Materials & Chemistry Division. "Computer simulation." NDT & E International 24, no. 4 (August 1991): 227. http://dx.doi.org/10.1016/0963-8695(91)90364-9.

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10

Satoh, Shuichi. "Computer Simulation." REVIEW OF HIGH PRESSURE SCIENCE AND TECHNOLOGY 8, no. 4 (1998): 242. http://dx.doi.org/10.4131/jshpreview.8.242.

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11

Grossman, David C. "Computer Simulation." Archives of Pediatrics & Adolescent Medicine 155, no. 9 (September 1, 2001): 992. http://dx.doi.org/10.1001/archpedi.155.9.992.

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12

Wang, A. T. "Finite-Simulation Error Bounds for Estimated K-Year Forces." Journal of Offshore Mechanics and Arctic Engineering 111, no. 4 (November 1, 1989): 273–77. http://dx.doi.org/10.1115/1.3257095.

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Анотація:

Computer simulations have been used frequently to calculate design loads associated with a specific return period for offshore structures. However, two important questions persistently confront engineers who simulate load events on computers to estimate k-year forces: 1) How accurate is the estimated k-year force (say, 100-yr force) obtained through a computer simulation of n years (e.g., n = 1000) compared to that which would result from a much longer simulation? 2) When can we stop a computer simulation? Or how many simulation years are needed to reach a specified level of reliability for a certain k-year force? This paper presents solutions to these two questions under the assumption that the input parameters are completely known and the formulas used to compute loads are one hundred percent correct. Given a confidence level C (e.g., C = 80 or 90 percent) and an arbitrary but fixed number of simulation years, a method is identified to find an estimated k-year force and an error bound α, such that Pr(|estimator−k-yearforce|<α)>C (1) In addition, when the required confidence C and error bound α are given, a procedure is given to stop a computer simulation as soon as inequality (1) is satisfied. These results are not dependent on the statistical distribution of the underlying force distribution. Therefore, one does not have to assume that forces are of a specific probability distribution (such as lognormal, exponential, etc.).

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13

Foley, Michael J., Patrick S. Cottler, Silvia S. Blemker, Arlen D. Denny, and Jonathan S. Black. "Computer Simulation and Optimization of Cranial Vault Distraction." Cleft Palate-Craniofacial Journal 55, no. 3 (December 14, 2017): 356–61. http://dx.doi.org/10.1177/1055665617738999.

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Objective: The objective of this study was to validate the proof of concept of a computer-simulated cranial distraction, demonstrating accurate shape and end volume. Design: Detailed modeling was performed on pre- and postoperative computed tomographic (CT) scans to generate accurate measurements of intracranial volume. Additionally, digital distraction simulations were performed on the preoperative scan and the resultant intracranial volume and shape were evaluated. Setting: Tertiary Children’s Hospital. Patients, Participants: Preoperative and postoperative CT images were used from 10 patients having undergone cranial distraction for cephalocranial disproportion. Interventions: None; computer simulation. Main Outcome Measure: Computer simulation feasibility of cranial vault distraction was demonstrated through creation of digital osteotomies, simulating distraction through translating skull segments, followed by simulated consolidation. Accuracy of the model was evaluated through comparing the intracranial volumes of actual and simulated distracted skulls. Results: The developed digital distraction simulation was performed on the CT images of 10 patients. Plotting the relationship between the actual and simulated postdistraction volumes for the 10 patients yielded a slope of 1.0 and a correlation coefficient of 0.99. The average actual resultant volume change from distraction was 77.0 mL, compared to a simulated volume change of 76.9 mL. Conclusions: Digital simulation of cranial distraction was demonstrated through manipulation of the CT images and confirmed by comparing the actual to simulated volume change. This process may provide objective data in designing an individual distraction plan to optimize volume expansion and resultant cranial shape as well as patient education.

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14

Nazaré, Thalita E., Erivelton G. Nepomuceno, Samir A. M. Martins, and Denis N. Butusov. "A Note on the Reproducibility of Chaos Simulation." Entropy 22, no. 9 (August 29, 2020): 953. http://dx.doi.org/10.3390/e22090953.

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Анотація:

An evergreen scientific feature is the ability for scientific works to be reproduced. Since chaotic systems are so hard to understand analytically, numerical simulations assume a key role in their investigation. Such simulations have been considered as reproducible in many works. However, few studies have focused on the effects of the finite precision of computers on the simulation reproducibility of chaotic systems; moreover, code sharing and details on how to reproduce simulation results are not present in many investigations. In this work, a case study of reproducibility is presented in the simulation of a chaotic jerk circuit, using the software LTspice. We also employ the OSF platform to share the project associated with this paper. Tests performed with LTspice XVII on four different computers show the difficulties of simulation reproducibility by this software. We compare these results with experimental data using a normalised root mean square error in order to identify the computer with the highest prediction horizon. We also calculate the entropy of the signals to check differences among computer simulations and the practical experiment. The methodology developed is efficient in identifying the computer with better performance, which allows applying it to other cases in the literature. This investigation is fully described and available on the OSF platform.

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15

Lasquety-Reyes, Jeremiah A. "Towards Computer Simulations of Virtue Ethics." Open Philosophy 2, no. 1 (September 26, 2019): 399–413. http://dx.doi.org/10.1515/opphil-2019-0029.

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AbstractThis article presents two approaches for computer simulations of virtue ethics in the context of agent-based modeling, a simple way and a complex way. The simple way represents virtues as numeric variables that are invoked in specific events or situations. This way can easily be implemented and included in social simulations. On the other hand, the complex way requires a PECS framework: physical, cognitive, emotional, and social components need to be implemented in agents. Virtue is the result of the interaction of these internal components rather than a single variable. I argue that the complex way using the PECS framework is more suitable for simulating virtue ethics theory because it can capture the internal struggle and conflict sometimes involved in the practice of virtue. To show how the complex way could function, I present a sample computer simulation for the cardinal virtue of temperance, the virtue that moderates physical desires such as food, drink, and sex. This computer simulation is programmed in Python and builds upon the well-known Sugarscape simulation.1

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16

Eastwood, J. W. "Computer simulation and computer algebra." Computer Physics Communications 54, no. 1 (April 1989): 199. http://dx.doi.org/10.1016/0010-4655(89)90045-3.

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17

Chatterji, B. N. "Analog Computer Simulation." IETE Journal of Education 34, no. 1 (January 1993): 27–38. http://dx.doi.org/10.1080/09747338.1993.11436397.

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18

Mirabelli, Andre. "Computer simulation concerns." Physics Teacher 26, no. 4 (April 1988): 200. http://dx.doi.org/10.1119/1.2342481.

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19

Zhang, Tong-Yi. "Computer Simulation of Semipbrittle Fracture/ Computer-Simulation des Semi-Sprödbruchs." International Journal of Materials Research 81, no. 1 (January 1, 1990): 63–69. http://dx.doi.org/10.1515/ijmr-1990-810109.

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20

Algimantas, FEDARAVIČIUS, RAČKAUSKAS Saulius, and SURVILA Arvydas. "Numerical Study on Internal Ballistics Characteristics of a Solid Propellant Rocket Motor." Mechanics 25, no. 3 (July 1, 2019): 187–96. http://dx.doi.org/10.5755/j01.mech.25.3.23742.

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The viability of numerical simulations to design a solid propellant rocket motor, as opposed to static testing, is discussed in this paper. The results demonstrate that an objectively more detailed insight into the processes taking place within the motor in active combustion can be gained by employing computer simulations. The abundance of readily available computing power allows complex simulations to be run on personal desktop computers, thus, saving money by eliminating the need for static testing during the design phase of the motor. The computer simulation results are validated by a comparison with test data gathered during static tests on the motor. In this study, an RM-12K solid propellant rocket motor is designed and developed, which is currently used in real-world air defence training applications, and therefore, sufficient empirical data are available. The numerical method, based on computer simulations using personal computers, is sufficiently accurate to allow for motor design decisions and an adequate substitute for static testing. Even though a single simulation can take up to 715 h on an 8-core personal machine, it is still an effective solution.

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21

Sussman, Daniel, and Joseph Lowman. "Hard-copy versus Computer Presentation of the SuperShrink Interview Simulation." Teaching of Psychology 16, no. 4 (December 1989): 227–30. http://dx.doi.org/10.1207/s15328023top1604_17.

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Анотація:

The effects of realism and perceived control on student satisfaction with the SuperShrink interview simulation were investigated via a 2 × 2 comparison of active/passive and computer/hard-copy conditions. Students rated the computer versions as more satisfying and as promoting a greater sense of realism than the printed materials. Students perceived having more control in the active than the passive conditions, but this perception was not accompanied by differences in satisfaction. These data suggest that computers are superior to hard-copy simulations of human interaction, perhaps because they enhance realism rather than control.

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22

Marupov, Jasur R. "COMPUTER SIMULATION IN THE ANALYSIS OF POLITICAL PROCESSES." Oriental Journal of History, Politics and Law 02, no. 02 (April 1, 2022): 56–62. http://dx.doi.org/10.37547/supsci-ojhpl-02-02-08.

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As a rule, when considering socio-political processes, the system under consideration becomes one of the subjects of politics: a political structure, a state, a state institution, a party, a political leader. Each subject has its own environment and a hypersystem that unites them (country, administrative-territorial community, production team, legislative body, apparatus, party, etc.). The purpose of the study may be to identify the conditions for the stability of a given system (preservation of systemic quality), achieving the goal while maintaining stability or programmed decay, adaptation modalities, etc. Depending on the task, a model is created that describes the most necessary elements and relationships, from which, for example, , to a large extent depends on the development and maintenance of the quality of the system or the achievement of goals. The result of modeling a real system is its model, which becomes the subject of direct study. Modeling is the process of creating a secondary object, in addition to modeling real processes on it. Especially when building mathematical and computer models, one often has to deal with a skeptical attitude towards modeling. At first glance, the reasons for skepticism are important. In fact, in this case, the real system is replaced by its simplified similarity.

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23

GHIMBASEANU, Ioan. "MONITOR THE SIMULATION OF MECHANICAL STRESSES BY COMPUTER." Review of the Air Force Academy 14, no. 1 (May 16, 2016): 105–10. http://dx.doi.org/10.19062/1842-9238.2016.14.1.15.

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24

Hanai, Kouzou, Tetsuya Horiuchi, Junko Sekiguchi, Yoshihisa Muramatsu, Ryutaro Kakinuma, Noriyuki Moriyama, Ryosuke Tuchiiya, and Noboru Niki. "Computer-Simulation Technique for Low Dose Computed Tomographic Screening." Journal of Computer Assisted Tomography 30, no. 6 (November 2006): 955–61. http://dx.doi.org/10.1097/01.rct.0000230011.16468.0e.

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25

Badcock, Christopher, Nigel Gilbert, and Jim Doran. "Simulating Societies: The Computer Simulation of Social Phenomena." British Journal of Sociology 46, no. 3 (September 1995): 544. http://dx.doi.org/10.2307/591863.

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26

Bollen, Kenneth A., Nigel Gilbert, and Jim Doran. "Simulating Societies: The Computer Simulation of Social Phenomena." Social Forces 74, no. 2 (December 1995): 745. http://dx.doi.org/10.2307/2580509.

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27

Janoško, Ivan, Tomáš Polonec, Peter Kuchar, Pavel Máchal, and Martin Zach. "Computer Simulation of Car Aerodynamic Properties." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 5 (2017): 1505–14. http://dx.doi.org/10.11118/actaun201765051505.

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The paper presents an optimization of car aerodynamic properties using the simulation software STAR‑CCM+. For real simulation was used tested car Fiat 127 which was modified on the performance car. The basic objective of this work is using computer simulations to obtain knowledge about the impact of individual body parts on the results of aerodynamic drag, downforce or lift. Based on the results, bodywork modifications will be designed to improve the aerodynamic characteristics of the body, but would not disrupt the basic shape and appearance of the vehicle. The modifications will be again subjected to tests in simulation software. On the modified body was significantly reduced torque of the front axle, while increased of rear axle (cca 1250 N.m). This caused a significant stabilizing effect on the rear axle. The results of simulation tests before and after use bodywork modifications are processed in graphical and numerical form.

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28

Möring, Sebastian. "The Metaphor-Simulation Paradox in the Study of Computer Games." International Journal of Gaming and Computer-Mediated Simulations 5, no. 4 (October 2013): 48–74. http://dx.doi.org/10.4018/ijgcms.2013100103.

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Анотація:

This article discusses the metaphor-simulation paradox in the study of computer games. It is derived from three observations. Firstly, often when authors use the concept of metaphor with regard to games they use it in conceptual and textual vicinity to the concept of simulation. Secondly, the concept of metaphor is often applied to signify seemingly abstract games in opposition to mimetic simulations. Both observations can be made within an artgame discourse of the study of computer games as well as within the more general discourse of the study of computer games. Thirdly, however, the definitions for simulation as well as for metaphor are strikingly similar which culminates in the metaphor-simulation paradox i.e. the notions of metaphor and simulation are not distinct enough in order to make the distinctions which are usually made with these notions with regard to computer games. In an attempt to reconcile both notions with regard to computer games this article will make three suggestions. Observing that simulations are often called metaphors with regard to their degree of reduction or abstraction the first suggestion argues that simulations are essentially synecdochic and hence metaphoric when following a broad notion of metaphor. Based on the assumption that simulation is not a matter of similarity the second suggestion proposes to distinguish between a first order simulation and a second order simulation which can then be considered metaphoric. As a third and final suggestion the author offers to consider simulation and metaphor as related via the notion of the model. Simulations are based on models and metaphors provide models such that one can speak of metaphor based simulations.

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29

Bati, Mégane, Stéphane Blanco, Christophe Coustet, Vincent Eymet, Vincent Forest, Richard Fournier, Jacques Gautrais, Nicolas Mellado, Mathias Paulin, and Benjamin Piaud. "Coupling Conduction, Convection and Radiative Transfer in a Single Path-Space: Application to Infrared Rendering." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–20. http://dx.doi.org/10.1145/3592121.

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In the past decades, Monte Carlo methods have shown their ability to solve PDEs, independently of the dimensionality of the integration domain and for different use-cases (e.g. light transport, geometry processing, physics simulation). Specifically, the path-space formulation of transport equations is a key ingredient to define tractable and scalable solvers, and we observe nowadays a strong interest in the definition of simulation systems based on Monte Carlo algorithms. We also observe that, when simulating combined physics (e.g. thermal rendering from a heat transfer simulation), there is a lack of coupled Monte Carlo algorithms allowing to solve all the physics at once, in the same path space, rather than combining several independent MC estimators, a combination that would make the global solver critically sensitive to the complexity of each simulation space. This brings to our proposal: a coupled, single path-space, Monte Carlo algorithm for efficient multi-physics problems solving. In this work, we combine our understanding and knowledge of Physics and Computer Graphics to demonstrate how to formulate and arrange different simulation spaces into a single path space. We define a tractable formalism for coupled heat transfer simulation using Monte Carlo, and we leverage the path-space construction to interactively compute multiple simulations with different conditions in the same scene, in terms of boundary conditions and observation time. We validate our proposal in the context of infrared rendering with different thermal simulation scenarios: e.g., room temperature simulation, visualization of heat paths within materials (detection of thermal bridges), heat diffusion capacity of thermal exchanger. We expect that our theoretical framework will foster collaboration and multidisciplinary studies. The perspectives this framework opens are detailed and we suggest a research agenda towards the resolution of coupled PDEs at the interface of Physics and Computer Graphics.

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30

Farahmand, Kambiz, Satpal Singh Wadhwa, and Mahmoud Mostafa. "INTEGRATING ANIMATION INTO TEACHING COMPUTER SIMULATION." INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION METHODOLOGY 7, no. 3 (August 30, 2016): 1176–81. http://dx.doi.org/10.24297/ijrem.v7i3.3827.

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Анотація:

Computer simulation is an experiment using a computer model to represent a unique system.Â Variables are defined and parameters to be study are monitored and recorded. Growing caÂpabilities and decreasing costs of microcomputers are placing this powerful tool at the fingertips of scientists and engineers. In the past, the use of digital computers in simulation required a considerable amount of programming effort. This is no longer a true statement.Â Simulation provides the student with a greater breadth and depth of information on which decisions could be made. It is also considered one of the most valuable and flexible decision making tools available. Flexible simulation and animation models developed using a multitude of softwareâ€™s available in the market today is considered a very powerful and effective approach in engineering education. Simulation and animation models could easily be used to solve complex and dynamic problems in both the classroom and real life.Computer simulation techniques and soft wares have been used for more than a decade to help engineers in development, trouble shooting, problem solving, and decision making process. The new paradigm in computer simulation is the use of animation and virtual reality to build engineering models and animation, simulate operations and performance. The fantastic progress in computer hardware and software industry has now opened a new and higher level of teaching computer simulation.

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31

Takahashi, Akiyuki, Masahiro Arita, and Masanori Kikuchi. "Computer Simulation of Irradiation Growth in Zirconium." Advanced Materials Research 33-37 (March 2008): 889–94. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.889.

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This paper describes the computer simulation of irradiation growths induced by neutron irradiations in zirconium using a combination of Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC) methods. First, we performed the MD simulation of the displacement cascade on a defect cluster to study the interaction between the defect cluster and the displacement cascade. The MD simulations provide a lot of information on the amount of the defect production and the subsequent morphological change in the defect cluster. The results are used to make simple models that describe the nature of the displacement cascade overlap on the defect clusters. The models are then implemented into the KMC simulation code to extend the length- and time-scale of the simulation, which allows us to evaluate directly the defect cluster accumulations during a long-term irradiation. The irradiation growth strain resulting from the defect cluster accumulations is simply evaluated, and compared to an available experimental data. The comparison suggests that the displacement cascade overlap plays an important role on the irradiation growth, and, consequently, the KMC method with the simple models must be appropriate for the simulations of the irradiation growth.

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32

Zhao, L., T. J. Montville, and D. W. Schaffner. "Computer Simulation of Clostridium botulinum Strain 56A Behavior at Low Spore Concentrations." Applied and Environmental Microbiology 69, no. 2 (February 2003): 845–51. http://dx.doi.org/10.1128/aem.69.2.845-851.2003.

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ABSTRACT It is generally assumed that spore behavior is independent of spore concentration, but recently published mathematical models indicate that this is not the case. A Monte Carlo simulation was employed in this study to further examine the independence assumption by evaluating the inherent variance in spore germination data. All simulations were carried out with @Risk software. A total of 500 to 4,000 iterations were needed for each simulation to reach convergence. Lag time and doubling time from a higher inoculum concentration were used to simulate the time to detection (TTD) at a lower inoculum concentration under otherwise identical environmental conditions. The point summaries of the simulated and observed TTDs were recorded for the 26 simulations, with kinetic data at the target inoculum concentration. The ratios of the median (Rm = medianobs/mediansim) and 90% range (Rr = 90% rangeobs/90% rangesim) were calculated. Most Rm and Rr values were greater than one, indicating that the simulated TTDs were smaller and more homogeneous than the observed ones. Rr values departed farther from one than Rm values. Ratios obtained when simulating 1 spore with 10,000 spores deviated the farthest from one. Neither ratio was significantly different from the other when simulating 1 spore with 100 spores or simulating 100 spores with 10,000 spores. When kinetic data were not available, the percent positive observed at the 95th percentile of the simulated TTDs was obtained. These simulation results confirmed that the assumption of independence between spores is not valid.

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33

Kovács, Tamás. "Computer simulation of roundabouts." Gradus 7, no. 3 (2020): 153–58. http://dx.doi.org/10.47833/2020.3.csc.003.

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Inthe last decades numerous traffic lamp controlled intersectionshave beenchanged to roundabouts on Hungarian traffic roads, hoping that this helps diminish the usual traffic jams in these traffic contexts.The most characteristic parameter of a roundabout is the capacity that is the number of vehicles can pass through the intersection in 1 hour. The capacity calculation of the new roundabouts, beyond a formula basedcalculation, often involves computer simulation as well, so as to get more reliable results. In the present paper we introduce a development of our traffic simulator so that it is able to determine the capacitiesof a roundabout. The simulation is tested by capacity calculation of a real roundabout and the dependency of the capacity on some key parameters is examined as well.

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34

Aidara Diouf, Alioune, and Bassirou Lo. "DIELECTRIC PROPERTIES: COMPUTER SIMULATION." International Journal of Advanced Research 8, no. 6 (June 30, 2020): 972–79. http://dx.doi.org/10.21474/ijar01/11178.

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35

Paul, Ray J., and Francis Neelamkavil. "Computer Simulation and Modelling." Journal of the Operational Research Society 38, no. 11 (November 1987): 1092. http://dx.doi.org/10.2307/2582236.

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Bradley, Drake R. "Computer simulation with DATASIM." Behavior Research Methods, Instruments, & Computers 21, no. 2 (March 1989): 99–112. http://dx.doi.org/10.3758/bf03205564.

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Scott, R., M. P. Allen, and D. J. Tildesley. "Computer Simulation of Liquids." Mathematics of Computation 57, no. 195 (July 1991): 442. http://dx.doi.org/10.2307/2938686.

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Ohnaka, Itsuo. "Computer simulation of casting." Journal of Japan Institute of Light Metals 54, no. 9 (September 30, 2004): 394–403. http://dx.doi.org/10.2464/jilm.54.394.

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39

Masic, Zlatan, Konny Lajhner, and Haris Pandza. "Computer Modelling and Simulation." International Journal on Biomedicine and Healthcare 9, no. 3 (2021): 173. http://dx.doi.org/10.5455/ijbh.2021.9.173-182.

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Красноухова, Евгения Алексеевна. "Computer simulation of solidification." Technology audit and production reserves 5, no. 2(7) (September 18, 2012): 59–60. http://dx.doi.org/10.15587/2312-8372.2012.4849.

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41

Nachbar, James M. "Computer Imaging/Surgical Simulation." Plastic and Reconstructive Surgery 100, no. 7 (December 1997): 1905–6. http://dx.doi.org/10.1097/00006534-199712000-00045.

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42

Moretti, Sabrina. "Computer Simulation in Sociology." Social Science Computer Review 20, no. 1 (February 2002): 43–57. http://dx.doi.org/10.1177/089443930202000105.

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43

Kadanoff, L. P. "Excellence in computer simulation." Computing in Science & Engineering 6, no. 2 (March 2004): 57–67. http://dx.doi.org/10.1109/mcise.2004.1267608.

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44

Fazarinc, Z. "Computer simulation in physics." IEEE Potentials 9, no. 2 (April 1990): 30–33. http://dx.doi.org/10.1109/45.52998.

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45

Small, Cathy. "Computer Simulation and Ethnogrpahy." Anthropology News 42, no. 4 (April 2001): 21. http://dx.doi.org/10.1111/an.2001.42.4.21.

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46

Rutan, Alan H. "Advances in computer simulation." ACM SIGSIM Simulation Digest 21, no. 3 (April 1, 1991): 2–6. http://dx.doi.org/10.1145/106073.106075.

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47

Rutan, Alan H. "Advances in computer simulation." ACM SIGSIM Simulation Digest 21, no. 3 (April 1991): 2–7. http://dx.doi.org/10.1145/106073.306832.

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48

KRÖSL, EVENTS P. "COMPUTER SIMULATION OF CIRCULATORY." Shock 2, Supplement (September 1994): 41. http://dx.doi.org/10.1097/00024382-199409001-00086.

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49

Magagnosc, David. "Simulation in computer organization." ACM SIGCSE Bulletin 26, no. 1 (March 12, 1994): 178–82. http://dx.doi.org/10.1145/191033.191100.

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50

Allen, M. P., D. J. Tildesley, and Jayanth R. Banavar. "Computer Simulation of Liquids." Physics Today 42, no. 3 (March 1989): 105–6. http://dx.doi.org/10.1063/1.2810937.

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