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Journal articles on the topic 'Simulation methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ripley, B. D., and M. D. Kirkland. "Iterative simulation methods." Journal of Computational and Applied Mathematics 31, no. 1 (1990): 165–72. http://dx.doi.org/10.1016/0377-0427(90)90347-3.

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2

Hoops, Christian, and Rahul Pathare. "Review and Simulation of Different Sampling Methods." International Journal of Scientific Engineering and Research 3, no. 3 (2015): 84–90. https://doi.org/10.70729/26031503.

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3

Fagbade, Adeyemi, and Stefan Heinz. "Continuous Eddy Simulation vs. Resolution-Imposing Simulation Methods for Turbulent Flows." Fluids 9, no. 1 (2024): 22. http://dx.doi.org/10.3390/fluids9010022.

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The usual concept of simulation methods for turbulent flows is to impose a certain (partial) flow resolution. This concept becomes problematic away from limit regimes of no or an almost complete flow resolution: discrepancies between the imposed and actual flow resolution may imply an unreliable model behavior and high computational cost to compensate for simulation deficiencies. An exact mathematical approach based on variational analysis provides a solution to these problems. Minimal error continuous eddy simulation (CES) designed in this way enables simulations in which the model actively r

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4

GEORGE, E., J. GLIMM, X. L. LI, et al. "Numerical methods for the determination of mixing." Laser and Particle Beams 21, no. 3 (2003): 437–42. http://dx.doi.org/10.1017/s0263034603213239.

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We present a Rayleigh–Taylor mixing rate simulation with an acceleration rate falling within the range of experiments. The simulation uses front tracking to prevent interfacial mass diffusion. We present evidence to support the assertion that the lower acceleration rate found in untracked simulations is caused, at least to a large extent, by a reduced buoyancy force due to numerical interfacial mass diffusion. Quantitative evidence includes results from a time-dependent Atwood number analysis of the diffusive simulation, which yields a renormalized mixing rate coefficient for the diffusive sim

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5

Podolskaya, Nina A. "Network Simulation: Tasks and Methods of Their Solution." International Journal of Computer Theory and Engineering 6, no. 5 (2014): 392–95. http://dx.doi.org/10.7763/ijcte.2014.v6.896.

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6

Zäh, Michael F., and Alexander Schober. "Innovative welding simulation methods." ATZproduktion worldwide 3, no. 1 (2010): 32–36. http://dx.doi.org/10.1007/bf03224215.

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7

Millington, James D. A., and John Wainwright. "Mixed qualitative-simulation methods." Progress in Human Geography 41, no. 1 (2016): 68–88. http://dx.doi.org/10.1177/0309132515627021.

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Across geography there has been variable engagement with the use of simulation and agent-based modelling. We argue that agent-based simulation provides a complementary method to investigate geographical issues which need not be used in ways that are epistemologically different in kind from some other approaches in contemporary geography. We propose mixed qualitative-simulation methods that iterate back-and-forth between ‘thick’ (qualitative) and ‘thin’ (simulation) approaches and between the theory and data they produce. These mixed methods accept simulation modelling as process and practice;

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8

Andersen, Torben G. "SIMULATION-BASED ECONOMETRIC METHODS." Econometric Theory 16, no. 1 (2000): 131–38. http://dx.doi.org/10.1017/s0266466600001080.

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The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods, with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of the standard linear model, linear simultaneous equations with the associated instrumental variable techniques, and maximum

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9

Tikhonov, V., and R. Veenhof. "GEM simulation methods development." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 478, no. 1-2 (2002): 452–59. http://dx.doi.org/10.1016/s0168-9002(01)01801-0.

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10

Guha, Ratan, and Mostafa Bassiouni. "Simulation Methods and Applications." Simulation Practice and Theory 9, no. 3-5 (2002): 91–93. http://dx.doi.org/10.1016/s0928-4869(01)00056-8.

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11

Elber, Ron. "Long-timescale simulation methods." Current Opinion in Structural Biology 15, no. 2 (2005): 151–56. http://dx.doi.org/10.1016/j.sbi.2005.02.004.

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12

Barrett, John H. "Methods of channeling simulation." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 44, no. 3 (1990): 367–72. http://dx.doi.org/10.1016/0168-583x(90)90652-b.

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13

Mahato, Ashok Kumar, Rahul Das, and Suresh Kumar Sahani. "Simulation of Realistic Motion in Computer Graphics Using Runge-Kutta Methods." African Multidisciplinary Journal of Sciences and Artificial Intelligence 2, no. 2 (2025): 325–42. https://doi.org/10.58578/amjsai.v2i2.5681.

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This article looks into the use of the fourth-order Runge-Kutta (RK4) method in realistic motion simulation within computer graphics. With dynamic animations, there is an emerging need to solve physical systems using ordinary differential equations, for which RK4 is particularly useful due to its accuracy, stability, and balanced computational cost and efficiency. We implement motion phenomena with damped spring-mass systems by changing second-order differential equations into first-order systems that can be integrated using RK4. The results are measured against Euler and Midpoint methods for

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14

Babakhadjaev, Rashid Xashimovich. "THE USE OF INTERACTIVE METHODS IN FOREIGN LANGUAGES." International journal of word art 5, no. 6 (2022): 4. https://doi.org/10.5281/zenodo.7393902.

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The active and interactive methods allows sharing information, receiving feedback, solving together the arising problems, simulating the educational situations, evaluating one’s own behavior and the actions of other participants, diving into the real atmosphere of business cooperation in solving problematic issues. Various methods exist of interaction between the teacher and students: cooperative learning methods, group discussions, debates, business simulation games, case situation analysis, project method, social-psychological training, moderation, computer simulations, and others. Tha

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15

Heinz, Stefan. "The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods." Fluids 9, no. 12 (2024): 278. http://dx.doi.org/10.3390/fluids9120278.

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Feasible and reliable predictions of separated turbulent flows are a requirement to successfully address the majority of aerospace and wind energy problems. Existing computational approaches such as large eddy simulation (LES) or Reynolds-averaged Navier–Stokes (RANS) methods have suffered for decades from well-known computational cost and reliability issues in this regard. One very popular approach to dealing with these questions is the use of machine learning (ML) methods to enable improved RANS predictions. An alternative is the use of minimal error simulation methods (continuous eddy simul

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16

Chen, Lei. "Comparisons of Explicit and Implicit Finite Element Methods for Sheet Metal Forming." Advanced Materials Research 936 (June 2014): 1836–39. http://dx.doi.org/10.4028/www.scientific.net/amr.936.1836.

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Sheet metal forming is one of the most commonly practiced fabrication processes in industry. Numerical simulations of the complex parts are possible by finite element method in the past thirty years. The most important problem of the simulation is the reliability of the model. Static implicit method (SI) and dynamic explicit method (DE) were used to simulation sheet metal forming process. It was found that simulation speed in dynamic explicit software has large effect on the simulation results. The best simulation speed is 5~10 m/s. Compared with the simulation and experimental results of thic

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17

Winterstein, S. R., and R. Torhaug. "Extreme Jack-Up Response: Simulation and Nonlinear Analysis Methods." Journal of Offshore Mechanics and Arctic Engineering 118, no. 2 (1996): 103–8. http://dx.doi.org/10.1115/1.2828817.

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The nonlinear dynamic response of a jack-up structure under random wave loads is considered. For a simplified jack-up model, average behavior and variability in extreme forces and responses are found from simulation over many 6-h seastates. Weibull and Hermite analytical models of response extremes are also presented and evaluated. These models use shorter, less expensive simulations to estimate a limited number of response statistics, such as moments or parameters of the response peak distribution, and fit analytical models to estimate global extremes. Necessary simulation lengths are establi

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18

Chu, Xiajing, Derek K. Chu, Junjie Ren, et al. "Completeness of reporting of simulation studies on responder analysis methods and simulation performance: a methodological survey." BMJ Open 15, no. 5 (2025): e096107. https://doi.org/10.1136/bmjopen-2024-096107.

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ObjectivesTo evaluate the completeness of reporting of simulation studies on responder analysis methods and simulation performance.DesignSystematic methodological survey.Data sourcesWe searched Embase, MEDLINE (via Ovid), PubMed and Web of Science Core Collection from inception to 9 October 2023.Eligibility criteriaWe included simulation studies comparing responder analysis methods and assessing simulation performance (bias, accuracy, precision or variance, power, type I and II errors and coverage).Data extraction and synthesisTwo independent reviewers extracted data and assessed simulation pe

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19

Liu, Shilong, Ioan Nistor, and Majid Mohammadian. "Evaluation of the Solid Boundary Treatment Methods in SPH." International Journal of Ocean and Coastal Engineering 01, no. 02 (2018): 1840002. http://dx.doi.org/10.1142/s252980701840002x.

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The smoothed particle hydrodynamics (SPH) method has been proved as a powerful algorithm for fluid mechanics, especially in the simulation of free surface flows with high speeds or drastic impacts. The solid boundary treatment method is important for the accuracy and stability of the numerical results, as the support domain of fluid particles is truncated near the vicinity of the boundary. This paper presents two commonly used methods for simulating a solid boundary in SPH simulations. Emphasis is placed on the description of the methods, definition of the boundary particles’ parameters, and d

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20

Paulovics, László, Jan Rohde-Brandenburger, and Csaba Tóth-Nagy. "Timing chain wear investigation methods: Review." FME Transactions 50, no. 3 (2022): 461–72. http://dx.doi.org/10.5937/fme2203461p.

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Several methods are used for investigating timing chain wear, from fired engine dynamometer tests through tribological model tests to simulations. Research over the past decade has shown that component or tribometer tests can replace expensive engine dynamometer tests in many cases. Simulation methods can further reduce the cost and time of development. Simulation models require experimentally defined input parameters; therefore, experiment-based methods cannot be completely avoided. However, a comprehensive comparison or validation of the various experimental and simulation techniques is diff

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21

Ishigami, Toshihiko. "Simulation Methods for Lamp Development." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 83, no. 1 (1999): 19–22. http://dx.doi.org/10.2150/jieij1980.83.1_19.

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22

Kanevskaya, R. D., and A. V. Novikov. "Methods of acid fracturing simulation." Automation, Telemechanization and Communication in Oil Industry, no. 3 (2018): 28–34. http://dx.doi.org/10.30713/0132-2222-2018-3-28-34.

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23

Davis, Jason P., Kathleen M. Eisenhardt, and Christopher B. Bingham. "Developing Theory Through Simulation Methods." Academy of Management Review 32, no. 2 (2007): 480–99. http://dx.doi.org/10.5465/amr.2007.24351453.

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24

Apithy, H., Y. Bouslimaniet, and H. Hamam. "Simulation methods in optical propagation." Canadian Journal of Electrical and Computer Engineering 30, no. 1 (2005): 39–48. http://dx.doi.org/10.1109/cjece.2005.1532605.

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25

Haile, J. M., Ian Johnston, A. John Mallinckrodt, and Susan McKay. "Molecular Dynamics Simulation: Elementary Methods." Computers in Physics 7, no. 6 (1993): 625. http://dx.doi.org/10.1063/1.4823234.

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26

Mezencev, Konstantin Nikolaevich, and Punam Jha. "Simulation Methods of Computer Networks." International Journal of Advanced Studies 4, no. 3 (2015): 9. http://dx.doi.org/10.12731/2227-930x-2014-3-2.

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27

Мезенцев, Константин Николаевич, and Джха Пунам. "Simulation methods of computer networks." Automation and Control in Technical Systems, no. 2 (January 4, 2015): 29. http://dx.doi.org/10.12731/2306-1561-2014-2-4.

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28

Kadau, Kai, John L. Barber, Timothy C. Germann, Brad L. Holian, and Berni J. Alder. "Atomistic methods in fluid simulation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1916 (2010): 1547–60. http://dx.doi.org/10.1098/rsta.2009.0218.

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Atomistic methods, such as molecular dynamics and direct simulation Monte Carlo, constitute a powerful and growing set of techniques for fluid-dynamics simulation. The more fundamental nature of such methods, which exhibit nonlinear transport effects and small-scale fluctuations, extends their modelling accuracy to a significantly wider range of scales and regimes than the more traditional Navier–Stokes-based continuum fluid-simulation techniques. In this paper, we describe the current state of the art in atomistic fluid simulation, from both a theoretical and a computational standpoint, and o

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29

KELTON, W. DAVID. "Random Initialization Methods in Simulation." IIE Transactions 21, no. 4 (1989): 355–67. http://dx.doi.org/10.1080/07408178908966242.

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30

Giraudo, Marı́a Teresa, and Laura Sacerdote. "Simulation methods in neuronal modelling." Biosystems 48, no. 1-3 (1998): 77–83. http://dx.doi.org/10.1016/s0303-2647(98)00052-5.

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31

Schmidt, G., and G. Lappus. "Digital Simulation Methods — A Tutorial." IFAC Proceedings Volumes 18, no. 11 (1985): 83–93. http://dx.doi.org/10.1016/s1474-6670(17)60113-7.

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32

Feijóo, Andrés, and Daniel Villanueva. "Assessing wind speed simulation methods." Renewable and Sustainable Energy Reviews 56 (April 2016): 473–83. http://dx.doi.org/10.1016/j.rser.2015.11.094.

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33

Heiss, Florian. "Discrete Choice Methods with Simulation." Econometric Reviews 35, no. 4 (2016): 688–92. http://dx.doi.org/10.1080/07474938.2014.975634.

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34

Seleim, A. Azab, and T. AlGeddawy. "Simulation Methods for Changeable Manufacturing." Procedia CIRP 3 (2012): 179–84. http://dx.doi.org/10.1016/j.procir.2012.07.032.

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35

Popov, V. L., and S. G. Psakhie. "Numerical simulation methods in tribology." Tribology International 40, no. 6 (2007): 916–23. http://dx.doi.org/10.1016/j.triboint.2006.02.020.

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36

Guilleaume, C., and A. Brosius. "Simulation methods for skew rolling." Procedia Manufacturing 27 (2019): 1–6. http://dx.doi.org/10.1016/j.promfg.2018.12.035.

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37

Yanez, Javier. "Neural Network and Simulation Methods." Neurocomputing 9, no. 2 (1995): 218–19. http://dx.doi.org/10.1016/0925-2312(95)90021-7.

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38

Ramos, J. I. "Computational Methods for Process Simulation." Applied Mathematical Modelling 14, no. 8 (1990): 445. http://dx.doi.org/10.1016/0307-904x(90)90103-c.

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39

Chronister, Connie, and Diane Brown. "Comparison of Simulation Debriefing Methods." Clinical Simulation in Nursing 8, no. 7 (2012): e281-e288. http://dx.doi.org/10.1016/j.ecns.2010.12.005.

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40

Joppich, W., and S. Mijalković. "Multigrid methods for process simulation." Microelectronics Journal 26, no. 2-3 (1995): xxvii—xxviii. http://dx.doi.org/10.1016/0026-2692(95)90020-9.

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41

Wagner, Janet M., Uri Shamir, and David H. Marks. "Water Distribution Reliability: Simulation Methods." Journal of Water Resources Planning and Management 114, no. 3 (1988): 276–94. http://dx.doi.org/10.1061/(asce)0733-9496(1988)114:3(276).

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42

Pb. "Molecular dynamics simulation, elementary methods." Journal of Molecular Structure: THEOCHEM 288, no. 3 (1993): 287–88. http://dx.doi.org/10.1016/0166-1280(93)87060-q.

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43

Varakin, A. I., V. V. Mazur, N. V. Arkhipova, and Yu V. Seryanov. "Simulation methods in biophysical pharmacokinetics." Biomedical Engineering 41, no. 3 (2007): 103–7. http://dx.doi.org/10.1007/s10527-007-0022-5.

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44

Paterson, W. R. "Computational methods for Process simulation." Chemical Engineering Science 47, no. 8 (1992): 2129–30. http://dx.doi.org/10.1016/0009-2509(92)80336-b.

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45

Gaffney, Betty J., and Harris J. Silverstone. "Simulation Methods for Looping Transitions." Journal of Magnetic Resonance 134, no. 1 (1998): 57–66. http://dx.doi.org/10.1006/jmre.1998.1526.

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46

Eigenberger, G. "Computational Methods for Process Simulation." Chemical Engineering and Processing: Process Intensification 30, no. 1 (1991): 59. http://dx.doi.org/10.1016/0255-2701(91)80010-m.

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47

Rajput, Abhishek, Alessandro Roggero, and Nathan Wiebe. "Hybridized Methods for Quantum Simulation in the Interaction Picture." Quantum 6 (August 17, 2022): 780. http://dx.doi.org/10.22331/q-2022-08-17-780.

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Conventional methods of quantum simulation involve trade-offs that limit their applicability to specific contexts where their use is optimal. In particular, the interaction picture simulation has been found to provide substantial asymptotic advantages for some Hamiltonians, but incurs prohibitive constant factors and is incompatible with methods like qubitization. We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations over known algorithms. These approaches show asymptotic improvements over the indivi

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48

Locatelli, M., L. Pellegrini, D. Accardo, E. Sulis, L. C. Tagliabue, and G. M. Di Giuda. "People flow management in a healthcare facility through crowd simulation and agent-based modeling methods." Journal of Physics: Conference Series 2600, no. 14 (2023): 142007. http://dx.doi.org/10.1088/1742-6596/2600/14/142007.

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Abstract The study investigates the optimization of user flow and space management in a hospital located in Turin by simulating activities and patient flows of the blood drawing center. The simulation aims to verify the maximum number of people allowed to occupy the spaces simultaneously, manage user flows, and verify compliance with COVID-19 pandemic restrictions. Pedestrian Dynamics, supported by Building Information Modeling (BIM) methods, and NetLogo are used to simulate and optimize user flow and space management relying on crowd simulation and Agent-Based Modeling. Patients’ movements an

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49

Wu, Zhibo, Yanbing Zhang, Chuanmeng Sun, Lei Feng, Shuangfeng Liu, and Bin Jiao. "Simulation Methods for MEMS S&A Devices for 2D Fuze Overload Loading." Micromachines 14, no. 8 (2023): 1566. http://dx.doi.org/10.3390/mi14081566.

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An experimental testing system for the two-dimensional (2D) fuze overload loading process was designed to address the loading issues of recoil overload and centrifugal overload in fuze safety and arming (S&A) device. By incorporating centrifuge rotation energy storage, impact acceleration simulation, and equivalent centrifugal rotation simulation, a block equipped with a fuze S&A device accelerated instantly upon having impact from a centrifuge-driven impact hammer, simulating recoil overload loading. The impact hammer was retracted instantaneously by adopting an electromagnetic brake,

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50

Liu, Zhimin, Zhigang Xu, Dan Wang, et al. "A Review on Molecularly Imprinted Polymers Preparation by Computational Simulation-Aided Methods." Polymers 13, no. 16 (2021): 2657. http://dx.doi.org/10.3390/polym13162657.

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Molecularly imprinted polymers (MIPs) are obtained by initiating the polymerization of functional monomers surrounding a template molecule in the presence of crosslinkers and porogens. The best adsorption performance can be achieved by optimizing the polymerization conditions, but this process is time consuming and labor-intensive. Theoretical calculation based on calculation simulations and intermolecular forces is an effective method to solve this problem because it is convenient, versatile, environmentally friendly, and inexpensive. In this article, computational simulation modeling methods

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