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Modeling and simulation in engineering

Overview

Mehmet Sahinoglu

Published Online: Apr 19 2013

DOI: 10.1002/wics.1254

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Abstract This review article will explore the innovative and popular theme of engineering modeling and simulation, predominantly in the manufacturing industry and cybersecurity world, citing severe challenges, advantages and time‐ and budget saving solutions and its future. The power of simulation is not an exaggeration but an understatement. The favorable outcomes since the advent of digital computers and software revolution could not have been achieved, especially without the multiple benefits of statistical simulation, which underlies the widespread use of modeling and simulation in engineering and sciences, stretching from A (Astronomy) to Z (Zoology). This refers not only to research findings in verifying a certain piece of theory, such as that of the recently discovered Higgs Boson, but in testing new products to innovate new discoveries so as to make our universe a more peaceful place by modeling and simulating the future projects and taking precautions before disasters occur. The review explores a cross section of engineering modeling and simulation practices illustrating a window of numerical examples. WIREs Comput Stat 2013, 5:239–266. doi: 10.1002/wics.1254 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Algorithms and Computational Methods > Random Number Generation Statistical Models > Simulation Models

P(UP) Cumulative reliability plot with 10,000 Monte Carlo simulation runs.

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A sample illustration of feasible transitions from Figure 3 implemented to subsections of Three‐State Sahinoglu Probability Model of Production Units (Monte Carlo Simulation).

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Three‐state Markov diagram of a repairable hardware unit with UP, DOWN and DER states.

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Given the input table, the p.d.f. of the two‐state SL is plotted for UP (r) and DOWN (q) for 90% confidence analytically showing mode (m), mean (E) with upper & lower confidence bounds.

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Computer modeling and interplay between experiments, simulation, and theory.25

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Uniform numbers testing; Ho: random versus Ha: not random for 250,000 runs. Ho is not rejected. After 40 cycles × 250K = 10,000K = 10,000,000 simulations there is still no reject Ho = Random Sequence. This may signal still no rejection of random sequence from the earlier safe threshold: 50K simulations for a JAVA coding uniform random number generator. Important Note: In this figure, buttons indicate: No of values = 250,000 (simulation runs), DF = 6 (Section on Generic Theory, by Knuth's Technique10,11), Significance level (Type‐I error) = 5%, Total Runs: 41,606 ×1 + 51,836 ×2 + 23,059 ×3 + 6583 ×4 + 1482 ×5 + 290 ×6.093 (average for >6) = 250,000, where bold numbers from 1 to >6 are calculated run sizes by Knuth's method. χ² calculated = 7.57 <χ² critical value = 12.59. Do NOT reject Ho: random sequence.

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Uniform numbers testing; Ho: random versus Ha: not random for 100,000 runs. Ho is NOT rejected. After 50 cycles × 100K = 5000K = 5,000,000 simulations, there is still no reject Ho = random sequence. This may signal still no rejection of random sequence from the earlier safe threshold: 50K for a JAVA coding uniform random number generator.

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Uniform numbers testing; Ho: random versus Ha: not random for 50,000 runs. Ho is NOT rejected. After 60 cycles × 50K = 3000K = 3,000,000 simulations there is still no reject Ho = random sequence. This may signal a cut‐off point of no rejection of random sequence from this point on. Safe threshold may be 50K for JAVA coding uniform random number generator.

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Uniform numbers testing; Ho: random versus Ha: not random for 10,000 runs. Ho is rejected. On the average, one out of 25 cycles of 10,000 = 250,000 simulations will end up rejecting Ho: random.

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Uniform numbers testing; Ho: random versus Ha: not random for 10000 runs. Ho is NOT rejected.

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Uniform numbers testing; Ho: random versus Ha: not random for 5000 runs. Ho is rejected. On the average, one out of 10 cycles of 5000 = 50,000 simulations will end up rejecting Ho: random.

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Uniform numbers testing; Ho: random versus Ha: not random for 5000 runs. Ho is NOT rejected.

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Uniform numbers testing; Ho: random versus Ha: not random for 500 runs. Ho is rejected. On the average, one out of 40 cycles of 500 runs = 20,000 simulations will end up rejecting Ho: random.

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Uniform numbers testing; Ho: random versus Ha: not random for 500 runs. Ho is not rejected.

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Monte Carlo simulations for the cyber server ($8000 asset) example with inputs in Table 8.

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The Monte Carlo (MC) simulation results of the 2 × 2 × 2 security meter sampling design.

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DES results of the 2 × 2 × 2 security meter sampling design.

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Simplest 2 × 2 × 2 tree diagram for two threats and for two vulnerabilities in a cyber‐risk scenario.

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Complex network of seven units with input data, where source: s = 1 and target: t = 7.

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Simple series system of two units.

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Relationships for distributions in statistical simulation where α₁ = α₂ or α₁≠α₂, and L = (β₁/β₂) for SL (α, β, L). (Dashed arrows indicate ‐→∝ Reprinted with permission from Ref 20 Copyright 2007, Wiley & Sons, Inc)

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Similar to Figure 12 but with Median (M), first and third quartiles as location measures for n = 100,000 simulation runs plotted for UP (r), DER (d), and DOWN (q).

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Given the input table on the l.h.s. column, the p.d.f.s of the three states are plotted for UP (r), DER (d), and DOWN (q) for a 90% confidence level showing mode (m), mean (E) with upper & lower confidence as centrality measures for n = 100,000 simulation runs.

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The input data in Table 1, and simulation results in Tables 2–4 and Figures 5–7 display the cumulative reliability plots of the three states for UP (r), DER (d), and DOWN (q).

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P(DOWN) Cumulative reliability plot with 10,000 Monte Carlo simulation runs.

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P(DER) Cumulative reliability plot with 10,000 Monte Carlo simulation runs.

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References

Available at: http://www.nucleonica.net/wiki/images/d/df/EMC_2008_short.pdf. (Accessed January 5, 2013).
Available at: http://web.student.tuwien.ac.at/∼e9527 412/history.html. (Accessed January 5, 2013).
Available at: http://www.britannica.com/EBchecked/topic/1252440/von‐Neumann‐machine. (Accessed January 5, 2013).
Available at: http://mathworld.wolfram.com/Cellular Automaton.html. (Accessed January 5, 2013).
Available at: http://www.math.com/students/wonders/life/life.html. (Accessed January 5, 2013).
Ledin, J. Simulation Engineering‐ Build Better Embedded Systems Faster. Taylor and Francis; CRC Press; BOCA Raton, London & Newyork 2001. Available at: http://books.google.com/books/about/Simulation_Engineering.html?id=GMRjR2shhXAC. (Accessed January 5, 2013)
Aoyama, M, Computing for the next‐generation automobile. IEEE Comput 2012, 45:32–37.
Zeigler, BP, Praehofer, H, Kim, TG, Theory of Modeling and Simulation, 2nd ed. Academic Press San Diego, CA 92101‐4495,USA; 2000.
Levene, H, Wolfowitz, J. The covariance matrix of runs up and down. Ann Mat Stat (AMS) 1944, 15:58–69
Knuth, DE, The Art of Computer Programming, Vol. 2: Seminumerical Algorithms. 3rd ed. Addison‐Wesley: Reading, MA; 1998.
Stewart, WJ. Probability, Markov Chains, Queues and Simulation. Princeton University Press Princeton, NJ, 08540, USA; 2009.
Available at: http://www.youtube.com/watch?v=_RBH0PeLhOk. (Accessed January 5, 2013).
Available at: http://www.nap.edu/openbook.php? record_id=10425%26page=77. (Accessed January 5, 2013).
Available at: http://www.guardian.co.uk/public‐leaders‐network/2011/may/20/local‐councils‐software‐hard‐cuts. (Accessed January 5, 2013).
Available at: http://www.albrechts.com/mike/DES/Annotated%20Bibliography%20.pdf. (Accessed January 5, 2013).
Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=%26arnumber=373979 (Accessed January 5, 2013).
Kuhl, E, Kistner, J, Costantini, K, Sudit, M. Cyber attack modeling and simulation for network security analysis. ACM Digital Library, Proceedings of the 39th Conference on Winter Simulation Conference (WSC`07), 2007.
Available at: http://www.youtube.com/watch?v = du6g__lgS3Q (Accessed January 5, 2013).
Available at: http://www.washingtonpost.com/wp‐dyn/content/article/2010/02/16/AR2010021605762.html (Accessed January 5, 2013).
Sahinoglu, M. Trustworthy Computing: Analytical and Quantitative Engineering Evaluation (CD ROM included). Hoboken, NJ: John Wiley %26 Sons Inc.; 2007.
Sahinoglu, M. Security meter—a practical decision tree model to quantify risk. IEEE Securit Privacy 2005, 3:18–24.
Sahinoglu, M. An input‐output measurable design for the security meter model to quantify and manage software security risk. IEEE Trans Instrum Measure 2008, 57:1251–1260.
Sahinoglu, M. Can we quantitatively assess and manage risk of software privacy breaches?. Int J Comput, Inform Technol Eng 2009, 3:65–70.
Sahinoglu, M. Yuan, Y‐L. Banks, D. Validation of a security and privacy risk metric using triple uniform product rule. Int J Comput Inform Technol Eng 2010, 4:125–135.
This file appeared originally in the Wikipedia article entitled Computer Simulation. It is licensed under the Creative Commons Attribution‐Share Alike 3.0 Unported License. Available at: http://en.wikipedia.org/wiki/File:Molecular_simulation_process.svg. (Accessed January 5, 2013).
Sahinoglu, M, Cueva‐Parra, L. CLOUD computing. Wiley Interdisciplinary Reviews Computational Statistics 2011, 3:47–68.
Jain, S, Foley, WJ. Basis for development of a generic FMS simulator. Proceedings of the Second ORSA/TIMS Conference on Flexible Manufacturing Systems: Operations Research Models and Applications. Amsterdam: Elsevier Science Publishers B.V.; 1986, 393–403.
Schriber, TJ, Stecke, KE. Machine utilizations and production rates achieved by using balanced aggregate FMS production ratios in a simulated setting. Proceedings of the Second ORSA/TIMS Conference on Flexible Manufacturing Systems: Operations Research Models and Applications. Amsterdam: Elsevier Science Publishers B.V.; 1986, 405–416.
Dee, ZJ, Co, HC, Wyek, RA. SIM‐Q: a simplified approach to simulation modelling of automated manufacturing systems. Proceedings of the Second ORSA/TIMS Conference on Flexible Manufacturing Systems: Operations Research Models and Applications. Amsterdam: Elsevier Science Publishers B.V.; 1986, 417–430.
Rao, MS, Naikan, VN, Pradesh, A. A hybrid markov system dynamics approach for availability analysis of degraded systems. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, Kula Lumpur, Malaysia, January 22–24, 2011.
Lins, ID, Droguett, EL. Multiobjective optimization of availability and cost in repairable systems design via genetic algorithms and discrete event simulation Pesqui. Oper. Jan/Apr 2009, 29.
Shah, A, Dhillon, BS. Reliability and availability analysis of three‐state device redundant systems with human errors and common‐cause failures. Int J Perform Eng 2007, 3:411–441.
Sahinoglu, M, Longnecker, MT, Ringer, LJ, Singh, C, Ayoub, AK. Probability distribution function for generation reliability indices‐analytical approach. IEEE Trans Power Apparat Syst (PAS) 1983, 104:1486–1493.
Sahinoglu, M, Libby, D, Das, SR. Measuring availability indices with small samples for component and network reliability using the Sahinoglu‐Libby probability model. IEEE Trans Instrum Measure 2005, 54:1283–1295.
Sahinoglu, M. Statistical inference on reliability performance index for electric power generation systems. Ph.D. Dissertation, The Institute of Statistics jointly with Electrical and Computer Eng., Texas A&M University, College Station, December 11, 1981.
Libby, DL. Multivariate fixed state utility assessment. Ph.D. Dissertation, University of Iowa, Iowa City, 1981.
Libby, DL, Novick, MR. Multivariate generalized β distributions with applications to utility assessment. J Ed Stat 1982, 7:271–294.
Sahinoglu, M, Yuan Y‐L, Capar S. Statistical inference on the two‐ and three‐ state availability of the repairable units with the Sahinoglu‐Libby Model, IPS018 (Invited Paper Session): statistical risk assessment in trustworthy computing. Proceedings (CD ROM) of International Statistical Institute, 58th Congress, Dublin, 2011.
Sahinoglu, M, Yuan Y‐L., Multivariate statistical modeling on the 3‐state (up, derated, down) availability of repairable hardware unit and their systems with the Sahinoglu‐Libby probability distribution using monte carlo simulation. Proceedings of GCMS`10, Ottawa, Canada, 12–14 July 2010, 253–260.
Lisniansky, A, Levitin, G. Multi‐State System Reliability: Assessment, Optimization and Applications. World Scientific Publishing Co Ptc.Ltd, Singapore 596224; 2003.
Billinton, R, Allan, R. Reliability Evaluation of Power Systems. New York: Plenum Press; 1996.
Murchland, J. Fundamental concepts and relations for reliability analysis of multistate systems, reliability and fault tree analysis. Theoret Appl Aspects System Reliab SIAM 1975:581–618.
Barlow, R, Wu, A. Coherent systems with multistate elements. Math Oper Res 1978, 3(4):275–281.
Billinton, R. Power System Reliability Evaluation. One Park Avenue, New York, NY: Gordon and Breach Science Publishers Inc. 1970.
Sahinoglu, M, Rice, B. Network reliability evaluation. Wiley Interdiscip Rev: Comput Stat 2010, 2:189–211.
Hogg, RV, Craig, AT. Introduction to Mathematical Statistics. 3rd ed. New York: MacMillan; 1970.
Snedecor, G, Cochran, W. Statistical Methods. Ames, IA: Iowa State University Press; 1989.
Rinaldi, SM. Modeling and simulating critical infrastructures and their interdependencies. IEEE Proceedings of the 37th Hawaii International Conference on System Sciences, Hawaii, 2004, 1–8
Kim, H, Chaudhuri, S, Parashar, M, Marty, C. Online risk analytics on the cloud. IEEE Computer Society Washington, DC, CCGRID`09. Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009, 484–489.
Sahinoglu, M, Cueva‐Parra, L, Ang, D. Game‐theoretic computing in risk analysis. WIREs Comp Stat 2012, 4:227–248. doi:10.1002/wics.1205.
Thompson, J. Forward simulation models. Wiley Interdiscip Rev Comput Stat 2010, 2:61–68.
Kroese, DP, Rubinstein, RV. Monte Carlo methods. WIREs Comp Stat 2012, 4:48. doi:10.1002/wics.194
Underwood, RG. A non‐trivial markov chain with explicit invariant distribution. Available at: http:// sciences‐srv.aum.edu/∼runderwo/papers/Markov_paper_underwood.pdf. (Accessed January 28, 2013).
Available at: www.aum.edu/csis. (Accessed January 5, 2013).
Johnson, NL. Kotz, S. Distributions in Statistics: Continuous Univariate Distributions. Wiley‐Interscience Newyork, USA; 1970.

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