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Comparación de metodologías de optimización de sistemas mecánicos bajo incertidumbre

dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributor.advisorCortes Ramos, Henry Octavio
dc.contributor.authorCalvo Ocampo, Rodrigo Andres
dc.date.accessioned2021-02-12T16:27:26Z
dc.date.available2021-02-12T16:27:26Z
dc.date.issued2020-12-04
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/79212
dc.description.abstractEn esta investigación se exploró el uso de diferentes formulaciones para la optimización bajo incertidumbre de sistemas mecánicos, con el fin de analizar su aplicabilidad y utilidad. Para ello, se abordan las principales formulaciones de optimización bajo incertidumbre consolidadas hasta la fecha en el área de optimización en ingeniería a saber: Optimización Basada en Confiabilidad (RBDO, Realibility Based Design Optimization), Optimización del Diseño Robusto (RDO, Robust Design Optimization), Optimización Bajo Riesgo (RO, Risk Based Design Optimization) y Optimización del diseño Robusto y basado en Confiabilidad (RBRDO, Reliability Based Robust Design Optimization). Adicionalmente se hizo una comparación de los resultados variando el algoritmo de optimización, para esto se usó: un algoritmo de búsqueda directa, un algoritmo basado en derivadas y un algoritmo genético. Se tomaron de la literatura ocho problemas (un problema matemático, un bastidor, un mecanismo, un sistema dinámico y cuatro problemas de estructuras). Para la formulación RDO los resultados muestran aplicabilidad alta en el 50% de los problemas y utilidad alta en el 63% de los problemas, destacando por su bajo costo computacional y robustez en la función objetivo. Para la formulación RBDO y RBRDO los resultados muestran una aplicabilidad alta en el 75% de los problemas y utilidad alta en el 50% de los problemas, destacando por su compromiso con el cumplimiento de la confiabilidad en las restricciones. Para la formulación RO los resultados muestran una aplicabilidad alta en el 38% de los problemas y utilidad alta en el 10% de los problemas, destacando por su equilibrio entre seguridad y economía (costo monetario).
dc.description.abstractIn this research, the use of different formulations for the optimization under uncertainty of mechanical systems (machines and structures) was explored, in order to analyze their applicability and utility. To this end, the main optimization formulations under uncertainty consolidated to date in the area of engineering optimization are addressed, namely: Reliability Based Optimization (RBDO, Reliability Based Design Optimization), Robust Design Optimization (RDO) and Low Risk Optimization (RO, Risk Based Design Optimization). Additionally, a comparison of the results was made by varying the optimization algorithm, for this we used: a direct search algorithm, one based on derivatives and a genetic algorithm. To obtain the results, eight problems were taken from the literature (a mathematical problem, a frame, a mechanism, a mechanical system and 4 trusses). For the RDO formulation the results show high applicability in 50% of the problems and high utility in 63% of the problems. This stands out for its low computational cost and robustness in the objective function. For the RBDO formulation, the results show high applicability in 75% of the problems and high utility in 50% of the problems. It stands out for its commitment to complying with the reliability of the restrictions. For the RO formulation, the results show a high applicability in 38% of the problems and a high utility in 10% of the problems. This stands out for its balance between security and economy (monetary cost).
dc.format.extent1 recurso en línea (129 páginas)
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
dc.titleComparación de metodologías de optimización de sistemas mecánicos bajo incertidumbre
dc.title.alternativeComparison of methodologies for optimization of mechanical systems under uncertainty
dc.typeOtro
dc.rights.spaAcceso abierto
dc.description.additionalLínea de Investigación: Optimización en ingeniería
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Mecánica
dc.description.degreelevelMaestría
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesL. Duan, G. Li, A. Cheng, G. Sun, and K. Song, “Multi-objective system reliability-based optimization method for design of a fully parametric concept car body,” Eng. Optim., vol. 49, no. 7, pp. 1247–1263, 2017, doi: 10.1080/0305215X.2016.1241780.
dc.relation.referencesS. S. Rane, A. Srividya, and A. K. Verma, “Multi-objective reliability based design optimization and risk analysis of motorcycle frame with strength based failure limit,” Int. J. Syst. Assur. Eng. Manag., vol. 3, no. 1, pp. 33–39, 2012, doi: 10.1007/s13198-012-0080-2.
dc.relation.referencesW. Yao, X. Chen, W. Luo, M. Van Tooren, and J. Guo, “Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles,” Prog. Aerosp. Sci., vol. 47, no. 6, pp. 450–479, 2011, doi: 10.1016/j.paerosci.2011.05.001.
dc.relation.referencesX. Lv, X. Gu, L. He, D. Zhou, and W. Liu, “Reliability design optimization of vehicle front-end structure for pedestrian lower extremity protection under multiple impact cases,” Thin-Walled Struct., vol. 94, pp. 500–511, 2015, doi: 10.1016/j.tws.2015.05.014
dc.relation.referencesB. D. Youn, K. K. Choi, R.-J. Yang, and L. Gu, “Reliability-based design optimization for crashworthiness of vehicle side impact,” Struct. Multidiscip. Optim., vol. 26, no. 3–4, pp. 272–283, 2004, doi: 10.1007/s00158-003-0345-0.
dc.relation.referencesX. Gu, J. Lu, and H. Wang, “Reliability-based design optimization for vehicle occupant protection system based on ensemble of metamodels,” Struct. Multidiscip. Optim., vol. 51, no. 2, pp. 533–546, 2015, doi: 10.1007/s00158-014-1150-7.
dc.relation.referencesA. T. Beck, W. J. S. Gomes, R. H. Lopez, and L. F. F. Miguel, “A comparison between robust and risk-based optimization under uncertainty,” Struct. Multidiscip. Optim., vol. 52, no. 3, pp. 479–492, 2015, doi: 10.1007/s00158-015-1253-9.
dc.relation.referencesY. Tsompanakis, N. D. Lagaros, and M. Papadrakakis, Structural design optimization considering uncertainties (Structures and Infrastructures 1), vol. 1. London: Taylor & Francis, 2008.
dc.relation.referencesS. M. J. Spence and M. Gioffré, “Efficient algorithms for the reliability optimization of tall buildings,” J. Wind Eng. Ind. Aerodyn., vol. 99, no. 6–7, pp. 691–699, 2011, doi: 10.1016/j.jweia.2011.01.017.
dc.relation.referencesM. Schelbergen, “Structural Optimization of Multi-Megawatt, Offshore Vertical Axis Wind Turbine Rotors,” Am. Inst. Aeronaut. Astronaut., pp. 1–19, 2013, doi: 10.2514/6.2014-1082.
dc.relation.referencesA. T. Beck and W. J. D. S. Gomes, “A comparison of deterministic, reliability-based and risk-based structural optimization under uncertainty,” Probabilistic Eng. Mech., vol. 28, pp. 18–29, 2012, doi: 10.1016/j.probengmech.2011.08.007.
dc.relation.referencesR. Reuven Y., Simulation and the Monte Carlo method, 1st ed. New York: John Wiley & Sons., 1981.
dc.relation.referencesS. K. Au and J. L. Beck, “Estimation of small failure probabilities in high dimensions by subset simulation,” Probabilistic Eng. Mech., vol. 16, no. 4, pp. 263–277, 2001, doi: 10.1016/S0266-8920(01)00019-4.
dc.relation.referencesV. Dubourg, B. Sudret, and J. M. Bourinet, “Reliability-based design optimization using kriging surrogates and subset simulation,” Struct. Multidiscip. Optim., vol. 44, no. 5, pp. 673–690, 2011, doi: 10.1007/s00158-011-0653-8.
dc.relation.referencesB. Sudret, “Meta-models for structural reliability and uncertainty quantification,” Fifth Asian-Pacific Symp. Struct. Reliab. its Appl., pp. 53–76, 2012.
dc.relation.referencesH. O. Cortés-Ramos, R. A. Calvo-Ocampo, and C. J. Camacho-López, “Comparación de algoritmos para optimización estructural basada en confiabilidad.,” Rev. Int. Métodos Numéricos para Cálculo y Diseño en Ing., pp. 1–9, 2017, doi: DOI: 10.23967/j.rimni.2017.7.003.
dc.relation.referencesR. J.-B. Wets, “Chapter VIII Stochastic programming,” Handbooks Oper. Res. Manag. Sci., vol. 1, pp. 573–629, 1989, doi: 10.1016/S0927-0507(89)01009-1.
dc.relation.referencesG. C. Marano, R. Greco, and S. Sgobba, “A comparison between different robust optimum design approaches: Application to tuned mass dampers,” Probabilistic Eng. Mech., vol. 25, no. 1, pp. 108–118, 2010, doi: 10.1016/j.probengmech.2009.08.004.
dc.relation.referencesB. K. Roy, S. Chakraborty, and S. K. Mihsra, “Robust optimum design of base isolation system in seismic vibration control of structures under uncertain bounded system parameters,” JVC/Journal Vib. Control, vol. 20, no. 5, pp. 786–800, 2014, doi: 10.1177/1077546312466577.
dc.relation.referencesW. Wang, S. Caro, F. Bennis, R. Soto, and B. Crawford, “Multi-Objective Robust Optimization Using a Postoptimality Sensitivity Analysis Technique: Application to a Wind Turbine Design,” J. Mech. Des., vol. 137, no. 1, p. 011403, 2015, doi: 10.1115/1.4028755.
dc.relation.referencesG. C. Marano, S. Sgobba, R. Greco, and M. Mezzina, “Robust optimum design of tuned mass dampers devices in random vibrations mitigation,” J. Sound Vib., vol. 313, no. 3–5, pp. 472–492, 2008, doi: 10.1016/j.jsv.2007.12.020.
dc.relation.referencesR. H. ; Lopez and A. T. Beck, “Reliability-based design optimization strategies based on FORM: a review,” J. Brazilian Soc. Mech. Sci. Eng., vol. XXXIV, no. 4, pp. 506–514, 2012, doi: 10.1590/S1678-58782012000400012.
dc.relation.referencesL. L. Corso, H. M. Gomes, G. P. Mezzomo, and A. Molter, “Otimização baseada em confiabilidade para uma célula de carga multiaxial utilizando algoritmos genéticos,” Rev. int. métodos numér. cálc. diseño ing., vol. 32, no. 4, pp. 221–229, 2016, doi: 10.1016/j.rimni.2015.07.002.
dc.relation.referencesA. T. Beck and C. C. Verzenhassi, “Risk optimization of a steel frame communications tower subject to tornado winds,” Lat. Am. J. Solids Struct., vol. 5, no. 3, pp. 187–203, 2008.
dc.relation.referencesW. J. S. Gomes and A. T. Beck, “A Novel Approach to Efficient Risk-Based Optimization,” Vulnerability, Uncertainty, Risk, pp. 155–164, 2014, doi: 10.1061/9780784413609.016.
dc.relation.referencesH. Hu and G. Li, “Granular Risk-Based Design Optimization,” vol. 23, no. 2, pp. 340–353, 2015.
dc.relation.referencesA. T. Beck, W. J. S. Gomes, and F. A. V. Bazan, “on the Robustness of Structural Risk Optimization With Respect To Epistemic Uncertainties,” Int. J. Uncertain. Quantif., vol. 2, no. 1, pp. 1–20, 2012, doi: 10.1615//Int.J.UncertaintyQuantification.2011003415.
dc.relation.referencesR. Ghanem, D. Higdon, and H. Owhadi, Handbook of Uncertainty Quantification. 2016.
dc.relation.referencesE. Zio and N. Pedroni, “Risk Analysis - Uncertainty Characterization in Risk Analysis for Decision- Making Practice,” p. 50, 2012, [Online]. Available: http://www.foncsi.org/fr/.
dc.relation.referencesT. J. Sullivan, Introduction to Uncertainty, vol. 8. Springer, 2011.
dc.relation.referencesH. J. Zimmermann, “Application-oriented view of modeling uncertainty,” Eur. J. Oper. Res., vol. 122, no. 2, pp. 190–198, 2000, doi: 10.1016/S0377-2217(99)00228-3.
dc.relation.referencesN. Karmarkar, “A new polynomial-time algorithm for linear programming,” Combinatorica, vol. 4, no. April, pp. 373–395, 1984, doi: https://doi.org/10.1007/BF02579150.
dc.relation.referencesP. Taylor, M. Street, L. Wt, and D. Kadelka, “Mathematische Operationsforschung und Statistik . Series Optimization : A Journal of Mathematical Programming and Operations Research On inventory problems with arbitrary cost pattern , demand pattern and demand distribution,” no. August 2013, pp. 37–41, 2007.
dc.relation.referencesB. N. Pshenichny and Y. M. Danilin, Numerical Methods in Extremal Problems. Moscow : Mir Publishers, 1978.
dc.relation.referencesA. D. Belegundu and C. Tirupathi R, OPTIMIZATION CONCEPTS AND APPLICATIONS IN ENGINEERING, vol. 53, no. 9. 2013.
dc.relation.referencesR. H. Byrd, J. C. Gilbert, and J. Nocedal, “A Trust Region Method Based on Interior Point Techniques for Nonlinear Programming,” Math. Program. Ser. B, vol. 89, no. 1, pp. 149–185, 2000, doi: 10.1007/PL00011391.
dc.relation.referencesR. Hooke and T. A. Jeeves, “‘Direct Search’’’ Solution of Numerical and Statistical Problems,’” J. ACM, vol. 8, no. 2, pp. 212–229, 1961, doi: 10.1145/321062.321069.
dc.relation.referencesD. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed. New York: Addison-Wesley Publishing Company, 1989.
dc.relation.referencesC. Mathworks, “Datafeed Toolbox TM User ’ s Guide R 2019 a,” 2019.
dc.relation.referencesN. V. Sahinidis, “Optimization under uncertainty: State-of-the-art and opportunities,” Comput. Chem. Eng., vol. 28, no. 6–7, pp. 971–983, 2004, doi: 10.1016/j.compchemeng.2003.09.017.
dc.relation.referencesK. L. Tsui, “Robust design optimization for multiple characteristic problems,” Int. J. Prod. Res., vol. 37, no. 2, pp. 433–445, 1999, doi: 10.1080/002075499191850.
dc.relation.referencesH. G. Beyer and B. Sendhoff, “Robust optimization - A comprehensive survey,” Comput. Methods Appl. Mech. Eng., vol. 196, no. 33–34, pp. 3190–3218, 2007, doi: 10.1016/j.cma.2007.03.003.
dc.relation.referencesA. D. Belegundu and S. Zhang, “Robustness of Design Through inimum Sensitivity,” J. Mech. Des., vol. 114, no. April 1990, pp. 213–217, 2016, doi: doi:10.1115/1.2916933.
dc.relation.referencesV. Rathod, O. P. Yadav, A. Rathore, and R. Jain, “Reliability-based robust design optimization: A comparative study,” IEEE Int. Conf. Ind. Eng. Eng. Manag., pp. 1558–1563, 2011, doi: 10.1109/IEEM.2011.6118179.
dc.relation.referencesS. Wang, Q. Li, and G. J. Savage, “Reliability-based robust design optimization of structures considering uncertainty in design variables,” Math. Probl. Eng., vol. 2015, 2015, doi: 10.1155/2015/280940.
dc.relation.referencesA. Forouzandeh Shahraki and R. Noorossana, “Reliability-based robust design optimization: A general methodology using genetic algorithm,” Comput. Ind. Eng., vol. 74, no. 1, pp. 199–207, 2014, doi: 10.1016/j.cie.2014.05.013.
dc.relation.referencesV. Dubourg, B. Sudret, and B. J.-M., “Reliability-based design optimization using kriging surrogates and subset simulation,” Struct. Multisciplinary Optim., 2011.
dc.relation.referencesE. Nikolaidis and R. Burdisso, “Reliability based optimization: A safety index approach,” Comput. Struct., vol. 28, no. 6, pp. 781–788, 1988, doi: 10.1016/0045-7949(88)90418-X.
dc.relation.referencesR. Rackwitz, “Optimization and risk acceptability based on the life quality index,” Struct. Saf., vol. 24, no. 2–4, pp. 297–331, 2002, doi: 10.1016/S0167-4730(02)00029-2.
dc.relation.referencesS.-K. Choi, R. V. . Grandhi, and R. A. Canfield, Reliability-based Structural Design. London: Springer, 2007.
dc.relation.referencesA. M. Hasofer and N. C. Lind, “An Exact and Invariant First-order Reliability Format,” J. Eng. Mech. Div., pp. 111–121, 1973.
dc.relation.referencesC. A. Cornell, “A probability-based structural code,” J. Am. Concr. Inst., vol. 66, no. 12, pp. 974–985, 1969.
dc.relation.referencesY. Aoues and A. Chateauneuf, “Benchmark study of numerical methods for reliability-based design optimization,” Struct. Multidiscip. Optim., vol. 41, no. 2, pp. 277–294, 2009, doi: 10.1007/s00158-009-0412-2.
dc.relation.referencesA. M. Hasofer and N. C. Lind, “Exact and Invariant Second-Moment Code Format,” J. Eng. Mech. Div., vol. 100, no. 1, pp. 111–121, 1974.
dc.relation.referencesO. Ditlevsen and P. Bjerager, “Methods of Structural Systems Reliability,” Struct. Saf., vol. 3, no. 3, pp. 195–229, 1986, doi: 10.1016/0167-4730(86)90004-4.
dc.relation.referencesO. Ditlevsen, “Life quality index revisited,” Struct. Saf., vol. 26, no. 4, pp. 443–451, 2004, doi: 10.1016/j.strusafe.2004.03.003.
dc.relation.referencesV. Papadopoulos and N. D. Lagaros, “Vulnerability-based robust design optimization of imperfect shell structures,” Struct. Saf., vol. 31, no. 6, pp. 475–482, 2009, doi: 10.1016/j.strusafe.2009.06.006.
dc.relation.referencesK. L. Tsui, “An Overview of Taguchi Method and Newly Developed Statistical Methods for Robust Design,” IIE Trans. (Institute Ind. Eng., vol. 24, no. 5, pp. 44–57, 1992, doi: 10.1080/07408179208964244.
dc.relation.referencesN. Lelièvre, P. Beaurepaire, C. Mattrand, N. Gayton, and A. Otsmane, “On the consideration of uncertainty in design: optimization - reliability - robustness,” Struct. Multidiscip. Optim., vol. 54, no. 6, pp. 1423–1437, 2016, doi: 10.1007/s00158-016-1556-5.
dc.relation.referencesE. E. Kostandyan and J. D. Sorensen, “Reliability assessment of IGBT modules modeled as systems with correlated components,” Proc. - Annu. Reliab. Maintainab. Symp., no. 2, pp. 1–6, 2013, doi: 10.1109/RAMS.2013.6517663.
dc.relation.referencesP.-E. AUSTRELL, O. DAHLBLOM, J. LINDEMANN, and A. OLSSON, CALFEM-a finite element toolbox, version 3.4. Sweden: Lund University, 2004.
dc.relation.referencesJ. A. Nelder and R. Mead, “A Simplex Method for Function Minimization,” Comput. J., vol. 7, no. 4, pp. 308–313, 1965, doi: 10.1093/comjnl/7.4.308.
dc.relation.referencesC. Montanaro, Machines and mechanisms. 2019.
dc.relation.referencesC. Zang, M. I. Friswell, and J. E. Mottershead, “A review of robust optimal design and its application in dynamics,” Comput. Struct., vol. 83, no. 4–5, pp. 315–326, 2005, doi: 10.1016/j.compstruc.2004.10.007.
dc.relation.referencesL. Cerrolio, “Metodología eficiente de optimización de diseño basada en fiabilidad aplicada a estructuras,” Ph.D. Thesis, Universidad de la Rioja, Logroño, España, 2013.
dc.relation.referencesW. J. S. Gomes, “Risk optimization of trusses using a new gradient estimation method,” 12th Int. Conf. Appl. Stat. Probab. Civ. Eng. ICASP 2015, no. August, 2015.
dc.relation.referencesA. Kaveh and S. Talatahari, “Size optimization of space trusses using Big Bang-Big Crunch algorithm,” Comput. Struct., vol. 87, no. 17–18, pp. 1129–1140, 2009, doi: 10.1016/j.compstruc.2009.04.011.
dc.relation.referencesR. Fletcher, Practical methods of optimization. Wiley, 1987.
dc.relation.referencesD. M. Himmelblau, Applied Nonlinear Programming. McGraw-Hill, 1972
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalOptimización basada en confiabilidad
dc.subject.proposalReliability-based optimization
dc.subject.proposalOptimización del diseño robusto
dc.subject.proposalRobust design optimization
dc.subject.proposalOptimización bajo riesgo
dc.subject.proposalRisk optimization
dc.subject.proposalAlgoritmos de optimización
dc.subject.proposalOptimization algorithms
dc.subject.proposalUncertainty
dc.subject.proposalIncertidumbre
dc.subject.proposalSistemas mecánicos
dc.subject.proposalMechanical systems
dc.subject.proposalFailure probability
dc.subject.proposalProbabilidad de fallo
dc.type.coarhttp://purl.org/coar/resource_type/c_1843
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
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oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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