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Статті в журналах з теми "RBDO (Reliability Based Design Optimisation)":
Al-Juboori, Muqdad, and Bithin Datta. "Optimum design of hydraulic water retaining structures incorporating uncertainty in estimating heterogeneous hydraulic conductivity utilizing stochastic ensemble surrogate models within a multi-objective multi-realisation optimisation model." Journal of Computational Design and Engineering 6, no. 3 (December 24, 2018): 296–315. http://dx.doi.org/10.1016/j.jcde.2018.12.003.
Chiralaksanakul, Anukal, and Sankaran Mahadevan. "First-Order Approximation Methods in Reliability-Based Design Optimization." Journal of Mechanical Design 127, no. 5 (October 8, 2004): 851–57. http://dx.doi.org/10.1115/1.1899691.
Zhang, Li-Xiang, Xin-Jia Meng, and He Zhang. "Reliability-Based Design Optimization for Design Problems with Random Fuzzy and Interval Uncertainties." International Journal of Computational Methods 17, no. 06 (April 4, 2019): 1950018. http://dx.doi.org/10.1142/s021987621950018x.
Youn, Byeng D., and Kyung K. Choi. "An Investigation of Nonlinearity of Reliability-Based Design Optimization Approaches." Journal of Mechanical Design 126, no. 3 (October 1, 2003): 403–11. http://dx.doi.org/10.1115/1.1701880.
Li, Xiaoke, Qingyu Yang, Yang Wang, Xinyu Han, Yang Cao, Lei Fan, and Jun Ma. "Development of surrogate models in reliability-based design optimization: A review." Mathematical Biosciences and Engineering 18, no. 5 (2021): 6386–409. http://dx.doi.org/10.3934/mbe.2021317.
Youn, Byeng D., Kyung K. Choi, and Young H. Park. "Hybrid Analysis Method for Reliability-Based Design Optimization." Journal of Mechanical Design 125, no. 2 (June 1, 2003): 221–32. http://dx.doi.org/10.1115/1.1561042.
Chen, Zhen Zhong, Hao Bo Qiu, Hong Yan Hao, and Hua Di Xiong. "A Reliability Index Based Decoupling Method for Reliability-Based Design Optimization." Advanced Materials Research 544 (June 2012): 223–28. http://dx.doi.org/10.4028/www.scientific.net/amr.544.223.
Zou, T., and S. Mahadevan. "Versatile Formulation for Multiobjective Reliability-Based Design Optimization." Journal of Mechanical Design 128, no. 6 (November 22, 2005): 1217–26. http://dx.doi.org/10.1115/1.2218884.
Elhami, Norelislam, Mhamed Itmi, and Rachid Ellaia. "Reliability-Based Design and Heuristic Optimization MPSO-SA of Structures." Advanced Materials Research 274 (July 2011): 91–100. http://dx.doi.org/10.4028/www.scientific.net/amr.274.91.
Tu, J., K. K. Choi, and Y. H. Park. "A New Study on Reliability-Based Design Optimization." Journal of Mechanical Design 121, no. 4 (December 1, 1999): 557–64. http://dx.doi.org/10.1115/1.2829499.
Дисертації з теми "RBDO (Reliability Based Design Optimisation)":
Chakchouk, Mohamed. "Conceptiοn d'un détecteur de système mécatronique mobile intelligent pour observer des molécules en phase gazeuse en ΙR". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR06.
This work anticipates that, in an ever-expanding digital technology world, technological breakthroughs in the analysis of data collected by spectroscopic devices will allow the almost instantaneous identification of known species observed in-situ in a specific environment, leaving the necessary in-depth analysis of unobserved species. The method derived from RBDO (Reliability Based Design Optimization) technology will be used to implement an artificial intelligence procedure to identify observed species from a mobile IR sensor. To successfully analyze the obtained data, it is necessary to appropriately assign molecular species from the observed IR data using appropriate theoretical models. This work focuses on the observation from mobile devices equipped with appropriate sensors, antennas, and electronics to capture and send raw or analyzed data from an interesting IR spectroscopic environment. It is therefore interesting if not essential to focus on symmetry-based theoretical tools for the spectroscopic analysis of molecules, which allows to identify the IR windows to be chosen for observation in the design of the device. Then, by fitting the theoretical spectroscopic parameters to the observed frequencies, the spectrum of a molecular species can be reconstructed. A deconvolution of the observed spectra is necessary before the analysis in terms of intensity, width and line center characterizing a line shape. Therefore, an adequate strategy is needed in the design to include data analysis during the observation phase, which can benefit from an artificial intelligence algorithm to account for differences in the IR spectral signature. In this regard, the analytical power of the instrument data can be improved by using the reliability-based design optimization (RBDO) methodology. Based on the multi-physics behavior of uncertainty propagation in the hierarchical system tree, RBDO uses probabilistic modeling to analyze the deviation from the desired output as feedback parameters to optimize the design in the first place. The goal of this thesis is to address IR observation window parameters to address reliability issues beyond mechatronic design to include species identification through analysis of collected data
Zhang, Peipei. "Diffuse response surface model based on advancing latin hypercube patterns for reliability-based design optimization of ultrahigh strength steel NC milling parameters." Compiègne, 2011. http://www.theses.fr/2011COMP1949.
Since variances in the input parameters of engineering systems cause subsequent variations in the product performance, and deterministic optimum designs that are obtained without taking uncertainties into consideration could lead to unreliable designs. Reliability-Based Design Optimization (RBDO) is getting a lot of attention recently. However, RBDO is computationally expensive. Therefore, the Response Surface Methodology (RSM) is often used to improve the computational efficiency in the solution of problems in RBDO. In this work, we focus on a Response Surface Methodology (RSM) adapted to the Reliability-Based Design Optimization (RBDO). The Diffuse Approximation (DA), a variant of the well-known Moving Least Squares (MLS) approximation based on a progressive sampling pattern is used within a variant of the First Order Reliability Method (FORM). The proposed method simultaneously uses points in the standard normal space (U-space) as well as the physical space (X-space). The two grids form a “virtual design of experiments” defined by two sets of points in the two design spaces, that are evaluated only when needed in order to minimize the number of ‘exact’ thus computationally expensive function evaluations. In each new iteration, the pattern of points is updated with points appropriately selected from the “virtual design of experiments”, in order to perform the approximation. As an original contribution, we introduce the concept of « advancing Latin Hypercube Sampling (LHS) » which extends the idea of Latin Hypercube Sampling (LHS) to maximally reuse previously computed points while adding a minimal number of new neighboring points at each step, necessary for the approximation in the vicinity of the current design. We propose panning, expanding and shrinking Latin hypercube patterns of sampling points and we analyze the influence of this specific kind of patterns on the quality of the approximation. Next we calculate the minimal number of data points required in order to get a well-conditioned approximation system. In the application part of this work, we investigate the optimization of the process parameters for Numerical Control (NC) milling of ultrahigh strength steel. The success of the machining operation depends on the selection of machining parameters such as the feed rate, cutting speed, and the axial and radial depths of cut. A variant of the First Order Reliability Method (FORM) is chosen to calculate the reliability index. The optimization constraints are expressed as functions of the computed reliability indices
Cho, Hyunkyoo. "Efficient variable screening method and confidence-based method for reliability-based design optimization." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4594.
Gaul, Nicholas John. "Modified Bayesian Kriging for noisy response problems and Bayesian confidence-based reliability-based design optimization." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1322.
Ndashimye, Maurice. "Accounting for proof test data in Reliability Based Design Optimization." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97108.
ENGLISH ABSTRACT: Recent studies have shown that considering proof test data in a Reliability Based Design Optimization (RBDO) environment can result in design improvement. Proof testing involves the physical testing of each and every component before it enters into service. Considering the proof test data as part of the RBDO process allows for improvement of the original design, such as weight savings, while preserving high reliability levels. Composite Over-Wrapped Pressure Vessels (COPV) is used as an example application of achieving weight savings while maintaining high reliability levels. COPVs are light structures used to store pressurized fluids in space shuttles, the international space station and other applications where they are maintained at high pressure for extended periods of time. Given that each and every COPV used in spacecraft is proof tested before entering service and any weight savings on a spacecraft results in significant cost savings, this thesis put forward an application of RBDO that accounts for proof test data in the design of a COPV. The method developed in this thesis shows that, while maintaining high levels of reliability, significant weight savings can be achieved by including proof test data in the design process. Also, the method enables a designer to have control over the magnitude of the proof test, making it possible to also design the proof test itself depending on the desired level of reliability for passing the proof test. The implementation of the method is discussed in detail. The evaluation of the reliability was based on the First Order Reliability Method (FORM) supported by Monte Carlo Simulation. Also, the method is implemented in a versatile way that allows the use of analytical as well as numerical (in the form of finite element) models. Results show that additional weight savings can be achieved by the inclusion of proof test data in the design process.
AFRIKAANSE OPSOMMING: Onlangse studies het getoon dat die gebruik van ontwerp spesifieke proeftoets data in betroubaarheids gebaseerde optimering (BGO) kan lei tot 'n verbeterde ontwerp. BGO behels vele aspekte in die ontwerpsgebied. Die toevoeging van proeftoets data in ontwerpsoptimering bring te weë; die toetsing van 'n ontwerp en onderdele voor gebruik, die aangepaste en verbeterde ontwerp en gewig-besparing met handhawing van hoë betroubaarsheidsvlakke. 'n Praktiese toepassing van die BGO tegniek behels die ontwerp van drukvatte met saamgestelde materiaal bewapening. Die drukvatontwerp is 'n ligte struktuur wat gebruik word in die berging van hoë druk vloeistowwe in bv. in ruimtetuie, in die internasionale ruimtestasie en in ander toepassings waar hoë druk oor 'n tydperk verlang word. Elke drukvat met saamgestelde materiaal bewapening wat in ruimtevaartstelsels gebruik word, word geproeftoets voor gebruik. In ruimte stelselontwerp lei massa besparing tot 'n toename in loonvrag. Die tesis beskryf 'n optimeringsmetode soos ontwikkel en gebaseer op 'n BGO tegniek. Die metode word toegepas in die ontwerp van drukvatte met saamgestelde materiaal bewapening. Die resultate toon dat die gebruik van proeftoets data in massa besparing optimering onderhewig soos aan hoë betroubaarheidsvlakke moontlik is. Verdermeer, die metode laat ook ontwerpers toe om die proeftoetsvlak aan te pas om sodoende by ander betroubaarheidsvlakke te toets. In die tesis word die ontwikkeling en gebruik van die optimeringsmetode uiteengelê. Die evaluering van betroubaarheidsvlakke is gebaseer op 'n eerste orde betroubaarheids-tegniek wat geverifieer word met talle Monte Carlo simulasie resultate. Die metode is ook so geskep dat beide analitiese sowel as eindige element modelle gebruik kan word. Ten slotte, word 'n toepassing getoon waar resultate wys dat die gebruik van die optimeringsmetode met die insluiting van proeftoets data wel massa besparing kan oplewer.
Zhao, Liang. "Reliability-based design optimization using surrogate model with assessment of confidence level." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1194.
Ezzati, Ghasem. "Reliability-based design optimisation methods in large scale systems." Thesis, Federation University Australia, 2015. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/99881.
Structural optimisation is an important field of applied mathematics, which has proved useful in engineering projects. Reliability-based design optimisation (RBDO) can be considered a branch of structural optimisation. Different RBDO approaches have been applied in real world problems (e.g. vehicle side impact model, short column design, etc.). Double-loop, single-loop, and decoupled approaches are three categories in RBDO. This research focuses on double-loop approaches, which consider reliability analysis problems in their inner loops and design optimisation calculations in their outer loops. In recent decades, double-loop approaches have been studied and modified in order to improve their stability and efficiency, but many shortcomings still remain, particularly regarding reliability analysis methods. This thesis will concentrate on development of new reliability analysis methods that can be applied to solve RBDO problems. As a local optimisation algorithm, the conjugate gradient method will be adopted. Furthermore, a new method will be introduced to solve a reliability analysis problem in the polar space. The reliability analysis problem must be transformed into an unconstrained optimisation problem before solving in the polar space. Two methods will be introduced here and their stability and efficiency will be compared with the existing methods via numerical experiments. Next, we consider applications of RBDO models to electricity networks. Most of the current optimisation models of these networks are categorised as deterministic design optimisation models. A probabilistic constraint is introduced in this thesis for electricity networks. For this purpose, a performance function must be defined for a network in order to define safety and failure conditions. Then, new non-deterministic design optimisation models will be formulated for electricity networks by using the mentioned probabilistic constraint. These models are designed to keep failure probability of the network below a predetermined and accepted safety level.
Chen, Qing. "Reliability-based structural design: a case of aircraft floor grid layout optimization." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39630.
Mansour, Rami. "Reliability Assessment and Probabilistic Optimization in Structural Design." Doctoral thesis, KTH, Hållfasthetslära (Avd.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183572.
QC 20160317
Villanueva, Diane. "Reliability Based Design Including Future Tests and Multi-Agent Approaches." Phd thesis, Saint-Etienne, EMSE, 2013. http://tel.archives-ouvertes.fr/tel-00862355.
Частини книг з теми "RBDO (Reliability Based Design Optimisation)":
Kharmanda, Ghias, Abedelkhalak El Hami, and Eduardo Souza De Cursi. "Reliability-based Design Optimization (RBDO)." In Multidisciplinary Design Optimization in Computational Mechanics, 425–58. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118600153.ch11.
Majumder, Rohan, and Sudib K. Mishra. "Reliability Based Design Optimization (RBDO) of Randomly Imperfect Thin Cylindrical Shells Against Post-Critical Drop." In Recent Developments in Structural Engineering, Volume 1, 47–55. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9625-4_5.
Tahir, Arslan, and Claus Kunz. "Reliability Based Rehabilitation of Existing Hydraulic Structures." In Lecture Notes in Civil Engineering, 578–90. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6138-0_50.
"Reliability-Based Design Optimization (RBDO)." In Structural Design Optimization Considering Uncertainties, 1–2. Taylor & Francis, 2008. http://dx.doi.org/10.1201/b10995-2.
"Reliability Based Design Optimization (RBDO)." In Encyclopedia of Ocean Engineering, 1451. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-10-6946-8_300642.
Rahmani, Shima, Elyas Fadakar, and Masoud Ebrahimi. "An Efficient Quantile-Based Adaptive Sampling RBDO with Shifting Constraint Strategy." In Avantgarde Reliability Implications in Civil Engineering [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110442.
Hurtado, Jorge. "Optimal Reliability-Based Design Using Support Vector Machines and Artificial Life Algorithms." In Intelligent Computational Paradigms in Earthquake Engineering, 59–79. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-099-8.ch004.
Тези доповідей конференцій з теми "RBDO (Reliability Based Design Optimisation)":
Coffey, Tiarnan, Christopher Rai, John Greene, and Stephen O’Brien Bromley. "Subsea Spare Parts Analysis Optimisation." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96100.
Chiralaksanakul, Anukal, and Sankaran Mahadevan. "Reliability-Based Design Optimization Methods." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57456.
Cho, Hyunkyoo, K. K. Choi, and David Lamb. "Confidence-Based Method for Reliability-Based Design Optimization." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34644.
Moon, Min-Yeong, K. K. Choi, Hyunkyoo Cho, Nicholas Gaul, David Lamb, and David Gorsich. "Reliability-Based Design Optimization Using Confidence-Based Model Validation for Insufficient Experimental Data." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60155.
Choi, Kyung K., and Byeng D. Youn. "Hybrid Analysis Method for Reliability-Based Design Optimization." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/dac-21044.
Choi, Kyung K., and Byeng D. Youn. "An Investigation of Nonlinearity of Reliability-Based Design Optimization Approaches." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34128.
Choi, Kyung K., Yoojeong Noh, and Liu Du. "Reliability Based Design Optimization With Correlated Input Variables Using Copulas." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35104.
Pugazhendhi, K., and A. K. Dhingra. "Reliability Based Design Optimization Using Automatic Differentiation." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65912.
Pan, Hao, Zhimin Xi, and Ren-Jye Yang. "Model Uncertainty Approximation Using a Copula-Based Approach for Reliability Based Design Optimization." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60071.
Oza, Kunjal, and Hae Chang Gea. "Two-Level Approximation Method for Reliability-Based Design Optimization." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57463.