Design Optimization of Hierarchically Decomposed Multilevel Systems Under Uncertainty

Kokkolaras, Michael; Mourelatos, Zissimos P.; Papalambros, Panos Y.

doi:10.1115/detc2004-57357

Cited by 34 publications

(5 citation statements)

References 51 publications

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“…From the above analysis, it is obvious that the quadratic response surface model (equation (1)) of MDAR analysis is decomposed into multilayer and multiple DRSFs like equations (5), (10), and (12). This method is called the DCRSM.…”

Section: Distributed Collaborative Response Surface Methodsmentioning

confidence: 99%

“…In this MDAR optimization design based on DCRSM, the output responses of each object and each discipline are not optimized; however, their analyses are only conducted to establish the CRSF just like equation (12). According to the basic idea minYðX, tÞ s:t:…”

Section: Direct Reliability Optimization Model M1mentioning

confidence: 99%

“…1 Hence the dynamic reliability optimization of assembly relationship for complex machinery like the HPT BTRRC is a multi-object multi-disciplinary (MOMD) reliability optimal design of dynamic assembly relationship. 5,[7][8][9][10] Although some works have been done to handle multi-object or multi-discipline problem by a deterministic analysis, [12][13][14] the proposed approaches and ideas cannot be applied to the dynamic assembly relationship reliability design because of incomplete theory. In order to conduct preferably the MOMD reliability design of dynamic assembly relationship, Bai et al 11 developed the mechanical dynamic assembly reliability (MDAR) theory and conceived a distributed collaborative response surface method (DCRSM) according to the MDAR analysis features, which provide a promising theory and method for the reliability optimization of mechanical dynamic assembly relationship.…”

Section: Introductionmentioning

confidence: 99%

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Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Fei

Bai

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BTRRC) was first accomplished based on the mechanical dynamic assembly reliability (MDAR) theory and distributed collaborative response surface method (DCRSM). Four optimization models of MDAR were developed based on the features of assembly machinery and the thought of DCRSM, which are, respectively, called as the direct reliability optimization model (denoted by M1), the multilayer reliability optimization models (denoted by M2), the direct reliability optimization model-based probabilistic analysis (denoted by M3), and the multilayer reliability optimization model-based probabilistic analysis (denoted by M4). Through the MDAR optimization design of BTRRC by the four standard optimization models, some conclusions are drawn as follows: (1) the DCRSM is proved to be effective and feasible for MOMD MDAR optimization design with high computational efficiency and precision; (2) all the reliability optimization results of BTRRC and assembly objects satisfy the requirements of optimization design, and the optimized BTTRC variations are reduced by about 10% and obey the normal distribution, which are quite promising in improving the design and control of HPT BTRRC; (3) in computational efficiency, the computing time of M1 and M3 is far less than those of M2 and M4, meanwhile M3 and M4 are superior to M1 and M2; (4) in computational accuracy, M1 and M2 are better than M3 and M4, as well as M2 and M4 are higher than M1 and M3 theoretically. The presented study does not only fulfill the HPT BTRRC dynamic assembly design from a probabilistic optimization perspective and improve the performance and reliability of gas turbine engine, but also provides a promising approach and four valuable optimization models for MDAR optimization design. Besides, the present efforts are of great significance in enriching the theory and method of mechanical reliability design.

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Section: Distributed Collaborative Response Surface Methodsmentioning

confidence: 99%

Section: Direct Reliability Optimization Model M1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Fei

Bai

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Recently, several researchers have used Analytical Target Cascading (ATC) to understand the propagation of uncertainty from the system to the subsystem level [18][19][20]. The original deterministic structure of the ATC remains unchanged for handling uncertainty.…”

Section: Probabilistic Target Cascadingmentioning

confidence: 99%

Multidisciplinary Analysis and Design Tools For Uncertainty Modeling

Hajela,

Sakalkar,

Mullur

2023

joast

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Design optimization methods have found increased application in problems of aerospace design.The interdisciplinary nature of this design problem has given rise to the field of multidisciplinary design optimization that focuses on the integration of numerically efficient analysis methods with reliable and robust design optimization algorithms.The focus of research has increasingly shifted towards the study of modeling and handling of uncertainty in different facets of the design problem. Uncertainties add to both the computational complexity and to numerical costs in multidisciplinary design.The paper discusses recent developments in computational tools for better managing the inclusion of imprecise information, including methods through which to quantify uncertainty, new formulations of the design problem that inherently contribute to lower computational costs, and numerically efficient algorithms for computing reliability measures in multidisciplinary design. The coupled nature of analysis in the design process is examined from the perspective of decomposition-based design, and appropriate problem formulations for these cases are presented.

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“…Alternatively, stochastic programming treats the uncertainty with probabilistic models and optimization is performed on an objective statement (and possibly constraints) involving some mean, variance, or other probabilistic criteria [19]. Uncertainty-based multidisciplinary design optimization methods seek to include considerations of reliability and robustness in a system-level design formulation [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Engineering Design with Digital Thread

Singh¹,

Willcox²

2017

Structural Health Monitoring 2017

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Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. Though Digital Thread is gaining traction as a digital communication framework to streamline design, manufacturing, and operational processes in order to more efficiently design, build and maintain engineering products, a principled mathematical formulation describing the manner in which Digital Thread can be used for critical design decisions remains absent. The contribution of this paper is to present such a formulation from the context of a data-driven design and decision problem under uncertainty. This formulation accounts for the fact that the design process is highly iterative and not all information is available at once. Output design decisions are made not only on what data to collect but also on the costs and benefits involved in experimentation and sensor instrumentation to collect that data. The mathematical formulation is illustrated through an example design of a structural fiber-steered composite component. In this example, the methodology highlights how different sequencing of small-scale experimentation with manufacturing and deployment lead to different designs and different associated costs.

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Design Optimization of Hierarchically Decomposed Multilevel Systems Under Uncertainty

Cited by 34 publications

References 51 publications

Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Multidisciplinary Analysis and Design Tools For Uncertainty Modeling

Engineering Design with Digital Thread

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