Propagation of Uncertainty in Optimal Design of Multilevel Systems: Piston-Ring/Cylinder-Liner Case Study

Chan, Kuei-Yuan; Kokkolaras, Michael; Papalambros, Panos Y.; Skerlos, Steven J.; Mourelatoes, Zissimos

doi:10.4271/2004-01-1559

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…The application of variability-based design optimization in piston assembly tribology was carried out by Hoffman et al (2003), Ejakov et al (2003) and Chan et al (2004). Rahman and Sun (2003) applied robust design and variability-based optimization to address the reliability problem of engine cooling system and the variation of top tank temperature.…”

Section: Previous Research In Reliability-based Design Optimizationmentioning

confidence: 99%

Optimization techniques in diesel engine system design

Xin¹

2013

Diesel Engine System Design

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Section: Previous Research In Reliability-based Design Optimizationmentioning

confidence: 99%

Optimization techniques in diesel engine system design

Xin¹

2013

Diesel Engine System Design

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“…The ring/liner assembly simulation takes as inputs the surface roughness of the ring and the liner and the Young's modulus and hardness and computes power loss due to friction. The root mean square (RMS) of asperity height [33] is used to represent asperity roughness. The membership functions for all fuzzy random variables are generated from pseudo-PDFs using Eq.…”

Section: Mathematical Example [26]mentioning

confidence: 99%

Integration of Possibility-Based Optimization and Robust Design for Epistemic Uncertainty

Youn

Choi

et al. 2006

Journal of Mechanical Design

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In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. Aleatory uncertainty is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data, whereas epistemic uncertainty is a subjective and reducible uncertainty that stems from lack of knowledge on input uncertainty data. This study attempts to develop a robust design optimization with epistemic uncertainty. For epistemic uncertainties, a possibility-based design optimization deals with the failure rate, while a robust design optimization minimizes the product quality loss. In general, product quality loss is described using the first two statistical moments for aleatory uncertainty: mean and standard deviation. However, there is no metric for product quality loss defined when having epistemic uncertainty. This paper proposes a new metric for product quality loss with epistemic uncertainty, such that a possibility-based design optimization is integrated to a robust design. For numerical efficiency and stability, an enriched performance measure approach (PMA+) is employed for possibility-based robust design optimization, and the maximal possibility search (MPS) is used for a possibility analysis. Three different types of robust objectives are considered for possibility-based robust design optimization: smaller-the-better type (S-Type), larger-the-better type (L-Type), and nominal-the-better type (N-Type). Examples are used to demonstrate the effectiveness of possibility-based robust design optimization using the proposed metric for produce quality loss with epistemic uncertainty. 2.

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“…Originated from the automotive industries, the ATC has been demonstrated in many engineering applications such as tolerance allocation of multistation assembly line [4], vehicle suspensions [5], military vehicle design [6], and engine piston ring/cylinder liner design [7]. In addition to the engineering field, the ATC is also applied in multidisciplinary field such as architecture [8], enterprise decision making [9], and marketing [10].…”

Section: Introductionmentioning

confidence: 99%

Sequential linearization in analytical target cascading for optimization of complex multilevel systems

Chan

2010

Proceedings of the Institution of Mechanical Engineers, Part C:

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Large-scale design problems are high dimensional and deeply coupled in nature. The complexity of such large-scale systems prevents designers from solving them as a whole. Analytical target cascading (ATC) provides a systematic approach in solving decomposed large-scale systems that has solvable subsystems. By coordinating between subsystems, ATC can obtain the same optima as they were undecomposed. However, a convergent coordination requires series of ATC iterations that may hinder the efficiency of ATC. In this research, a sequential linearization technique is proposed to improve the efficiency of ATC. The proposed linearization technique is applied to each ATC iteration, and therefore each iteration has all linear subsystems that can be solved with high efficiency. The global convergence of this approach is ensured by a filter to determine the acceptance of the optima at each iteration and the corresponding trust region. One further motivation of the proposed strategy is its perceived potential in handling multilevel problems with random design variables. As previously studied, the sequential linear programming (SLP) algorithm provides a good balance between efficiency, accuracy, and convergence for single-level design optimization under random design variables. The proposed linearization technique can integrate with the SLP algorithm for multilevel systems. In this work, a geometric programming example is used to demonstrate the efficiency of the proposed method over standard ATC solution process without loss of accuracy.

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Propagation of Uncertainty in Optimal Design of Multilevel Systems: Piston-Ring/Cylinder-Liner Case Study

Cited by 15 publications

References 9 publications

Optimization techniques in diesel engine system design

Optimization techniques in diesel engine system design

Integration of Possibility-Based Optimization and Robust Design for Epistemic Uncertainty

Sequential linearization in analytical target cascading for optimization of complex multilevel systems

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