Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Liu, Yu; Huang, Hong‐Zhong; Liu, Dan

doi:10.1080/00207721.2011.617887

Cited by 27 publications

(12 citation statements)

References 42 publications

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“…Nowadays, mathematical models are widely used in many fields, such as structural reliability, performance evaluation of mechanics, nuclear safety and behaviour of power grid, since practical experiments are often expensive or even impracticable (Liu, Huang, and Ling 2013). As the complexity of these models keeps increasing, it becomes more often than ever before when engineers have to take all the input factors into consideration thoroughly and find which of them are more important to the model.…”

Section: Introductionmentioning

confidence: 99%

Point estimate-based importance analysis for structural models with correlated variables

Zhou

Lü

Ren

2013

International Journal of Systems Science

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Importance analysis is conducted to find the contributions of the inputs to the output uncertainty. In this work, a point estimate-based importance analysis algorithm is established for models involving correlated input variables, and the variance contribution by an individual correlated input variable is decomposed into correlated contribution and uncorrelated contribution. In the established algorithm, the correlated variables are orthogonalised to generate corresponding independent variables, and the performance function is reconstructed in the independence space. Then, the point estimate is employed to compute the variance-based importance measures in the independence space, by which the variance contribution of the original correlated variables, including the correlated part and uncorrelated part, can be obtained. Different point estimate methods can be employed in the proposed algorithm; thus, the algorithm is adaptable and improvable. The proposed algorithm avoids the sampling procedure, which usually consumes a heavy computational cost. Discussion of numerical and engineering examples in this work has demonstrated that the proposed algorithm provides an effective tool to deal with uncertainty analysis involving correlated inputs.

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Section: Introductionmentioning

confidence: 99%

Point estimate-based importance analysis for structural models with correlated variables

Zhou

Lü

Ren

2013

International Journal of Systems Science

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“…Richardson et al (2011) implemented one of these software environments in the design of a small jet powered aircraft and found it to be useful when developing novel geometries. Other solutions have included quickly analyzing designs for characteristics such as lifetime (Bohm et al 2010), reliability (Liu, Huang, and Ling 2013), complexity (Caprace and Rigo 2012), and cost, (Cheng, Tsai, and Sudjono 2010, Lin, Lee and Bohez 2012, Mellichamp 2013). …”

Section: Engineering Design Workflowmentioning

confidence: 99%

Orthogonal decomposition as a design tool: With application to a mixing impeller

Sloan

2013

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“…The capabilities of these tools have included algorithms that make design decisions based upon previous experiences, 27,28 automatic mesh generation for the design of concepts, 29 graphs that quickly present pertinent information based upon user generated concepts, 30 and structural analysis of generated concepts. 31 This has included quickly analyzing designs for characteristics such as lifetime, 32,33 reliability, 34 complexity, 35 and cost. 36,37 Several researchers have examined how to examine more information during the preliminary design phase while performing faster analysis to support a quicker down select process.…”

Section: B Engineering Design Workflowsmentioning

confidence: 99%

Developing an Integrated Computational Environment for the Detailed Design of a Mixing Impeller

Sloan

Suram

Bryden

2014

52nd Aerospace Sciences Meeting

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Digital manufacturing significantly reduces the expense and time required to produce custom products. Utilizing this technology, a customized product can be quickly manufactured. But the timescale and expense of the engineering design workflows used to develop these customized products have not been adapted from the workflows used in mass production due to the development of high fidelity models. But with digital manufacturing the economies of scale are eliminated such that producing many unique designs or many of the same designs result in the same manufacturing cost. Developing a design workflow that utilizes the developed and validated high fidelity models of the already produced design can reduce the amount of time and expense developing a slightly varied customized design. Reduced order modeling enables this reuse and magnification of existing high fidelity models by creating a computationally inexpensive representation of a model from high fidelity data. This thesis explores the integration of reduced order modeling and detailed analysis into the engineering design workflow developing a customized design using digital manufacturing. Specifically detailed analysis is coupled with proper orthogonal decomposition to enable the exploration of the design space while simultaneously shaping the model representing the design. This revised workflow is examined using the design of a laboratory scale overhead mixing impeller. The case study presented here is compared with the design of the Kar Dynamic Mixer developed by the Dow Chemical Company. The result of which is a customized design for a refined set of operating conditions with improved performance. Digital manufacturing significantly reduces the expense and time required to produce custom products. Utilizing this technology, a customized product can be quickly manufactured. But the timescale and expense of the engineering design workflows used to develop these customized products have not been adapted from the workflows used in mass production due to the development of high fidelity models. But with digital manufacturing the economies of scale are eliminated such that producing many unique designs or many of the same designs result in the same manufacturing cost. Developing a design workflow that utilizes the developed and validated high fidelity models of the already produced design can reduce the amount of time and expense developing a slightly varied customized design. Reduced order modeling enables this reuse and magnification of existing high fidelity models by creating a computationally inexpensive representation of a model from high fidelity data. This thesis explores the integration of reduced order modeling and detailed analysis into the engineering design workflow developing a customized design using digital manufacturing. Specifically detailed analysis is coupled with proper orthogonal decomposition to enable the exploration of the design space while simultaneously shaping the model representing the design. This revised workflow is examined ...

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Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

Cited by 27 publications

References 42 publications

Point estimate-based importance analysis for structural models with correlated variables

Point estimate-based importance analysis for structural models with correlated variables

Orthogonal decomposition as a design tool: With application to a mixing impeller

Developing an Integrated Computational Environment for the Detailed Design of a Mixing Impeller

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