Investigation of unsteady aerodynamics effects in cycloidal rotor using RANS solver

Hu, Yu; Du, Fengshan; Zhang, H. L.

doi:10.1017/aer.2016.38

Cited by 30 publications

(17 citation statements)

References 14 publications

(21 reference statements)

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“…The real world is very complicated; complex network is used to model the uncertainty and complexity [50][51][52][53][54]. Facility location problem can be seen as a network optimization [55].…”

Section: Problem Descriptionmentioning

confidence: 99%

Location of Facility Based on Simulated Annealing and “ZKW” Algorithms

Dong

Wang

Feng-hua

et al. 2017

Mathematical Problems in Engineering

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To cope with the facility location problem, a method based on simulated annealing and "ZKW" algorithm is proposed in this article. The method is applied to some real cases, which aims to deploy video content server at appropriate nodes in an undirected graph to satisfy the requirements of the consumption nodes with the least cost. Simulated annealing can easily find the optimum with less reliance on the initial solution. "ZKW" algorithm can find the shortest path and calculate the least cost from the server node to consumption node quickly. The results of three kinds of cases illustrate the efficiency of our method, which can obtain the optimum within 90 s. A comparison with Dijkstra and Floyd algorithms shows that, by using "ZKW" algorithm, the method can have large iteration with limited time. Therefore, the proposed method is able to solve this video content server location problem.

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Section: Problem Descriptionmentioning

confidence: 99%

Location of Facility Based on Simulated Annealing and “ZKW” Algorithms

Dong

Wang

Feng-hua

et al. 2017

Mathematical Problems in Engineering

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“…After conducting a criticality assessment using the method of Yang et al, FM 8 ranked only at the seventh place, while FM 3 , FM 7 , and FM 4 ranked fourth, fifth, and sixth, respectively. However, a closer look at the values of the risk factors for FM4 and FM 8 reveals that FM 8 has the highest value of O and S. Similarly, the values of O for FM…”

mentioning

confidence: 91%

“…Failure mode and effects analysis (FMEA) is widely used to define, identify, and eliminate known or potential failures, errors, etc from a system, design, or process to prevent an unexpected failure . Because of its reliability, safety, and simplicity, FMEA plays an important role in the design of industrial products such as structures operating in power, aeronautics, and astronautics . Failure mode and effects analysis can be a solution for various reliability problems in many industries including aerospace, nuclear, chemical, and manufacturing …”

Section: Introductionmentioning

confidence: 99%

Failure mode and effects analysis based on D numbers and TOPSIS

Bian

Zheng

Yin

et al. 2018

Quality & Reliability Eng

174

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Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.

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“…Modeling complex systems are very complicated since there are many factors interacting with each other [4][5][6][7][8]. A key component of model validation is the specific, rigorous disposal of numerous sources and different types of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Evidential Model Validation under Epistemic Uncertainty

Deng

2018

Mathematical Problems in Engineering

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This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model. Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered. Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation. The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance. Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined. Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA. The validity of proposed methods is illustrated through a numerical example.

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Investigation of unsteady aerodynamics effects in cycloidal rotor using RANS solver

Cited by 30 publications

References 14 publications

Location of Facility Based on Simulated Annealing and “ZKW” Algorithms

Location of Facility Based on Simulated Annealing and “ZKW” Algorithms

Failure mode and effects analysis based on D numbers and TOPSIS

Evidential Model Validation under Epistemic Uncertainty

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