Global moment‐independent sensitivity analysis of single‐stage thermoelectric refrigeration system

et al. 2020

Int J Energy Res

Self Cite

Summary In this paper, a mathematical model of two‐stage thermoelectric refrigeration system is established considering the influence of external heat transfer and its performance is analysed based on finite‐time thermodynamics and Newton's heat transfer law. Taking the cooling capacity and coefficient of performance of the two‐stage thermoelectric refrigeration system as separate objective functions, the general relationship between cooling capacity, coefficient of performance, and working design parameters of the system is determined. The influence of the fluctuation of the input design parameters on the output performance parameters is studied using a global sensitivity analysis based on the variance response. The main and total Global sensitivity indices of input parameters that affect the output performance are calculated, and the related sensitivity ranking are obtained. The results can be used to guide the performance analysis and parameter optimization of two‐stage thermoelectric refrigeration system in application.

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of two‐stage thermoelectric refrigeration system based on response variance

Zhang

et al. 2020

Int J Energy Res

Self Cite

“…Due to imprecise manufacturing, imperfect knowledge, environment change and so on, uncertainty inevitably exists in products, which leads to uncertainty in product response 1–3 . Therefore, quantifying the reliability and robustness of products is of great significance in practical engineering problems.…”

Section: Introductionmentioning

confidence: 99%

An effective evidence theory‐based reliability analysis algorithm for structures with epistemic uncertainty

Quality & Reliability Eng

Xue

Gao

2020

The purpose of this article is to develop an effective method to evaluate the reliability of structures with epistemic uncertainty so as to improve the applicability of evidence theory in practical engineering problems. The main contribution of this article is to establish an approximate semianalytic algorithm, which replaces the process of solving the extreme value of performance function and greatly improve the efficiency of solving the belief measure and the plausibility measure. First, the performance function is decomposed as a combination of a series of univariate functions. Second, each univariate function is approximated as a unary quadratic function by the second‐order Taylor expansion. Finally, based on the property of the unary quadratic function, the maximum and minimum values of each univariate function are solved, and then the maximum and minimum values of performance function are obtained according to the monotonic relationship between each univariate function and their combination. As long as the first‐ and second‐order partial derivatives of the performance function with respect to each input variable are obtained, the belief measure and plausibility measure of the structure can be estimated effectively without any additional computational cost. Two numerical examples and one engineering application are investigated to demonstrate the accuracy and efficiency of the proposed method.

“…Global sensitivity, also known as the importance measure, is one of the powerful tools for quantifying this influence degree. 3–6 In general, importance analysis methods can be divided into two categories: probability based importance analysis method 7–9 and non-probability 10–12 based importance analysis method. The probability based importance analysis method requires the accurate distribution type and distribution parameters of the variable.…”

Section: Introductionmentioning

confidence: 99%

An efficient non-probabilistic importance analysis method based on MDRM and Taylor series expansion

Proceedings of the Institution of Mechanical Engineers, Part O:

2020

The input variable of engineering structure inevitable has certain uncertainty. How to quantify the influence of those uncertainties on the uncertainty of structural response is an important issue in structural design. Non-probabilistic reliability importance analysis is one of the methods to quantify this influence when variable data information is insufficient. Although the method has great advantages for variables with insufficient data information, there is no efficient calculation method at present, and the excessive computational cost seriously hinders its application in actual engineering structures. In this paper, the multiplicative dimensional reduction method, Taylor series expansion and unary quadratic function are combined to put forward an efficient algorithm to estimate two non-probabilistic reliability importance indices. With the proposed method, all the calculation processes used to solve the extreme value of function are replaced by an approximate analytical solution. Since the proposed method is an approximate analytical solution, the calculation efficiency is extremely high. Three examples are investigated to verify the accuracy and efficiency of the proposed method.