Multidisciplinary Reliability Research Articles

A reliability-based multidisciplinary design parallel optimization method based on double-layer approximation model for nuclear fuel assembly bottom nozzle

The design of intricate systems such as the nuclear fuel assembly necessitates elevated levels of multi-disciplinary performance and reliability. In current reliability-based multidisciplinary design optimization methods, the decoupling strategy between optimization and reliability assessment involves sequential optimization and reliability assessment, with reliability serving as a constraint for optimization. This approach often suffers failure in locating the global optimal solution. In light of this issue, a reliability-based multidisciplinary design parallel optimization method based on double-layer approximation model is hereby proposed, achieving parallel separation and global efficient optimization of deterministic disciplinary performances and reliability, while performing efficient and accurate calculation on the multi-disciplinary reliability numerical solution. This method has been implemented for the multi-disciplinary uncertain optimization of the nuclear fuel assembly bottom nozzle. In comparison to conventional optimization methods, this approach demonstrates enhanced global optimization effectiveness.

Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

AbstractFailure mode and effect analysis (FMEA), a multidisciplinary reliability analysis tool based on team evaluations, has been widely used in various industries. There are three critical issues in FMEA: the conversion of linguistic evaluations, the weights of risk factors, and the ranking mechanism of failure modes. Scholars have used various fuzzy theories and multi‐attribute decision‐making (MADM) methods to improve traditional FMEA, but there are still deficiencies. In this paper, the hesitant intuitionistic fuzzy set (HIFS), a concept that combines the intuitionistic fuzzy set (IFS) and the hesitant fuzzy set (HFS), is introduced into FMEA to convert linguistic evaluations. Some operators based on HIFS are proposed to process the converted data. Among them, a hesitant intuitionistic fuzzy comprehensive weighted Hamming distance (HIFCWHD) operator is proposed to compute the ordered comprehensive weight, effectively weakening the effect of extreme scores on results. The gray relational projection (GRP) method is adopted to determine the risk priority order of the failure modes. Finally, we give an illustrative case to demonstrate the effectiveness of the proposed FMEA method.

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Multidisciplinary reliability design optimization is considered an effective method for solving complex product design optimization problems under the influence of uncertainty factors; however, the high computational cost seriously affects its application in practice. As an important part of multidisciplinary reliability design optimization, multidisciplinary reliability analysis plays a direct leading role in its computational efficiency. At present, multidisciplinary reliability analysis under mixed uncertainty is still nested or sequential execution mode, which leads to the problem of poor disciplinary autonomy and inefficiency in the reliability analysis of complex products. To this end, a multidisciplinary reliability assessment method integrating deep neural networks and probabilistic computational models under mixed uncertainty is proposed for the problem of multidisciplinary reliability analysis under mixed uncertainty. The method considers the stochastic-interval-fuzzy uncertainty, decouples the nested multidisciplinary probability analysis, multidisciplinary likelihood analysis, and multidisciplinary interval analysis, uses deep neural networks to extract subdisciplinary high-dimensional features, and fuses them with probabilistic computational models. Moreover, the whole system is divided into several independent subsystems, then the collected reliability data are classified, and the fault data are attributed to each subsystem. Meanwhile, the environmental conditions of the system are considered, and the corresponding environmental factors are added as input neurons along with each subsystem. In this paper, the effectiveness of the proposed method is verified on numerical calculations and real inverter power failure data.

Multidisciplinary Collaborative Design and Optimization of Turbine Rotors Considering Aleatory and Interval Mixed Uncertainty under a SORA Framework

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified.

An Efficient Sequential Reliability Analysis Method for Multidisciplinary System

Multidisciplinary reliability analysis (MRA) is an important part of reliability-based multidisciplinary design optimization (RBMDO). In order to solve the problem of low computational efficiency caused by nested loops in multidisciplinary reliability analysis, an efficient multidisciplinary reliability analysis method based on decoupling idea is proposed. The method decouples system analysis, system sensitivity analysis and reliability analysis of Single discipline to form a recursive cycle, which eliminates the complete optimization calculation of reliability analysis model by system-level optimizer in each iteration. In the process of system sensitivity analysis, the global sensitivity equation is used to perform the concurrent subspace sensitivity analysis, which improves the efficiency of MRA. The decoupled multidisciplinary reliability analysis can get rid of the restriction that the traditional reliability analysis can only be carried out by using nonlinear optimization method, and the modified advanced mean value (MAMV) and other most probable point (MPP) search algorithms can be used to solve the reliability problem. Finally, three examples are used to verify the effectiveness of the method.

Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system

AbstractDue to integrated structures and multiple functions, complex systems, such as large‐scale equipment and aerospace vehicle, faced prominent reliability problems. However, in real‐world applications, collecting sufficient reliability data is costly and time‐consuming. To overcome this difficulty, the information‐poor variables are modeled with interval models and the corresponding reliability analysis method for complex system is studied in this paper. First, we establish a multidisciplinary nonprobabilistic reliability model based on interval analysis, which is an optimization framework with an objective of nonprobabilistic reliability index and two constraints of interdisciplinary consistency equation and limit state equation. Second, two types of algorithms for the above model are studied. Based on typical methods of multidisciplinary design optimization (MDO), the direct algorithms including RA‐MDF and RA‐IDF are developed. Then, a decoupled reliability analysis algorithm is proposed to realize parallel multidisciplinary reliability analysis, in which two methods of multidisciplinary interval uncertainty analysis are formulated to decouple the coupling relationship between disciplines. Finally, three examples are employed to illustrate the validity and efficiency of the proposed methods.

A Decoupling Strategy for Reliability Analysis of Multidisciplinary System with Aleatory and Epistemic Uncertainties

In reliability-based multidisciplinary design optimization, both aleatory and epistemic uncertainties may exist in multidisciplinary systems simultaneously. The uncertainty propagation through coupled subsystems makes multidisciplinary reliability analysis computationally expensive. In order to improve the efficiency of multidisciplinary reliability analysis under aleatory and epistemic uncertainties, a comprehensive reliability index that has clear geometric meaning under multisource uncertainties is proposed. Based on the comprehensive reliability index, a sequential multidisciplinary reliability analysis method is presented. The method provides a decoupling strategy based on performance measure approach (PMA), probability theory and convex model. In this strategy, the probabilistic analysis and convex analysis are decoupled from each other and performed sequentially. The probabilistic reliability analysis is implemented sequentially based on the concurrent subspace optimization (CSSO) and PMA, and the non-probabilistic reliability analysis is replaced by convex model extreme value analysis, which improves the efficiency of multidisciplinary reliability analysis with aleatory and epistemic uncertainties. A mathematical example and an engineering application are demonstrated to verify the effectiveness of the proposed method.

Reliability-based multidisciplinary concurrent design optimization method for complex engineering systems

Reliability-based multidisciplinary design optimization (RBMDO) has been considered as an efficient technology for addressing complex engineering design optimization problems under uncertainties. Current RBMDO methods, however, perform design optimization either in a sequential manner or in an all-at-once manner, which does not support design autonomy in various disciplines and has low computational efficiency. In this article, a reliability-based multidisciplinary concurrent design optimization (RBMCDO) method is proposed to solve the problems of lack of design autonomy and low computational efficiency in current RBMDO methods. In RBMCDO, the multidisciplinary reliability constraints are first transformed into deterministic constraints using the multidisciplinary probabilistic constraint transformation method, developed in this work based on the performance measure approach and Karush–Kuhn–Tucker conditions. The RBMDO problem is then decomposed into several optimization problems of a single discipline, which are solved in parallel based on the collaborative optimization strategy. Two examples are used to verify the proposed method.

A Reliability based Multidisciplinary Design Optimization Method with Multi-Source Uncertainties

The complexity of engineering systems is increasing greatly, and more coupled disciplines and multi-source uncertainties are involved in the design and development of complex engineered systems (CESs). Actually, uncertainties are ubiquitous in design of CESs, which can be mainly classified into alteatory uncertainty (AU) and epistemic uncertainty (EU). To gain high reliability and safety of CESs, the Reliability-based MDO (RBMDO) which considers uncertainties of design variables and parameters has become a hot research topic. In this paper, the quantification of aleatory and epistemic uncertainties based on the probability theory and convex set-theory, multidisciplinary comprehensive reliability evaluation index, adaptive collaborative optimization strategy based on the intelligent optimization algorithm, sequential multidisciplinary reliability analysis method based on the concurrent subspace optimization (CSSO) and performance measure approach (PMA), and hierarchical and hybrid sequential optimization and reliability assessment (HSORA) RBMDO strategy under multi-source uncertainties have been researched. All of the researches can expand and improve the RBMDO theoretical system, and also provide the effective method for the design and optimization of CESs under multi-source uncertainties.

Multidisciplinary reliability analysis of turbine blade with shape uncertainty by Kriging model and free-form deformation methods

This work presents an integrated approach for the multidisciplinary reliability analysis of turbine blades with shape uncertainty, including the metamodel, the free-form deformation, and the Monte Carlo simulation. The multidisciplinary analysis of turbine blade includes fluid, structure, and thermal analyses, which is time-consuming during integration with multidisciplinary reliability analysis. The metamodel is constructed by adaptive sampling to reduce computational cost. The shape uncertainty with small size changes in reliability analysis should be considered. The geometry-based multidisciplinary analysis may fail to capture the small size changes during the geometry and mesh regeneration process. The main contribution of this article is to introduce the free-form deformation in multidisciplinary reliability analysis to overcome the aforementioned problems. The mesh-based method supported by free-form deformation is proposed. Failure probability analysis of the multidisciplinary blade system is performed using the Monte Carlo simulation and the surrogate model. Through the numerical simulation, it is found that the failure probability increases as the blade shape uncertainty becomes larger. The methodology in this article provides a valuable and applicative way to calculate the risk of blade in multidisciplinary system.

Life reliability assessment of twin-web disk using the active learning kriging model

Compared with the traditional single turbine disk, the novel twin-web turbine disk (TWD) has a bright future in the application of the aero high-pressure disk due to its advantages in weight loss and heat transfer. With the strong coupling of multiple disciplines and the uncertain factors during the manufacturing process, it is necessary to analyze the multidisciplinary reliability of its fatigue life cycles in an efficient way. In this paper, based on the stratified sampling, a new sampling method called modified quadrangular grid (MQG) method, which has better efficiency and stability of sampling, is applied with the active learning Kriging (ALK) surrogate model and the Monte Carlo simulation. The distance based on sampling efficiency (DBSE) and information entropy are proposed in this paper as the criteria to evaluate the current sampling methods. A platform is then built for analyzing its fluid characteristic and structural strength performance. And the reliability of low-cycle fatigue (LCF) life is analyzed by Monte Carlo simulation with ALK model (ALK-MC). Results show the failure probability of current design is higher than the criterion which needs further reliability-based optimization design.

Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statistics

Considering the coupling among aerodynamic, heat transfer and strength, a reliability based multidisciplinary design optimization method for cooling turbine blade is introduced. Multidisciplinary analysis of cooling turbine blade is carried out by sequential conjugated heat transfer analysis and strength analysis with temperature and pressure interpolation. Uncertainty data including the blade wall, rib thickness, elasticity Modulus and rotation speed is collected. Data statistics display the probability models of uncertainty data follow three-parameter Weibull distribution. The thickness of blade wall, thickness and height of ribs are chosen as design variables. Kriging surrogate model is introduced to reduce time-consuming multidisciplinary reliability analysis in RBMDO loop. The reliability based multidisciplinary design optimization of a cooling turbine blade is carried out. Optimization results shows that the RBMDO method proposed in this work improves the performance of cooling turbine blade availably.

Multidisciplinary reliability design optimization under time-varying uncertainties

Degradation failure is one of the main reasons for complex mechanical systems losing their functions. Research on multidisciplinary design optimization under uncertainties should shift from static uncertainties to time-varying uncertainties. Aiming at time-varying uncertainties in mechanical systems, we put forward a multidisciplinary reliability design optimization method using stochastic process theory. First, we investigated the characteristics of time-varying uncertainties in complex mechanical systems, and then utilized stochastic process theory to quantify time-varying uncertainties. Second, through combining the multidisciplinary simultaneous analysis and design optimization method, the model of multidisciplinary design optimization under time-varying uncertainties is established. Moreover, a mathematical problem and an engineering example are provided to illustrate the accuracy and effectiveness of the proposed method.

Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis

For high reliability calculation efficiency and evaluation accuracy, saddlepoint approximation technology has been introduced into design and optimization under uncertainties. When using saddlepoint approximation, there are two prerequisites: all random information is tractable and saddlepoint equations are easy to be solved. However, the above requirements cannot always be met in complex multidisciplinary systems. Random variables sometimes are intractable, or saddlepoint equations are highly nonlinear. To tackle these problems, in this study, an efficient reliability-based multidisciplinary design optimization using the combination method of saddlepoint approximation and third-moment is given. A simplified alternative cumulant generating function can be constructed by saddlepoint approximation and third-moment with the first, second and third moments of a random variable effectively. Then, this cumulant generating function can be utilized to calculate the cumulative distribution function and the probability density function of this random variable approximately. Moreover, to obtain better efficiency, the framework of sequential optimization and reliability analysis is introduced in this study. The corresponding formula of the proposed reliability-based multidisciplinary design optimization is given in detail. Two test problems are solved to show the application of the proposed method.

Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO). However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO) is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP) search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.

Sequential Multidisciplinary Reliability Design and Optimization Based on Approximate Sensitivity Method under Random and Interval Uncertainties

Sequential Multidisciplinary Reliability Design and Optimization Based on Approximate Sensitivity Method under Random and Interval Uncertainties

An efficient strategy for multidisciplinary reliability design and optimization based on CSSO and PMA in SORA framework

The conventional reliability-based multidisciplinary design optimization (RBMDO) integrates the reliability-based design optimization and multidisciplinary design optimization (MDO) directly, which leads to a triple-level nested optimization loop. Especially, the multidisciplinary reliability analysis in the middle layer dominates the whole efficiency of RBMDO. To tackle this problem, first of all, a sequential multidisciplinary reliability analysis (SMRA) approach that integrates the concurrent subspace optimization (CSSO) strategy and the performance measure approach is proposed, in which the multidisciplinary analysis, system sensitivity analysis and reliability analysis are decoupled and arranged sequentially, making a recursive loop. The multidisciplinary analysis and system sensitivity analysis provide the value and gradient information of limit-state function for reliability analysis respectively. As a result, a great number of repeated iterations of the whole reliability analysis are eliminated. Secondly, the CSSO has been integrated with the sequential optimization and reliability assessment (SORA) to decouple the triple-level nested RBMDO procedures into a sequence of cycles of deterministic MDO and multidisciplinary reliability analysis. Therefore, the expensive computation of the whole reliability analysis model in each iteration of RBMDO is avoided. And also, the CSSO is adopted in the deterministic MDO to deal with medium-scale and coupled multidisciplinary systems. The procedures of the proposed approaches are presented in detail. The effectiveness of the proposed strategies is demonstrated and verified with two design examples.

A Collaborative Approach for Multidisciplinary Systems Reliability Design and Optimization

In order to improve the efficiency and robustness of reliability-based multidisciplinary design optimization (RBMDO), a new collaborative strategy (named C-RBMDO) which integrates performance measure approach (PMA) and concurrent subspace optimization strategy (CSSO) is proposed. Both the mathematical model and optimization procedure are put forward. The traditional triple-level nested flowchart of RBMDO is decoupled with the sequential optimization and reliability assessment (SORA). The deterministic multidisciplinary design optimization and the multidisciplinary reliability analysis are executed by CSSO and PMA respectively. Finally, the proposed method is verified through the design example of gear transmission.

A PMA-Based Collaborative Strategy for Reliability Design and Optimization of Multidisciplinary Systems

To tackle the computing efficiency and robustness problems caused by the reliability index approach (RIA) in reliability-based multidisciplinary design optimization (RBMDO), a new performance measure approach-based method for RBMDO is proposed. Meanwhile, the traditional triple-level nested flowchart of RBMDO is decoupled through the main idea of sequential optimization and reliability assessment (SORA). Both deterministic multidisciplinary design optimization and the multidisciplinary reliability analysis are executed by collaborative optimization (CO). Finally, the proposed method is verified through the design example of gear transmission.

Proposal of the Method for Multidisciplinary Reliability Analysis Based on Statistical and Probabilistic Methods

In the packaging design of electronic devices, the reliability margins for each design specification have been reduced because of high-quality specifications. Therefore, at an early stage of design, it is important to analyze various design margins such as signal integrity, cooling characteristics and thermal fatigue life of solder joints for a number of design solutions, taking into consideration the element of uncertainty in the design. In this paper, the Response Surface Method (RSM), the First Order Reliability Method (FORM) and Structural Equation Modeling (SEM) were introduced in order to realize the reliability evaluation and optimization in the multidisciplinary design. This method was applied to the packaging design of CPU (Central Power Unit) module for the purpose of the validity verification.