Debiao Meng

Reliability-Based Multidisciplinary Design Optimization Using Subset Simulation Analysis and Its Application in the Hydraulic Transmission Mechanism Design

The Monte Carlo simulation (MCS) can provide high reliability evaluation accuracy. However, the efficiency of the crude MCS is quite low, in large part because it is computationally expensive to evaluate a very small failure probability. In this paper, a subset simulation-based reliability analysis (SSRA) approach is combined with multidisciplinary design optimization (MDO) to improve the computational efficiency in reliability-based MDO (RBMDO) problems. Furthermore, the sequential optimization and reliability assessment (SORA) approach is utilized to decouple an RBMDO problem into a sequential of deterministic MDO and reliability evaluation problems. The formula of MDO with SSRA within the framework of SORA is proposed to solve a design optimization problem of a hydraulic transmission mechanism. [DOI: 10.1115/1.4029756]

Interaction balance optimization in multidisciplinary design optimization problems

As a bi-level optimization method, collaborative optimization can solve multidisciplinary design optimization problems in practical engineering effectively. However, if there are high-dimensional couplings in a multidisciplinary design optimization problem, a large number of compatibility constraints will be required in collaborative optimization. In this situation, collaborative optimization will not be suitable to be utilized because of low computational efficiency or divergence issue. To solve this problem, an efficient interaction balance optimization method is proposed in this article. In interaction balance optimization method, the simple coordination strategy of interaction balance principle and the distributed optimization strategy of collaborative optimization can be integrated effectively. Lagrange multipliers are used instead of compatibility constraints to maintain the consistency between any two coupled disciplines. Two examples are given to show the effectiveness of the proposed method.

A concurrent reliability optimization procedure in the earlier design phases of complex engineering systems under epistemic uncertainties

Concurrent engineering has obtained increasing attention to solve the design problems of multidisciplinary systems. In practical engineering, there are epistemic uncertainties during whole design cycle of complex systems. Especially in earlier design phases, the effects of epistemic uncertainties are not usually easy to be quantified. It is because design information is insufficient. Furthermore, commonly used probability theory is also not suitable to be utilized. In this situation, epistemic uncertainties will be introduced unavoidably by mathematical models or simulation tools and may affect the performance of complex system significantly. To solve this problem, evidence theory is introduced and combined with the collaborative optimization method in this study. An evidence-based collaborative reliability optimization method is also proposed. Evidence theory is a powerful approach to handle epistemic uncertainties by Plausibility and Belief. Meanwhile, collaborative optimization is widely utilized in the concurrent design of complex systems. An aircraft conceptual design problem is utilized to show the application of the proposed method.

Reliability analysis of direct drive electrohydraulic servo valves based on a wear degradation process and individual differences

Electrohydraulic servo valves play critical roles in modern servo control systems, which require high reliability and high safety. The reliability analysis of a direct drive electrohydraulic servo valve is conducted in this article. First, the failure mechanism of the direct drive electrohydraulic servo valve is investigated by analyzing the structure and the working principle of the direct drive electrohydraulic servo valve. It shows that clamping stagnation, internal leakages and spring fatigue are the main failure modes of direct drive electrohydraulic servo valve. The structure degradation caused by wear enlarges the clearance and results in the increase in null leakages. Then, a gamma process is adopted to describe the internal structure degradation based on the failure mechanism analysis. Heterogeneity among different samples of direct drive electrohydraulic servo valves is studied and handled by introducing unit-specific random effects into the gamma process degradation model. Additionally, in this article, a Bayesian method is used to facilitate the degradation analysis and reliability estimation. The reliability models of sealing, springs and spool valves are presented. Finally, a brief introduction of the experiment of the direct drive electrohydraulic servo valves and an illustrative example of reliability analysis are presented to demonstrate the introduced failure mechanism analysis and the proposed reliability analysis method for direct drive electrohydraulic servo valves.

Collaborative Reliability Analysis under the Environment of Multidisciplinary Design Optimization

Uncertainties in multidisciplinary design optimization (MDO) have a significant influence on the whole design process of engineering systems. The most probable point (MPP) based reliability analysis is an approach that utilizes the safety index β to measure the effect of uncertainties. Collaborative optimization (CO) is a two-level optimization method specially created for large-scale distributed-analysis applications. Simulated annealing-based collaborative optimization (SA—CO) is one of the improved forms of CO that overcomes the difficulty of convergence given the existing of highly nonlinear consistency constraints. By combining the MPP-based reliability analysis method with SA—CO, we present a new collaborative reliability analysis method under the environment of MDO to deal with uncertainties existing in MDO, that is, MPP—SA—CO. Demonstrated by two typical examples, the proposed method inherits the advantages of CO. Also, accurate and efficient results are obtained by employing simulated annealing algorithmic as the system level optimizer and it features response surface instead of disciplinary optimization.

Interaction prediction optimization in multidisciplinary design optimization problems.

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

A New Multidisciplinary Design Optimization Method Accounting for Discrete and Continuous Variables under Aleatory and Epistemic Uncertainties

Abstract Various uncertainties are inevitable in complex engineered systems and must be carefully treated in design activities. Reliability-Based Multidisciplinary Design Optimization (RBMDO) has been receiving increasing attention in the past decades to facilitate designing fully coupled systems but also achieving a desired reliability considering uncertainty. In this paper, a new formulation of multidisciplinary design optimization, namely RFCDV (random/fuzzy/continuous/discrete variables) Multidisciplinary Design Optimization (RFCDV-MDO), is developed within the framework of Sequential Optimization and Reliability Assessment (SORA) to deal with multidisciplinary design problems in which both aleatory and epistemic uncertainties are present. In addition, a hybrid discrete-continuous algorithm is put forth to efficiently solve problems where both discrete and continuous design variables exist. The effectiveness and computational efficiency of the proposed method are demonstrated via a mathematical proble...

A multi-team competitive optimization algorithm for bearing fault diagnosis

To recognize different bearing fault patterns under different operating conditions, a novel multi-team competitive optimization (MTCO) algorithm is proposed. The algorithm is inspired by the competitive behaviors among multiple teams. In the structure, it consists of a three-level optimization organization structure, so that the more potential optimal areas can be searched. Meanwhile, some new strategies imitating human thinking mode are proposed to increase the diversity and guide the members jumping out of location optimal areas, which include acceptable vector designed for imitating some uncertain or random events influencing the decision-making, betrayal mechanism and replacement mechanism designed for ensuring the reasonable turnover of staffs and leaders. Moreover, to reduce fault pattern recognition errors caused by inseparability of data with nonlinear distribution, a kernel function is introduced to increase the separability of data and improve recognition accuracies. Finally, the MTCO algorithm has been applied to diagnose real-world bearings faults. In this case, the MTCO algorithm has proved to have better recognition accuracies and to be effective.

First-Order Second-Moment based multidisciplinary design optimization

Uncertainties in multidisciplinary design optimization (MDO) have a significant influence on the whole design process of the engineering system. If uncertainty in the engineering system is not considered, optimization results may be unreliable. On the basis of the existing methods for uncertainty analysis, a new method for MDO under uncertainty, i.e. First-Order Second-Moment based collaborative optimization (FOSM-CO), is proposed, and its particular computational process is described in detail. The FOSM-CO is an optimization approach that utilizes reliability index to evaluate the probability that the random variables are in the safe region and instead of the original constraints with the probability. The computation results of the example indicate that FOSM-CO is an effective reliability design method for MDO under uncertainty.

A modeling and analysis strategy of constellation availability using on-orbit and ground added launch backup and its application in the reliability design for a remote sensing satellite

To evaluate the system reliability and the constellation availability of remote sensing satellite, in this study, a novel single satellite availability modeling and analysis strategy using on-orbit backup and ground added launch backup is proposed. First, considering the characteristics of remote sensing satellites, the calculation formulas of constellation availability are given in detail. Then, the process of system reliability modeling is also illustrated step by step. Finally, an availability evaluation example of remote sensing satellite constellation is introduced to show the effectiveness of the proposed method.