Collaborative optimization with inverse reliability for multidisciplinary systems uncertainty analysis

This article introduces a method which combines the collaborative optimization framework and the inverse reliability strategy to assess the uncertainty encountered in the multidisciplinary design process. This method conducts the sub-system analysis and optimization concurrently and then improves the process of searching for the most probable point (MPP). It reduces the load of the system-level optimizer significantly. This advantage is specifically more prominent for large-scale engineering system design. Meanwhile, because the disciplinary analyses are treated as the equality constraints in the disciplinary optimization, the computation load can be further reduced. Examples are used to illustrate the accuracy and efficiency of the proposed method.     

Principal components analysis in sensitivity studies of dynamic systems

Local sensitivity analysis is a modeling tool for determining the effects of single parameter variations on the output of a first order differential system. To determine the effects of multi-parameter variations, the local sensitivity matrix can be used in a first order Taylor series to approximately model the variance of the system output. For dynamic systems, a principal component analysis based on this time varying approximation reveals the evolution of the directions and magnitudes of greatest variation of the system output derived from input variability in the parameters. Such an analysis acts as a means of modeling the robustness of dynamic differential systems.     

Optimized independent components for parameter regression

In this paper, a modified ICR algorithm is proposed for quality prediction purpose. The disadvantage of original Independent Component Regression (ICR) is that the extracted Independent Components (ICs) are not informative for quality prediction and interpretation. In the proposed method, to enhance the causal relationship between the extracted ICs and quality variables, a dual-objective optimization which combines the cost function w^TX^TYv in Partial Least Squares (PLS) and the approximations of negentropy in Independent Component Analysis (ICA) is constructed in the first step for feature extraction. It simultaneously considers both the quality-correlation and the independence, and then the ICR-MLR (Multiple Linear Regression) method is used to obtain the regression coefficients. The proposed method is applied to the quality prediction in continuous annealing process and Tennessee Eastman process. Applications indicate that the proposed approach effectively captures the relations in the process variables and use of proposed method instead of original PLS and ICR improves the regression matching and prediction ability.     

Reliability analysis for multidisciplinary systems with the mixture of epistemic and aleatory uncertainties

Epistemic and aleatory uncertain variables always exist in multidisciplinary system simultaneously and can be modeled by probability and evidence theories, respectively. The propagation of uncertainty through coupled subsystem and the strong nonlinearity of the multidisciplinary system make the reliability analysis difficult and computational cost expensive. In this paper, a novel reliability analysis procedure is proposed for multidisciplinary system with epistemic and aleatory uncertain variables. First, the probability density function of the aleatory variables is assumed piecewise uniform distribution based on Bayes method, and approximate most probability point is solved by equivalent normalization method. Then, important sampling method is used to calculate failure probability and its variance and variation coefficient. The effectiveness of the procedure is demonstrated by two numerical examples. Copyright © 2013 John Wiley & Sons, Ltd.     

Operations sequencing in automated warehousing systems

The block sequencing strategy, where retrievals are sequenced and executed in blocks, is commonly assumed in many works on operations management in automated warehousing systems. The nearest neighbour heuristic is a popular sequencing method. In this work, the destructive effects of block sequencing on the system's performance: waiting times and queue length, in a non-deterministic environment, is demonstrated. Dynamic application of the nearest neighbour as a dispatching rule is proposed as an alternative. The performance level of the dispatching nearest neighbour rule is surprisingly high     

Reliability and risk analysis of large systems with ageing components

Applications of limit reliability functions to the reliability evaluation of large multi-state systems composed of independent components are considered. The main emphasis is on multi-state systems with ageing components because of the importance of such an approach in safety analysis, assessment and prediction, and analysing the effectiveness of operation processes of real technical systems. The results concerned with multi-state series systems are applied to the reliability evaluation and risk function determination of a homogeneous bus transportation system. Results on limit reliability functions of a homogeneous multi-state “m out of n” system are applied to durability evaluation of a steel rope. A non-homogeneous series-parallel pipeline systems composed of several lines of pipe segments is estimated as well. Moreover, the reliability evaluation of the model homogeneous parallel-series electrical energy distribution system is performed.     

Sensitivity analysis of flexible multibody systems using composite materials components

In this paper, a general formulation for the computation of the first‐order analytical sensitivities based on the direct method using automatic differentiation of flexible multibody systems is presented. The direct method for sensitivity calculation is obtained by differentiating the equations that define the response of the flexible multibody systems of composite materials with respect to the design variables, which are the ply orientations of the laminated. In order to appraise the benefits of the approach suggested and to highlight the risks of the procedure, the analytical sensitivities are compared with the numerical results obtained by using the finite difference method. For the beam composite material elements, the section properties and their sensitivities are found using an asymptotic procedure that involves a two‐dimensional (2‐D) finite element analysis of their cross section. The equations of the sensitivities are obtained by automatic differentiation and integrated in time simultaneously with the equations of motion of the multibody systems. The equations of motion and the sensitivities of the flexible multibody system are solved and the accelerations, velocities and the sensitivities of accelerations and velocities are integrated in time using a multi‐step multi‐order integration algorithm. Through the application of the methodology to two simple flexible multibody systems the difficulties and benefits of the procedure, with respect to finite difference approaches or to the direct implementation of the analytic sensitivities, are discussed. Copyright © 2008 John Wiley & Sons, Ltd.     

复杂系统的多学科设计优化综述

从设计和分析的本质出发,结合复杂系统的特点,通过分析传统设计优化流程在面对复杂系统时存在的困难和缺陷,指出多学科设计优化(multidisciplinary design optimization,MDO)方法是解决复杂系统设计优化问题的一种有效措施.在此基础上,介绍了多学科优化方法的基本思想,总结了子系统耦合方式及MDO在处理耦合时的基本方法,归纳了MDO的知识框架和主要研究内容.最后在现有研究成果的基础上,对MDO今后的研究提出了几点参考意见.     

Performance optimization of multidisciplinary mechanical systems subject to uncertainties

This paper presents a methodology to solve a new class of stochastic optimization problems for multidisciplinary systems (multidisciplinary stochastic optimization or MSO) wherein the objective is to maximize system mechanical performance (e.g. aerodynamic efficiency) while satisfying reliability-based constraints (e.g. structural safety). Multidisciplinary problems require a different solution approach than those solved in earlier research in reliability-based structural optimization (single discipline) wherein the goal is usually to minimize weight (or cost) for a structural configuration subject to a limiting probability of failure or to minimize probability of failure subject to a limiting weight (or cost). For the problems solved herein, the objective is to maximize performance over the range of operating conditions, while satisfying constraints that ensure safe and reliable operation. Because the objective is performance based and because the constraints are reliability based, the random variables used in the objective must model variability in operating conditions, while the random variables used in the constraints must model uncertainty in extreme values (to ensure safety). Thus, the problem must be formulated to treat these two different types of variables at the same time, including the case when the same physical quantity (e.g. a particular load) appears in both the objective function and the constraints. In addition, the problem must be formulated to treat multiple load cases, which can again require modeling the same physical quantity with different random variables. Deterministic multidisciplinary optimization (MDO) problems have advanced to the stage where they are now commonly formulated with multiple load cases and multiple disciplines governing the objective and constraints. This advancement has enabled MDO to solve more realistic problems of much more practical interest. The formulation used herein solves stochastic optimization problems that are posed in this same way, enabling similar practical benefits but, in addition, producing optimum designs that are more robust than the deterministic optimum designs (since uncertainties are accounted for during the optimization process). The methodology has been implemented in the form of a baseline MSO shell that executes on both a massively parallel computer and a network of workstations. The MSO shell is demonstrated herein by a stochastic shape optimization of an axial compressor blade involving fully coupled aero-structural analysis.     

Optimized support systems for spaceborne dewars

In a long-lifetime dewar, heat loads to the cryogen fall into two distinct categories: parasitic heat leak (i.e. conduction and radiation from the vacuum shell) and internal heat loads (i.e. electrical power dissipation and telescope aperture heating in the case of cooled telescope systems). To accommodate launch loads, the cryogen tank support system of a spaceborne dewar must be stout, and heat conducted from the vacuum shell through the supports can have a large influence on cryogen loss rate. Sophisticated support systems using low conductivity composite materials for thermal optimization have consequently been developed. The most thermally efficient support system used to date in flight hardware is fibreglass/epoxy tension straps. To provide even better thermal efficiency than straps, various attempts have been made over the last two decades to develop dual support concepts in which a primary support reacts launch loads and a smaller, more thermally efficient support then holds the cold assembly in place after launch. This Paper compares predicted dewar performance for several cases using both tension straps and the best developed dual support approach, passive orbital disconnect struts (PODS). Results show that cryogen loss rates are very similar for both systems. Straps provide a slight advantage for storage of lighter cryogens such as helium and hydrogen, and PODS are slightly better for heavier cryogens such as nitrogen and oxygen. The PODS system is more complex than straps, and a flightworthy design does not yet exist. The performance of a flight system using straps is at this time more predictable than one using PODS.     

Identifying latent dynamic components in biological systems

In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real‐world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable''s time course and influence on the other species is estimated in a two‐step procedure involving spline approximation, maximum‐likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.Inspec keywords: biology computing, RNA, splines (mathematics), maximum likelihood estimation, approximation theory, biochemistryOther keywords: latent dynamic components, biological systems, computational system biology, regulatory models, multivariate readouts, biological applications, external regulations, real‐world examples, microRNA, metabolic fluxes, latent dynamic variables, variable time course, two‐step procedure, spline approximation, maximum‐likelihood estimation, model selection, signalling pathway model, real data, incomplete network structures     

Segment set-based part input sequencing in flexible manufacturing systems

In competitive and uncertain business environments, manufacturing firms face various challenges requiring quick response, customisation and cost effectiveness to sustain competitive advantage. This paper proposes a segment set-based approach for the part input sequencing problem of flexible manufacturing systems (FMSs). The segment set theorems are built for the development of the segment set-based approach for the problem. A numerical study is done using simulation, and validated through statistical analyses. Our approach results in the significant performance improvement of the FMSs. Managerial implications are discussed. Further research is also provided.     

The optimal frequency and sequencing of tests in the inspection of multicharacteristic components

IIE Transactions - A product (component) often requires inspection on its characteristics, of which nonconformance of one would result in the rejection of the component. However, the need for...     

Reliability based design optimization using response surfaces in application to multidisciplinary systems

Traditionally, reliability based design optimization (RBDO) is formulated as a nested optimization problem. For these problems the objective is to minimize a cost function while satisfying the reliability constraints. The reliability constraints are usually formulated as constraints on the probability of failure corresponding to each of the failure modes or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. The difficulty in evaluating reliability constraints comes from the fact that modern reliability analysis methods are themselves formulated as an optimization problem. Solving such nested optimization problems is extremely expensive for large scale multidisciplinary systems which are likewise computationally intensive. In this research, a framework for performing reliability based multidisciplinary design optimization using approximations is developed. Response surface approximations (RSA) of the limit state functions are used to estimate the probability of failure. An outer loop is incorporated to ensure that the approximate RBDO converges to the actual most probable point of failure. The framework is compared with the exact RBDO procedure. In the proposed methodology, RSAs are employed to significantly reduce the computational expense associated with traditional RBDO. The proposed approach is implemented in application to multidisciplinary test problems, and the computational savings and benefits are discussed.     

Reliability and performance analysis of hardware–software systems with fault-tolerant software components

This paper presents an algorithm for evaluating reliability and expected execution time for systems consisting of fault-tolerant software components running on several hardware units. The components are built from functionally equivalent but independently developed versions characterized by different reliability and execution time. Different number of versions can be executed simultaneously depending on the number of available units. The system reliability is defined as the probability that the system produces a correct output in a specified time.     

含惯容和杠杆元件的减振系统参数优化及性能分析

为了提高减振性能,设计了两种基于惯容-弹簧-阻尼器结构的含放大机构的减振系统,探讨了模型在受到外部激励时的减振效果。根据牛顿第二定律建立了系统的动力学方程,并得到了其解析解,发现幅频曲线都存在独立于阻尼比的两个固定点。基于H∞和H2优化准则,分别得到了系统的最优参数,并研究了惯容质量比和放大比对模型减振性能的影响。发现在一定范围内,惯容质量比与放大比增大,幅频曲线峰值降低,两共振峰间距拉大,并通过数值仿真验证了解析解的正确性。与其他减振模型在简谐激励和随机激励情况下比较,所设计模型大幅降低了共振振幅,并且拓宽了有效频带,表明其具有更好的减振性能。     

On selecting components for redundancy in coherent systems

We study the problem of selecting components for redundancy in coherent systems to maximize system reliability. For this optimization problem most of the previous results are obtained under the assumption that the values of component reliabilities are known, or at least an ordering of them must be known in advance. However, in many practical situations such information about component reliabilities is unobtainable. In this paper we propose selection criteria based solely on structural information, and show that the resulting selections from the criteria are optimal when certain conditions are satisfied. Some numerical examples illustrate that the selection methods we propose are easy to apply and perform quite well in practical structures.     

Bayesian reliability analysis of a products of probabilities model for parallel systems with dependent components

In a Bayesian reliability analysis of a system with dependent components, an aggregate analysis (i.e. system-level analysis) or a simplified disaggregate analysis with independence assumptions may be preferable if the estimations obtained from employing these two approaches do not deviate substantially from those derived through a disaggregate analysis, which is generally considered the most accurate method. This study was conducted to identify the key factors and their range of values that lead to estimation errors of great magnitude. In particular, a copula-based Bayesian reliability model was developed to formulate the dependence structure for a products of probabilities model of a simple parallel system. Monte Carlo simulation, regionalised sensitivity analysis and classification tree learning were employed to investigate the key factors. The resulting classification tree achieved favourable predictive accuracy. Several decision rules suggesting the optimal approach under different combinations of conditions were also extracted. This study has made a methodological contribution in laying the groundwork for investigating systems with dependent components using copula-based Bayesian reliability models. With regard to practical implications, this study also derived useful guidelines for selecting the most appropriate analysis approach under different scenarios with different magnitude of dependence.     

Production scheduling and sequencing for multi-stage production systems

Summary This paper focuses on multistage, multiproduct dynamic production systems. Up to now little work has been done on such realistic problems, the reasons being that multiproduct models are subject to capacity considerations and to the general sequencing problem. Optimal and heuristic solution methods are presented for this combined sequencing/scheduling problem. Our computational experience indicates that the proposed heuristic is efficient and differs marginally from the optimal solution.

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Zusammenfassung In diesem Beitrag werden mehrstufige Mehrprodukt-Produktionssysteme mit dynamischem Zeitablauf betrachtet. Da die entsprechenden Modelle es erfordern, gleichzeitig Seriengröße und -folge zu bestimmen derart, daß ein zulässiger Produktionsplan erhalten wird, hat man sich bisher wenig mit solchen realistischen Problemen beschäftigt. Optimale und heuristische Lösungsverfahren für dieses kombinierte Entscheidungsproblem werden vorgestellt. Die Rechenerfahrungen deuten darauf hin, daß das vorgeschlagene heuristische Lösungsverfahren effektiv ist und die erreichten Lösungen sich von einer Optimallösung nur wenig unterscheiden.

     

Modelling,measurement and evaluation of sequencing flexibility in manufacturing systems

Sequencing flexibility refers to the possibility of interchanging the order in which required manufacturing operations are performed. In this paper, we address several issues related to the modelling, measurement and performance evaluation of this flexibility in manufacturing systems. In particular, we introduce several representation and measurement schemes for sequencing flexibility and discuss the usefulness and limitations each. We then propose a new performance-based approach for quantifying the value of flexibility. To this effect, we study the relationship between flexibility and system performance under a variety of design assumptions and operating conditions. These relationships are used to identify key characteristics of a measure of flexibility that is reflective of system performance. This measure is to allow system designers and managers to predict performance based on existing levels of flexibility and/or determine the amount of flexibility required to achieve a certain level of performance. This measure is shown to be easier to calculate than traditional performance measures and to be possible to use as a substitute for these measures in estimating the effect of flexibility on performance.