BLISS方法的基本理论及应用

在分析、研究BLISS方法理论的基础上，提出了针对BLISS方法的一般性系统分解准则及其子系统的目标函数的确定方法，并结合齿轮减速器的算例证明了该方法的可行性和有效性。该方法可以作为BLISS方法在实际应用中的补充和完善。     

基于Isight的BLISS算法框架

介绍多学科优化设计(Multidisciplinary Design Optimization,MDO)算法的核心和应用价值,概述BLISS算法的总体框架.以2个相互耦合系统的方程组为算例,利用Isight搭建BLISS算法流程并进行求解.计算结果表明:BLISS算法的优化效果良好,收敛速度快,因此BLISS是很有价值的MDO算法.     

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.     

The BLISS programming language: a history

The BLISS programming language was invented by William A. Wulf and others at Carnegie‐Mellon University in 1969, originally for the DEC PDP‐10. BLISS‐10 caught the interest of Ronald F. Brender of DEC (Digital Equipment Corporation). After several years of collaboration, including the creation of BLISS‐11 for the PDP‐11, BLISS was adopted as DEC's implementation language for use on its new line of VAX computers in 1975. DEC developed a completely new generation of BLISSs for the VAX, PDP‐10 and PDP‐11, which became widely used at DEC during the 1970s and 1980s. With the creation of the Alpha architecture in the early 1990s, BLISS was extended again, in both 32‐ and 64‐bit flavors. BLISS support for the Intel IA‐32 architecture was introduced in 1995 and IA‐64 support is now in progress. BLISS has a number of unusual characteristics: it is typeless, requires use of an explicit contents of operator (written as a period or ‘dot’), takes an algorithmic approach to data structure definition, has no goto , is an expression language, and has an unusually rich compile‐time language. This paper reviews the evolution and use of BLISS over its three decade lifetime. Emphasis is on how the language evolved to facilitate portable programming while retaining its initial highly machine‐specific character. Finally, the success of its characteristics are assessed. Copyright © 2002 John Wiley & Sons, Ltd.     

Comparison of MDO methods with mathematical examples

Recently, engineering systems are quite large and complicated. The design requirements are fairly complex and it is not easy to satisfy them by considering only one discipline. Therefore, a design methodology that can consider various disciplines is needed. Multidisciplinary design optimization (MDO) is an emerging optimization method that considers a design environment with multiple disciplines. Seven methods have been proposed for MDO. They are Multiple-discipline-feasible (MDF), Individual-discipline-feasible (IDF), All-at-once (AAO), Concurrent subspace optimization (CSSO), Collaborative optimization (CO), Bi-level integrated system synthesis (BLISS), and Multidisciplinary design optimization based on independent subspaces (MDOIS). Through several mathematical examples, the performances of the methods are evaluated and compared. Specific requirements are defined for comparison and new types of mathematical problems are defined based on the requirements. All the methods are coded and the performances of the methods are compared qualitatively and quantitatively.