Stephen M. Batill

Concurrent Subspace Optimization Using Design Variable Sharing in a Distributed Computing Environment

This paper reviews recent implementation advances and modifications in the continued development of a Concurrent Subspace Op timization (CSSO) algorithm for Multidisciplinary Design Optimization (MDO) The CSSO MDO algorithm implemented in this research incor porates a Coordination Procedure of System Approximation (CP-SA) for design updates This study also details the use of a new discipline based decomposition strategy which provides for design variable sharing across discipline design regimes (i e subspaces) A graphical user interface is developed which provides for menu driven execution of MDO algorithms and results display this new programming environment highlights the modularity of the CSSO algorithm The algorithm is implemented in a distributed computing environment using the graphical user interface providing for truly concurrent discipline design Implementation studies introduce two new multidisciplinary design test problems the optimal design of a high performance, low cost structural system and the preliminary sizing of a general aviation aircraft concept for optimal perfor mance Significant time savings are observed when using distributed computing for concurrent design across disciplines The use of design vari able sharing across disciplines does not introduce any difficulties in implementation as the design update in the CSSO MDO algorithm is gener ated in the CP-SA Application of the CSSO algorithm results in a considerable decrease in the number of system analyses required for optimization in both test problems More importantly for the fully coupled aircraft concept sizing problem a significant reduction in the number of individual contributing analyses is observed

Decision-Based Collaborative Optimization

In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg [1,2] is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

MODELING AND SIMULATION UNCERTAINTY IN MULTIDISCIPLINARY DESIGN OPTIMIZATION

This paper is intended to contribute to the ongoing discussion of selected concepts related to the topic of technical risk or uncertainty in the model-based design of physical artifacts. The paper focuses on the use of analytic models and numerical simulation in the multidisciplinary design optimization process. It considers how issues of physical process variability, information uncertainty and the use of models and simulations influence the design decision process. This paper only qualitatively addresses these issues but the goal is to provide a focus for discussion of concepts associated with information uncertainty as applied to model-based multidisciplinary design and optimization.

DECOMPOSITION STRATEGIES FOR RELIABILITY BASED OPTIMIZATION IN MULTIDISCIPLINARY SYSTEM DESIGN

A trust region based reliability analysis that performs a bounded Most Probable Point search is developed. This facilitates screening of non-critical failure modes as well as makes possible the use of Multidisciplinary Design Optimization (MDO) approaches to perform reliability analysis for multidisciplinary problems. A Reliability Based Optimization (RBO) framework that uses MDO approaches in the reliability analysis as well as in the optimization is presented. The method allows the use of both hard and deterministic constraints, and also allows constraint screening within the RBO framework. The framework is implemented for a simple demonstration problem. The trust region based reliability analysis is implemented for a variation of a control augmented structures problem used in various MDO studies. The trust region based reliability analysis was successful in performing bounded MPP searches in the constraint screening process and was instrumental in the success of the MDO version of the reliability analysis.

Flow about a circular cylinder with a single large-scale surface perturbation

An experimental study of the flow around a cylinder with a single straight perturbation was conducted in a wind tunnel. With this bluff body, positioned in a uniform crossflow, the vortex shedding frequency and other flow characteristics could be manipulated.The Strouhal number has been shown to be a function of the perturbation angular position, θp, as well as the perturbation size and Reynolds number. As much as a 50% change in Strouhal number could be achieved, simply by changing θp by 1°. The perturbation size compared to the local boundary layer thickness, δ, was varied from approximately 1 δ to about 20 δ. The Reynolds number was varied from 10,000 to 40,000. For perturbation sizes approximately 5 δ to 20 δ and Reynolds numbers of 20,000 to 40,000, a consistent Strouhal number variation with θp was observed.A detailed investigation of the characteristic Strouhal number variation has shown that varying θp had a significant influence on the boundary layer separation and transition to turbulence. These significant changes occurring in the boundary layer have been shown to cause variations in the spacing between the shear layers, base pressure, drag, lift, and the longitudinal spacing between the vortices in the vortex street.

Morphing UAV Pareto Curve Shift for Enhanced Performance

Research in unmanned aerial vehicles (UAVs) has grown in interest over the past couple decades. Historically, UAVs were designed to maximize endurance and range, but demands for UAV designs have changed in recent years. In addition to the traditional demands for endurance and range, today customer demands include maneuverability. Therefore, UAVs are being designed to morph, to change their geometrical shape during flight, for enhanced maneuvering capability. In this investigation the morphing UAV concept under study is referred to as the buckle wing. The design of the buckle-wing airfoil geometries is posed as a multilevel, multiobjective optimization problem. This buckle-wing design problem includes two competing objectives of maneuverability and long range/endurance. Multiobjective problems have many optimal solutions each depicting a dierent compromise scenario. Each optimal solution is a Pareto point, and the set of all these points represents the Pareto curve. This is a powerful means of showing the global picture of the solution eld. The goal of this paper is to explore and compare the Pareto curves of the buckle-wing UAV to that of a conventional non-morphing UAV. In order to make this performance comparison, Compromise Programming is used as the optimizing method, and the VortexPanel Method is used in calculating the aerodynamics. The buckle-wing UAV’s enhanced capabilities are demonstrated both quantitatively and graphically.

Framework for Multidisciplinary Design Based on Response-Surface Approximations

A framework has been proposed to allow for the multidisciplinary design of coupled, nonhierarchic systems. This approach is based on the ability to decompose a model-based analysis of a coupled system into subspaces or contributing disciplines. These subspaces are defined in terms of the design variables that they can influence and the information they contribute to the characterization of the complete system. The subspace coupling is based on the information they exchange. By using a set of response surface approximations, experts responsible for a particular subspace can make design decisions with the goals of improving the complete system merit and satisfying system constraints. Because design variables can be shared between subspaces, coordination of the subspace design decisions is achieved by the solution of a fully approximate optimization problem involving the complete set of system design variables. The implementation of this framework using two flight vehicle concept design problems is presented.

Multilevel Variable Fidelity Optimization of a Morphing Unmanned Aerial Vehicle

The Morphing Aircraft Structures (MAS) program has grown substantially in technology and has received more attention in recent years. One main initiative of this project is to develop aerial vehicles that are capable of radical shape change. Such shape changes should enable the vehicle to efficiently perform single missions that normally would require two or more different aircraft. Many design optimization issues arise for such systems. Morphing aircraft have multiple configurations which create multiobjective and possibly multilevel optimization problems. The problems are multiobjective because of the performance trade-offs occurring between each morphed state. In many morphing concepts one configuration must be defined in order to design a second state; this produces a multilevel design problem. The complexity of the simulation model and the nested formulation of the multilevel design problem results in a very computationally intensive optimization problem. This paper includes a discussion of solution strategies for these multiobjective, multilevel morphing aircraft design problems. Two design tools are explored to combat the described issues of the optimization problem: conversion to a single-level design problem and the use of variable fidelity optimization. A study is performed comparing the results obtained from the optimization processes of both a multi-level design problem and its corresponding single level problem. Finally a variable fidelity optimization framework is discussed and applied to design a morphing concept; the two fidelity models include a high fidelity computational fluid dynamics simulation and a low fidelity panel method.

Concurrent Subspace Optimization of Mixed Continuous/Discrete Systems

An extension of the method of Concurrent Subspace Optimization (CSSO) has been developed to accomodate mixed continuous/discrete design problems. The mixed CSSO framework employs artificial neural networks to provide approximations to the design space, which are the means of coordinating design decisions in the individual disciplines. This approach is applied to a nonhierarchic test problem which contains continuous and discrete design variables. The results demonstrate that the mixed CSSO framework is able to locate optimal designs and did reduce the number of the complete system analyses required by conventional optimization techniques. Computational resources remain a concern, however, due to the large number of contributing (disciplinary) analyses required to perform mixed optimization at the discipline level. Results demonstrate that the database of design information assembled during CSSO can be exploited to enhance the efficiency of subsequent runs, even if the requirements of the system design problem are altered.

AN ITERATIVE CONCURRENT SUBSPACE ROBUST DESIGN FRAMEWORK

The purpose of this paper was to evaluate a framework for obtaining robust designs of complex, coupled systems. This framework referred to as Iterative Concurrent Subspace Robust Design (ICSRD), is based upon the use of global response surface approximations of the design space. ICSRD incorporates a robust optimization formulation, using a linearization approach. It generates approximate robust designs from artificial Neural Network (NN) approximations in an iterative fashion. Two benchmark problems are presented, one being an analytic problem with two design variables and the other a control-structures problem, which is characterized by complex discipline coupling. Two variations of the latter problem are considered, one with modified bounds for certain design variables and the other with a reduced number of design variables with original bounds. It is observed that the NN training plays a significant role in obtaining a good robust optimum. It is also observed that ICSRD framework yields reasonable robust designs for the test cases implemented.