Ilan Kroo

Implementation and Performance Issues in Collaborative Optimization

Collaborative optimization is a multidisciplinary design architecture that is well-suited to large-scale multidisciplinary optimization problems. This paper compares this approach with other architectures, examines the details of the formulation, and some aspects of its performance. A particular version of the architecture is proposed to better accommodate the occurrence of multiple feasible regions. The use of system level inequality constraints is shown to increase the convergence rate. A series of simple test problems, demonstrated to challenge related optimization architectures, is successfully solved with collaborative optimization.

Collaborative Optimization Using Response Surface Estimation

The use of response surface estimation in collaborative optimization, an architecture for large-scale multidisciplinary design is described. Collaborative optimization preserves the autonomy of individual disciplines while providing a mechanism for coordinating the overall design problem and progressing toward improved designs. Collaborative optimization is a two-level optimization architecture, with discipline-specific optimizations free to specify local designs, and a global optimization that ensures that all of the discipline designs eventually agree on a single value for those variables that are shared in common. Results demonstrate how response surface models of subproblem optimization results improve the performance of collaborative optimization. The utility of response surface estimation in collaborative optimization depends on the generation of inexpensive accurate response surface models and the refinement of these models over several fitting cycles. Special properties of the subproblem optimization formulation are exploited to reduce the number of required subproblem optimizations to develop a quadratic model from O(n 2 ) to O(n/2). Response surface refinement is performed using ideas from trust region methods. Results for the combined approaches are compared through the design optimization of a tailless unmanned air vehicle in 44 design variables.

Collaborative Approach to Launch Vehicle Design

Collaborative optimization is a new design architecture specie cally created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-dee ned number of subspace optimization problems that are driven toward interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specie c issues to be accommodated by disciplinary analysts while requiring interdisciplinary decisions to be reached by consensus. This investigation focuses on application of the collaborative optimization architecture to the multidisciplinary design of a singlestage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled in this problem, which is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the ine uencethat an a priori ascent-abort criterion has on the vehicle design and the distinction between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecture include minimal framework integration requirements, the ability to use domain-specie c analyses, which already provide optimization without modie cation, inherent system e exibility and modularity, a distributed analysis and optimization capability, and a signie cant reduction in interdisciplinary communication requirements.

AN AUTOMATED METHOD FOR SENSITIVITY ANALYSIS USING COMPLEX VARIABLES

The complex-step method for calculating sensitivities and its use in numerical algorithms is presented. A general procedure for the implementation of this method is described in detail and a script is developed that automates its implementation. The numerical examples include the automatic conversion of a structural finite element and a two-dimensional computational fluid dynamics code. In both of these examples, the complex-step method is compared with other existing methods, namely finitedierencing, automatic dierentiation and an analytic method. The complex-step method is shown to have implementation advantages over automatic dierentiation and computational advantages over finite-dierencin g.

Framework for Aircraft Conceptual Design and Environmental Performance Studies

Although civil aircraft environmental performance has been important since the beginnings of commercial aviation, continuously increasing air traffic and a rise in public awareness have made aircraft noise and emissions two of the most pressing issues hampering commercial aviation growth today. This, in turn, has created the demand for an understanding of the impact of noise and emissions requirements on the design of the aircraft. In response, the purpose of this research is to explore the feasibility of integrating noise and emissions as optimization objectives at the aircraft conceptual design stage, thereby allowing a quantitative analysis of the tradeoffs between environmental performance and operating cost. A preliminary design tool that uses a multiobjective genetic algorithm to determine optimal aircraft configurations and to estimate the sensitivities between the conflicting objectives of low noise, low emissions, and operating costs was developed. Beyond evaluating the ability of a design to meet regulations and establishing environmental performance trades, the multidisciplinary design tool allows the generation of conventional but extremely low-noise and low-emissions designs that could, in the future, dramatically decrease the environmental impact of commercial aviation, albeit at the expense of increased operating cost. The tool incorporates ANOPP, a noise prediction code developed at NASA Langley Research Center, NASA Glenn Research Centers Engine Performance Program engine simulator, and aircraft design, analysis, and optimization modules developed at Stanford University. The trend that emerges from this research among the seemingly conflicting objectives of noise, fuel consumption, and NO x emissions is the opportunity for significant reductions in environmental impact by designing the aircraft to fly slower and at lower altitude.

Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results

Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

Use of the Collaborative Optimization Architecture for Launch Vehicle Design

Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an a priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecutre in a multidisciplinary design environment are also discussed.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

Subsonic wing planform design using multidisciplinary optimization

This article presents basic results from wing planform optimization for minimum drag with constraints on structural weight and maximum lift. Analyses in each of these disciplines are developed and integrated to yield successful optimization of wing planform shape. Results demonstrate the importance of weight constraints, compressibility drag, maximum lift, and static aeroelasticity on wing shape, and the necessity of modeling these effects to achieve realistic optimized planforms.

Persistent Surveillance Using Multiple Unmanned Air Vehicles

Search and exploration using multiple autonomous sensing platforms has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage or exploration problem, in that the target area needs to be continuously searched, minimizing the time between visitations to the same region. This difference does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. In this research we investigate techniques that are scalable, reliable, efficient, and robust to problem dynamics. These are tested in a multiple unmanned air vehicle (UAV) simulation environment, developed for this program. A semi-heuristic control policy for a single UAV is extended to the case of multiple UAVs using two methods. One is an extension of a reactive policy for a single UAV and the other involves allocation of sub-regions to individual UAVs for parallel exploration. An optimal assignment procedure (based on auction algorithms) has also been developed for this purpose. A comparison is made between the two approaches and a simplified optimal result. The reactive policy is found to exhibit an interesting emergent behavior as the number of UAVs becomes large. The control policy derived for a single UAV is modified to account for actual aircraft dynamics (a 3 degree-of-freedom nonlinear dynamics simulation is used for this purpose) and improvements in performance are observed. Finally, we draw conclusions about the utility and efficiency of these techniques.