The suitability of selected multidisciplinary design and optimization techniques to conceptual aerospace vehicle design

Abstract

Aerospace vehicle conceptual design is dominated by interactions among various traditional engineering disciplines. Aerodynamics, propulsion, performance, weights, sizing, and others are usually highly coupled, and complete vehicle analysis requires an iterative process with efficient methods of communication among the disciplines. Progress to computerize the analysis process has been fast in recent years, producing analysis tools such as NASA-Langley's AVID and EASIE. Given a configuration, the capability exists to quickly analyze it in order to determine its overall characteristics and performance. However, the vehicle designer/ integrator still largely depends on intuition to make systems level changes to the configuration and components in order to improve or optimize the overall design. "What if" studies are typically performed by perturbing the design variables one at a time in an attempt to locate a better design. A complete reanalysis of the entire system is then required for each variable change. This method is a time consuming process that may or may not lead to a more desirable vehicle design. Several mathematically based design techniques have recently emerged that could help the system designer make necessary improvements. These new methods serve to bridge the gap between analysis and design. This paper attempts to give a brief overview of four such techniques, system decomposition, sensitivity analysis, Taguchi methods, and for comparison, classical optimization. References to examples of successful uses of each technique are provided. The goal of this paper is to assess the pros and cons of each technique and their applicability to aerospace vehicle conceptual design. *-graduate student, mech. and aero eng. dept.

A Modeling Framework for the Concurrent Design of Complex Space Systems

“…Many solutions to this complex problem have been developed in the past years, all of them addressing the issues of problem decomposition (modeling) and problem formulation 7,10,11,12,15,16 , software architectures 8,9,13 , and problem solution and optimization 14,17,18 . As the name suggest, MDO is a branch of system design, which mainly deals with enabling synergies between different disciplines to efficiently design a system.…”

Comparative Analysis of Global Techniques for Performance and Design Optimization of Launchers

“…The payload mass and the final orbit are fixed by the mission objectives, and the MDO problem therefore consists of the minimization of the ELV's total mass and of the cumulative error on the orbital parameters, as in Eq. (2). A minimum takeoff thrust-to-weight constraint is imposed, in addition to the same trajectory-path constraints mentioned in Sec.…”

“…However, it is still not clear how to obtain reliable design information in the conceptual and early preliminary phases when accepting the compromises in design fidelity that are necessary to achieve reasonable computational times. The first steps in the development of multidisciplinary models for space transportation systems (STSs) were undertaken in the 1990s by Olds [2,3], Braun et al [4,5], and others. However, the lack of computational power restrained the application to the study of specific launcher configurations and prevented introduction into the optimization cycle of complex disciplinary models.…”

Reduced-Order Techniques for Sensitivity Analysis and Design Optimization of Aerospace Systems

“…[3][4][5][6][7][8][9][10][11][12][13][14][15][16] The effectiveness of these techniques is closely tied to topics such as design of experiments (DOE), both from general statistics as well as the specific circumstances of computational experiments, as can be seen in various example applications. 15,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] Multidisciplinary analysis and optimization (MDA/MDO), including formal optimization, coupled system theory, and sensitivity analysis, is also relevant to this research work. Traditional optimization has a long history, rooted in mathematical programming, and has well developed tools.…”