by National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, [Springfield, Va., For sale by the National Technical Information Service] in [Washington, D.C.] .
Written in English
|Statement||Augustine R. Dovi and Gregory A. Wrenn.|
|Series||NASA contractor report -- 4328., NASA contractor report -- NASA CR-4328.|
|Contributions||Wrenn, Gregory A., Langley Research Center.|
|The Physical Object|
|Pagination||v, 23 p.|
|Number of Pages||23|
Techniques for Aircraft Conceptual Design for Mission Performance Comparing Nonlinear Multiobjective Optimization Methods Augustine R. Dovi Gregory A. Wrenn Lockheed Engineering and Sciences Co. Hampton, Virginia I. INTRODUCTION In this chapter, a recently developed technique which converts a constrained optimization problem to an unconstrained one where conflicting figures of merit may be simultaneously considered has been combined with Author: Augustine R. Dovi, Gregory A. Wrenn. Aircraft design for mission performance using non-linear multiobjective optimization methods considered has been combined with a complex mission analysis system. The method is compared with existing single and multiobjective optimization methods. A primary benefit from this new method for multiobjective optimization is the elimination of Author: Gregory A. Wrenn and Augustine R. Dovi. Aircraft design is a highly nonlinear problem and inherently multidiscip linary activity that involves a large number of design variables and different mod els and tools for various aspects of. The method of multidisciplinary analysis and optimization exploits the synergism of interacting computational domains. This has thoroughly changed the way in which the design of complex engineering system is organized. Essential aspects of multidisciplinary optimization are system decomposition, multilevel and multi‐objective optimization.
Aircraft design for mission performance using nonlinear multiobjective optimization methods existing single and multiobjective optimization methods. A primary benefit from this new method for. Ostermeier  and the ε-MOEA (epsilon-dominance multi-objective evolutionary algorithm) by Deb et al. . In the following, each com-ponent of the overall coupled method is introduced. Aircraft Design Code, iFly Since evolutionary algorithm optimization methods typically require thousands of function evaluations, selecting a performance. Close Drawer Menu Close Drawer Menu Menu. Home; Journals. AIAA Journal; Journal of Aerospace Information Systems; Journal of Air Transportation; Journal of Aircraft; Journal of . The key steps of the process are detailed in 10 chapters that encompass aircraft constraint analysis, aircraft mission analysis, engine parametric (design point) analysis, engine performance (off-design) analysis, engine installation drag and sizing, and the design of inlets, fans, compressors, main combustors, turbines, afterburners, and.
6. Aircraft Weight Analysis Introduction The Content of this Section Deﬁnitions Fundamental Weight Relations Mission Analysis Initial Weight Analysis Methods Method 1: Initial Gross Weight Estimation Using Historical Relations Method 2: Historical Empty Weight Fractions Aircraft Design for Mission Performance Using Nonlinear Multiobjective Optimization Methods, J. of Aircraft, Vol. 27, No. 12, , pp. – CrossRef Google Scholar . Consoli, R.D. and Sobieszczanski-Sobieski, J., “Application of Advanced Multidisciplinary Analysis and Optimization Methods to Vehicle Design Synthesis,” ICAS Paper , 17th Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden, September , pp. – Google Scholar. The aim of this paper is to demonstrate that the aerodynamic design of an aircraft for its optimal mission performance can be obtained using a multipoint optimization based on the Gradient Span Analysis (GSA).6 In this way, the operating conditions ranges are sampled such that the computation cost is minimal and the problem is well-poised. 2 of