Robust Design Considerations in the Multi-Objective Problem of Aircraft Conceptual Design

2010 ◽  
Author(s):  
Matthew Daskilewicz ◽  
Brian German ◽  
Timothy Takahashi ◽  
Shane Donovan ◽  
Arvin Shajanian
Keyword(s):  
Conceptual Design ◽  
Robust Design ◽  
Multi Objective ◽  
Design Considerations ◽  
Aircraft Conceptual Design

Effects of disciplinary uncertainty on multi-objective optimization in aircraft conceptual design

2011 ◽  
Vol 44 (6) ◽  
pp. 831-846 ◽  
Author(s):  
Matthew J. Daskilewicz ◽  
Brian J. German ◽  
Timothy T. Takahashi ◽  
Shane Donovan ◽  
Arvin Shajanian
Keyword(s):  
Conceptual Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Aircraft Conceptual Design

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

Robust Compressor Blades for Desensitizing Operational Tip Clearance Variations

2014 ◽  
Author(s):  
Pranay Seshadri ◽  
Shahrokh Shahpar ◽  
Geoffrey T. Parks
Keyword(s):  
Robust Design ◽  
Total Pressure ◽  
Pressure Rise ◽  
Side Surface ◽  
Tip Clearance ◽  
Design Parameters ◽  
Compressor Blades ◽  
Robust Designs ◽  
Multi Objective ◽  
Mean And Variance

Robust design is a multi-objective optimization framework for obtaining designs that perform favorably under uncertainty. In this paper robust design is used to redesign a highly loaded, transonic rotor blade with a desensitized tip clearance. The tip gap is initially assumed to be uncertain from 0.5 to 0.85% span, and characterized by a beta distribution. This uncertainty is then fed to a multi-objective optimizer and iterated upon. For each iteration of the optimizer, 3D-RANS computations for two different tip gaps are carried out. Once the simulations are complete, stochastic collocation is used to generate mean and variance in efficiency values, which form the two optimization objectives. Two such robust design studies are carried out: one using 3D blade engineering design parameters (axial sweep, tangential lean, re-cambering and skew) and the other utilizing suction and pressure side surface perturbations (with bumps). A design is selected from each Pareto front. These designs are robust: they exhibit a greater mean efficiency and lower variance in efficiency compared to the datum blade. Both robust designs were also observed to have significantly higher aft and reduced fore tip loading. This resulted in a weaker clearance vortex, wall jet and double leakage flow, all of which lead to reduced mixed-out losses. Interestingly, the robust designs did not show an increase in total pressure at the tip. It is believed that this is due to a trade-off between fore-loading the tip and obtaining a favorable total pressure rise and higher mixed-out losses, or aft-loading the tip, obtaining a lower pressure rise and lower mixed-out losses.

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