scholarly journals Integrated uncertainty techniques in aircraft derivative design optimization.

2021 ◽  
Author(s):  
Hyeong-Uk Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Design Process ◽  
Aircraft Design ◽  
Collaborative Optimization ◽  
Aircraft Manufacturing ◽  
Analysis Tools ◽  
Design Variables ◽  
The Cost ◽  
Aircraft Conceptual Design

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

scholarly journals Integrated uncertainty techniques in aircraft derivative design optimization.

2021 ◽  
Author(s):  
Hyeong-Uk Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Design Process ◽  
Aircraft Design ◽  
Collaborative Optimization ◽  
Aircraft Manufacturing ◽  
Analysis Tools ◽  
Design Variables ◽  
The Cost ◽  
Aircraft Conceptual Design

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

scholarly journals Enhance Derivative Design Considering Global Sensitivity of Design Parameters

2013 ◽  
Author(s):  
Hyeong-UK Park ◽  
Kamran Behdinan ◽  
Joon Chung ◽  
Jae-Woo Lee
Keyword(s):  
Sensitivity Analysis ◽  
Design Process ◽  
Life Cycle Cost ◽  
Global Sensitivity Analysis ◽  
Collaborative Optimization ◽  
Design Parameters ◽  
Fast Method ◽  
Engineering Product ◽  
Global Sensitivity ◽  
Design Variables

An engineering product design considers derivatives to reduce the life cycle cost and to increase the efficiency on operation when it has new demands. The proposed design process in this study obtains derivative designs based on sensitivity of design variable. The efficiency and accuracy of the derivative design process can be enhanced by implementing global sensitivity analysis. Sensitivity analysis sensors the design variables accordingly and variables with low sensitivity for objective function can be neglected, since computational effort and time is not necessary for a design with less priority. In this research, e-FAST method code for global sensitivity analysis module was developed and implemented on Multidisciplinary Design Optimization (MDO) problem. The wing design was considered for MDO problem that used aerodynamics and structural disciplines. The global sensitivity analysis method was applied to reduce the number of design variables and Collaborative Optimization (CO) was used as MDO method. This research shows the efficiency of reduction of dimensionality of complex MDO problem by using global sensitivity analysis. In addition, this result shows important design variables for design requirement to student when they solving design problem.

scholarly journals Development of a Derivative Design Process Considering Uncertainties from Low Fidelity Analysis Tools

2011 ◽  
Author(s):  
Hyeong-Uk Park ◽  
Kamran Behdinan ◽  
Jae-Woo Lee ◽  
Joon Chung
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Design Parameters ◽  
Engineering Product ◽  
Reliability Based Design ◽  
Analysis Tools ◽  
Design Variables ◽  
Business Jet ◽  
Design Requirements ◽  
Important Design

A sensitivity analysis and an expert system have been applied to find the important design parameters for designing derivative aircraft subject to new design requirements. Additionally, the Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods are used to consider uncertainties on low fidelity analysis tools. This design process can used to reduce the time and cost for derivative design of engineering product by reducing the number of design variables. In this study, the process is applied to a conceptual design of light business jet aircraft.

Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

2012 ◽  
Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen
Keyword(s):  
Design Optimization ◽  
Support Vector Regression ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Initial Point ◽  
Ship Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Support Vector ◽  
Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

scholarly journals Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shujuan Wang ◽  
Qiuyang Li ◽  
Gordon J. Savage
Keyword(s):  
Sensitivity Analysis ◽  
Design Optimization ◽  
Robust Design ◽  
Structural Reliability ◽  
Computational Cost ◽  
Optimization Process ◽  
Robust Design Optimization ◽  
Global Approximation ◽  
Design Variables ◽  
Uncertainty In Design

This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

Global Sensitivity Analysis for Stay Vane and Casing Design of Reaction Hydraulic Turbine

2019 ◽  
Author(s):  
Hyunkyoo Cho ◽  
Ujjwal Shrestha ◽  
Young-Do Choi ◽  
Jungwan Park
Keyword(s):  
Sensitivity Analysis ◽  
Design Process ◽  
Performance Measures ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Surrogate Models ◽  
Sensitivity Index ◽  
Global Sensitivity ◽  
Design Variables ◽  
Hydraulic Turbines

Abstract Global sensitivity analysis (GSA) estimates influence of design variables in the entire design domain on performance measures. Hence, using GSA, important design variables could be found for an engineering application with high dimension which require computationally expensive analyses. Then, similar engineering applications could use selected variables to carry out design process with smaller dimension and affordable computational cost. In this study, GSA has been carried out for the performance measures in design of stay vane and casing of reaction hydraulic turbines. Global sensitivity index method is used for GSA because it can fully capture the effect of interaction between the design variables. For efficiency, genetic aggregation surrogate models are constructed using the responses of computational fluid dynamic (CFD) analysis. Global sensitivity indices for the performance measures of stay vane and casing have been evaluated using the surrogate models. It is found that less than three design variables among 12 are effective in the design process of stay vane and casing in reaction hydraulic turbines.

Aircraft conceptual design for optimal environmental performance

2012 ◽  
Vol 116 (1175) ◽  
pp. 1-22 ◽  
Author(s):  
R. P. Henderson ◽  
J. R. R. A. Martins ◽  
R. E. Perez
Keyword(s):  
Operating Cost ◽  
Aircraft Design ◽  
Aircraft Emissions ◽  
Design Optimisation ◽  
Commercial Aviation ◽  
Fuel Burn ◽  
Design Variables ◽  
Multidisciplinary Design Optimisation ◽  
Aircraft Conceptual Design ◽  
Geometry Engine

Abstract Consideration of the environmental impact of aircraft has become critical in commercial aviation. The continued growth of air traffic has caused increasing demands to reduce aircraft emissions, imposing new constraints on the design and development of future airplane concepts. In this paper, an aircraft design optimisation framework is used to design aircraft that minimise specific environmental metrics. Multidisciplinary design optimisation is used to optimise aircraft by simultaneously considering airframe, engine and mission. The environmental metrics considered in this investigation are CO2 emissions — which are proportional to fuel burn — and landing-takeoff NOx emissions. The results are compared to those of an aircraft with minimum direct operating cost. The design variables considered in the optimisation problems include aircraft geometry, engine parameters, and cruise settings. An augmented Lagrangian particle swarm optimiser and a genetic algorithm are used to solve the single objective and multi-objective optimisation problems, respectively.

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

2017 ◽  
Vol 232 (1) ◽  
pp. 52-64 ◽  
Author(s):  
Fan Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Dong Guan
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Region Of Interest ◽  
Kriging Model ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Learning Function ◽  
Design Variables ◽  
Interval Variables ◽  
Hybrid Reliability

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2001 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

Abstract In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed approach, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

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