Multidisciplinary Design Optimization of Airframe and Trajectory Considering Cost, Noise, and Fuel Burn (original) (raw)
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Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft
Journal of Aerospace Technology and Management, 2017
An optimization strategy is constructed to solve the aerodynamic and structural optimization problems in the conceptual design of double-swept flying wing aircraft. Aircraft preliminary aerodynamic and structural design optimization is typically based on the application of a deterministic approach of optimizing aerodynamic performance and structural weight. In aerodynamic optimization, the objective is to minimize induced drag coefficient, and the structural optimization aims to find the minimization of the structural weight. In order to deal with the multiple objective optimization problems, an optimization strategy based on collaborative optimization is adopted. Based on the optimization strategy, the optimization process is divided into system level optimization and subsystem level optimization. The system level optimization aims to obtain the optimized design which meets the constraints of all disciplines. In subsystem optimization, the optimization process for different disciplines can be executed simultaneously to search for the consistent schemes. A double-swept configuration of flying wing aircraft is optimized through the suggested optimization strategy, and the optimization results demonstrate the effectiveness of the method.
Metamodel-based Multidisciplinary Design Optimization of a General Aviation Aircraft
12th World Congress on Structural and Multidisciplinary Optimisation, 2017
Computational burden is still a significant challenge in the in multidisciplinary design optimization (MDO) of complex engineering systems. This challenge can be arising from the curse of dimensionality of the design space and the multiplicity of disciplines involved in the design problem. Tremendous efforts have been made to improve the computational efficiency, especially in the field of MDO. Meta-modeling is one of the powerful tools to facilitate this problem and has been received increasing attention in the past decades. Meta-models are used to provide simpler models instead of the complex original models and by admitting a small percentage of error reduces computing time of the problem. Kriging meta-model, due to its high efficiency in medium dimension problems has been attracted the attention of many researchers. Due to lack of continuity in the complex design problems, creating a comprehensive and appropriate meta-model with acceptable accuracy to cover the entire design space is difficult and almost impossible. This paper proposed a strategy to improve the accuracy of the created meta-models using the elimination of outlier data from sampled points and re-designing the effective Kriging meta-model parameters. The proposed strategy is applied to the conceptual design of a General Aviation Aircraft (GAA) using MDO methodology and appropriate Kriging meta-model. Meta-models of the design disciplines including propulsion, aerodynamics, weight and sizing, performance criteria and stability disciplines are created and integrated based on Multidisciplinary Design Feasibility (MDF) structure to improve the aircraft performance. The gross weight of the aircraft and cruise phase range are considered as the objective functions. The NSGA-II multi-objective evolutionary optimization algorithm is utilized to demonstrate a set of possible answers in the form of the Pareto front.
Genetic Optimization Applied in Conceptual and Preliminary Aircraft Design
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Optimum Multidisciplinary and Multi-Objective Wing Design in CFD Using Evolutionary Techniques
Computational Fluid Dynamics 2004, 2006
This paper details some current extensions and applications of hierarchical asynchronous parallel evolutionary algorithms (HAPEA) for multidisciplinary and multi-objective wing design and optimisation problems. In this work the search for the solution takes place in separate hierarchical layers comprising different CFD solvers or resolutions. The performance and advantages of the algorithm are compared to that of a classical EA which would normally use only a single complex model and involve larger computational expense. The formulation and implementation of the algorithm are described and a test case for a multidisciplinary transonic wing design in structures and aerodynamics is presented. The trade-off between the objective functions produced a set of compromise designs represented in an optimal Pareto front. Results indicate that the algorithm is fast and robust for multi-objective and multidisciplinary optimisation problems and as designed produces classical as well as alternative wing configurations.
The purpose of this chapter is to give an overview of evolutionary algorithms and describe a particular multi-objective EA (MOEA) named Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEA) and its application to aeronautical design and optimisation problems. The first chapter provides an overview of evolutionary algorithms introduces the main advantages of this derivative free approach and details the HAPEA method. Then the paper focuses on the application of the method to mathematical test problems for which non-dominated solutions of the Pareto front are known. Finally several practical examples illustrate the potential of the method, related to conceptual and detailed multi objective and multi disciplinary design problems in aeronautics.
Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
Studies in Computational Intelligence, 2011
Optimization problems in many industrial applications are very hard to solve. Many examples of them can be found in the design of aeronautical systems. In this field, the designer is frequently faced with the problem of considering not only a single design objective, but several of them, i.e., the designer needs to solve a Multi-Objective Optimization Problem (MOP). In aeronautical systems design, aerodynamics plays a key role in aircraft design, as well as in the design of propulsion system components, such as turbine engines. Thus, aerodynamic shape optimization is a crucial task, and has been extensively studied and developed. Multi-Objective Evolutionary Algorithms (MOEAs) have gained popularity in recent years as optimization methods in this area, mainly because of their simplicity, their ease of use and their suitability to be coupled to specialized numerical simulation tools. In this chapter, we will review some of the most relevant research on the use of MOEAs to solve multi-objective and/or multi-disciplinary aerodynamic shape optimization problems. In this review, we will highlight some of the benefits and drawbacks of the use of MOEAs, as compared to traditional design optimization methods. In the second part of the chapter, we will present a case study on the application of MOEAs for the solution of a multi-objective aerodynamic shape optimization problem.
Multi-Objective Design Exploration for Aerodynamic Configurations
35th AIAA Fluid Dynamics Conference and Exhibit, 2005
A new approach, Multi-Objective Design Exploration (MODE), is presented to address Multidisciplinary Design Optimization problems. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of Kriging Model, Adaptive Range Multi Objective Genetic Algorithms, Analysis of Variance and Self-Organizing Map. The main emphasis of this approach is visual data mining. Two data mining examples using high fidelity simulation codes are presented: four-objective aerodynamic optimization for the fly-back booster and Multidisciplinary Design Optimization problem for a regional-jet wing. The first example confirms that two different data mining techniques produce consistent results. The second example illustrates the importance of the present approach because design knowledge can produce a better design even from the brief exploration of the design space.
ENHANCING AIRCRAFT CONCEPTUAL DESIGN USING MULTIDISCIPLINARY OPTIMIZATION
Research into the improvement of the Aircraft Conceptual Design process by the application of Multidisciplinary Optimization (MDO) is presented. Aircraft conceptual design analysis codes were incorporated into a variety of optimization methods including Orthogonal Steepest Descent (full-factorial stepping search), Monte Carlo, a mutationbased Evolutionary Algorithm, and three variants of the Genetic Algorithm with numerous options. These were compared in the optimization of four notional aircraft concepts, namely an advanced multirole export fighter, a commercial airliner, a flyingwing UAV, and a general aviation twin of novel asymmetric configuration. To better stress the methods, the commercial airliner design was deliberately modified for certain case runs to reflect a very poor initial choice of design parameters including wing loading, sweep, and aspect ratio.