Ruben E. Perez

Evaluation of Multidisciplinary Optimization Approaches for Aircraft Conceptual D esign

This paper presents the evaluation of different MDO architectures using an extended set of metrics, which take into consideration optimization and formulation structure characteristics. Demonstrative comparisons are made for analytic and supersonic business jet conceptual design examples. Results show the promising features of the proposed evaluation metrics to define a standardized guideline when dealing with multidisciplinary optimization formulations which can be applied to aircraft conceptual design problems.

AIRCRAFT CONCEPTUAL DESIGN USING GENETIC ALGORITHMS

Nomenclature Aircraft design is a complex multidisciplinary process to determine aircraft configuration variables that satisfy a set of mission requirements. It is very hard for aircraft designers to foresee the consequences of changing certain variables. Furthermore, conventional optimization processes are limited by the type and number of parameters used, resulting in sub-optimal designs. The objective of this research is to test the functionality and implementation of a multidisciplinary aircraft conceptual design optimization method using an adaptive genetic algorithm (GA), as a feasible alternative to the existing sizing and optimization methods. To illustrate the approach the algorithm is used to optimize a medium range commercial aircraft, with takeoff weight as an optimization goal, subjected to constraints in performance and geometric parameters. Adaptive and traditional formulations for the handling of constraints by the GA are tested and compared. Results show the ability of the adaptive GA to unbiased search through the design space of aircraft conceptual designs, leading to more viable aircraft configurations than the traditional GA approach at reduced timeframes, with a lower cost than current aircraft design optimization procedures.

Multidisciplinary optimization framework for control-configuration integration in aircraft conceptual design

The emerging flight-by-wire and flight-by-light technologies increase the possibility of enabling and improving aircraft design with excellent handling qualities and performance across the flight envelope. As a result, it is desired to take into account the dynamic characteristics and automatic control capabilities at the early conceptual stage. In this paper, an integrated control-configured aircraft design sizing framework is presented. It makes use of multidisciplinary design optimization to overcome the challenges which the flight dynamics and control integration present when included with the traditional disciplines in an aircraft sizing process. A commercial aircraft design example demonstrates the capability of the proposed methodology. The approach brings higher freedom in design, leading to aircraft that exploit the benefits of control configuration. It also helps to reduce time and cost in the engineering development cycle.

Particle Swarm Optimization in Structural Design

Optimization techniques play an important role as a useful decision making tool in the design of structures. By deriving the maximum benefits from the available resources, it enables the construction of lighter, more efficient structures while maintaining adequate levels of safety and reliability. A large number of optimization techniques have been suggested over the past decades to solve the inherently complex problem posed in structural design. Their scope varies widely depending on the type of stru ctural problem to be tackled. Gradient-based methods, for example, are highly effectively in finding local optima when the design space is convex and continuous and when the design problem involves large number of design variables and constraints. If the problem constraints and objective function are convex in nature, then it is possible to conclude that the local optimum will be a global optimum. In most structural problems, however, it is practically impossible to check the convexity of the design space, therefore assuring an obtained optimum is the best possible among multiple feasible solutions. Global non-gradient-based methods are able to traverse along highly non-linear, non-convex design spaces and find the best global solutions. In this category many unconstrained optimization algorithms have been developed by mimicking natural phenomena such as Simulated Annealing (Kirkpatrick et al., 1983), Genetic Algorithms (Goldberg, 1989), and Bacterial Foraging (Passino, 2002) among others. Recently, a new family of more efficient global optimization algorithms have been developed which are better posed to handle constraints. They are based on the simulation of social interactions among members of a specific species looking for food sources. From this family of optimizers, the two most promising algorithms, which are the subject of this book, are Ant Colony Optimization (Dorigo, 1986), and Particle Swarm Optimization or PSO. In this chapter, we present the analysis, implementation, and improvement strategies of a particle swarm optimization suitable for constraint optimization tasks. We illu strate the functionality and effectiveness of this algorithm, and explore the effect of the different PSO setting parameters in the scope of classical structural optimization problems.

Decomposition-Based Simultaneous Stabilization with Optimal Control

Simultaneous stabilization addresses the stability of multiple plants under a single feedback controller. It is desired for aircraft flight control operating under different conditions, when they are represented by a collection of linear dynamic models. The single controller brings continuity and a level of reliability. This paper presents a decomposition approach for the solution to the simultaneous stabilization problem. A bilevel design optimization architecture is adopted in which design of each individual plant (flight condition) is taking place at the bottom level, and the top-level optimization aims for single-control convergence of those individual controllers. Furthermore, performance requirements can be taken into account concurrently with the stabilization process, thanks to the separate bilevel decomposition concept. The effectiveness of the proposed approach is illustrated by different aircraft control system design test cases.

Simultaneous Stabilization of Linear Plants Using A Multidisciplinary Design Optimization Analogy

Simultaneous stabilization (SS) addresses the stability of multiple plants under one single feedback controller. It is desired for aircraft flight control operating under different conditions, represented by a collection of flight dynamic models. This paper presents an SS design approach using a multidisciplinary design optimization (MDO) paradigm. The unique bilevel design architecture is adopted where design of each individual plant (flight condition) is taken place at the bottom level while the top-level optimization aims at convergence of those individual controllers. Furthermore, performance requirements can be taken into account concurrently with the stabilization process thanks to the separate bi-level decomposition concept. The effectiveness of the proposed design approach is illustrated by two aircraft control design cases.

Flight Dynamics and Control Multidisciplinary Integration in Aircraft Conceptual Design Optimization

This paper describes the integration of flight dynamics and control in an aircraft conceptual design process, using a modified Collaborative Optimization methodology. Control design and handling quality evaluations are performed concurrently with oth er disciplinary analysis. This provides the freedom of configuration change at an early conceptual stage. A commercial aircraft design example demonstrates that the proposed integration achieves better aircraft configurations than those obtain from a tradi tional process.

ADVANCED BUSINESS JET CONCEPTUAL DESIGN AND COST OPTIMIZATION USING A GENETIC ALGORITHM APPROACH

The present challenge of the business and regional aircraft markets is to obtain a highperformance aircraft with a premium on passenger comfort at a very low price. With the maturity of the high subsonic aircraft markets, a significant increase in performance efficiency had been obtained, but the main challenge still lies in the tradeoffs between the possible obtained performance and aircrafts cost. This research discusses the application of a Genetic Algorithm (GA) in conceptual design and optimization to obtain the optimum external configuration for a long range, eight passenger business aircraft to meet the above objectives. Operating Cost of the aircraft is considered as the objective function to be minimized, and constraints are imposed in performance and geometric parameters based on the given aircraft requirements. Continuous and discrete aircraft variables are defined within the GA optimization process to provide a more accurate aircraft characterization. Improvement approaches are discussed as well as comparison with other global optimization methods is performed. The results obtained in this case study show the ability of GAs to explore the design domain, effectively finding optimum aircraft designs characteristics, and meeting the specified performance goals at reduced operating costs.