Joon Chung

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.

Boundary Layer and Near-Wake Measurements of NACA 0012 Airfoil at Low Reynolds Numbers

A study on the boundary layers and near-wake behavior of a NACA 0012 airfoil at low Reynolds numbers was conducted in order to gain knowledge about flow properties applicable to turbine blades and MAVs. Hot-wire anemometers were used to measure the boundary layers and near-wakes at angles-of-attack of α =0°, 3°, and 6°; and Reynolds Numbers of Rec=2.3×10, 3.3×10, and 4.8×10. The boundary layers were measured with small intervals along the direction normal to the airfoil surface by a 3-D traverse system. The near-wakes were measured at four downstream stations of x/C=0.1, 0.3, 0.5, and 1.0. The results show that the laminar separation of the boundary layers occurred at α =3°. The reattachment of the separated boundary layers was clearly observed for the case of Re=4.8×10 at α =6°, showing the formation of a long separation bubble. The properties of the long separation bubble were then observed and found to be different compared to the properties of short separation bubbles studied previously. The shear flow instability wave was investigated with respect to the formation of coherent vortical structures in the separated shear layer. It was found that the Reynolds number critically influenced the shear layer instability wave rather than the angle-of-attack. The near-wakes in the present paper were analyzed in detail from the boundary layer characteristics that directly affect nearwakes. In particular, the near-wake properties for Rec=4.8×10 and α =6° represented a clearly different evolutions compared to the other Reynolds number cases owing to reattachment.

Uncertainty-based MDO for aircraft conceptual design

Purpose – The purpose of this paper is to study the consideration of uncertainty from analysis modules for aircraft conceptual design by implementing uncertainty-based design optimization methods. Reliability-Based Design Optimization (RBDO), Possibility-Based Design Optimization (PBDO) and Robust Design Optimization (RDO) methods were developed to handle uncertainties of design optimization. The RBDO method is found suitable for uncertain parameters when sufficient information is available. On the other hand, the PBDO method is proposed when uncertain parameters have insufficient information. The RDO method can apply to both cases. The RBDO, PBDO and RDO methods were considered with the Multidisciplinary Design Optimization (MDO) method to generate conservative design results when low fidelity analysis tools are used. Design/methodology/approach – Methods combining MDO with RBDO, PBDO and RDO were developed and have been applied to a numerical analysis and an aircraft conceptual design. This research eva...

Development of Aircraft Conceptual Design Optimization Software

This paper describes the development of computer software designed to assist in the process of conceptual air- crap design. A Multi-Objective Genetic Algorithm (MOGA) optimizer and an aircraft performance simulation package was developed and integrated with an aircraft component database to produce useful suggestions to aircraft designers during the conceptual design phase. The results include the conceptual design of two types of aircraft; a Very Light Jet (VU) and an Unmanned Aerial Vehicle (UAV).

Unmanned Aerial Vehicle Conceptual Design Using a Genetic Algorithm and Data Mining

Aircraft design is a complex process involving multiple co-dependent design variables and many design decisions. For commercial aircraft design, this di‐culty is ofiset somewhat by the wealth of knowledge available. Observing existing designs has provided useful empirical relationships and insights for the designer to apply yielding a relatively well deflned problem. The wide variety of conflguration possibilities, mission proflles, and the relative lack of historical data leave the problem of unmanned aerial vehicle (UAV) design less deflned. The purpose of this research was to develop a robust optimization package for UAV design using data mining to aid conflguration decisions and to develop empirical relationships applicable to a wide variety of mission proflles. An optimization software package was developed using a Genetic Algorithm (GA) and Data Mining. The algorithm proved succesful in carrying out the preliminary design phase of a number of test cases similar to existing UAVs. Designs produced by the algorithm promise improved performance and reduced development time. Future work will introduce high fldelity analysis to the framework developed in this research.

Reliability and Possibility Based Multidisciplinary Design Optimization for Aircraft Conceptual Design

In recent years, uncertainties have been considered in engineering problem. The uncertainties are inherited in each phase of simulation based design process. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle these uncertainties on design optimization. In this paper, the RBDO and PBDO methods were considered with a Multidisciplinary Design Optimization (MDO) method to accomplish the conservative design result when low fidelity analysis tools are used. This method has been applied to an aircraft conceptual design case. This research evaluates the characteristic of the RBDO and PBDO methods. It is shown that the RBDO results depend on the accuracy of uncertain parameters while the PBDO results shows more conservative results.

An Approach to Multi-Objective Aircraft Design

Aircraft design is a complex process subject to many competing disciplinary analyses and is constrained by many performance targets, airworthiness requirements, environmental regulations, and many other factors. Designers must explore a broad range of possible decisions to find the best trade-offs between many competing performance goals and design constraints while ensuring that the resulting design complies with certification and airworthiness standards. A modular Multi-Disciplinary Optimization (MDO) framework is being developed with the ability to handle multiple simultaneous objectives while considering any airworthiness constraints that can be assessed at the conceptual level. The algorithm implements a multi-objective Genetic Algorithm (GA) within an MDO framework. The problem consists of four core disciplinary analysis including structural weight estimation, aerodynamics, performance, and stability.

Aircraft Conceptual Design Optimization with Uncertain Contributing Analyses

This paper outlines the development of a multi-disciplinary design optimization (MDO) architecture for aircraft conceptual design that includes the assessment of uncertainties introduced by approximate equations or computational methods in the contributing disciplinary analyses. Aircraft conceptual design traditionally harnesses prior knowledge in the form of empirical or statistical equations and low fidelity analysis. This approach is computationally inexpensive and allows for rapid design iterations. However, the use of approximate methods introduces uncertainties that can lead to an optimum conceptual design that, when subjected to more detailed analysis later in the design process, is found to fail one or more of the design goals. This may lead to costly revision. By assessing the uncertainty of the contributing analyses using Reliability Based Design Optimization (RBDO) methods, the probability of failure of a given conceptual design can be estimated and minimized.

Unmanned aerial vehicle derivative design optimization based on light sport aircraft

Manufacturers often develop new products by modifying and extending existing products in order to achieve new market demands while minimizing development time and manufacturing costs. In this research, an efficient derivative design process was developed to efficiently adapt existing aircraft designs according to new requirements. The proposed design process was evaluated using a case study that derives an unmanned aerial vehicle design from a baseline manned 2-seatlight sport aircraft. Multiple unmanned aerial vehicle operational scenarios were analysed to define the requirements of the derivative aircraft. These included patrol, environmental monitoring, and communications relay missions. Each mission has different requirements and therefore each resulting derivative unmanned aerial vehicle design has different geometry, devices, and performance. The derivative design process involved redefining the design requirements and identifying the minimum design variable set that needed to be considered in order to efficiently adapt the baseline design. Uncertainty was considered as well to enhance the reliability of the optimized result when it considered different conditions for each mission. An optimization method based on the possibility based design optimization was proposed to handle uncertainty that arises in the design requirements for the multi-role nature of unmanned aerial vehicles. In this paper, the possibility based design optimization method was implemented with multidisciplinary design optimization technique to derive the derivative unmanned designs based on originally manned aircraft. This approach prevented constraint violation via uncertainty variations in the operating altitude and payload weight for each. The unmanned aerial vehicle derivative designs satisfying the requirements of three different missions were derived from the proposed design process.

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.