Daniel Neufeld

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.