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In current design practices, safety, operational and handling criteria are often overlooked until late design stages due to the difficulty in capturing such criteria early enough in the design cycle and in the presence of limited and uncertain knowledge. Virtual (flight) testing and evaluation, based on autonomous modeling and simulation, is proposed as a solution to this shortcoming. The methodology enables one to evaluate vehicle behavior in relatively complex situations through a series of specific flight scenarios. Bringing this methodology to conceptual design requires the creation of an automatic link between the design database and the autonomous flight simulation environment. This paper describes the creation of such a link and an implementation of the Virtual Testing and Evaluation methodology with the use of an advanced design concept.

This paper discusses the use of a Multi-Objective Genetic Algorithm to optimize a technology portfolio for a commercial transport. When incorporating technologies into a conceptual design, there are often multiple competing objectives that determine the benefits and costs of a certain portfolio. The set of designs that achieves the best values of these objectives will fall along a Pareto front that outlines the tradeoffs which will give the optimal design. Multi-Objective Genetic Algorithms determine the Pareto set by giving higher priority to dominant portfolios in the evolutionary optimization techniques of selection and reproduction. When determining the final Pareto optimal set it is important to ensure that only compatible portfolios of technologies are present.

Although market research has indicated that there is significant demand for a supersonic business aircraft, development of a feasible concept has proven difficult. Two factors contributing to this difficulty are the uncertain nature of the vehicle’s requirements and the fact that conventional design methods are inadequate to solve such non-traditional problems. This paper describes the application of a multiobjective genetic algorithm to the design space exploration of such a supersonic business jet. Results obtained using this method are presented, and give insight into the important decisions that must be made at the early stages of a design project.

The success of a modern, complex engineering program is inherently a dynamic economic exercise. Because of this it is not possible to fully grasp what decisions are important to the success of a program using only the typical static or “frozen” design methods and processes. This paper attempts to provide a basic understanding of these design processes and illustrate what they leave to be desired when used in a true market environment. Further, this paper illustrates a dynamic method using tools from engineering, management, and finance to overcome these weaknesses. The dynamic environment allows decision parameters and metrics to change, along with the potential for true competition. Furthermore, it allows the engineer to determine which design choices matter most to the creation of a successful program and how to make the most appropriate choices in the face of uncertainty.

This paper critically evaluates the use of Neural Networks (NNs) as metamodels for design applications. The specifics of implementing a NN approach are researched and discussed, including the type and architecture appropriate for design-related tasks, the processes of collecting training and validation data, and training the network, resulting in a sound process, which is described. This approach is then contrasted to the Response Surface Methodology (RSM). As illustrative problems, two equations to be approximated and a real-world problem from a Stability and Controls scenario, where it is desirable to predict the static longitudinal stability for a High Speed Civil Transport (HSCT) at takeoff, are presented. This research examines Response Surface Equations (RSEs) as Taylor series approximations, and explains their high performance as a proven approach to approximate functions that are known to be quadratic or near quadratic in nature.

The ultimate goal of knowledge-based aircraft design, pilot training and flight operations is to make flight safety an inherent, built-in feature of the flight vehicle, such as its aerodynamics, strength, economics and comfort are. Individual flight accidents and incidents may vary in terms of quantitative characteristics, circumstances, and other external details. However, their cause-and-effect patterns often reveal invariant structure or essential causal chains which may re-occur in the future for the same or other vehicle types. The identification of invariant logical patterns from flight accident reports, time-histories and other data sources is very important for enhancing flight safety at the level of the ‘pilot - vehicle -operational conditions’ system. The objective of this research project was to develop and assess a method for ‘mining’ knowledge of typical cause-and-effect patterns from flight accidents and incidents.

One of the most essential contributors in the aircraft sizing and synthesis process is the creation and utilization of accurate drag polars. An improved general procedure to generate drag polars for conceptual and preliminary design purposes in the form of Response Surface Equations is outlined and discussed in this paper. This approach facilitates and supports aerospace system design studies as well as Multi-disciplinary Analysis and Optimization. The analytically created Response Surface Equations replace the empirical aerodynamic relations or historical data found in sizing and synthesis codes, such as the Flight Optimization System (FLOPS). These equations are commonly incorporated into system level studies when a configuration falls beyond the conventional realm. The approach described here is a statistics-based methodology, which combines the use of Design of Experiments and Response Surface Method (RSM).

Creation and utilization of accurate drag polars is essential in the aircraft sizing and synthesis process. Existing sizing and synthesis codes are based on historical data and cannot capture the aerodynamics of a non-conventional aircraft at the conceptual design phase. The fidelity of the aerodynamic analysis should be enhanced to increase the designer’s confidence in the results. Hence, there is need for a physics-based approach to generate the drag polars of an aircraft lying outside the conventional realm. The deficiencies of the legacy codes should be removed and replaced with higher fidelity meta-model representations. This is facilitated with response surface methodology (RSM), which is a mathematical and statistical technique that is suited for the modeling and analysis of problems in which the responses, the drag coefficients in this case, are influenced by several variables. The geometric input variables are chosen so that they represent a multitude of configurations.

By Combining the Response Surface Methodology with a classical Design of Experiments formulation, a robust method was developed to facilitate the aerodynamic analysis of conceptual designs. These aerodynamic predictions, presented in a parametric form, can then be furnished to a sizing and synthesis code for further evaluation of the concept at the system level. The computational basis of this methodology is a set of numerical codes that work in unison to both optimize the geometry for minimal drag and evaluate key aerodynamic parameters such as lift, friction, wave and induced drag coefficients. Code fidelity and sensitivity to a wide variety of input parameters such as aircraft geometry, panel layout, number of panels used, flow theory used within the numerical code, etc. was investigated. The numerical results were compared with experimental data for different configurations, and the code input parameters required for the best correlation were grouped according to aircraft type.

Current robust design methods rely heavily on meta-modeling techniques to reduce the total computational effort of probabilistic explorations to a combinatorially manageable size. Historically most of these meta-models were in the form of Response Surface Equations (RSE). Recently there has been interest in supplementing the RSE with techniques that better handle non-linear phenomena. One technique that has been identified is the Gaussian Process (GP). The GP has fewer initial assumptions when compared to the linear methods used by RSEs and, therefore, fewer limitations. The initial implementation and employment techniques proposed in current literature for use with the GP are barely modified versions of those used for RSEs. A better, more tailored technique needs to be developed to properly make use of the nature of the GP, and minimize the effect of some of its limitations. Such a technique would allow for rapid development of a reusable, computationally efficient and accurate GP.

A technique is proposed for examining complex behaviors in the “pilot - vehicle - operational conditions” system using an autonomous situational model of flight. The goal is to identify potentially critical flight situations in the system behavior early in the design process. An exhaustive set of flight scenarios can be constructed and modeled on a computer by the designer in accordance with test certification requirements or other inputs. Distinguishing features of the technique include the autonomy of experimentation (the pilot and a flight simulator are not involved) and easy planning and quick modeling of complex multi-factor flight cases. An example of mapping airworthiness requirements into formal scenarios is presented. Simulation results for various flight situations and aircraft types are also demonstrated.

A supersonic engine for a high Mach interceptor mission is modeled, and the requirements for the engine at different flight conditions are discussed. These include low fuel consumption at a non-afterburning supersonic dash Mach number for interception, and high thrust, both afterburning and non-afterburning, at a high subsonic Mach number for combat engagement. In addition, the engine should have low frontal area and low weight for a given sea level thrust rating. For the design point, the sea level static, standard day non-afterburning thrust is fixed at 20,000 lbs. The primary independent parameters varied in the study are fan pressure ratio, overall pressure ratio, turbine inlet temperature, throttle ratio, and extraction ratio. A design of experiments (DoE) is set up to vary the independent parameters to produce a meta-model for engine performance, geometry and weight.