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A key issue in complex systems design is measuring the ‘goodness’ of a design, i.e. finding a criterion through which a particular design is determined to be the ‘best’. Traditional choices in aerospace systems design, such as performance, cost, revenue, reliability, and safety, individually fail to fully capture the life cycle characteristics of the system. Furthermore, current multi-criteria optimization approaches, addressing this problem, rely on deterministic, thus, complete and known information about the system and the environment it is exposed to. In many cases, this information is not be available at the conceptual or preliminary design phases. Hence, critical decisions made in these phases have to draw from only incomplete or uncertain knowledge. One modeling option is to treat this incomplete information probabilistically, accounting for the fact that certain values may be prominent, while the actual value during operation is unknown.

Variable cycle engines (VCEs) hold promise as an enabling technology for supersonic business jet (SBJ) applications. Fuel consumption can potentially be minimized by modulating the engine cycle between the subsonic and supersonic phases of flight. The additional flexibility may also contribute toward meeting takeoff and landing noise and emissions requirements. Several different concepts have been and are currently being investigated to achieve variable cycle operation. The core-driven fan stage (CDFS) variable cycle engine is perhaps the most mature concept since an engine of this type flew in the USAF Advanced Tactical Fighter prototype program in the 1990s. Therefore, this type of VCE is of particular interest for potential commercial application. To investigate the potential benefits of a CDFS variable cycle engine, a parametric model is developed using the NASA Numerical Propulsion System Simulation (NPSS).

Significant advances have been made recently in applying probabilistic methods to aerospace vehicle concepts. Given the explosive changes that are occurring in today’s political, social, and technological climate, it makes practical sense to try and extrapolate these methods to the campaign analysis level. This would allow the assessment of rapidly changing threat environments as well as technological advancements, aiding today’s decision makers. The following paper summarizes attempts to apply these methods directly to campaign analysis, and discusses the resulting issues that were identified as potential problem areas. A new approach is postulated which includes the application of probabilistic methods to a fully linked analysis environment. Applying and validating these new methods is an ongoing project.

A fuel cell powered airplane has been designed and constructed at the Georgia Insitute of Technology to develop an understanding of the design and implementation challenges of fuel cell-powered unmanned aerial vehicles (UAVs). A custom 448W net output proton exchange membrane fuel cell powerplant has been constructed and tested. A demonstrator aircraft was designed and built to accommodate this powerplant and the fuel cell powered aircraft has performed seven test flights to date. Test data show that the aircraft performance validates the models used for design and optimization and that the fuel cell aircraft is capable of longer endurance, higher performance test flights.

This paper outlines a comprehensive, structured, and robust methodology for decision making in the early phases ofaircraft design. The proposed approach is referred to as the Technology Identification, Evaluation, and Selection (TIES) method. The seven-step process provides the decision maker/designer with an ability to easily assess and trade-off the impact of various technologies in the absence of sophisticated, time-consuming mathematical formulations. The method also provides a framework where technically feasible alternatives can be identified with accuracy and speed. This goal is achieved through the use of various probabilistic methods, such as Response Surface Methodology and Monte Carlo Simulations. Furthermore, structured and systematic techniques are utilized to identify possible concepts and evaluation criteria by which comparisons could be made.

This paper presents a quantitative process to track the progress of technology developments within NASA’s Vehicle Systems Program (VSP) as implemented on a Supersonic Business Jet (SBJ). The process, called the Technology Metric Assessment and Tracking (TMAT) process, accounts for the temporal aspects of technology development programs such that technology portfolio assessments, in the form of technological progress towards VSP sector goals, may be tracked and assessed. Progress tracking of internal research and development programs is an essential element to successful strategic endeavors and justification of the pursuit of capital projects [1].

Societal demands of recent years have increasingly pressured the development of greener technologies in all sectors of the nation's transportation infrastructure, including that of civilian aviation. This study explores the concept of electric-drive aeropropulsion, aided by high-temperature superconducting technology, as an enabler for enhancing the environmental characteristics at the air-vehicle level. Potential improvements in the areas of aircraft noise, emissions, and energy efficiency are discussed in the context of supporting the latest strategic goals of leading governmental organizations.

Commercial and independent market assessments continue to reveal a strong market desire for a supersonic business jet capable of meeting the requirements for supersonic, overland flight. However, the challenge of meeting the as-yet undefined regulations for overland flight, as well as meeting current and future noise and emission regulations, is daunting. An integrated modeling and simulation environment, based on the creation of response surface metamodels, allows for the rapid evaluation of a design space. From this environment the effects on metrics such as emissions, economics, sonic boom profiles and noise levels can rapidly be seen and manipulated. Such an environment also allows the application of technologies to the vehicle in order to evaluate their potential impact on the system-level metrics.

Unfortunately, the promises of efficient, clean, quiet power that fuel cells offer are balanced by extremely low power densities and great infrastructure-related challenges. Studies by government and industry have investigated their feasibility for primary propulsion in light aircraft. These studies have produced mixed results but have tended to rely on integrating fuel cells into existing airframes, with respectably-performing light sport planes being turned into underpowered show planes with horribly compromised range and payload capabilities. Fuel cells today are in the earliest phases of technological development. As an aircraft propulsion system, they are as advanced as the Wright's reciprocating engine was a hundred years ago.

Recent market studies indicate a renewed interest for a quiet Supersonic Business Jet (SBJ). The success of such a program will be strongly dependent upon the achievement of stringent engine noise, emissions and fuel consumption goals. This paper demonstrates the use of advanced design methods to develop a parametric design space exploration environment which will be ultimately used for the identification of an engine concept capable of satisfying acoustic levels imposed by FAR part 36 (stage IV) and NOx and CO2 standards as stated in the 1996 ICAO. The engine performance is modeled through the use of Response Surface and Design of Experiments Techniques, enabling the designer/decision-maker to change initial engine parameter values to detect the effects of the responses in a time efficient manner. Engine performance and engine weight results are obtained through physics-based engine analysis codes developed by NASA.

The High Speed Civil Transport (HSCT), a supersonic commercial transport currently under development, presents several challenges to traditional conceptual design. The current historical database used by many commercial transport design processes only include data for subsonic transports and therefore does not apply to innovative new configurations such as the HSCT. Therefore, physics-based, preliminary design tools must be used to model the characteristics of advanced aircraft in conceptual sizing routines. In addition, the evaluation of the aircraft design space often requires the analysis of many configurations in order to assess the impact of design constraints and determine the attainable range of system level metrics, a process which is very time consuming in both modeling and computer run time.

An evolving aircraft synthesis simulation environment which offers improvements to existing methods at multiple levels of a design process is described in this paper. As design databases become obsolete due to the introduction of new technologies and classes of vehicles and as sophisticated analysis codes are often too computationally expensive for iterative applications, the design engineer may find a lack of usable information needed for decision making. Within the environment developed in this paper, rapid sensitivity analysis is possible through a unique representation of the relationship between fundamental design variables and system objectives. The combined use of the Design of Experiments and Response Surface techniques provides the ability to form this design relationship among system variables and target values, which is termed design-oriented in nature.

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.

Aircraft survivability is a key metric that contributes to the overall system effectiveness of military aircraft as well as to a lower life cycle cost. The aircraft designer, thus, must have a complete and thorough understanding of the interrelationships between the components of survivability and the other traditional disciplines as well as how they affect the overall life cycle cost of the aircraft. If this understanding occurs, the designer can then evaluate which components and technologies will create the most robust aircraft system with the best system effectiveness at the lowest cost. A synthesis and modeling environment is formulated and presented that will allow trade-off studies and analysis of survivability concepts to be conducted. This environment then becomes the testbed used to develop a comprehensive and structured probabilistic methodology, called the Probabilistic System of System Effectiveness Methodology (POSSEM), that will allow these trades to be conducted.

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.

In today's business climate, aerospace companies are more than ever in need of rational methods and techniques that provide insights as to the best strategies which may be pursued for increased profitability and risk mitigation. However, the use of subjective, anecdotal decision-making remains prevalent due to the absence of analytical methods capable of capturing and forecasting future needs. Negotiations between airframe and engine manufacturers could benefit greatly from a structured environment that facilitates efficient, rational, decision-making. Creation of such an environment can be developed through a parametric physics-based, stochastic formulation that uses Response Surface Equations as meta-models to expedite the process.

A Hypersonic Strike Fighter (HSF) would provide many benefits over current fighters, including increased effectiveness and survivability. However, there are many design challenges to developing such a vehicle. Therefore the conceptual design of an HSF requires the development of new tools and methods to analyze and select vehicle concepts. A parametric method was developed to determine aerodynamic characteristics of hypersonic vehicles in a rapid, automated way. This parametric method and other tools were then used to select a baseline design and optimize this baseline for the notional mission.

This paper evaluates and compares a variety of sampling techniques, including both classical and modern Designs of Experiments, to create a more structured approach to selecting the most apt DoE for a specific type of problem. Six different designs are investigated through a design analysis for a notional commercial aircraft. The appropriateness of each sampling technique is determined based on a number of criteria, including code execution time, independent variable correlation, and distribution of data points throughout the design space. Additionally, the resulting models are evaluated using a systematic procedure for checking quality to quantify the accuracy and predictive capability of a given model.

Market forecasts predict a potentially large market for a Quiet Supersonic Business Jet provided that several technical hurdles are overcome prior to fielding such a vehicle. In order to be economically viable, the QSJ must be able to fly at supersonic speeds overland and operate from regional airports in addition to meeting government noise and emission requirements. As a result of these conflicting constraints on the design, the process of selecting a configuration for low sonic boom is a difficult one. Response Surface Methodology along with physics-based analysis tools were used to create an environment in which the sonic boom can be studied as a function of design and mission parameters. Ten disciplinary codes were linked with a sizing and synthesis code by using a commercial wrapper in order to calculate the required responses with the desired level of fidelity.

A method to identify the topology of an aerospace system’s requirements space, specifically the location and type of the discontinuities that occur at the boundaries of the available technology and the physics of the system, allows the designer to make decisions as to the desirability of a specific solution state. Additionally, since a given set of requirements may produce multiple solutions the designer can compare his/her solution to other potential solutions. This allows an assessment of the requirements risk associated with a specific design. This paper addresses the need to visualize and understand the topology of the requirements space for a Rapid Response Strike System.