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Turboelectric propulsion is a technology that can potentially reduce aircraft noise, increase fuel efficiency, and decrease harmful emissions. In a turbo-electric system, the propulsor (fans) is no longer connected to the turbine through a mechanical connection. Instead, a superconducting generator connected to a gas turbine produces electrical power which is delivered to distributed fans. This configuration can potentially decrease fuel burn by 10% [1]. One of the primary challenges in implementing turboelectric electric propulsion is designing the power distribution system to transmit power from the generator to the fans. The power distribution system is required to transmit 40 MW of power from the generator to the electrical loads on the aircraft. A conventional aircraft distribution cannot efficiently or reliably transmit this large amount of power; therefore, new power distribution technologies must be considered.

Over the past few years, modern aircraft design has experienced a paradigm shift from designing for performance to designing for affordability. This paper contains a probabilistic approach that will allow traditional deterministic design methods to be extended to account for disciplinary, economic, and technological uncertainty. The probabilistic approach was facilitated by the Fast Probability Integration (FPI) technique; a technique which allows the designer to gather valuable information about the vehicle's behavior in the design space. This technique is efficient for assessing multi-attribute, multi-constraint problems in a more realistic fashion. For implementation purposes, this technique is applied to illustrate how both economic and technological uncertainty associated with a Very Large Transport aircraft may be assessed.

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.

As an element of a design optimization study of high speed civil transport (HSCT), response surface equations (RSEs) were developed with the goal of accurately predicting the sideline, takeoff, and approach noise levels for any combination of selected design variables. These RSEs were needed during vehicle synthesis to constrain the aircraft design to meet FAR 36, Stage 3 noise levels. Development of the RSEs was useful as an application of response surface methodology to a previously untested discipline. Noise levels were predicted using the Aircraft Noise Prediction Program (ANOPP), with additional corrections to account for inlet and exhaust duct lining, mixer-ejector nozzles, multiple fan stages, and wing reflection. The fan, jet, and airframe contributions were considered in the aircraft source noise prediction.

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 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 objective of this paper is to examine ways in which to implement probabilistic design methods in the aircraft engine preliminary design process. Specifically, the focus is on analytically determining the impact of uncertainty in engine component performance on the overall performance of a notional large commercial transport, particularly the impact on design range, fuel burn, and engine weight. The emphasis is twofold: first is to find ways to reduce the impact of this uncertainty through appropriate engine cycle selections, and second is on finding ways to leverage existing design margin to squeeze more performance out of current technology. One of the fundamental results shown herein is that uncertainty in component performance has a significant impact on the overall aircraft performance (it is on the same order of magnitude as the impact of the cycle itself).

Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique.

An integrated, multidisciplinary environment of a tactical aircraft platform has been created by leveraging the powerful capabilities of both MATLAB/Simulink and Numerical Propulsion System Simulation (NPSS). The overall simulation includes propulsion, power, and thermal management subsystem models, which are integrated together and linked to an air vehicle model and mission profile. The model has the capability of tracking temperatures and performance metrics and subsequently controlling characteristics of the propulsion and thermal management subsystems. The integrated model enables system-level trade studies involving the optimization of engine bleed and power extraction and thermal management requirements to be conducted. The simulation can also be used to examine future technologies and advanced thermal management architectures in order to increase mission capability and performance.

The projection of aeroelastic constraints in the design space has long been a want in the design process of vehicles. These properties are usually not established accurately until later phases of design. The desire is to bring another interactive constraint to the conceptual design phase and allow the designer to see the impact of design decisions on aeroelastic characteristics. Even though a number of analysis and optimization tools have been developed to support aeroelastic analysis and optimization in the flight vehicle design process, the toolbox is far from being complete. The results often cannot be obtained in a manner timely enough and the natural division of the engineering team into specialty groups is not supported very well by the aerodynamic-structures monolithic codes typically in the above toolbox. The monolithic codes are also not amenable to the use of concurrent processing now made available by computer technology.

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.

One of the major impetuses for the development of modern, robust design methodologies is the need for affordable aerospace systems. Because the affordability of a system is directly tied to the economics of developing, manufacturing, operating, and disposing of that system, it has become common practice to perform an economic analysis of a potential system to evaluate its viability. Additionally, as needs for improved modeling, analysis, and evaluation capability have arisen, several techniques which have proved themselves popular in economics have been adopted. While adopting these techniques has improved the capabilities of the designer/engineer, they do not proceed far enough. That is aerospace systems design, and consequently all complex systems design, could actually be considered an exercise in economics. All of the players, i.e. designers, firms, end users, and the systems themselves can be considered microeconomic entities.

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.

In this paper, a brief overview is given of the different alternatives to an integrating computational framework. A new framework will be introduced, which incorporates the latest computational techniques and more importantly a mind-set emphasizing flexibility, modularity, portability and re-usability. This introduction will include a thorough review of the fundamental design decisions that went into developing this new integrated computational framework. Distributed object computing extends an object-oriented system which allows objects to interact across heterogenous networks and interoperate as a unified whole. Integrated computing frameworks are discussed, together with data transport techniques such as Extensible Markup Language (XML) and Simple Object Access Protocol (SOAP) to achieve platform, code and meta-model independent integration.

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.

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.

In today’s emerging parametric and probabilistic design environments, disciplinary or multidisciplinary analysis data are represented efficiently with the use of metamodels. Each metamodel is an efficient replacement for a particular design analysis tool. An object oriented library is developed in this paper to represent vehicle configuration in a generic manner and assist the analysis data collection for the metamodeling process. The library is used to produce input files for design analysis tools. It can also be used to create preprocessors for integration environments used in the design process. This allows for smoother integrations of analysis programs within such environments as the environment now needs only replace data in one central input file rather than a file for each analysis tool.

Affordable, reliable endo- and exoatmospheric transportation, for both the military and commercial sectors, grows in importance as the world grows smaller and space exploration and exploitation increasingly impact our daily lives. However, the impact of disciplinary, operational, and technological uncertainties inhibit the design of the requisite hypersonic vehicles, an inherently multidisciplinary and non-deterministic process. Without investigation, these components of design uncertainty undermine the designers’ decision-making confidence. In this paper, the authors propose a new probabilistic design method, using Bayesian Statistics techniques, which allows assessment of the impact of disciplinary uncertainty on the confidence in the design solution. The proposed development of a two-stage reusable launch vehicle configuration highlights the means to first quantify the fidelity of the disciplinary analysis tools utilized, then propagate such to the vehicle system level.

A solid business case is highly dependent upon a strategic technology research and development plan in the early phases of product design. The embodiment of a strategic technology development plan is the identification and subsequent funding of high payoff technology programs that can maximize a company’s return on investment, which entails both performance and economic objectives. This paper describes a technique whereby the high payoff technologies may be identified across multiple platforms to quantitatively justify resource allocation decisions and investment opportunities. A proof of concept investigation was performed on a fleet of subsonic, commercial aircraft.

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.