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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.

A mean value based sizing and simulation model has been developed for use in the conceptual design and sizing of hydrogen fueled spark-ignition internal combustion engines (HICE) in the aerospace industry, here ‘mean value’ includes mean effective pressure (MEP), mean piston speed, mean specific power, etc. This model is developed since there is currently no such model readily available for this purpose. When sizing the HICE, statistical data and common practice for gasoline internal combustion engines (GICE) are used to obtain preliminary sizes of the HICE, such as total cylinder volume, bore and stroke; to capture the effect of low volumetric efficiency, the preliminary results are adjusted by a volumetric correction factor until the cycle parameters of HICE are reasonable. A non-dimensional combustion model with hydrogen as fuel is incorporated with existing GICE methods. With this combustion model, the high combustion temperature and high combustion pressure are captured.

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.

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.

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.

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.

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.

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.

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.

One the most critical barriers to the advancement of Personal Air Vehicles in today's market environment is that the technological capabilities can never seem to outweigh the risks associated with financing such an endeavor. To address such a need, a system dynamics approach with the capability to model the uncertainties in the supply chain is presented in this paper. The overall modeling framework is first presented and the modeling process of the various relevant elements, such as demand prediction and manufacturer analysis, is then described. The aim of this research is ultimately to assess the viability of a next-generation aircraft program beyond the static confines of a net present value approach, through the inclusion of dynamic events and uncertainties that can occur throughout the life-cycle of the aircraft.

Several approaches to robust design have been proposed in the past. Only few acknowledged the paradigm shift from performance based design to design for cost. The incorporation of economics in the design process, however, makes a probabilistic approach to design necessary, due to the inherent ambiguity of assumptions and requirements as well as the operating environment of future aircraft. The approach previously proposed by the authors, linking Response Surface Methodology with Monte Carlo Simulations, has revealed itself to be cumbersome and at times impractical for multi-constraint, multi-objective problems. In addition, prediction accuracy problems were observed for certain scenarios that could not easily be resolved. Hence, this paper proposes an alternate approach to probabilistic design, which is based on a Fast Probability Integration technique.