Mavris, Dimitri N.

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Now showing 1 - 5 of 5
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    Adaptive Selection of Aircraft Engine Technologies in the Presence of Risk
    (Georgia Institute of Technology, 2002-06) Roth, Bryce Alexander ; Graham, Matthew ; Mavris, Dimitri N. ; Macsotai, Noel I.
    The objective of this paper is to describe a method for selecting optimal engine technology solution sets while simultaneously accounting for the presence of technology risk. This method uses a genetic algorithm in conjunction with Technology Identification, Evaluation, and Selection methods to find optimal combinations of technologies. The unique feature of this method is that the technology evaluation itself is probabilistic in nature. This allows the performance impact and associated risk of each technology to be quantified in terms of a distribution on key engine technology metrics. The resulting method can best be characterized as a concurrent genetic algorithm/Monte Carlo analysis that yields a performance- and risk-optimal technology solution set. This solution set is inherently a robust solution because the method will naturally strive to find those technologies representing the best compromise between performance improvement and technology risk. Finally, a practical demonstration of the method and accompanying results is given for a typical commercial aircraft engine technology selection problem.
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    Adaptive Selection of Pareto Optimal Engine Technology Solution Sets
    (Georgia Institute of Technology, 2002) Roth, Bryce Alexander ; Graham, Matthew ; Mavris, Dimitri N. ; Macsotai, Noel I.
    Successful selection of propulsion system technologies for development and incorporation into new engine designs requires careful balance among many competing design objectives (i.e. performance, cost, risk, etc.). One seldom has sufficient development resources available to fully explore all promising concepts and must therefore choose a few technologies that show the greatest promise to meet program objectives. This paper describes a method of selecting optimal combinations of engine technologies. This method employs a technology impact forecasting environment in conjunction with genetic algorithms to find Pareto-optimal technology solution sets. These results are illustrated using Technology State Transition Diagrams to show how technologies move into and out of the Pareto-optimal sets. An edge search procedure is introduced as a means to efficiently characterize the objective space, the results of which are presented in the form of ternary plots. These plots show how technologies benefit multiple (oftenconflicting) objectives and help find robust or compromise technology combinations. Finally, these methods are applied to select engine technology combinations for a commercial engine system of current interest.
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    Adaptive Selection of Engine Technology Solution Sets from a Large Combinatorial Space
    (Georgia Institute of Technology, 2001) Roth, Bryce Alexander ; German, Brian Joseph ; Mavris, Dimitri N. ; Macsotai, Noel I.
    This paper describes a method to assist in selecting technology concepts from amongst a pool of candidates such that the resulting concepts yield the best compromise between conflicting sign performance and technology risk. The heart of this method is a unique technology impact forecasting environment that is used in conjunction with a genetic algorithm as a tool to efficiently explore the technology combinatorial space. The technique is applied to a commercial turbofan engine technology selection problem of practical interest. A pool of forty technology concepts is proposed and evaluated, the objective being to determine which subset of technologies is the best candidate to go forward into development given conflicting objectives on performance, engine manufacturing cost, and design risk (i.e. cumulative technology readiness). Introduction manufacturing cost, design risk, etc. School of Aerospace, Georgia Tech. Member, AIAA. Director, ASDL. Associate Fellow, AIAA.
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    A Method for Probabilistic Sensitivity Analysis of Commercial Aircraft Engines
    (Georgia Institute of Technology, 1999-09) Mavris, Dimitri N. ; Roth, Bryce Alexander ; Macsotai, Noel I.
    The objective of this paper is to illustrate how probabilistic methods can be utilized to rationally and analytically make design decisions in the presence of uncertainty, with emphasis on the use of probabilistic sensitivities in the aircraft gas turbine engine preliminary design process. A brief review of risk and uncertainty in the engine design process is given, and the role of probabilistic methods is discussed. Probabilistic sensitivity analysis, used in conjunction with response surface methods, is proposed as a computationally-efficient method to address defined sources of uncertainty and risk in engine design from a system level perspective. The method outlined is then applied to the analysis of engine component performance uncertainty impact on the performance of a notional four-engine wide-body commercial transport. More specifically, uncertainty in engine design parameters is shown to have a direct and quantifiable impact on aircraft system figures of merit such as design range and fuel burn. The methods developed are then used to create a set of contour plots showing the behavior of vehicle performance uncertainty over the design space of interest.
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    A Probabilistic Design Methodology for Commercial Aircraft Engine Cycle Selection
    (Georgia Institute of Technology, 1998-09) Mavris, Dimitri N. ; Macsotai, Noel I. ; Roth, Bryce Alexander
    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 t he same order of magnitude as the impact of the cycle itself). However, this paper shows that uncertainties in component efficiencies, pressure losses, and cooling flow losses do not have a significant influence on the variance of aircraft performance. This paper also shows that the probabilistic method is very useful for formulating direct trades of design margin against performance or other figures of merit such as engine weight, thus enabling the existing design margin to be capitalized upon in the interest of obtaining better system performance. In terms of a comparison between techniques, one can conclude that the probabilistic approach is inherently more computationally intensive that the deterministic approach. It therefore behooves the designer to choose wisely when setting up the problem in order to avoid unnecessary work. However, a properly formulated probabilistic method provides a much clearer picture of how the various system trades another and enables the ultimate cycle selection to be analytically determined based on the level of risk that is consistent with program objectives.