Organizational Unit:

Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

Organizational Unit

Aerospace Systems Design Laboratory (ASDL)

Organizational Unit

Air Transportation Laboratory

Organizational Unit

Cognitive Engineering Center

Organizational Unit

Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 9 of 9

Assessment of Uncertainty in Aerospace Propulsion System Design and Simulation

(Georgia Institute of Technology, 2003-12) Mavris, Dimitri N. ; Roth, Bryce Alexander

The subject of uncertainty analysis in complex systems design is a broad and bourgeoning field of study. This paper focuses on only three very specific areas of current propulsion research wherein uncertainty plays a pivotal role in the problem formulation. The first is probabilistic approaches to matching engine cycle models to test data. Engine cycle models must have a high confidence of representing the actual engine performance accurately. These models must be matched in the presence of measurement, manufacturing, and other sources of uncertainty. Moreover, the optimal model match tends to change with time such that the problem is stochastic in nature. Current efforts are focusing on using Bayesian statistics to enable a comprehensive (stochastic) treatment of the problem. The second research area of interest is probabilistic analysis methods for estimation of part life in life-limited gas turbine engines. There are many sources of uncertainty in estimating part life, including material properties, material cleanliness/flaw size, part loads, and usage profile. Moreover, life limited parts are subject to accumulated damage over time, and the damage accumulation rate is a strong function of vehicle mission profile and usage. Current efforts are therefore aimed at linking detailed part analysis (finite element and materials models) with higher-level system and mission-level parameters to enable rapid and accurate analysis with the least possible effort. Finally, the role of uncertainty in engine materials selection and insertion is discussed. The materials development process for critical turbine engine parts is very lengthy and subject to considerable uncertainty with regards to the optimal balance of materials properties required for a given application. This is an area of research that will benefit from the development of materials selection methods designed to yield robust materials applicable to the greatest possible number of engines.
Simulation-Based Parametric Reliability Modeling Using Covariate Theory

(Georgia Institute of Technology, 2003-11) Wallace, Jon Michael ; Mavris, Dimitri N.

Reliability analysis methods used for preliminary safety assessments of complex systems typically assume a predetermined and invariant set of input variable statistical properties. However, during the product development phase of the system and especially during in service operation, the characteristics of the random variables can themselves be subject to variation. Thus, the resulting failure probability distribution can vary greatly from early predictions. The objective of this paper is to explore a technique used to create a general parametric failure probability distribution as a function of key input variables. This technique is constructed around covariate theory which is the basis of the familiar Accelerated Life Testing and Proportional Hazards Modeling approaches. Where these approaches have traditionally been used with physical experiments, they are applied within this study to Monte Carlo simulation data generated using an available component modeling and simulation environment of a gas turbine airfoil limited by a single failure mode. Necessary modifications to the traditional form of the covariate approach are identified for application to controlled Monte Carlo simulation data. Implications to potential safety improvements early on in the product development phase are discussed.
Implementation of a Physics-Based Decision-Making Framework for Evaluation of the Multidisciplinary Aircraft Uncertainty

(Georgia Institute of Technology, 2003-09) Briceño, Simón Ignacio ; Mavris, Dimitri N.

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. This paper describes the implementation of such an approach in order to demonstrate the types of insights that might be gained as an engine manufacturer tries to forecast the effects of the associated airframe uncertainties (structural, aerodynamic, etc. design changes) on engine related characteristics for the design of a hypothetical regional business jet.
Estimation of Turbofan Engine Performance Model Accuracy and Confidence Bounds

(Georgia Institute of Technology, 2003-09) Roth, Bryce Alexander ; Mavris, Dimitri N. ; Doel, David L.

This paper explores the application of Inference and Bayesian Updating principles as a means to efficiently incorporate probabilistic data into the turbine engine status model matching process. This approach allows efficient estimation of nominal model match parameters from test data and also enables quantification of model accuracy and confidence bounds. The basic concepts are developed in detail and formulated into a status matching approach. This method is then applied to a simple surrogate matching problem using a cantilever beam matching exercise to illustrate the methods in a clear and easy-to-understand way. Typical results are presented and are directly analogous to status matching of a gas turbine engine cycle model.
Modeling and Cost Optimization of Combined Cycle Heat Recovery Generator Systems

(Georgia Institute of Technology, 2003-06) Zhao, Yongjun ; Chen, Hongmei ; Mavris, Dimitri N.

The combined cycle power plant is made up of three major systems, the gas turbine engine, the heat recovery steam generator and the steam turbine. Of the major systems the gas turbine engine is a fixed design offered by a manufacturer, and the steam turbine is also a fairly standard design available from a manufacturer, but it may be somewhat customized for the project. In contrast, the heat recovery steam generator (HRSG) offers many different design options, and its design is highly customized and integrated with the steam turbine. The objective of this project is to parametrically investigate the design and cost of the HRSG system, and to demonstrate the impact on the overall cost of electricity (COE) of a combined cycle power plant. There are numerous design parameters that can affect the size and complexity of the HRSG, and it is the plan for the project to identify all the important parameters and to evaluate each. For this study, the design parameter chosen for evaluation is the exhaust gas pressure drop across the HRSG. This parameter affects the performance of both the gas turbine and steam turbine and the size of the heat recovery unit. Single-pressure, two-pressure and three-pressure HRSGs are all investigated, with the tradeoffs between design point size, performance and cost evaluated for each system. A genetic algorithm is used in the design optimization process to minimize the investment cost of the HSRG. Several system level metrics are employed to evaluate a design. They are gas turbine net power, steam turbine net power, fuel consumption of the power plant, net cycle efficiency of the power plant, HRSG investment cost, total investment cost of the power plant and the operating cost measured by the cost of electricity (COE). The impacts of HRSG exhaust gas pressure drop and system complexity on these system level metrics are investigated.
Robust Design of a Creep Limited Gas Turbine Component Using the Taguchi Approach

(Georgia Institute of Technology, 2003-06) Wallace, Jon Michael ; Wojcik, Stephanie ; Mavris, Dimitri N.

System quality and reliability are becoming increasingly more important for maintaining market place competitiveness and customer satisfaction. The objective of this paper is to demonstrate the application of the Taguchi Method as a straightforward means of improving produce quality using simulatin based prdictions of the operating life of a critical gas turbine component. The method is applied to statistically select a set of creep life simulations that will illuminate a bucket design which is least sensitive to component and operating environment variation. Implications of this method to improving turbine component quality at the design level are discussed. The results of the Taguchi Method are compared to a robust design solution found using the more time consuming, yet more accurate Response Surface Monte Carlo method. Apparent advantages and limitations of the Taguchi Method as applied to turbine component design are discussed.
An Unstructured Wave Drag code for the Preliminary Design of Future Supersonic Aircraft

(Georgia Institute of Technology, 2003-06) Rallabhandi, Sriram Kishore ; Mavris, Dimitri N.

In a preliminary design environment, the designer needs to have freedom to quickly evaluate different configurations and come up with the most promising configuration. In the supersonic regime, most linearized codes that are available today can only handle specific shapes and configurations. These codes only aid in optimizing conventional configurations and do not span the entire space of possible shapes, which include revolutionary and unconventional configurations. This paper proposes using a set of GNU libraries and analyses codes to overcome the shortcomings of the legacy codes. It is known that any surface can be discretized into triangles using efficient Delaunay triangulation algorithms. The proposed method involves creating a triangulated aircraft from a generic CAD environment, using the set of geometric libraries and then performing necessary surface operations for the desired result, which in our case is the calculation of the wave drag. Linearized methods for wave drag estimation call for the calculation of the intercepted areas of the aircraft with a Mach cone and the GNU libraries help us in obtaining these areas. Finally, in order to validate the code, the new code is used to compute the wave-drag of a Sears-Haack body and F-16 and the results are compared to the results from AWAVE, the Harris Wave Drag program.
Creep Life Uncertainty Assessment of a Gas Turbine Airfoil

(Georgia Institute of Technology, 2003-04) Wallace, Jon Michael ; Mavris, Dimitri N.

Hot gas path turbine components are exposed to very severe and complex boundary conditions and many other sources of variation during their design, production, and operation. Consequently, the useful life of these components can exhibit considerable scatter. A complex, multi-physics environment has been created to automate the bulk creep life assessment of a gas turbine airfoil for a land-based, heavy duty power generation unit. An uncertainty assessment using the combined response surface Monte Carlo method is conducted with the developed environment. Results of this study are given and found to be in agreement with a more theoretical solution using power series approximation. Some widely used assumptions in conducting component level reliability assessments are investigated and discussed.
Development of a Strategic Business Decision-Making Environment for Commercial Jet Engine Selection

(Georgia Institute of Technology, 2003-01) Mavris, Dimitri N. ; Fernandez, Ismael ; Briceño, Simón Ignacio

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 meta-models to expedite the process. This paper describes such an approach in order to demonstrate the types of insights that might be gained as an engine manufacturer tries to forecast the effects of uncertainties and future vehicle requirements on engine related characteristics for the design of a hypothetical regional business jet. Game theory concepts are suggested as a potential means by which one can attach business payoffs to the selection of any engine design point.