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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

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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Viable Designs Through a Joint Probabilistic Estimation Technique

(Georgia Institute of Technology, 1999-10) Bandte, Oliver ; Mavris, Dimitri N. ; DeLaurentis, Daniel A.

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. Hence, to account for a multi-criteria as well as a probabilistic approach to systems design, a joint-probabilistic formulation is needed to accurately estimate the probability of satisfying the criteria concurrently. When criteria represent objective/ aspiration functions with corresponding goals, this ?int probability?can also be called viability. The proposed approach to probabilistic, multi-criteria aircraft design, called the Joint Probabilistic Decision Making (JPDM) technique, will facilitate precisely this estimate.
Determination of System Feasibility and Viability Employing a Joint Probabilistic Formulation

(Georgia Institute of Technology, 1999-01) Mavris, Dimitri N. ; Bandte, Oliver ; DeLaurentis, Daniel A.

The present paper outlines a method for probabilistic multi-criteria decision making. Recognizing the limitations of traditional probabilistic methods in accounting for multiple decision criteria in conceptual or preliminary design, this new method combines probabilistic treatment of uncertain information with a multi-criteria decision making technique. The paper describes how the method addresses a need in Multi-Disciplinary Optimization and Analysis as well as the advanced technology selection process in conceptual and preliminary design. The mathematical foundations of a general joint probabilistic formulation are outlined. Two specific functions are introduced that compute the joint probability: the joint empirical distribution function and the joint probability model. The utility of both functions is demonstrated in a proof of concept study for two criteria, applying both functions to a challenging aircraft design problem, the High Speed Civil Transport. This example application addresses two pressing issues: the identification of a feasible design space for a given design concept and the evaluation of viability of a given aircraft design. Finally, the advantages and limitations of the empirical distribution function method as well as the joint probability model are summarized.
The Impact of Supportability on the Economic Viability of a High Speed Civil Transport

(Georgia Institute of Technology, 1998-06) Mavris, Dimitri N. ; Nottingham, Carrie R. ; Bandte, Oliver

There is a significant investment being made within the design community developing tools identifying metrics for all considerable recyclability, etc.) of design in an attempt to bring these issues into the early stages of a design process. Additionally, within the aerospace community there has been a shift in focus from a design solely for performance to a design that emphasizes the vehicle's economic viability. This shift in focus intensified the need for an accurate cost estimation in the early stages of design. In this paper these two areas are integrated through the evaluation of the impact of supportability issues on the overall economic viability of a High Speed Civil Transport. The first step involved making modifications to an aircraft cost estimation tool called Aircraft Life Cycle Cost Analysis (ALCCA). ALCCA originally contained a module that calculates the direct cost associated with line maintenance labor, material, and burden. However, the tool did not account for the cost associated with heavy maintenance visits and supporting new technologies, nor did it account for the revenue loss associated with aircraft downtime due to both preventive as well as corrective maintenance. Modules were added to the ALCCA code which represent each of these areas of supportability concerns and, using the modified version of ALCCA, the impact of supportability for the HSCT on the overall economic viability was evaluated. The results of this analysis represent an initial step towards understanding the impact of supportability on economic viability.
A Stochastic Approach to Multi-disciplinary Aircraft Analysis and Design

(Georgia Institute of Technology, 1998-01) Mavris, Dimitri N. ; DeLaurentis, Daniel A. ; Bandte, Oliver ; Hale, Mark A.

Within the context of multi-disciplinary aircraft analysis and design, a new approach has been formulated and described which allows for the rapid technical feasibility and economic viability assessment of multi- attribute, multi-constrained designs. The approach, referred to here as Virtual Stochastic Life Cycle Design, facilitates the multi-disciplinary consideration of a system, accounting for life-cycle issues in a stochastic fashion. The life-cycle consideration is deemed essential in order to evaluate the emerging, all encompassing system objective of affordability. The stochastic treatment is employed to account for the knowledge variation/uncertainty that occurs in time through the various phases of design. Variability found in the treatment of assumptions, ambiguous requirements, code fidelity (imprecision), economic uncertainty, and technological risk are all examples of categories of uncertainty that the proposed probabilistic approach can assess. For cases where the problem is over-constrained and a feasible solution is not possible, the proposed method facilitates the identification and provides guidance in the determination of potential barriers which will have to be overcome via the infusion of new technologies. The specific task of examining system feasibility and viability is encapsulated and outlined in a series of easy to follow steps. Finally, the method concludes with a brief description and discussion of proposed decision making techniques to achieve optimal designs with reduced variability. This decision making is achieved through a combined utility theory and Robust Design Simulation approach.
A Probabilistic Approach to Multivariate Constrained Robust Design Simulation

(Georgia Institute of Technology, 1997-10) Mavris, Dimitri N. ; Bandte, Oliver

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. The paper critically reviews the combined Response Surface Equation/ Monte Carlo Simulation methodology and compares it against the Advanced Mean Value (AMV) method, one of several Fast Probability Integration techniques. Both methods are used to generate cumulative distribution functions, which are being compared in an example case study, employing a High Speed Civil Transport concept. Based on the outcome of this study, an assessment and comparison of the analysis effort and time necessary for both methods is performed. The Advanced Mean Value method shows significant time savings over the Response Surface Equation/Monte Carlo Simulation method, and generally yields more accurate CDF distributions. The paper also illustrates how by using the AMV method for distribution generation, robust design solutions to multivariate constrained problems may be obtained. These robust solutions are optimizing the objective function for a given level of risk the decision maker is willing to take.
Comparison of Two Probabilistic Techniques for the Assessment of Economic Uncertainty

(Georgia Institute of Technology, 1997-05) Mavris, Dimitri N. ; Bandte, Oliver

Several approaches to probabilistic 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 uncertainty of assumptions and the circumstances of operating environments of the future aircraft. The approach previously proposed by the authors, linking Response Surface Methodology with Monte Carlo Simulations, has revealed itself to be inadequate for multi-constraint, multi-objective problems. In addition accuracy problems were observed that could not be resolved with the methodology. Hence, this paper proposes an alternate approach to probabilistic design, which is based on a Fast Probability Integration (FPI) technique. The paper critically reviews the combined Response Surface Equation/ Monte Carlo Simulation methodology and compares it against the Advanced Mean Value (AMV) method, one of several Fast Probability Integration techniques. The Advanced Mean Value method is a probability estimation method based on a Most Probable Point (MPP) analysis. The paper describes the method employed to identify the Most Probable Point and obtain a cumulative probability distribution. The resulting distribution function is compared to the one generated by the Response Surface Equation/Monte Carlo Simulation method. For this comparison a case study is formulated, employing a High Speed Civil Transport concept. Based on the outcome of this study an assessment and comparison of the analysis effort and time necessary for both methods is performed. If the Most Probable Point can be found efficiently, the Advanced Mean Value method shows significant time savings over the Response Surface Equation/Monte Carlo Simulation method, and generally yields more accurate CDF distributions.
Application of Probabilistic Methods for the Determination of an Economically Robust HSCT Configuration

(Georgia Institute of Technology, 1996-09) Mavris, Dimitri N. ; Bandte, Oliver ; Schrage, Daniel P.

This paper outlines an approach for the determination of economically viable robust design solutions using the High Speed Civil Transport (HSCT) as a case study. Furthermore, the paper states the advantages of a probability based aircraft design over the traditional point design approach. It also proposes a new methodology called Robust Design Simulation (RDS) which treats customer satisfaction as the ultimate design objective. RDS is based on a probabilistic approach to aerospace systems design, which views the chosen objective as a distribution function introduced by so called noise or uncertainty variables. Since the designer has no control over these variables, a variability distribution is defined for each one of them. The cumulative effect of all these distributions causes the overall variability of the objective function. For cases where the selected objective function depends heavily on these noise variables, it may be desirable to obtain a design solution that minimizes this dependence. The paper outlines a step by step approach on how to achieve such a solution for the HSCT case study and introduces an evaluation criterion which guarantees the highest customer satisfaction. This customer satisfaction is expressed by the probability of achieving objective function values less than a desired target value.
Effect of Mission Requirements on the Economic Robustness of an HSCT Concept

(Georgia Institute of Technology, 1996-06) Mavris, Dimitri N. ; Bandte, Oliver ; Schrage, Daniel P.

Design for robustness and its subset design for economic robustness and viability are two areas in current design methodology and optimization research attracting a lot of attention, as the increasing number of publications and industry position papers in this field indicate. In fact, these publications attempt to address the paradigm shift taking place in industry, where design for performance is being replaced by design for affordability. That is designing and optimizing a system for a high yield while reducing the variation from that optimum yield. The study presented here can be viewed as a proof of concept for a proposed approach to design for robustness, called Robust Design Simulation (RDS). The paper outlines an alternative approach to Taguchi's, assigning probability distributions to uncontrollable factors (noise variables) which result in a distribution for the design objective instead of a point solution. The study also illustrates that indeed one is able to manipulate the mean and variance of the design objective concurrently, hence, optimizing a new Overall Evaluation Criterion (OEC) that is comprised of both the mean and variance of the design objective. The High Speed Civil Transport (HSCT) was utilized as an illustrative case to demonstrate the implementation of RDS. The objective of this case study is to show and quantify the effects of mission and aircraft sizing parameters on the mean and variance of direct and total operating cost as well as the required average yield per revenue passenger mile ($/RPM). Finally, the optimal mission requirement settings which yield an OEC that concurrently minimizes the mean $/RPM as well as its variance are identified for the HSCT configuration studied.
A Method for the Identification and Assessment of Critical Technologies Needed for an Economically Viable HSCT

(Georgia Institute of Technology, 1995-09) Mavris, Dimitri N. ; Bandte, Oliver ; Brewer, Jason T.

Researchers from the Aerospace Systems Design Laboratory (ASDL) at the School of Aerospace Engineering at Georgia Tech have been developing over the past three years a comprehensive methodology for the integration of aircraft design and manufacturing. NASA's High Speed Civil Transport (HSCT) concept has been selected as a pilot project for this study because of its potential global transportation payoffs and impact on U.S. world competitiveness. The proposed methodology is based on a Concurrent Engineering/ IPPD approach, and, in this case, is specifically applied to the design of an HSCT. The procedure employs the use of a Design of Experiments approach to facilitate the development of Response Surface Equations which capture the essence of sophisticated, computationally intense disciplinary analyses tools and replace them by simple second order polynomial equations. Since this aircraft has to be economically competitive to current subsonic transports, emphasis has been given throughout this study on understanding and assessing its economic viability. The determination of this objective is based on the required average yield per Revenue Passenger Mile ($/RPM), a metric that captures the concerns of all interested parties. The latest developments of ASDL's new methodology for the design of such affordable and reliable aircraft are outlined in this paper. However, the main objective of this paper is to describe the overall approach from concept formulation to concept feasibility and the identification and assessment of all possible means of achieving economic viability. Finally, different means of improving the economic viability of a hypothetical HSCT are examined, and their relative impact is quantified.
Economic Uncertainty Assessment Using a Combined Design of Experiments/Monte Carlo Simulation Approach with Application to an HSCT

(Georgia Institute of Technology, 1995-05) Mavris, Dimitri N. ; Bandte, Oliver

Recently, the aerospace industry has felt the impact of the combined effect of increasing aircraft systems costs and budget restrictions and is reacting through a series of initiatives to help minimize their overall Life Cycle Costs. These growing concerns have prompted the appearance of various risk assessment and reduction techniques. These techniques have been incorporated into a Robust Aircraft Design Simulation methodology which is based on an Integrated Product and Process Development (IPPD) approach. This IPPD environment accounts for the effects of each discipline (i.e. aerodynamics, structures, propulsion, producibility, supportability, etc.) and their corresponding technological advances on the overall system evaluation criterion, the average yield per revenue passenger mile. This paper reviews this IPPD methodology by describing the techniques on which it is based, such as the Design of Experiments, Response Surface Methods, and Monte Carlo Simulation, and illustrates the steps taken for its implementation for the economic uncertainty assessment of a High Speed Civil Transport vehicle.