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Aerospace Systems Design Laboratory (ASDL)

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https://hdl.handle.net/1853/70744

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Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1135

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Now showing 1 - 10 of 11

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.
Elements of an Emerging Virtual Stochastic life Cycle Design Environment

(Georgia Institute of Technology, 1999-10) Mavris, Dimitri N. ; DeLaurentis, Daniel A. ; Hale, Mark A. ; Tai, Jimmy C. M.

The challenge of designing next-generation systems that meet goals for system effectiveness, environmental compatibility, and cost has grown to the point that traditional design methodologies are becoming ineffective. Increases in the analysis complexity required, the number of objectives and constraints to be evaluated, and the multitude of uncertainties in today? design problems are primary drivers of this situation. A new environment for design has been formulated to treat this situation. It is viewed as a testbed, in which new techniques in such areas as design-oriented/physics-based analysis, uncertainty modeling, technology forecasting, system synthesis, and decision-making can be posed as hypotheses. Several recent advances in elements of this multidisciplinary environment, termed the Virtual Stochastic Life Cycle Design Environment, are summarized in this paper.
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.
A Stochastic Design Approach for Aircraft Affordability

(Georgia Institute of Technology, 1998-09) Mavris, Dimitri N. ; DeLaurentis, Daniel A.

A novel approach to assessing aircraft system feasibility and viability over time is presented, with special emphasis on modeling and estimating the impact of new technologies. The approach is an integral part of an overall stochastic, life-cycle design process under development by the authors, which is to address the new measure for system value, affordability. Stochastic methods are proposed since the design process is immersed in ambiguity and uncertainty, both of which vary with time as knowledge increases about the system behavior. The specific task of examining system feasibility and viability is encapsulated and explained in five steps in this paper. The probabilistic approach contained in these steps is compared to more traditional, deterministic means for examining a design space and evaluating technology impacts. Finally, the techniques are implemented on an example problem to highlight the additional realism and information that is obtained. The example is based on a High Speed Civil Transport vehicle design, and is meant to illustrate the power of the technique on a current problem of significant interest to the international aerospace community.
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.
Probabilistic Assessment of Handling Qualities Constraints in Aircraft Preliminary Design

(Georgia Institute of Technology, 1998-01) Mavris, Dimitri N. ; DeLaurentis, Daniel A. ; Soban, Danielle Suzanne

A method is introduced and demonstrated which uses parametric stability derivative data (in the form of regression equations) and probabilistic analysis techniques to evaluate the impact of uncertainty on the handling qualities characteristics of a family of aircraft alternatives. While the method is based on the use of elementary design parameters familiar to the configuration designer, it enables the computation of responses more familiar to the stability and control engineer. This connection is intended to bring about a more complete accounting of stability and handling quality characteristics in aircraft design, based on engineering analysis instead of historical data. Another key advantage of the method is that it allows for the quantification of analysis imprecision and information quantity/quality trades through fidelity uncertainty models. The metrics for these quantifications are the cumulative distribution function and probability sensitivity derivatives. The method is exemplified through the investigation of the longitudinal handling qualities trends for a defined High Speed Civil Transport design space, in the presence of fidelity uncertainty in the stability derivatives.
Generating Dynamic Models Including Uncertainty for Use in Aircraft Conceptual Design

(Georgia Institute of Technology, 1997-08) DeLaurentis, Daniel A. ; Mavris, Dimitri N. ; Calise, Anthony J. ; Schrage, Daniel P.

Accurate stability and control derivative information is essential to the configuration designer. As new, non-conventional aircraft are being designed, however, the trusted stability and control estimates usually used in conceptual design may no longer be useful. Using sophisticated analysis to compute every derivative in the highly iterative design environment is not a viable approach either. This paper proposes a method for addressing this dilemma by combining experimental design techniques for model building with vortex lattice aerodynamics for analysis. The careful implementation of this method results in parametric regression equations for three important derivatives as a function of the variables of most interest to the designer (e.g. wing, tail geometry, center of gravity location, etc.). These equations are based on actual analysis and not historical trends. Finally, uncertainty associated with this method is introduced and an initial technique for analyzing the effect of such uncertainty is presented.
Reduced Order Guidance Methods and Probabilistic Techniques in Addressing Mission Uncertainty

(Georgia Institute of Technology, 1996-09) DeLaurentis, Daniel A. ; Mavris, Dimitri N. ; Calise, Anthony J. ; Schrage, Daniel P.

Recognizing that vehicle synthesis fulfills the role of integrator of the mutually interacting disciplines, difficulties persist in intelligently implementing disciplinary analysis into this synthesis process. This paper develops and describes analytical and statistical approximation techniques used to create design-oriented analyses which are implementable in the process. Specifically, techniques related to the vehicle guidance discipline are examined. The ultimate goal is to investigate the economic viability of an aerospace system in the face of uncertainty at the system and discipline design levels. The notion of a requirement is replaced by a modeling of mission variability, since future aircraft will likely fly a variety of missions. Aircraft guidance laws are key components in the mission analysis portion of an aircraft sizing code, and thus they must be included in the investigation. Through the use of statistical modeling techniques, a link between mission uncertainty, optimal guidance, wing planform, and economic objectives is obtained. This linkage allows for the investigation of guidance and mission effects on such quantities as gross weight and ticket price (on a per mile basis). Further, the resulting solutions are robust since they are obtained by choosing control parameters which maximize the probability of meeting a target while simultaneously assuring that appropriate constraints (which are also probabilistic) are met.
System Synthesis in Preliminary Aircraft Design Using Statistical Methods

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

This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).
A New Approach to Integrated Wing Design in Conceptual Synthesis and Optimization

(Georgia Institute of Technology, 1996-09) DeLaurentis, Daniel A. ; Cesnik, Carlos Eduardo Stolf ; Lee, Jae-Moon ; Mavris, Dimitri N. ; Schrage, Daniel P.

Design-oriented analysis has become increasingly important as more and more problems traditionally solved in isolation are being approached from a multidisciplinary point of view. One such problem is the aeroelastic optimization of supersonic transport wings. Whereas simplified analytical techniques may not be sophisticated enough, and complex numerical models may be too cumbersome, this paper puts forward a new approach to achieving a balance between modeling fidelity and required accuracy. Higher fidelity analysis techniques, usually associated with design stages where key geometric variables have been fixed, are used to model a design space consisting of these important geometric variables. This is accomplished through the combined use of a Design of Experiment/Response Surface Method technique and parametric analysis tools (including an automated finite element grid generation procedure). The result is a prediction method for the structural weight of an aeroelastically optimized wing for use in an Integrated Product and Process Development environment, where cost, performance, and manufacturing trades can be accomplished. The technique is to be demonstrated on the aeroelastic design of a wing for a generic High Speed Civil Transport, based on a select set of planform and airfoil design variables. Finally, a framework for evaluating new technologies within the aeroelastic optimization is outlined.