Aerospace Systems Design Laboratory

Allenstown Elementary School, United States

Aerospace Systems Design Laboratory

Allenstown Elementary School, United States
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Mavris D.N.,Georgia Institute of Technology | Mavris D.N.,Aerospace Systems Design Laboratory | Griendling K.,Georgia Institute of Technology | Griendling K.,Research Engineer II | Dickerson C.E.,Loughborough University
Journal of Aircraft | Year: 2013

In this paper, a new framework for performing early technology tradeoff and design studies, the relational-oriented systems engineering and technology tradeoff analysis framework, is developed and applied to an initial case study to conduct a trade between two candidate technologies for potential application on a commercial jet Relational-oriented systems engineering and technology tradeoff analysis leverages the relational-oriented systems engineering methodology coupled with the exploitation of transformations used in modeling and simulation to create a direct association between the quality function deployment methodology and standard quantitative conceptual design space exploration techniques leveraged in technology forecasting and trade studies. This association brings precision to quality function deployment that is model driven and mathematically founded. The approach highlights key deficiencies in quality function deployment when applied to early phase design and technology tradeoff studies for the development of systems. Relational-oriented systems engineering and technology tradeoff analysis proposes a more rigorous and generalized mathematical framework for conducting generic quality-function- deployment-type exercises to support decision-making in early systems engineering and design, and the advantages of the relationaloriented systems engineering and technology tradeoff analysis framework are demonstrated through the application of relational-oriented systems engineering and technology tradeoff analysis to a small-scale aerospace technology tradeoff. Relational-oriented systems engineering and technology tradeoff analysis provides a means to begin to formalize and strengthen the relationship between quality function deployment, modeling and simulation, and theoretical mathematics, and it allows translation between these three approaches to engineering problems. © 2013 by Dimitri Mavris, Kelly Griendling, and Charles Dickerson. Publishedbythe American Institute of Aeronautics and Astronautics, Inc.,.

Gatian K.N.,Georgia Institute of Technology | Gatian K.N.,Aerospace Systems Design Laboratory | Mavris D.N.,Georgia Institute of Technology | Mavris D.N.,Director Aerospace Systems Design Laboratory
15th AIAA Aviation Technology, Integration, and Operations Conference | Year: 2015

When aggressive performance goals for next generation aircraft systems are set, technology development programs must select the appropriate sub-set of technologies to achieve them. Evaluating technologies with respect to their performance when they are not fully developed is diflcult because the results are the result of a forecasting exercise. Furthermore, the uncertainty surrounding each technology's performance should be investigated to provide a clear picture to decision makers. This research provides processes for technology portfolio formulation and selection based upon quantitative, probabilistic performance assessments that incorporate advanced technology forecasting and uncertainty quantification techniques. The processes were tested on an environmentally-motivated case study through the use of a physics-based, aircraft design and analysis tool. The results of the implementation show that the impact a technology has on system performance metrics can be used for technology prioritization. Furthermore, it is demonstrated that the probabilistic analysis procedure outlined for portfolio evaluation enables portfolio down-selection. © 2015, American Institute of Aeronautics and Astronautics Inc.

Garmendia D.C.,Georgia Institute of Technology | Garmendia D.C.,Aerospace Systems Design Laboratory | Mavris D.N.,Georgia Institute of Technology | Mavris D.N.,Aerospace Systems Design Laboratory
Journal of Aircraft | Year: 2016

Conventional trim optimization problem formulations can produce poor results for vehicles with redundant multiaxis control surfaces. Trimming these vehicles requires solving the nonlinear equations of motion for their roots or equilibrium points. The hybrid wing-body vehicle has many redundant elevons and tends to result in an underdetermined system. As a consequence, there may be an infinite number of deflection combinations that satisfy the same moments. Challenges associated with using conventional trim optimizations with this type of vehicle are demonstrated, followed by two alternative approaches to relieve these problems. Control allocation techniques are integrated into the conventional approach to reduce the number of variables and attempt to make the problem more convex. A reformulation of the trim optimization from root seeking to drag minimization was also explored. These alternative methods were judgedonhow often they converged,trimmed drag and thrust, and commanded deflections. Copyright © 2015 by Daniel C. Garmendia and Dimitri N. Mavris. Published by the American Institute of Aeronautics and Astronautics, Inc.

Jimenez H.,Georgia Institute of Technology | Jimenez H.,Aerospace Systems Design Laboratory | Pfaender H.,Georgia Institute of Technology | Pfaender H.,Aerospace Systems Design Laboratory | Mavris D.,Georgia Institute of Technology
Journal of Aircraft | Year: 2012

A method for assessing the impact of vehicle technologies and new aircraft concepts at the fleet level is presented. Various aspects of the method constitute a departure from standard practice intended to address known shortcomings and advance the state of the art. In particular, an operational activity growth routine is implemented where operational sets are grown to match top-level and airport-level forecasts that are fully balanced for all origin- destination airport pairs. The proposed approach also features a novel fleet evolution scheme where replacements are devised on the basis of mission capabilities for aircraft types rather than on seat classes, and the retirement of aircraft is applied to models introduced beyond the reference year. Surrogate models of fuel burn are regressed from gold-standard modeling tools and are shown to be suitable for system-wide assessments on the basis of model representation accuracy. Fleet assessment results are shown for various technology introduction and growth scenarios and indicate that an introduction of NASA vehicle technologies across the fleet in 2025 can yield up to 436:05 × 109 kg of fuel savings through 2050, or up to 94.6 million additional operations for that same period assuming a 2050 total fuel-burn cap at 2006 levels.

Bernardo J.E.,Georgia Institute of Technology | Kirby M.,Georgia Institute of Technology | Kirby M.,Aerospace Systems Design Laboratory | Mavris D.,Georgia Institute of Technology | Mavris D.,Aerospace Systems Design Laboratory
50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition | Year: 2012

Future air transportation demand forecasts suggest that environmental concerns such as noise will be exacerbated beyond their current level. Although detailed airport noise modeling is available with the current fleet, a rapid, flexible, and more generic method is desired to evaluate fleet-level metrics with respect to new technologies or forecasted changes in demand. Although some work has been done to this extent, only the area of the 65 dB contour has been considered as the relevant metric. These methods cannot account for the shape of the contour, which has far-reaching implications for the ultimate metric: population affected. Moreover, the 65 dB contour as the lone contour of interest is a paradigm that, recently, has begun shifting to include other contour levels. This paper presents a generic fleet-level noise methodology that leverages the fidelity of detailed modeling software. By performing generic aircraft operations up front, these events can be rapidly recombined later to perform trades of various noise mitigation strategies. By moving the detailed noise modeling 'off-line' and making the appropriate assumptions, some of the fidelity of these models can be propagated earlier into the decision-making process. The methodology was used to demonstrate two simple proofs of concept that evaluate the method by accuracy and process criteria. Finally, discussions for validation of assumptions and future work to improve accuracy are included. Copyright © 2012 by Jose E. Bernardo.

Pfaender H.,Georgia Institute of Technology | Mavris D.,Aerospace Systems Design Laboratory
12th AIAA Aviation Technology, Integration and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference | Year: 2012

This paper explores the effects of fuel prices on aviation technology and environmental outcomes. The concern is that significant efficiency improvements could potentially increase the environmental impact of aviation instead of reducing it by reducing costs and also increasing economic activity at the same time. Here we explore this effect known as Jevons' Paradox, by exploring the dynamic interplay of efficiency, demand feedback, airline decision making, and aircraft technologies. The results produced through dynamic modeling show that it seems unlikely that this effect would altogether overwhelm efficiency gains. However, we find that the effect does reduce vehicle efficiency gains at the system level, not only due to extensive fleet turn-over times, but also due to demand bounce back effects. © 2012 by Holger Pfaender and Dimitri Mavris.

Silva-Martinez J.,Georgia Institute of Technology | Silva-Martinez J.,Aerospace Systems Design Laboratory | Schrage D.,Georgia Institute of Technology
52nd AIAA Aerospace Sciences Meeting - AIAA Science and Technology Forum and Exposition, SciTech 2014 | Year: 2014

This paper is a continuation of the analysis performed in support of the development of a Collaborative Aerospace Lifecycle Systems Engineering Master's Program (CALSEMP). CALSEMP addresses six focus areas identified by the NDIA including systems engineering trade study and design decision methodologies; system integration, assembly, and test analyses modeling; enterprise level supply chain design; electrical, mechanical, and assembly yield modeling; quantitative analyses; and life cycle cost modeling. The proposed research master's program is divided into five variants designed to meet CALSEMP objectives. Those variants are explored and analyzed at Georgia Tech using the integrated product and process design approach to systems engineering, with qualitative and quantitative tools, such as the house of quality, morphological matrix, Pugh and TOPSIS methods. Through the systems engineering plan presented to aid with the execution of this proposed program and its variants, we hope to enable aerospace industries to compete on the global aerospace market by providing them with tools that help them integrate more tightly manufacturing, producibility, life cycle costs, and large scale system integration concerns from the beginning of the aerospace design process.

Lee K.,Georgia Institute of Technology | Lee K.,Aerospace Systems Design Laboratory | Mavris D.N.,Georgia Institute of Technology | Mavris D.N.,Aerospace Systems Design Laboratory
AIAA Journal | Year: 2010

In aerospace engineering, various problems such as restoring impaired experimental flow data can be handled by gappy proper orthogonal decomposition. Similar to gappy proper orthogonal decomposition, probabilistic principal component analysis can approximate missing data with the help of an expectation-maximization algorithm, yielding an expectation-maximization algorithm for probabilistic principal component analysis (expectation- maximization principal component analysis). Although both gappy proper orthogonal decomposition and expectationmaximization principal component analysis address the same missing-data-estimation problem, their antithetical formulation perspectives hinder their direct comparison; the development of the former is deterministic, whereas that of the latter is probabilistic. To effectively differentiate both methods, this research provides a unifying leastsquares perspective to qualitatively dissect them within a unified least-squares framework. By virtue of the unifying least-squares perspective, gappy proper orthogonal decomposition and the expectation-maximization principal component analysis turn out to be similar in that they are twofold: basis and least-squares coefficient evaluations. On the other hand, they are dissimilar because the expectation-maximization principal component analysis, unlike gappy proper orthogonal decomposition, dispenses with either a gappy norm or a proper orthogonal decomposition basis. To illustrate the theoretical analysis of both methods, numerical experiments using simple and complex data sets quantitatively examine their performance in terms of convergence rates and computational cost. Finally, comprehensive comparisons, including theoretical and numerical aspects, establish that the expectationmaximization principal component analysis is simpler and thereby more efficient than gappy proper orthogonal decomposition.

Locascio D.B.,Georgia Institute of Technology | Ramee C.L.,Georgia Institute of Technology | Cooksey K.D.,Georgia Institute of Technology | Mavris D.N.,Georgia Institute of Technology | Mavris D.N.,Aerospace Systems Design Laboratory
16th AIAA Aviation Technology, Integration, and Operations Conference | Year: 2016

A vital requirement of the modern combat environment is to gain and maintain situational awareness to facilitate effective squad-level decision making. This paper presents a part of the research undertaken by Georgia Institute of Technology (Georgia Tech) in collaboration with the Army Research Laboratory (ARL) in developing design capabilities for small unmanned aerial systems (sUAS). As part of this effort the team developed a toolset capable of creating mission-specific fixed wing aircraft assets that can be rapidly tailored and manufactured at a forward operating base. The toolset includes a physics-based analysis model to generate feasible aircraft designs from a family of designs, a decision making tool to select the optimal design for a mission, and a parametric CAD model. The CAD model accepts sizing parameters from the design algorithm and uses them to scale baseline part files, which can then be used to rapidly manufacture vehicle parts. Several sets of mission requirements were chosen, leading to unique fixed wing aircraft designs which were manufactured and flown. The process described herein can be used to develop and fabricate small unmanned airplane designs to fulfill rapidly changing squad-level mission-specific operational needs, but can also be applied to other vehicle architectures. © 2016 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

Renganathan S.A.,Georgia Institute of Technology | Renganathan S.A.,Aerospace Systems Design Laboratory | Mavrisy D.N.,Georgia Institute of Technology
51st AIAA/SAE/ASEE Joint Propulsion Conference | Year: 2015

The conceptual design of a runway-based, fully re-usable space launch system to deliver payload to 100 nautical mile Low Earth Orbit (LEO), driven by the requirements of Space Solar Power (SSP), is performed. A two-stage-to-orbit (TSTO) system is considered which includes a large supersonic carrier vehicle capable of take-off and landing on conventional runways and a hypersonic vehicle air-launched from the carrier vehicle, capable of accelerating to orbital speeds and return to earth safely, thereby making the system re-usable and hence cost effective. The energy based constraint analysis by Mattingly1 is used to size the carrier vehicle while local surface inclination methods2 are used to size the hypersonic vehicle. An unified environment that can rapidly size the TSTO system is developed. This paper presents the preliminary results of the environment which is undergoing ongoing development. Focus is laid more on the analytical representation of the hypersonic vehicle shape and its drag estimation which is critical in the conceptual design of the vehicle. The propulsion system feasibility for the launch system is also discussed. © 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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