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Daniel Guggenheim School of Aerospace Engineering

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End date: 2017 × Start date: 2017 × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: Aerospace Systems Design Laboratory (ASDL) × search.filters.applied.f.isOrgUnitOfPublicationTitle: Daniel Guggenheim School of Aerospace Engineering ×

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A Categorical Model for Airport Capacity Estimation Using Hierarchical Clustering

2017-12 , Cinar, Gokcin , Jimenez, Hernando , Mavris, Dimitri N.

Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditions airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7-10%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate other such models with different data sets is encouraged.

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A risk-value-based methodology for enterprise-level decision-making

2017-07-31 , Burgaud, Frederic

Despite its long lasting existence, aerospace remains a non-commoditized field. To sustain their market domination, the major companies need to commit to large capital investments and constant innovation, in spite of multiple sources of risk and uncertainty, and significant chances of failure. This makes aerospace programs particularly risky. However, successful programs more than compensate the costs of disappointing ones. In order to maximize the chances of a favorable outcome, a business-driven, multi-objective, and multi-risk approach is needed to ensure success, with particular attention to financial aspects. Additionally, aerospace programs involve multiple divisions within a company. Besides vehicle design, finance, sales, and production are crucial disciplines with decision power and influence on the outcome of the program. They are also tightly coupled, and the interdependencies existing between these disciplines should be exploited to unlock as much program-level value potential as possible. An enterprise-level approach should, therefore, be used. Finally, suborbital tourism programs are well suited as a case study for this research. Indeed, they are usually small companies starting their projects from scratch. Using a full enterprise-level analysis is thus necessary, but also more easily feasible than for larger groups. These motivations lead to the formulation of the research objective: to establish a methodology that enables informed enterprise-level decision-making under uncertainty and provides higher-value compromise solutions. The research objective can be decomposed into two main directions of study. First, current approaches are usually limited to the design aspect of the program and do not provide the optimization of other disciplines. This ultimately results in a de-facto sequential optimization, where principal-agent problems arise. Instead, a holistic implementation is proposed, which will enable an integrated enterprise-level optimization. The second part of this problem deals with decision-making with multiple objectives and multiple risks. Current methods of design under uncertainty are insufficient for this problem. First, they do not provide compelling results when several metrics are targeted. Additionally, variance does not properly fit the definition of risk, as it captures both the upside and downside uncertainty. Instead, the deviation of the Conditional Value at Risk (called here downside deviation) is used as a measure of value risk. Furthermore, objectives are categorized and aggregated into risk and value scores to facilitate convergence, visualization, and decisionmaking. As suborbital vehicles are complex non-linear systems, with many infeasible concepts and computationally expensive M&S environments, a time-efficient way to estimate the downside deviation needs to be used. As such, a new uncertainty propagation structure is used that involves regression and classification neural networks, as well as a Second-Order Third-Moment (SOTM) technique to compute statistical moments. The proposed process elements are combined, and integrated into a method following a modified Integrated Product and Process Development (IPPD) approach, using five main steps: establishing value, generating alternatives, evaluating alternatives, and making decisions. A new M&S environment is implemented and involves a design framework to which several business disciplines are added. A bottom-up approach is used to study the four research questions of this dissertation. At the lowest level of the implementation, an enhanced financial analysis is evaluated. Common financial valuation methods used in aerospace have heavy limitations: all of them rely on a very arbitrary discount rate despite its critical impact on the final value of the NPV. The proposed method provides detailed analysis capabilities and helps capture more value by enabling the optimization of the company’s capital structure. A sensitivity analysis also verifies the importance of the added factors in the proposed method. The second implementation step is to time-efficiently evaluate downside deviation. As such, regression and classification neural networks are implemented to estimate the base costs of the vehicle and speed up the vehicle sizing process. Business analyses are already time-efficient and therefore maintained. These neural networks ultimately show good validation prediction root-mean-square error (RMSE), which confirms their accuracy. The SOTM method is also checked and shows a downside deviation prediction accuracy equivalent to a 750-point Monte Carlo method. From a computation time standpoint, the use of neural networks is required for a reasonable convergence time, and the SOTM used jointly with neural networks results in an optimization time below 1 hour. The proposed approach for making risk/value trade-offs in the presence of multiple risks and objectives is then tested. First, the importance of using downside deviation is demonstrated by showing the risk estimation error made when using the standard deviation rather than the actual downside deviation. Additionally, the use of risk and value scores also helps decision-making from a qualitative and quantitative point of view. Indeed, it facilitates visualization by supplying a two-dimensional Pareto frontier, while still being able to color it to observe program features and cluster patterns. Furthermore, the problem with risk and value scores provides more optimal solutions, compared to the non-aggregated case, unless very large errors in weightings are committed. Finally, the proposed method provides good capabilities for identifying, ranking, and selecting optimal concepts. The last research question presents the following interrogation: does an enterpriselevel approach help improve the optimality of the overall program, and does it result in significantly different decision-making? Two elements of the enterprise-level approach are tested: the integrated optimization, and the use of additional enterprise-level objectives. In both cases, the resulting Pareto frontiers are significantly dominating their counterparts, demonstrating the usefulness of the enterprise-level approach from a quantitative point of view. It also shows that the enterprise-level approach results in significantly different decisions, and should, therefore, be applied early in the design process. Hence, the method provided the capabilities sought in the research objective. This research resulted in contributions in the financial analysis of aerospace programs, in design under multiple sources of uncertainty with multiple objectives, and in design optimization by proposing the adoption of an enterprise-level approach.

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Sizing, Integration and Performance Evaluation of Hybrid Electric Propulsion Subsystem Architectures

2017-06 , Cinar, Gokcin , Mavris, Dimitri N. , Emeneth, Mathias , Schneegans, Alexander , Riediger, Carsten , Fefermann, Yann , Isikveren, Askin

This paper presents a methodology for the sizing and synthesis of power generation and distribution (PG&D) subsystems. The PG&D subsystem models developed in a previous work done by the authors were applied within a parallel hybrid electric propulsion architecture using the Dornier 328 as the baseline aircraft. The hybridization took place only during the cruise segment. Analyses were performed in Pacelab SysArc, a system architecture design tool, to assess the impact of different hybrid electric propulsion architectures and changing PG&D subsystem characteristics at aircraft and mission levels. To this end, sensitivity analysis was conducted to reveal the sensitivity to the subsystem level characteristics. Moreover, six different architectures were compared in terms of their mission level performance. These architectures included the PG&D subsystems with current state of the art technology, NASA 15-year technology goals and a more advanced battery technology. Although neither the current state of the art PG&D subsystems nor NASA 15-year technology goals were advanced enough to match the design range requirement of the baseline aircraft, some of the competing architectures met the practical range target while enjoying substantial amount of fuel reductions. Finally, it was observed that in order to reach a break-even point in terms of the design mission range, a battery specific energy of 5 kWh/kg was necessary for a 50% level of hybridization during cruise. In this work the Dornier 328 was used as a testbed, however the methodology can be generalized for all parallel hybrid electric propulsion applications.

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Identifying Instantaneous Anomalies in General Aviation Operations

2017-06 , Mavris, Dimitri N. , Puranik, Tejas G.

Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, data mining techniques are emerging as an important enabler in the aviation safety domain with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. There are two types of anomalies typically identified - flight-level (where the entire flight exhibits patterns deviating from nominal operations) and instantaneous (where a subset or few instants of the flight deviate significantly from nominal operations). Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis using energy-based metrics is proposed. Each data record is split by sliding a moving window across the multi-variate series of evaluated energy metrics. A mixture of gaussian models is then used to perform clustering using the values of energy metrics and their variability within each window. The trained models are then used to identify anomalies that may indicate increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment (exceedance detection).

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A methodology for risk-informed launch vehicle architecture selection

2017-11-13 , Edwards, Stephen James

Modern society in the 21st century has become inseparably dependent on human mastery of the near-Earth regions of space. Billions of dollars in on-orbit assets provide a set of fundamental, requisite services to such diverse domains as telecom, military, banking, and transportation. While orbiting satellites provide these services, launch vehicles (LVs) are unquestionably the most critical piece of infrastructure in the space economy value chain. The past decade has seen a significant level of activity in LV development, including some fundamental changes to the industry landscape. Every space-faring nation is engaged in new program developments; most notable, however, is the surge in commercial investments and development efforts, which has been spurred by a combination of private investments by wealthy individuals, new government policies and acquisition strategies, and the increased competition that has resulted from both. In all the LV programs of today, affordability is acknowledged as the single biggest objective. Governments seek assured access to space that can be realized within constrained budgets, and commercial entities vie for survival, profitability, and market-share. From literature, it is clear that the biggest opportunity for affecting affordability resides in improving decision-making early on in the design process. However, a review of historical LV architecture studies shows that very little has changed over the past 50 years in how early architecting decisions are analyzed. In particular, architecture analyses of alternatives are still conducted deterministically, despite uncertainty being at its highest in the very early stages of design. This thesis argues that the ``design freedom'' that exists early on manifests itself as volitional uncertainty during the LV architect's deliberation, motivating the objective statement ``to develop a methodology for enabling risk-informed decision making during the architecture selection phase of LV programs.'' NASA's Risk-Informed Decision Making process is analyzed with respect to the particulars of the LV architecture selection problem. The most significant challenge is found to be LV performance modeling via trajectory optimization, which is not well suited to probabilistic analysis. To overcome this challenge, an empirical modeling approach is proposed. However, this in turn introduces the challenge of generalizing the empirical model, as creating distinct performance models for every architecture concept under consideration is considered infeasible. A review of the main drivers in LV trajectory performance observes T/W not only to be one of the parameters with most sensitivity, but also reveals it to be a functional in its true form. Based on the performance-driving nature of the T/W profile, and the fact that in its infinite-dimensional form it offers a common basis for representing diverse architectures, functional regression techniques are proposed as a potential means of constructing an architecture-spanning empirical performance model. A number of techniques are formulated and tested, and prove capable of supporting the LV performance modeling in support of risk-informed architecture selection.

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Methodology for interoperability-enabled adaptable strategic fleet mix planning

2017-07-28 , Bernstein, Shai

Fleet sizing and mix problems, together with fleet mix scheduling problems, are frequently used to plan acquisitions over multiple time periods. However, no fleet sizing and mix problems have assessed the impact of interoperability between assets on the fleet purchasing decision. A challenge to investigating this question is the inability of many such methods to address a more generalized fleet planning problem: one in which fleets that have multi-mission assets and problem scenarios in which mission modeling at the operational level involves assets numbers that are orders of magnitude lower than at the strategic acquisitions level. Furthermore, in strategic decision making environments that are characterized as volatile, uncertain, complex, and ambiguous, prior approaches frequently do not take into account whether a decision set is adaptable to changing mission or budget priorities. In this work, a methodology is created to allow the investigation of the effects of interoperability on the fleet purchasing decision by first addressing the gaps in prior methods. A fleet scaling method is developed in order to bridge the gap between operational-level missions and strategic-level fleets in a way that is computationally inexpensive. Next, a discussion regarding how best to capture trade-offs associated with the adaptability of fleet plans leads to the adaptation of a method from decision-theory literature to the problem. Finally, requirements and criteria for capturing the effects of interoperability modeling are created. This methodology, which serves as an initial framework for assessing this large problem, is instantiated with existing methods where possible to show that it does indeed enable the desired investigation to be conducted. A sample case study based on two World War II operations is used to form the basis of the multi-mission analysis, and to walk through each step of the methodology. The sample study compares the effects of interoperability on fleet plan adaptability compared to other asset design variables, the number of assets, and fleet cost. Interoperability is shown to be a significant enough effect in this simple example that there is evidence for including it in future assessments of fleet plan adaptability. Furthermore, the usefulness of a unified fleet plan adaptability methodology that can account for asset requirements, mission capability, and budget and mission preference uncertainty, is demonstrated.

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Integrated Sizing and Multi-objective Optimization of Aircraft and Subsystem Architectures in Early Design

2017-06 , Rajaram, Dushhyanth , Cai, Yu , Chakraborty, Imon , Puranik, Tejas G. , Mavris, Dimitri N.

The aerospace industry's current trend towards novel or More Electric architectures results in some unique challenges for designers due to both a scarcity or absence of historical data and a potentially large combinatorial space of possible architectures. These add to the already existing challenges of attempting to optimize an aircraft design in the presence of multiple possible objective functions while avoiding an overly compartmentalized approach. This paper uses the Integrated Subsystem Sizing and Architecture Assessment Capability to pursue a multi-objective optimization for a Large Twin-aisle Aircraft and a Small Single-aisle Aircraft using the Non-dominated Sorting Genetic Algorithm II with parallel function evaluations. One novelty of the optimization setup is that it explicitly considers the impacts of subsystem architectures in addition to those of traditional aircraft-level design variables. The optimization yielded generations of non-dominated designs in which substantially electrified subsystem architectures were found to predominate. As a first assessment of the impact of epistemic uncertainty on the results obtained, the optimization was re-run with altered sensitivities for the thrust-specific fuel consumption penalties due to shaft-power and bleed air extraction. This analysis demonstrated that the composition of architectures on the Pareto frontier is sensitive to the secondary power extraction penalties, but more so for the Small Single-aisle Aircraft than the Large Twin-aisle Aircraft.

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Integrated architecture analysis and technology evaluation for systems of systems modeled at the subsystem level

2017-11-13 , Trent, Douglas James

A lack of knowledge during conceptual design results in two primary challenges: overruns in cost and schedule due to frequent design changes and combinatorial explosion of alternatives due to large, discrete categorical design spaces. Due to the significant impact subsystem-level technologies have on the cost and schedule of a design, they should be considered during the conceptual design of systems of systems in an effort to reduce this lack of knowledge. To integrate architecture analysis and technology evaluation at the subsystem level, several questions and hypotheses are posed during a discussion of a general concept exploration process to guide the development of a new framework. The Dynamic Rocket Equation Tool (DYREQT) and a collection of subsystem-level in-space transportation models were developed to provide a modeling and simulation environment capable of producing the necessary data for experimentation. DYREQT provides the capability to integrate user-developed subsystem models for space transportation architecture analysis and design. Results from the experiments led to conclusions which guided the definition of the Integrated Architecture and Technology Exploration (IntegrATE) framework. This new framework enables integrated architecture analysis and technology evaluation at the subsystem level in an effort to increase design knowledge during the conceptual design process. IntegrATE provides flexibility such that it can be tailored to a wide range of problems. It also provides a high degree of transparency throughout to help reduce the likelihood of bias towards individual architectures or technologies. Finally, the IntegrATE framework and DYREQT were demonstrated on a notional manned Mars 2033 design study to highlight the utility of these new developments.

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A Comprehensive Energy Monitoring Environment for District Energy Grid Systems

2017-07 , Lewe, Jung-Ho , Duncan, Scott J. , Song, Kisun , Oh, Sehwan , Solano, David , Yarbasi, Efe Y. , Ahuja, Jai , Johnston, Hunter B. , Mavris, Dimitri N.

By conducting active meter monitoring and performance analysis for the buildings and the plants at the main campus of the Georgia Institute of Technology, it is possible for campus facilities managers to achieve significant efficiency improvements. A key challenge, however, is gathering and making sense of the large volumes of utilities data. In response, a comprehensive web-based building and plant energy-monitoring environment is presented that collects data from multiple energy grids. From the gathered data, particular attention is given to heating, cooling, and ventilation to assess building and ultimately campus energy performance through various analytics. First, techniques for data gathering, organization, and filtering are described, followed by several novel metrics and ways of visualizing them via a comparative method. Data filtering and classification strategies have also been implemented into a framework capable of evaluating a fleet of buildings with respect to a data-driven or model-driven baseline. The resulting monitoring system is shown to reduce the number of variables that campus managers of campus utilities and facilities need to track and make it more obvious where energy efficiency opportunities exist across a large fleet of buildings. Implications and future extensions of the monitoring platform are discussed.

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A Simulation-Based Framework for Structural Loads Assessment during Dynamic Maneuvers

2017-06 , Goron, Gael , Duca, Ruxandra , Sarojini, Darshan , Shah, Somil R. , Chakraborty, Imon , Briceno, Simon , Mavris, Dimitri N.

Federal Aviation Regulations pertaining to structural integrity are key drivers in aircraft design and certification, and often involve critical loads occurring during dynamic maneuvers. In the context of increasing costs of testing and the general trend towards parametric design, there is a need for a more thorough consideration of such dynamic load cases earlier in the design process. In this work, a simulation framework is introduced to assess structural requirements stemming from such dynamic load conditions. Relevant aspects of the dynamics of the aircraft, the control system, and the pilot are modeled in order to simulate the maneuver and thereafter obtain inertial and aerodynamic loads on the empennage during the simulated maneuver. The loads are then translated into structural shear forces and bending moments through structural post-processing routines. This approach is demonstrated for the case of a representative business jet during the checked pitch maneuver. The analyses are repeated for three weight conditions and over the flight envelope for the aircraft from which the load cases resulting in the most constraining loads are determined.

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