Organizational Unit:

Aerospace Systems Design Laboratory (ASDL)

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

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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

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Publication Search Results

Now showing 1 - 10 of 156

A methodology to support relevant comparisons of Earth-Mars communication architectures

(Georgia Institute of Technology, 2018-12-11) Duveiller, Florence B.

Because of the human imperative for exploration, it is very likely that a manned mission to Mars occurs by the end of the century. Mars is one of the two closest planets to Earth. It is very similar to the Earth and could be suitable to host a manned settlement. Sending humans to Mars is a technological challenge above all. Among the technologies needed, some of the most important relate to communications. Women and men on Mars need to be able to receive support from the Earth, communicate with other human beings on Earth and to send back the data collected. A reliable and continuous communication link has to be provided between Earth and Mars to ensure a safe journey to Mars. However, the communication between the Earth and Mars is challenging because of the distance between the two planets and because of the obstruction by the Sun that occurs for about 21 days every 780 days. Because of the cost of communication systems and the number of exploration missions to Mars, it has been established that a permanent communication architecture between the Earth and Mars is the most profitable option. From these observations, the research goal established for this thesis is to enable reliable and continuous communications between the Earth and Mars through the design of a permanent communication architecture. A literature review of the communication architectures between Earth and Mars revealed that a lot of concepts have been offered by different authors over the last thirty years. However, when investigating ways to compare the variety of existing architectures, it becomes very apparent that there were no robust, traceable and rigorous approach to do so. The comparisons made in the literature were incomplete. The requirements driving the design the architectures were not defined or quantified. The assumptions on which the comparisons are based were different from one architecture to another, and from one comparative study to another. As a result, all the comparisons offered were inconsistent. This thesis addresses those gaps by developing a methodology that enables relevant and consistent comparisons of Earth-Mars communication architectures and supports gap analysis. The methodology is composed of three steps. The first step consists in defining the requirements and organizing them to emphasize their interactions with the different parts of the communication system (the architecture, the hardware and the software). A study of the requirements for a deep-space communication architecture supporting manned missions is performed. A set of requirements is chosen for the present work. The requirements are mapped against the communication system. The second step consists in implementing and evaluating the architectures. To ensure the consistency, the repeatably and the transparency of the methodology developed, a unique approach enabling the assessment of all the architectures based on the same assumptions has to be provided. A framework is designed in a modeling and simulation environment for this purpose. The environment chosen for this thesis is the software Systems Tool Kit (STK) because of its capabilities. A survey of the existing architectures is performed, the metrics to evaluate the architectures are defined, and the architectures are evaluated. The third step of the methodology consists in ranking the alternatives for different weighting scenarios. Four weighting scenarios are selected to illustrate some interesting trades. The ranking of the architectures is performed through a decision-making algorithm, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results from the different weighting scenarios are discussed. They underline the incompleteness of the comparisons performed in past studies, the lack of design space exploration for Earth-Mars communication architectures and the importance of the definition of the set of requirements when designing and comparing architectures. This research provides a transparent and repeatable methodology to rank and determine the best Earth-Mars communication architectures for a set of chosen requirements. It fills several gaps in the comparison of Earth-Mars communication architectures: the lack of definition of the requirements, the lack of a unique approach to implement and assess the architectures based on the same assumptions, and the lack of a process to compare all the architectures rigorously. Before the present research, there was no robust, consistent and rigorous means to rank and quantitatively compare the architectures. The methodology not only ranks but also quantitatively compares the architectures; it can quantifies the differences between architectures for an infinite number of scenarios. It has various capabilities including ranking Earth-Mars architectures based on a chosen set of requirements, performing gap analysis and sensitivities analysis on communication technologies and protocols, and performing design space exploration on architectures. The methodology developed is demonstrated on a restricted scope, it aims at being extended.
Development and validation of 3-D cloud fields using data fusion and machine learning techniques

(Georgia Institute of Technology, 2018-12-11) Huguenin, Manon

The impact of climate change is projected to significantly increase over the next decades. Consequently, gaining a better understanding of climate change and being able to accurately predict its effects are of the upmost importance. Climate change predictions are currently achieved using Global Climate Models (GCMs), which are complex representations of the major climate components and their interactions. However, these predictions present high levels of uncertainty, as illustrated by the very disparate results GCMs generate. According to the International Panel on Climate Change (IPCC), there is high confidence that such high levels of uncertainty are due to the way clouds are represented in climate models. Indeed, several cloud phenomena, such as the cloud-radiative forcing, are not well- modeled in GCMs because they rely on miscroscopic processes that, due to computational limitations, cannot be represented in GCMs. Such phenomena are instead represented through physically-motivated parameterizations, which lead to uncertainties in cloud representations. For these reasons, improving the parameterizations required for representing clouds in GCMs is a current focus of climate modeling research efforts. Integrating cloud satellite data into GCMs has been proved to be essential to the development and assessment of cloud radiative transfer parameterizations. Cloud-related data is captured by a variety of satellites, such as satellites from NASA’s afternoon constellation (also named the A-train), which collect vertical and horizontal data on the same orbital track. Data from the A-train has been useful to many studies on cloud prediction, but its coverage is limited. This is due to the fact that the sensors that collect vertical data have very narrow swaths, with a width as small as one kilometer. As a result, the area where vertical data exists is very limited, equivalent to a 1-kilometer-wide track. Thus, in order for satellite cloud data to be compared to global representations of clouds in GCMs, additional vertical cloud data has to be generated to provide a more global coverage. Consequently, the overall objective of this thesis is to support the validation of GCMs cloud representations through the generation of 3D cloud fields using cloud vertical data from space-borne sensors. This has already been attempted by several studies through the implementation of physics-based and similarity-based approaches. However, such studies have a number of limitations, such as the inability to handle large amounts of data and high resolutions, or the inability to account for diverse vertical profiles. Such limitations motivate the need for novel approaches in the generation of 3D cloud fields. For this purpose, efforts have been initiated at ASDL to develop an approach that leverages data fusion and machine learning techniques to generate 3D cloud field domains. Several successive ASDL-led efforts have helped shape this approach and overcome some of its challenges. In particular, these efforts have led to the development of a cloud predictive classification model that is based on decision trees and integrates atmospheric data to predict vertical cloud fraction. This model was evaluated against “on-track” cloud vertical data, and was found to have an acceptable performance. However, several limitations were identified in this model and the approach that led to it. First, its performance was lower when predicting lower-altitude clouds, and its overall performance could still be greatly improved. Second, the model had only been assessed at “on-track” locations, while the construction of data at “off-track” locations is necessary for generating 3D cloud fields. Last, the model had not been validated in the context of GCMs cloud representation, and no satisfactory level of model accuracy had been determined in this context. This work aims at overcoming these limitations by taking the following approach. The model obtained from previous efforts is improved by integrating additional, higher-accuracy data, by investigating the correlation within atmospheric predictors, and by implementing additional classification machine learning techniques, such as Random Forests. Then, the predictive model is performed at “off-track” locations, using predictors from NASA’s LAADS datasets. Horizontal validation of the computed profiles is performed against an existing dataset containing the Cloud Mask at the same locations. This leads to the generation of a coherent global 3D cloud fields dataset. Last, a methodology for validating this computed dataset in the context of GCMs cloud-radiative forcing representation is developed. The Fu-Liou code is implemented on sample vertical profiles from the computed dataset, and the output radiative fluxes are analyzed. This research significantly improves the model developed in previous efforts, as well validates the computed global dataset against existing data. Such validation demonstrates the potential of a machine learning-based approach to generate 3D cloud fields. Additionally, this research provides a benchmarked methodology to further validate this machine learning-based approach in the context of study. Altogether, this thesis contributes to NASA’s ongoing efforts towards improving GCMs and climate change predictions as a whole.
A methodology for conducting design trades related to advanced in-space assembly

(Georgia Institute of Technology, 2018-12-07) Jara de Carvalho Vale de Almeida, Lourenco

In the decades since the end of the Apollo program, manned space missions have been confined to Low Earth Orbit. Today, ambitious efforts are underway to return astronauts to the surface of the Moon, and eventually reach Mars. Technical challenges and dangers to crew health and well-being will require innovative solutions. The use of In-Space Assembly (ISA) can provide critical new capabilities, by freeing designs from the size limitations of launch vehicles. ISA can be performed using different strategies. The current state-of-the-art strategy is to dock large modules together. Future technologies, such as welding in space, will unlock more advanced strategies. Advanced assembly strategies deliver smaller component pieces to orbit in highly efficient packaging but require lengthy assembly tasks to be performed in space. The choice of assembly strategy impacts the cost and duration of the entire mission. As a rule, simpler strategies require more deliveries, increasing costs, while advanced strategies require more assembly tasks, increasing time. The effects of these design choices must be modeled in order to conduct design trades. A methodology to conduct these design trades is presented. It uses a model of the logistics involved in assembling a space system, including deliveries and assembly tasks. The model employs a network formulation, where the pieces of a structure must flow from their initial state to a final assembly state, via arcs representing deliveries and assembly tasks. By comparing solutions obtained under different scenarios, additional design trades can be performed. This methodology is applied to the case of an Artificial Gravity Space Station. Results for the assembly of this system are obtained for a baseline scenario and compared with results after varying parameters such as the delivery and storage capacity. The comparison reveals the sensitivities of the assembly process to each parameter and the benefits that can be gained from certain improvements, demonstrating the effectiveness of the methodology.
A proactive safety enhancement methodology for general aviation using a synthesis of aircraft performance models and flight data analysis

(Georgia Institute of Technology, 2018-11-09) Min, Sanggyu

As general aviation (GA) industry and its operations have grown along with the aviation industry development, improving aircraft safety has been a key interest in the GA industry. According to the U.S. Department of Transportation, GA in the U.S. has been suffering higher fatal accident rate compared to that of scheduled airline flights. This statistic indicates that safety enhancement effort is inevitable and reduction of GA aircraft fatality rate needs to be a prioritized goal in the GA community. The increasing pervasiveness of data-driven-safety programs such as flight data monitoring (FDM) in commercial aviation has permeated GA, giving rise to a growing body of quantitative safety analysis opportunities. FDM and other data-driven programs such as flight operations quality assurance (FOQA) feature a retrospective analysis of flight data records that identify potential safety-critical phenomena and the formulation and implementation of corrective actions. Thus, quantitative aircraft performance modeling and simulation capabilities emerge as critical enablers for safety analysis, particularly when coupled with flight data records that produce a rich and meaningful picture of operational safety. However, the intended application of the operational safety analysis imposes essential requirements on GA aircraft models and flight data records to be used by safety analysts. First, models must provide predictive capabilities with high flexibility and accuracy over the wide range of operational conditions. Also, to maximize the benefits of data-driven safety analysis, securing tidy data that is ready to be analyzed is as important as the ongoing collection and analysis of flight data records. Thus, the objective of this study is to develop a proactive operational safety analysis method by introducing a realistic and flexible performance modeling method and an efficient data noise removal technique for a fixed-wing GA aircraft. To accomplish the research goal, this study explores various existing performance modeling methods to propose a more cost-effective data-driven aerodynamic model that can adequately predict aircraft performance and capture the unsafe aerodynamic behavior of a fixed-wing GA aircraft. Furthermore, this study examines various data noise filtering techniques in both time and frequency domains to suggest an affordable and effective data cleaning process while preserving true aircraft behavior. Finally, this research suggests a quantification methodology for an operational safety assessment of GA fixed-wing aircraft using the previously obtained accurate and affordable aerodynamic model and clean flight data. The suggested safety assessment procedure enables a better understanding of realistic aircraft performance by adding flexibility to identifying operational limits and ensuring the reliability of collected flight data.
A methodology for predicting and mitigating loss of control incidents for general aviation aircraft

(Georgia Institute of Technology, 2018-11-09) Harrison, Evan David

In comparison with other modes of transportation, aviation has earned a clear distinction as the safest mode of travel. In recent years aviation has also achieved steady improvement in the accident rates, further distinguishing the safety of aviation with respect to other transportation modes. When aviation accidents do occur, however, it has been found that the most likely cause of these accidents is loss of control (LOC). Annual analysis of accident data indicates that LOC is consistently the most common cause of aviation accidents and fatalities for commercial aircraft worldwide and the Federal Aviation Administration (FAA) identifies LOC as the most important safety concern for general aviation (GA) as well. Recent work to identify and mitigate LOC events has been largely successful in identifying the sequence of events that typically precedes a LOC incident. Using this knowledge, several proposals have been made to break this sequence through application of advanced techniques and methods to detect, mitigate, or recover from events that may lead to LOC. These methods often assume the presence of advanced vehicle systems, such as advanced avionic systems and automated aircraft control, which imply intended application to future aircraft systems. Many existing aircraft are not equipped with such systems, leaving a gap between existing aircraft capability and the proposed solutions to address LOC. This is particularly true for GA, where the average age of an active vehicle in the GA fleet is estimated by the FAA to be 40 years old, suggesting that the typical GA aircraft lack such advanced on-board systems. The objective of this dissertation is to develop a methodology which enables the identification and mitigation of LOC for a typical GA fixed wing aircraft. The methodology which is developed within this work seeks to satisfy this objective through a combination of three key components. First, as LOC is understood within the existing literature as a deviation of the aircraft from normal operation, an appropriately defined LOC envelope will enable the prediction of LOC onset. Then to monitor this envelope during flight all necessary states of the vehicle must also be either observed or estimated. As it is assumed that only data collected by personal electronic devices is available, unobserved aircraft states and control actions of the pilot must be estimated within the methodology using existing or developed techniques. Finally, the methodology will aid in recovery of the aircraft in the event of LOC through synthesis of LOC recovery strategies which would be communicated to a human pilot through aural cues. This proposed methodology is summarized as a method of Mitigation by Envelope Restriction for Loss-of-control INcidents (MERLIN). The dissertation presents tools for implementing each of these components and includes a set of methods for synthesizing a dynamic vehicle model for use alongside the method. The various aspects of this methodology are also tested through a series of experiments. First the primary sources of uncertainty which affect the LOC envelope estimation process are identified and studied, yielding quantification of the effects of this uncertainty on the envelopes and a strategy for compensation. Secondly the expected error of the estimation of flight states is analyzed and the impact that this error has within the mitigation effort is accounted for through quantification of this error and the implementation of a strategy for mitigating the likelihood that this error causes erroneous evaluations of the vehicle's condition. Finally a full demonstration of the MERLIN method is presented within a simulation framework which includes the simulation of vehicle dynamics, pilot behavior, LOC envelope definition and real-time monitoring, and the communication of simplified recovery actions.
An approach for eﬃcient, conceptual-level aerospace structural design using the static condensation reduced basis element method

(Georgia Institute of Technology, 2018-11-06) Lee, Mario A.

In order to improve the energy eﬃciency and environmental compliance of future aircraft, the aviation industry has sought to investigate the inclusion of a variety of new technologies that are capable of enabling these goals. Among these technologies is a suite of structural technologies that are aimed at reducing airframe weight. At the conceptual level of aircraft design, the issues of vehicle weight and technology impact are of paramount importance. In aerospace engineering literature, there is a consensus that the ﬁnite element method (FEM) is the most accurate numerical method for determining the structural behavior and consequently, the weight of structural concepts that do not have vast empirical weight data. In the areas of conceptual and preliminary level design, the ﬁnite element method is often used in tandem with numerical optimization techniques to enable design space exploration and for ﬁnding suitable candidates that meet the requirements for the design problem. Unfortunately, the inclusion of detailed ﬁnite element analysis into conceptual level design environments has traditionally been prohibitive because of the associated computational expense. Recently, there has been signiﬁcant interest in the development of reduced order modeling strategies that are capable of expediting analyses performed by high ﬁdelity simulations. Among these methods, a class of techniques known as Reduced Basis Approximation or Reduced Basis Methods has gained popularity because of their ability to replicate the accuracy of the higher ﬁdelity analyses but at a very small fraction of the computational cost. In particular, a recently proposed approach known as the “Static Condensation Reduced Basis Element (SCRBE) method” is quite attractive because of its versatility of modeling a wide variety of ﬁnal problem conﬁgurations with a relatively small data set. This approach has been demonstrated on large-scale problems with physical problem domains that can be constructed from a several repeated, underlying reference sub-domains or components. Unlike traditional reduced order modeling approaches, the SCRBE method performs the model reduction at the sub-domain level. This feature of the method enables the creation and analysis of a large variety of ﬁnal problem domain conﬁgurations that can all be modeled with underlying physics. The aim of this work is to develop an approach that uses the SCRBE method to enable conceptuallevel, linear-static, structural design/optimization. While there has been extensive development in the SCRBE method since its inception, the author was unable to ﬁnd many published, academic work that investigates the extension of this method to enable numerical optimization. Instead, most of the papers in literature focus on determining the state variable/ solution of the weak form of the underlying partial diﬀerential equation being modeled and then one or more outputs that depend on this solution. In the case of gradient-based optimization, one also needs the gradients of these outputs. For largescale problems, numerical diﬀerentiation is not viable due to the computational expense associated with the “curse-of-dimensionality.” This work presents an approach to estimate common, conceptual-level structural design metrics and their gradients under the SCRBE paradigm. Another observation from the literature is that there tends to be a disparity between the computational time required to compose the equations to be solved in the SCRBE method and the time required to actually solve these equations. The literature recommends certain operational procedures that can be taken advantage of to tackle this overhead. This includes the use of repeated, cloned sub-domains and interactive design. However, these methods may not be applicable during numerical optimization. Admittedly, certain implementation strategies (such as the use of parallel computation) can be used to help to alleviate this overhead. This thesis proposes a technique that addresses this computational overhead and is perhaps most beneﬁcial in situations where there are limited to moderate computational resources available. This technique leverages the matrix Discrete Empirical Interpolation Method (mDEIM). The developments in this thesis are illustrated on a simple canonical problem of the strength design of a membrane-loaded, patched, variable-stiﬀness, composite plate. The ﬁndings of the experiments indicate that the SCRBE method, plus the techniques that are added to address the eﬃciency of the method have the potential to enable eﬃcient conceptual-level structural design. It is anticipated that this approach can eventually be extended to conceptual-level studies of larger subsystems commonly featured in aerospace construction and forms an exciting avenue for future research.
A methodology for dynamic sizing of electric power generation and distribution architectures

(Georgia Institute of Technology, 2018-11-01) Cinar, Gokcin

Electric and hybrid electric aircraft pose a significant architecture challenge, as these concepts not only deal with considerably high electrical loads, but also are extremely weight-sensitive. Ideally, a design space exploration should be conducted for each aircraft design including the transient electric propulsion and generation subsystem models for different propulsion architectures. However, such transient and detailed models require significantly small time steps (generally in the order of microseconds) during simulations, compared to the time steps required for aircraft mission analysis (generally in the order of minutes). Hence, the inclusion of such models bring enormous computational burden to the early design phases, and therefore are usually neglected in the aircraft conceptual design stage. Combined with the lack of historical data, the uncertainty in the design and performance estimation of these subsystems can have a cascading impact on the vehicle design and mission performance, which results in non-optimal designs with weight and performance penalties. The over-arching objective of this thesis is to develop a methodology to perform the sizing, integration and performance evaluation of electric power generation and distribution subsystems (EPGDS) and architectures within electric and hybrid electric aircraft concepts. To this end, this dissertation presents the creation of a novel methodological framework, called Electric Propulsion Sizing and Synthesis (E-PASS), which integrates EPGDS considerations into the aircraft sizing and synthesis process to enable quantitative and adequate comparisons between different types of electric and hybrid electric propulsion architectures. E-PASS has three main capabilities to overcome the aforementioned limitations. First, the traditional sizing and synthesis approach is modified to incorporate a modular weight estimation technique along with an energy-based mission analysis approach which stems from the conservation laws. The new, generalized approach enables the design and performance evaluation of any vehicle configuration, including the electric and hybrid electric aircraft. Second, a power split schedule optimization algorithm is wrapped around the sizing and synthesis capability to ensure that the candidate architectures at their optimum performance. Third, the dynamic nature of the EPGDS is taken into account by developing bi-level, physics-based and parametric models in addition to the adaptive step sizing capability which enables performing transient analysis at the conceptual design stage without sacrificing valuable computational resources. As a result, the transient analysis are performed only when required so that the knowledge about the subsystem design is maximized while minimizing the computational burden. Consequently, E-PASS incorporates these elements and provides a capability to integrate subsystem performance and dynamics of novel architectures to the aircraft sizing process at early design phases, enabling adequate comparisons between competing architectures.
Architecture-based selection of modeling type for system of systems analysis

(Georgia Institute of Technology, 2018-08-23) Bagdatli, Burak

A methodology for selecting computer modeling methods for system of systems problems was proposed. Two hypotheses were stated and supported by subsequent experimentation: "system architectures are very closely related to conceptual models" and "depending on the architecture views deemed to be essential to describe a system of systems, there are a number of modeling techniques required to adequately model it". The experiments were conducted in a systematic fashion using "element maps" that connect system of systems architecture elements to computer modeling elements. These element maps were developed to provide a repeatable scaffold in the translation of architectures into executable models. Using the element maps, eleven tests were performed on four different architectures varying in size and purpose. A process flow is designed and detailed that helps system of systems engineers with selecting computer modeling types and translating architectures into conceptual models that can be implemented in any computer language.
Development of a Methodology for Parametric Analysis of STOL Airpark Geo-Density

(Georgia Institute of Technology, 2018-06) Robinson, Joseph N. ; Sokollek, Max-Daniel ; Justin, Cedric Y. ; Mavris, Dimitri N.

Vehicles designed for urban air mobility (UAM)or on-demand mobility (ODM) applications typically adopt an architecture enabling vertical takeoff and landing (VTOL) capabilities. UAM or ODM systems featuring these capabilities typically have a smaller ground footprint but are subject to a number of performance compromises that make sizing and optimizing the vehicles more challenging. These design challenges can be further compounded when additional environmental considerations are taken into account and in particular if electric propulsion is considered. Alternative architectures such as short takeoff and landing (STOL) and super-short takeoff and landing (SSTOL) vehicles are thus investigated because they present possible advantages in terms of energy efficiency, overall vehicle performance, and noise footprint. However, the larger ground footprint of the infrastructure necessary to operate these systems means that these systems may be more difficult to integrate into a urban and suburban environment. One objective of this research is to estimate the geo-density of airparks suitable for STOL and SSTOL operations based on vehicle performance and ground footprint parameters. In turn, this helps establish requirements for the field performances of STOL and SSTOL vehicles to be considered for ODM and UAM applications. This research proposes and interactive and parametric design and trade-off analysis environment to help decision makers assess the suitability of candidate cities for STOL and SSTOL operations. Preliminary results for the Miami metropolitan area show that an average airpark geo-density of 1.66 airparks per square mile can be achieved with a 300 foot long runway.
Framework to Assess Effects of Structural Flexibility on Dynamic Loads Developed in Maneuvering Aircraft

(Georgia Institute of Technology, 2018-06) Sarojini, Darshan ; Duca, Ruxandra ; Solano, Heriberto D. ; Chakraborty, Imon ; Briceno, Simon ; Mavris, Dimitri N.

Sizing loads for major aircraft structural components are often experienced during dynamic maneuvers, several of which are described within the Federal Aviation Regulations as part of certification requirements. A simulation and analysis framework that permits such dynamic loads to be assessed earlier in the design process is an advantage for designers and aligned with the trend towards certification by analysis. Such a framework is demonstrated in this paper using the case of a business jet performing a longitudinal checked pitch maneuver. The maneuver is simulated with a six degree-of-freedom MATLAB/Simulink simulation model, using the aircraft aerodynamic characteristics, mass properties, and an adequate level of modeling for the flight control system and pilot control action. The effects of structural flexibility and deformation of the lifting surfaces and fuselage under maneuver loads are modeled by tracking a number of structural degrees-of-freedom for each. The modular nature of the simulation setup facilitates the assessment of multiple maneuvers, analysis of sensitivity to uncertainty, as well as the identification of the impact of structural flexibility through flexible versus rigid maneuver simulations.