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 118

An architecture model of the U.S. air transportation network

(Georgia Institute of Technology, 2019-11-26) Song, Kisun

For almost a century, the U.S. Air Transportation Network (ATN) has continuously and successfully adapted to its changing environment as if it were a living organism. Today, the complexity of the network encompasses various exogenous as well as endogenous factors: fuel price, socioeconomic and political climates, atmospheric conditions, varying interests of stakeholders, and growing dependence on technology, to name a few. Its sophisticated interactions among diverse factors affecting the ATN have captivated many network researchers. Some researchers have attempted to retrieve an order out of seemingly chaotic constructions, while others have analyzed historical variations in its properties to understand the ATN’s behavioral mechanisms. However, its mathematical representation led by the known components and rules is yet to be developed. Thus, this thesis develops an architecture model of the ATN that mathematically represents the components and rules with realism. In the model, the network evolves in a virtual environment comprising three time-variant components – demand, airport, and aircraft technology – built upon extensive realistic datasets. Then the network is constructed by the active agents – airlines – performing multi-tiered network evolutionary processes and evolves into a strong hub-and-spoke (H&S) structure network that mimics the function of its reference: real-world ATN. The validated model provides various opportunities to conduct extensive analyses and studies on the past, current, and future of the ATN. Finally, a case study has been performed: forecasting the future ATN disruption caused by the technological revolution of civil supersonic transports. It provided an opportunity to experience the exploratory and interpretative capability of the architecture model, which shed light on performing future researches with better realism.
A reduced-order modeling methodology for the multidisciplinary design and analysis of boundary layer ingestion configurations

(Georgia Institute of Technology, 2019-11-08) Bozeman, Michael Dwain

In response to the increasingly stringent requirements for subsonic transport aircraft, NASA has established aggressive goals for the noise, emissions, and fuel burn of the next generations of aircraft. This has led to the investigation of a variety of unconventional configurations and new technologies. Boundary Layer Ingestion (BLI) propulsion has been identified as a promising technology to reduce fuel burn. Preliminary studies show that BLI propulsion can offer 3-12% reduction in fuel burn, depending on the configuration. Traditionally, the design and analysis of the airframe and propulsion system has been performed in a decoupled manner. For BLI configurations, the propulsion system is tightly integrated into the airframe resulting in strong interactions between the airframe aerodynamics and propulsion system performance. As a result, the design and analysis of BLI configurations requires coupled multidisciplinary analysis (MDA) consisting of an aerodynamic analysis in an iterative loop with a propulsion system analysis. This is a very expensive analysis considering the requirement for high-fidelity models. Additionally, the design of highly-coupled configurations cannot rely solely on intuition to make design decisions. Advanced methods including Multidisciplinary Analysis and Optimization (MDAO) and design space exploration are needed to allow for the design decisions to be made based directly on a system-level objective (e.g., fuel burn) and to allow for design studies to provide insight into the multidisciplinary trades associated with BLI configurations. However, MDAO and design space exploration using coupled, high-fidelity analysis models are not practical. In this work, reduced-order modeling (ROM) is proposed as a potential solution to reduce the computational cost associated with the coupled MDA of BLI configurations and to enable these advanced design methods. An interpolation-based POD ROM is developed based on the CFD analysis to allow for predictions of the aerodynamics over a range of propulsor operating conditions for a simplified tail-cone thruster (TCT) configuration. The resulting ROM is then coupled to a propulsion model to perform ROM-based, coupled MDA. Finally, the ROM-based, coupled MDA approach is employed for coupled MDAO to assess the performance benefit offered relative to equivalent CFD- and adjoint-based approaches. The results show that the ROM-based, coupled MDA approach offers an improvement in performance relative to the current state of the art. Relative to the equivalent CFD-based approach, the ROM-based, coupled MDA method demonstrated significant computational savings for even a single optimization. However, the ROM-based approach requires multiple optimizations to offer a computational benefit over the adjoint-based approach. This result highlights the benefit of the proposed approach for optimization studies and design space exploration.
An architecture-based growth approach for industrial gas turbine product development

(Georgia Institute of Technology, 2019-07-30) Fu, Haoyun

In this research, an architecture-based methodology has been developed to understand and interpret the ascending performance trajectory of industrial gas turbines from a growth perspective. Historical data depicting the product evolvement are examined to reveal trends and features that can be tied to the published design philosophy and practices in this industry. Quantifiable growth metrics are introduced and deployed in an established framework that offers a scientific product development environment to emulate the prevalent product development practices. Furthermore, the capability established by this methodology is expected to support performance prediction and planning for future gas turbine products.
Resilience-enhanced control reconfiguration for autonomous systems

(Georgia Institute of Technology, 2019-07-26) Oh, Sehwan

Unmanned systems keep replacing manned systems as a paradigm shift. According to the Unmanned Autonomous Systems (UAS) market forecast reports, the UAS market value is expected to grow two to three times higher in ten years. Considering the economic impacts of UAS application in job markets and component manufacturing industries, the UAS market value may very well exceed, which is predicted in the reports. However, regulations have limited the effective utilization of UAS due to safety concerns. These restrictive regulations significantly delay the potential usefulness of civilian and commercial UAVs. According to the Unmanned Aerial Vehicle (UAV) incidence reports, mechanical failures come out to be one of the top reasons for the incidents except for human errors. Technically, it is impossible to avoid any fault or failure in any systems. However, it can be possible to save the faulty system if the faults are treated properly. In this regard, this research has reviewed the state-of-the-art techniques regarding system safety improvement in the presence of a critical fault mode. Promising concepts are resilience engineering and Active Fault Tolerant Control (AFTCS) systems. Resilience engineering has been more focus on system design and resilience assessment methods. AFTCS mainly contributes to the fast and stable operating point recovery without the consideration of long-term system performances or mission success. Prognostics-enhanced reconfigurable control frameworks have proposed the online prognosis for a Remaining Useful Life (RUL) prediction within the control scheme but do not address comprehensive mission capability trade-offs. The objective of this study is to design a resilience-enhanced reconfigurable control framework for unmanned autonomous systems in the presence of a critical fault mode during the operation. The proposed resilience-enhanced reconfigurable control framework is composed of three fundamental modules: 1) immediate performance recovery by Model Predictive Control (MPC) and Differential Dynamic Programming (DDP) approaches, 2) long-term mission capability trade-offs by an optimization routine, and 3) situational awareness by a particle filtering-based fault diagnosis and Case-Based Reasoning (CBR). Critical development of this thesis is an introduction of an adaptation parameter in an MPC formulation (Module 1) and optimization process to find an optimal value for the adaptation parameter (Module 2). Module 3 enables long-term mission capability reasoning when a new fault growth pattern is observed. In order to test the efficacy of the proposed framework, under-actuated hovercraft as a testbed and an insulation degradation of an electrical thrust motor as a critical fault mode are introduced. The experiments explore the effect of the adaptation parameter on long-term mission capabilities and identify the necessity of the proper trade-offs. Further experiments investigate the efficacy of each module and the integrated framework. The experiment results show that the adaptation parameter adjusts a control strategy, so that mission capabilities are optimized while vulnerable long-term mission capabilities are recovered. The integrated framework presents the improvement to the probability of mission success in the presence of a critical fault mode. Lastly, as a generalization of the design process for the resilience-enhanced reconfigurable control framework, a design methodology suggests a step-by-step design procedure. Assumptions of the research have guided the required steps and limitations of the proposed framework.
A methodology for the prediction of non-volatile particulate matter from aircraft gas turbine engine

(Georgia Institute of Technology, 2019-07-26) Choo, Kyung Hak

There is growing concern about the adverse effects of particulate matter emissions on human health and the environment. New regulation standards on aviation particulate matters are expected in the near future. As particulate emissions become one of the important design constraints, they must be evaluated during the conceptual design of an aircraft engine. Prediction of soot emission from gas turbine combustion is a major subject in this research. Soot is a non-volatile primary particulate matter emitted directly from the combustion chamber. Current soot prediction methods utilize engine-specific information. As the current methods cannot handle engines with different cycles and sizes, they are not suitable for conceptual design. Three hypotheses addressing air partitioning, sizing methodology, and statistical distribution are established to develop the prediction environment, capable of a variety of cycles of engines with different size and thrust. The prediction environment consists of a Combustor Flow Circuit model, Statistical Distribution Model with the unmixedness curve, Chemical Reactor Networks (CRN), and Soot Evaluation Model. The integrated prediction environment developed with the proposed methodology demonstrates good predictability for cycles of different size and thrust engines. As the input of the prediction environment is a cycle, the proposed methodology is adequate for the prediction of non-volatile PM during the conceptual design of an aircraft engine.
An unsteady aerodynamics reduced-order modeling method for maneuvering, flexible flight vehicles

(Georgia Institute of Technology, 2019-07-24) Hiller, Brett R.

Accurate aerodynamic predictions remain a cornerstone of the aircraft design process due to their significance in determining the performance and stability and control (S&C) characteristics of aircraft. Traditional flight dynamics modeling has historically relied on the use of quasi-steady stability derivatives, often calculated using simplified linear aerodynamic methods. These models are inherently incapable of predicting the nonlinear, unsteady aerodynamics encountered by modern flexible aircraft. Inaccurate predictions of such phenomena can lead to suboptimal vehicle performance and/or inaccurate control law design, potentially leading to loss of control of the vehicle. Recent advances in digital computing have motivated interest towards Virtual Flight Simulation (VFS), for which multidisciplinary numerical simulations are used to evaluate an aircraft’s performance and S&C characteristics at full-scale flight Reynolds and Mach numbers. Despite their demonstrated feasibility, these simulations often require thousands of computational hours, limiting their adoption and widespread application for aircraft design and analysis. Reduced-order modeling is viewed as a key enabler for the viable application of VFS methods. Reduced-order models (ROMs) are mathematical models aimed at accurately predicting the fundamental dynamics of a system at a computational cost much less than what is required in solving the original governing equations. These methods approximate the full-order numerical simulations of a system by extracting and reconstructing pertinent dynamic data solutions, without any limiting physical model assumptions. Significant progress in reduced-order modeling has been made over the past two decades with the development of a variety of reduced-order models for efficient unsteady aerodynamic predictions. However, most unsteady aerodynamic ROMs have been primarily used for either predicting the aerodynamic response of rigid maneuvering vehicles or identifying aeroelastic instabilities, such as a flutter. Multidisciplinary ROMs for virtual flight simulations remain a desirable, yet relatively unexplored area of research. The objective of this dissertation was to develop a ROM capable of providing quantitatively accurate, yet computationally efficient predictions of the nonlinear, unsteady aerodynamics encountered by maneuvering flexible flight vehicles. Indicial response theory is a nonintrusive ROM approach, which characterizes a system’s dynamics through identification of the system response due to numerically simulated unit step changes in a system’s inputs. A CFD-based dynamic modal aeroelastic analysis was proposed for identification of indicial responses with respect to the vehicle motion parameters. Linear and nonlinear indicial response ROMs, based on the mathematical principle of convolution, were extended to predict the unsteady aerodynamic response of flexible flight vehicles subject to arbitrary vehicle maneuvers. The aeroelastic ROM is tested through comparisons to dynamic aeroelastic simulations of NASA’s X-56A Multi-Utility Technology Testbed (MUTT) aircraft performing a series of harmonic forced oscillations and a right turn flight test maneuver. Once identified, the aeroelastic indicial response ROM method is shown to accurately capture the entire frequency spectrum of the system, resolving the unsteady aerodynamic response of flexible flight vehicles at any feasible motion rate. Quantified assessments of the computational cost and accuracy of the aeroelastic ROM demonstrate rapidly increasing performance benefits relative to high fidelity aeroelastic simulations as multiple maneuver are considered. Furthermore, traditional stability-derivative models are efficiently extracted from aeroelastic indicial responses and are shown to accurately predict slowly-varying maneuvers with reduced computational costs. For more dynamic motions, the ROM is shown to accurately resolve the complex fluid-structure interactions present for maneuvering, flexible flight vehicles that are incapable of being modeled by quasisteady models.
A framework for concurrent design and route planning optimization of unmanned aerial vehicle based urban delivery systems

(Georgia Institute of Technology, 2019-05-21) Choi, Younghoon

With the emergence of new technologies for small Unmanned Aircraft Systems (sUAS), such as lightweight sensors and high-efficiency batteries, the operation of small Unmanned Aerial Vehicles (sUAVs) has expanded from military use to commercial use. A promising commercial application of sUAS is package delivery because of its potential to reduce acquisition and operating costs of the last-mile delivery system while enabling new services such as same-day delivery. Furthermore, in urban areas, sUAVs can deliver packages to customers without negatively affecting street traffic. This thesis addresses an extended sizing and synthesis process that considers the performance of a sUAS in its total cost optimized routing to size the vehicles for sUAS-based delivery systems. This problem is called the concurrent aircraft design and routing problem. Based on decomposition approaches, this problem can be divided into three parts: the sizing, the route planning, and the integration of the two. However, the existing methods have mainly focused either UAV design or optimization of operations of UAVs. Although a concurrent UAV design and routing problem is addressed, only simple routing problems are studied without considering the obstructed environment like an urban area. To fully address the concurrent aircraft design and routing problem for sUAS-based delivery systems, this thesis presents a novel modular framework including all three parts of this problem: the UAV design module, the UAV routing module, and the integration method. First, for the UAV routing module, this thesis presents an endurance-constrained Multi-Trip Vehicle Routing Problem with time windows (MTVRPTW) optimization model that is an extension of the MTVRPTW optimization model. The MTVRPTW model builds a vehicle's schedule including a reuse plan for an on-demand delivery system. However, the MTVRPTW model does not consider the property of sUAV's limited endurance. To alleviate the limitation of the MTVRPTW model, the endurance-constrained MTVRPTW model employs maximum endurance constraints that trace flight time of each vehicle and restrict flight time to vehicle's maximum endurance. Moreover, to address the urban environment with the optimization model, this thesis presents a framework for creating a two-layered urban flight network as an input graph of a Vehicle Routing Problem (VRP) optimization model. The urban flight network is built by feeding airborne Light Detection And Ranging (LiDAR) sensor data into an algorithm that uses a Voronoi diagram to create collision-free paths. By integrating the endurance-constrained MTVRPTW model with the two-layered urban flight network, vehicle's schedule for sUAS-based urban delivery is created. Second, for the UAV design module, a component-based sizing and synthesis process for small fixed-wing VTOL UAVs is implemented. The sizing and synthesis process is an extension of traditional fixed-wing aircraft sizing and synthesis tool. The implemented process can consider vertical flight capacity and provide a take-off weight optimized combination of components of the propulsion system. The main intent of implementing the sizing and synthesis process is to make the framework for the concurrent UAV design and routing problem. Thus, the framework can be extended by integrating other sizing and synthesis tools if the interface is matched. Lastly, to integrate the UAV design module with the UAV routing module, existing methods have used a sequential approach; after conducting the sizing module, the vehicle routing problem is solved. However, the input and output of the two modules are coupled each other. Thus, the methods cannot address a converged solution for both modules. To alleviate the limitation, this thesis presents a novel modular framework for the concurrent aircraft design and routing problem for sUAS-based delivery systems, which is based on a Fixed Point Iteration (FPI) method to find a converged solution of the coupled problem. The presented framework can provide an optimal vehicle design and routing for the sUAS-based delivery system concurrently. This thesis uses the developed framework for concurrent UAV design and routing to study a possible package delivery using sUAS in San Diego, CA. The result shows that the developed framework can take into account both planning vehicle operation on the flight network in which it will be operating and designing the flight network capable of addressing the obstructed environment as part of the vehicle design process.
A bi-Level framework for aircraft design uncertainty quantification and management

(Georgia Institute of Technology, 2019-04-04) Mines, John Mark

Aircraft design and development is a high-risk process. The recent obstacles with the Boeing 787 Dreamliner and Lockheed Martin F-35 show that the level of risk facing aircraft designers and manufactures has yet to be addressed. A review of work in this area reveals that methods do exist that quantify design uncertainty as well as capture common safeguards against unfavorable uncertainty realizations; however, three main capability gaps currently inhibit the effectiveness of uncertainty quantification and management: physics-based analysis, data-based uncertainty quantification, and a bi-level integrated design environment. Filling these three gaps are the contributions of this work. The geometry is explicitly modeled to retroactive changes to the design can be made in response to unfavorable uncertainty. Richardson's Extrapolation Method generates the error data needed parametric distribution fitting and correlation testing. A convergence loop based on structural weight converges two design environments of varying levels of fidelity. These three contributions are combined to form a new framework to design uncertainty quantification and management called the Reliability Assessment using Bi-level Design Analysis (RABiDA) framework.
Fidelity assessment for model selection (FAMS): A framework for Initial comparison of multifidelity modeling options

(Georgia Institute of Technology, 2019-04-01) Cox, Adam William

Model development and selection are crucial to the process of design and analysis. Ideally, model selection would entail a rigorous quantitative approach, through comparison of model data to truth data. However, if sufficient data were available to guarantee model credibility and applicability, modeling would not be needed. As such, given a problem definition, the enumeration of, and selection from, relevant modeling options relies on expert opinion. These processes are typically performed ad hoc, relying as much on familiarity and availability as on model fidelity, and the modeling options and justifications for decision-making are rarely captured. Additionally, even if a model could be proven to be complete and perfect representation of the physical system, such a model would likely require an infeasible amount of time to run. As such, compromises in fidelity must always be made in the interest of meeting cost, or runtime, requirements. To address this, a framework is developed to provide a method for capturing expert knowledge in initial comparison of multifidelity modeling options and providing justification for decision-making in terms of both fidelity and efficiency. Fidelity is a term that many have worked to define in a more usable manner. In the literature, resolution and abstraction have been used to describe fundamental aspects of a model that drive much of its behavior. In addition to those two attributes, scope, or how much of the system the model represents, is presented in this work as the third fundamental characteristic of fidelity. Through the comparison of these characteristics, an understanding of the relative fidelity of models can be estimated, even before model data is available. As model data becomes available, it should be used to update the magnitudes of the relative fidelity assessments based on model agreement, and help to identify deficiencies that were not previously considered, or were overlooked in verification. Whether or not model data is available, the understanding of fidelity should be combined with information regarding the efficiency of models to find the non-dominated set of multifidelity combinations and compare them to the fidelity and efficiency of individual models. This can be used to justify single or multi-model selection based on the current set of fidelity and cost requirements, and should be revisited as more data is generated or requirements change. This framework is developed and tested using notional models, a set of finite element models (FEM) representing an I-beam, and a use case involving FEM estimation of aircraft wing weight.
A numerical investigation into the aerodynamic effects of tubercles in wind turbine blades

(Georgia Institute of Technology, 2019-04-01) Abate, Giada

Wind turbine performance is clearly affected by complicated environmental effects such as atmospheric turbulence, ground boundary layer, and variation of free-stream wind direction and amplitude. Since the main goal of a wind turbine is energy production, the irregular nature of the wind is considered the main obstacle to a constant power output. Sinusoidal modifications (i.e., tubercles) placed on the leading edge of wind turbine blades seem to be a promising solution to this problem, since they generate vortices able to delay flow separation and improve the aerodynamic performance in the post-stall regime. The main objective of the present study is to give insights into the application of tubercles applied on the leading edge of wind turbine blades, specifically the NREL Phase VI wind turbine, such that performance enhancement can be achieved. Tubercles are sinusoidal bumps located at the leading edge of humpback whale flippers, which are able to improve flow attachment by acting like flow control devices similar to vortex generators. This discovery was the starting point for the development of several projects in the application of tubercles in different areas. In the present work, tubercles have been applied to the NREL Phase VI wind turbine blade to study their effects on blade aerodynamics and wind turbine performance. In particular, tubercle effects on shaft torque and annual energy production (AEP) have been analyzed; more specifically, tubercle amplitude, wavelength, and spanwise location have been considered as design variables. Moreover, since the physical phenomenon behind tubercles is still not fully clear, a physical analysis has been conducted to understand their working principles and to compare the new findings with previous works. Since past research on wind turbine application considers random values of tubercle geometric parameters (amplitude and wavelength), in the present work a more systematic study has been made by using a design of experiments (DoE) for the generation of tubercle configurations to test by a three-dimensional computational fluid dynamics (CFD) analysis. In particular, the thesis research has been developed in three main phases. Firstly, amplitude and wavelength have been considered as two design variables for a Latin hypercube DoE, and 20 blades have been generated. Then, since it has been observed that tubercles on whale flippers are unevenly distributed and placed closer to the tip, only the tubercle spanwise location has been varied, keeping fixed amplitude and wavelength. Finally, all three design variables listed above (i.e., amplitude, wavelength, and spanwise location) have been considered together in a 57-case hybrid DoE (Latin hypercube + full factorial). All the blade geometries have been simulated by a CFD analysis, which was embedded in a high-performance computing simulation framework made of a geometry creation code, a mesher, and a CFD solver. Results in terms of shaft torque and AEP have been compared with the baseline turbine underlying the importance of tubercles especially in the off-design conditions, when the blade is fully stalled and characterized by a strong spanwise flow, which is partially blocked by the streamwise vortices generated by tubercles. The CFD results have been also used as training points for a surrogate model generation, which helps to identify the regions in the design space where the performance improvement is relevant. In particular, tubercles seem to be beneficial in the design condition when they are placed closer to the blade tip; in the off-design regime, they can be extended over the entire blade with particular attention to the second half, which is the most influential in the power generation. Values of tubercle amplitude and wavelength that positively affect the performance have been also identified in a limited region of the design space, which varies depending on the wind speed considered.