Organizational Unit:

Daniel Guggenheim School of Aerospace Engineering

Permanent Link

https://hdl.handle.net/1853/70742

Parent Organization

Organizational Unit

College of Engineering

Includes Organization(s)

Organizational Unit

Aerospace Design Group

Organizational Unit

Aerospace Systems Design Laboratory (ASDL)

Organizational Unit

Air Transportation Laboratory

Organizational Unit

Cognitive Engineering Center

Organizational Unit

Space Systems Design Laboratory (SSDL)

Show 1 more

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/106

Full item page

Publication Search Results

Now showing 1 - 10 of 233

Multi-mission sizing and selection methodology for space habitat subsystems

(Georgia Institute of Technology, 2019-12-11) Boutaud, Agathe Kathia

Future space missions aim to set up exploration missions in further space and establish settlements on other celestial bodies like the Moon or Mars. In this context, subsystem sizing and selection is crucial, not only because resource management is critical for the astronauts’ survival, but also because subsystems can account for more than 20% of the total mass of the habitat, so reducing their size can greatly impact the cost of the mission. A few tools already exist to size space habitat subsystems and assess their performance. However, these tools are either very high-fidelity and very slow or instantaneous but steady-state. Steady-state tools do not allow to take risks or mission variations into account and the dynamic, slower tools are less performing at helping stakeholders evaluate the impact of technology trade-offs because of their long running time. Faster sizing tools would also allow to implement additional capabilities, such as multi-mission sizing, which could be used to develop lunar or martian settlements. These tools are also used in the context of point-based design, which focuses on the development of one design throughout the process. Such approach can lead to a sub-optimal design because the selection of an alternative is made early in the design process, based on low-fidelity analyses. In addition, because the costs and design choices are committed early in the design process, requirements or design changes can have very significant cost consequences. This research proposes a new sizing capability, developed using HabNet [1], a dynamic space habitat simulation tool. It is faster than existing dynamic sizing tools and it allowed to develop a multi-mission sizing methodology using Design Space Exploration. Finally, leveraging the faster sizing tool developed to create surrogate models for the size of the elements in the habitat, it was shown that trade-off analyses can be used to support set-based design during the conceptual design phase. Consequently, the methodology proposed is faster than what is currently used to size and select space habitat subsystem technologies. It gives more insight to the user because it can perform instantaneous trade-offs. However, the quality of the surrogate models generated is not sufficient to validate the multi-mission sizing method and environment developed during this thesis. This methodology could be used as a basis for the development of a set-based design method for space habitats. Numerous capabilities, including the evaluation of the impact of disruptions or the level of uncertainty associated with the various alternatives considered, could be easily implemented and added to the existing tool.
Development of a Multidisciplinary Design Analysis Framework for Unmanned Electric Flying Wings

(Georgia Institute of Technology, 2019-12-03) Whitmore, William Valentin

Small-scale subsonic unmanned aerial vehicles have become common tools in both military and civil applications. A vehicle configuration of special interest is the flying wing (aka all-wing or tailless aircraft). This configuration can potentially reduce drag, increase structural efficiency, and decrease detectability. When combined with an electric propulsion system, it produces no observable emissions and possesses fewer maintenance issues. Unfortunately, strong couplings between disciplinary analyses hinder the design of unmanned electric flying wings. In particular, achieving adequate stability characteristics degrades the aerodynamic efficiency of the vehicle, and constrains the available volume in which subsystem components may be placed. Exploiting the potential advantages of electric flying wings therefore necessitates a multidisciplinary perspective. In order to overcome the identified challenges of unmanned electric flying wing design, a multidisciplinary design analysis framework was conceptualized, implemented, and evaluated. The Python-based framework synthesizes automated analysis modules that model geometry, weight distribution, electric propulsion, aerodynamics, stability, and performance. Virtual experiments demonstrated the framework’s utility in quickly exploring a wide design space and assessing design robustness. Two important stand-alone contributions developed for the framework are (1) an algorithm for densely packing battery cells within a wing shape and (2) a parametric electric propulsion analysis code. In short, the framework supports the design of small-scale (i.e. 0-55lb weight range) subsonic unmanned electric flying wings with a host of valuable capabilities that were previously unavailable within traditional design methods.
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 multi-UAV trajectory optimization methodology for complex enclosed environments

(Georgia Institute of Technology, 2019-05-02) Barlow, Sarah

Unmanned Aerial Systems (UAS) have become remarkably more popular over the past decade and demonstrate a continuous upward market trend. As UAS become more accessible and advanced, they are able to be incorporated into a broader range of applications and provide substantial operational benefits. In addition to exterior use cases, UAS are being investigated for interior use cases as well. An area that has great potential for UAV involvement are manufacturing and warehouse environments, as these typically occupy vast spaces. Warehouse logistics and operations are very complex and could significantly benefit from the integration of UAVs. Many companies are already exploring using UAS as a means to perform inventory audits to reduce labor costs and time, and improve accuracy and safety. To achieve the maximum benefit from this technology in these environments, multiple vehicles would be essential. The purpose of this thesis is to optimize the operations of multiple UAVs in complex and confined environments, using a warehouse model as a test case. There are added complexities when working with multiple vehicles; for example, ensuring that there are no collisions between vehicles. A great deal of research has been done on vehicle routing and trajectory optimization, but very little has been done with UAV optimization in confined spaces. This thesis further develops these algorithms and focuses in on the impact UAV involvement could have on operations in environments that are similar to warehouses. The proposed improvements from the current methods will help uncover the most optimal results by changing the process for finding solutions, the criteria under which solutions are ranked, and the operational/experimental setup. The new methodologies seek to resolve the sub-optimality issues from the existing approach to significantly reduce the mission time required to perform a warehouse inventory audit. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem and determining optimal deployment time without any collisions. To improve the sub-optimal results, this thesis introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These proposed improvements are assessed based on their degree of impact on the overall mission time compared to the current methods. They are also analyzed in comparison to one another and in combination with one another to better understand the effectiveness and sensitivities of the presented changes.