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Daniel Guggenheim School of Aerospace Engineering

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search.filters.applied.f.advisor: Mavris, Dimitri N. × End date: 2022 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Engineering ×

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Runway safety improvements through a data driven approach for risk flight prediction and simulation

2022-12-19 , Lee, Hyunki

Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.

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MBSE Enabled Conceptual Framework for Product Family and Platform Design

2022-08-26 , Karsten, Fatma Karagoz

In recent decades, competition in the global marketplace and demands for product variety have driven the need for different approaches to product design where strategies that help achieve high variety and growth while maintaining economies of scale and system complexity gained significant importance. Product family design is a complex problem by nature: the number of dimensions of the problem is high and there is a need to design and manage multiple products and their inter-dependencies simultaneously. Platform decisions are obfuscated by the lack of a singular approach to determining the proper level of commonality and modularity. The focus of this research is addressing these challenges and providing information for selecting the most cost-beneficial design approaches via a comprehensive comparison of individual versus family designs as well as sequential versus simultaneous design approaches for developing aircraft families. In particular, the methodology developed in this research is expected to enhance the conceptual design phase with a well-defined complex system description for product family design by coupling MBSE and matrix-based methods. Clustering methods are embedded to provide guidance for modularity and commonality identification, followed by a systematic evaluation of the potential benefits and penalties of these decisions. Furthermore, product family architecture variability representation brings rigor to the traditional way of identifying configuration solutions and helps guide the architectural decision process by elimination of configurational choices for early-stage system architecture representation.

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A Surrogate Modeling Approach for High-Speed Aerodynamics of Scramjet Inlets & Isolators

2022-07-30 , Baier, Dalton

Several unique challenges exist in the design & analysis of airbreathing hypersonic systems. One of the greatest challenges is caused by inclusion of an airbreathing propulsion system as it introduces multiple strong couplings and interactions with other disciplines in a system already characterized as highly coupled. Additionally, the propulsion system is a key driver in the design & analysis of these vehicles. Many current approaches for early stage design & analysis of scramjets rely on quasi-1D physics or empirical models due to low computational costs required for rapid/efficient design space exploration or conceptual design studies. In these approaches, computational accuracy is lost due to assumptions regarding physics of the problem in order to yield computationally efficient predictions, however these predictions typically do not capture important complex phenomena (e.g. SBLI). Viscous phenomena have been shown to exert first order effects on scramjet inlet performance, which makes it imperative to include viscous flow considerations in the final design; however these necessary physics-based analyses for scramjet systems are prohibitively expensive in early stage vehicle design. Current design methods typically focus on predicting overall vehicle performance scalars or transferring scalar quantities between disciplines, nevertheless inherent complexity of airbreathing hypersonic systems also requires field data to assess overall system performance. Surrogate modeling techniques present a possible solution to reducing the high expense associated with the required CFD analysis of scramjet systems; however none have been explored for field surrogate models of flows relevant to scramjets. Scramjet flowfields are complex and characterized by the presence of nonlinear phenomena such as shocks, viscous effects, SBLI, etc. These observations formulate the aim of this thesis: to identify and explore a surrogate modeling approach tailored to accurately and efficiently model the complex, nonlinear flowfields present in scramjet inlets & isolators. This research aims to investigate a neural network surrogate modeling approach to improve the information available earlier in the design process for the scramjet inlet/isolator, by yielding field data at a reduced cost compared to the full-order (CFD) models. Two loss functions are investigated with respect to representative single and multiple condition/geometry problems: 1) data-driven and 2) physics-informed, where the governing equations are included in the loss function. Additionally, the scalability of the approach is investigated and compared to full-order (CFD) solutions through a parametric scramjet mission relevant problem. Finally, the proposed neural network surrogate modeling approach is demonstrated on an inward turning inlet with bleed/vents at varying flow conditions. Distribution Statement A. Approved for public release. Distribution is unlimited. Abstract cleared: PA# AFRL-2022-2663 The views expressed are those of the author and do not necessarily reflect the official policy or position of the Department of the Air Force, Department of Defense, or the U.S. Government.

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A Graph-Based Methodology for Model Inconsistency Identification and Robust Architecture Exploration and Analysis

2022-05-03 , Duca, Ruxandra

The rise in complexity in aircraft design and the move towards non-conventional architectures lead to errors discovered late when changes are costly. A leading cause is the distributed design with isolated but interdependent models, which makes it difficult to maintain a consistent set of assumptions. Several gaps were identified, then a methodology was proposed to (1) to define a novel, internally feasible candidate architecture, (2) ensure that external analysis models are consistent with it, and (3) systematically extract cross-tool dependencies for multi-disciplinary analysis setup. In the first step, Model-Based Systems Engineering was leveraged to create a formal descriptive model of a baseline architecture. For this, an interface-based ontology was formulated using rules about component terminals and a standardized set of interactions. Incremental exploration was then enabled by developing a query-and-action process to find elements that must be added or removed after a local component replacement. The process was demonstrated by sequentially electrifying subsystems of a conventional baseline, resulting in numerous changes and restoring the system’s internal feasibility. In the second step, the application of inconsistency detection methods was enabled by automating the search for semantic overlap between analysis models and the central descriptive model. For this, data from the two was encoded into labeled digraphs and an algorithm was used to find the maximum common subgraph. It was demonstrated between the electrified candidate architecture from the first step and a conventional aircraft model as seen by an analysis tool. After finding the equivalent elements, the inconsistency detection method was demonstrated. The last step leveraged the results of the first two: analysis tools linked to a cross-disciplinary descriptive view of the whole system. Using the central model as an intermediary, cross-tool constraints were extracted, even when the relevant parameters were not exposed as inputs or outputs. This was demonstrated between the analysis model in the second step and a localized thermal model. With a formal, cross-disciplinary view of the candidate architecture and a set of properly configured tools and cross-tool constraints, this methodology enables the exploration of subsystem architectures during preliminary design with less effort than current methods, and with the prospective of fewer errors being discovered in later stages of design.

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A Controller Development Methodology Incorporating Unsteady, Coupled Aerodynamics and Flight Control Modeling for Atmospheric Entry Vehicles

2022-11-01 , Ernst, Zachary J.

Atmospheric entry vehicle aerodynamics, flight dynamics, and control mechanisms are inherently coupled and unsteady. The state-of-the-art disciplinary models used for Mars entry vehicle simulation do not directly account for these time-dependent interactions, resulting in increased model fidelity uncertainty that can negatively affect controller performance. This can be especially detrimental given the more rigorous landing precision requirements and increased technological and volitional uncertainty expected for future missions. This work seeks to formulate and implement an entry controller tuning methodology that directly accounts for coupled, unsteady entry vehicle aerodynamic and control system behavior. The methodology uses a 6-degree-of-freedom coupled CFD-rigid body dynamics (RBD) model, extended to include flight control system modeling, for flight simulation while preserving unsteady flow history. This is capable of high-fidelity simulation to evaluate the performance of a controller, but the high cost makes it infeasible to directly use the state-of-the-art methodology for controller tuning which relies on thousands of short-duration simulations. Instead, multifidelity optimization is used. The coupled model is run to evaluate promising designs at high fidelity, while a lower-fidelity model is used to rapidly explore the design space. Crucially, each time the coupled model is executed, it produces new time-accurate trajectory and aerodynamic data that can be added to the training data for the low-fidelity aerodynamic surrogate model. A multifidelity surrogate is then constructed to provide a correction between the low- and high-fidelity results. As tuning proceeds, knowledge of the model is thus gained both by data fusion of the controller performance metrics, and by decreasing aerodynamic error in the low-fidelity surrogate. The methodology was developed through numerical experimentation with an entry vehicle equipped with a single-axis internal moving mass actuator for pitch control. A feed-forward neural network architecture with better performance than a state-of-the-art database was identified for use as the low-fidelity aerodynamic surrogate. A fusion-based multifidelity optimization method is implemented to leverage the quasi-hierarchical nature of the coupled and low-fidelity models. The methodology is demonstrated for tuning an angle of attack controller, yielding a controller that has better performance than one that is tuned using the state-of-the-art methodology.

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A reduced order modeling methodology for the parametric estimation and optimization of aviation noise

2022-08-23 , Behere, Ameya Ravindra

The successful mitigation of aviation noise is one of the key enablers of sustainable aviation growth. Technological improvements for noise reduction at the source have been countered by increasing number of operations at most airports. There are several consequences of aviation noise including direct health effects, effects on human and non-human environments, and economic costs. Several mitigation strategies exist including reduction of noise at source, land-use planning and management, noise abatement operational procedures, and operating restrictions. Most noise management programs at airports use a combination of such mitigation measures. To assess the efficacy of noise mitigation measures, a robust modeling and simulation capability is required. Due to the large number of factors which can influence aviation noise metrics, current state-of-the-art tools rely on physics-based and semi-empirical models. These models help in accurately predicting noise metrics in a wide range of scenarios; however, they are computationally expensive to evaluate. Therefore, current noise mitigation studies are limited to singular applications such as annual average day noise quantification. Many-query applications such as parametric trade-off analyses and optimization remain elusive with the current generation of tools and methods. There are several efforts documented in literature which attempt to speed up the process using surrogate models. Techniques include the use of pre-computed noise grids with calibration models for non-standard conditions. These techniques are typically predicated on simplifying assumptions which greatly limit the applicability of such models. Simplifying assumptions are needed to downsize the number influencing factors to be modeled and make the problem tractable. Existing efforts also suffer due to the inclusion of categorical variables for operational profiles which are not conducive to surrogate modeling. In this research, a methodology is developed to address the inherent complexities of the noise quantification process, and thus enable rapid noise modeling capabilities which can facilitate parametric trade-off analysis and optimization efforts. To achieve this objective, a research plan is developed and executed to address two major gaps in literature. First, a parametric representation of operational profiles is proposed to replace existing categorical descriptions. A technique is developed to allow real-world flight data to be efficiently mapped onto this parametric definition. A trajectory clustering method is used to group similar flights and representative flights are parametrized using an inverse-map of an aircraft performance model. Next, a field surrogate modeling method is developed based on Model Order Reduction techniques to reduce the high dimensionality of computed noise metric results. This greatly reduces the complexity of data to be modeled, and thus enables rapid noise quantification. With these two gaps addressed, the overall methodology is developed for rapid noise quantification and optimization. This methodology is demonstrated on a case study where a large number of real-world flight trajectories are efficiently modeled for their noise results. As each such flight trajectory has a unique representation, and typically lacks thrust information, such noise modeling is not computationally feasible with existing methods and tools. The developed parametric representations and field surrogate modeling capabilities enable such an application.

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A METHODOLOGY FOR THE MODULARIZATION OF OPERATIONAL SCENARIOS FOR MODELLING AND SIMULATION

2022-07-29 , Muehlberg, Marc

As military operating environments and potential global threats rapidly evolve, military planning processes required to maintain international security and national defense increase in complexity and involve unavoidable uncertainties. The challenges in the field are diverse, including dealing with reemergence of long-term, strategic competition over destabilizing effects of rogue regimes, and the asymmetric non-state actors’ threats such as terrorism and international crime. The military forces are expected to handle increased multi-role, multi-mission demands because of the interconnected character of these threats. The objective of this thesis is to discuss enhancing system-of-systems analysis capabilities by considering diverse operational requirements and operational ways in a parameterized fashion within Capabilities Based Assessments process. These assessments require an open-ended exploratory approach of means and ways, situated in the early stages of planning and acquisition processes. In order to enhance the reflection of increased demands in the process, the integration of multi-scenario capabilities into a process with low-fidelity modelling and simulation is of particular interest. This allows the consideration of a high quantity of feasible alternatives in a timely manner, spanning across a diverse set of dimensions and its parameters. A methodology has been devised as an enhanced Capabilities Based Assessment approach to provide for a formalized process for the consideration and infusion of operational scenarios, and properly constrain the design space prior to computational analysis. In this context, operational scenarios are a representative set of statements and conditions that address a defined problem and include testable metrics to analyze performance and effectiveness. The scenario formalization uses an adjusted elementary definition approach to decompose, define, and recompose operational scenarios to create standardized architectures, allowing their rapid infusion into environments, and to enable the consideration of diverse operational requirements in a conjoint approach overall. Pursuant to this process, discrete event simulations as low-fidelity approach are employed to reflect the elementary structure of the scenarios. In addition, the exploration of the design and options space is formalized, including the collection of alternative approaches within different materiel and non-materiel dimensions and subsequent analysis of their relationship prior to the creation of combinatorial test cases. In the progress of this thesis, the devised methodology as a whole and the two developed augmentations to the Capabilities Based Assessment are tested and validated in a series of experiments. As an overall case study, the decision-making process surrounding the deployment of vertical airlift assets of varying type and quantity for Humanitarian Aid and Disaster Relief operations is utilized. A demonstration experiment is provided exercising the entire methodology to test specifically for its suitability to handle a variety of different scenarios through process, as well as a comprehensive set of materiel and non-materiel parameters. Based on a mission statement and performance targets, the status quo could be evaluated and alternative options for the required performance improvements could be presented. The methodology created in this thesis enables the Capabilities Based Assessment and general defense acquisition considerations to be initially approached in a more open and less constrained manner. This capability is provided through the use of low-fidelity modelling and simulation that enables the evaluation of a large amount of alternatives. In advances to the state of the art, the methodology presented removes subject-matter expert and operator driven constraints, allowing the discovery of solutions that would not be considered in a traditional process. It will support the work of not only defense acquisition analysts and decision-makers, but also provide benefits to policy planners through its ability to instantly revise and analyze cases in a rapid fashion.

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A Framework for Offline Risk-aware Planning of Low-altitude Aerial Flights during Urban Disaster Response

2022-10-05 , Harris, Caleb M.

Disaster response missions are dynamic and dangerous events for first responders. Active situational awareness is critical for effective decision-making, and unmanned aerial assets have successfully extended the range and output of sensors. Aerial assets have demonstrated their capability in disaster response missions via decentralized operations. However, literature and industry lack a systematic investigation of the algorithms, datasets, and tools for aerial system trajectory planning in urban disasters that optimizes mission performance and guarantee mission success. This work seeks to develop a framework and software environment to investigate the requirements for offline planning algorithms and flight risk models when applied to aerial assets exploring urban disaster zones. This is addressed through the creation of rapid urban maps, efficient flight planning algorithms, and formal risk metrics that are demonstrated in scenario-driven experiments using Monte Carlo simulation. First, rapid urban mapping strategies are independently compared for efficient processing and storage through obstacle and terrain layers. Open-source data is used when available and is supplemented with an urban feature prediction model trained on satellite imagery using deep learning. Second, sampling-based planners are evaluated for efficient and effective trajectory planning of nonlinear aerial dynamic systems. The algorithm can find collision-free, kinodynamic feasible trajectories using random open-loop control targets. Alternative open-loop control commands are formed to improve the planning algorithm’s speed and convergence. Third, a risk-aware implementation of the planning algorithm is developed that considers the uncertainty of energy, collisions, and onboard viewpoint data and maps them to a single measure of the likelihood of mission failure. The three modules are combined in a framework where the rapid urban maps and risk-aware planner are evaluated against benchmarks for mission success, performance, and speed while creating a unique set of benchmarks from open-source data and software. One, the rapid urban map module generates a 3D structure and terrain map within 20 meters of data and in less than 5 minutes. The Gaussian Process terrain model performs better than B-spline and NURBS models in small-scale, mountainous environments at 10-meter squared resolution. Supplementary data for structures and other urban landcover features is predicted using the Pix2Pix Generative Adversarial Network with a 3-channel encoding for nine labels. Structures, greenspaces, water, and roads are predicted with high accuracy according to the F1, OIU, and pixel accuracy metrics. Two, the sampling-based planning algorithm is selected for forming collision-free, 3D offline flight paths with a black-box dynamics model of a quadcopter. Sampling-based planners prove successful for efficient and optimal flight paths through randomly generated and rapid urban maps, even under wind and noise uncertainty. The Stable-Sparse-RRT, SST, algorithm is shown to improve trajectories for minimum Euclidean distance more consistently and efficiently than the RRT algorithm, with a 50% improvement in finite-time path convergence for large-scale urban maps. The forward propagation dynamics of the black-box model are replaced with 5-15 times more computationally efficient motion primitives that are generated using an inverse lower-order dynamics model and the Differential Dynamic Programming, DDP, algorithm. Third, the risk-aware planning algorithm is developed that generates optimal paths based on three risk metrics of energy, collision, and viewpoint risk and quantifies the likelihood of worst-case events using the Conditional-Value-at-Risk, CVaR, metric. The sampling-based planning algorithm is improved with informative paths, and three versions of the algorithm are compared for the best performance in different scenarios. Energy risk in the planning algorithm results in 5-35% energy reduction and 20-30% more consistency in finite-time convergence for flight paths in large-scale urban maps. All three risk metrics in the planning algorithm generally result in more energy use than the planner with only energy risk, but reduce the mean flight path risk by 10-50% depending on the environment, energy available, and viewpoint landmarks. A final experiment in an Atlanta flooding scenario demonstrates the framework’s full capability with the rapid urban map displaying essential features and the trajectory planner reporting flight time, energy consumption, and total risk. Furthermore, the simulation environment provides insight into offline planning limitations through Monte Carlo simulations with environment wind and system dynamics noise. The framework and software environment are made available to use as benchmarks in the field to serve as a foundation for increasing the effectiveness of first responders’ safety in the challenging task of urban disaster response.

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Rotor Fatigue Life Prediction and Design for Revolutionary Vertical Lift Concepts

2022-08-08 , Robinson, Joseph Nathaniel

Despite recent technological advancements, rotorcraft still lag behind their fixed-wing counterparts in the areas of flight safety and operating cost. Competition with fixed-wing aircraft is difficult for applications where vertical takeoff and landing (VTOL) capabilities are not required. Both must be addressed to ensure the continued competitiveness of vertical lift aircraft, especially in the context of new military and civilian rotorcraft programs such as Future Vertical Lift and urban air mobility, which will require orders-of-magnitude improvements in reliability, availability, maintainability, and cost (RAM-C) metrics. Lifecycle costs and accident rates are strongly driven by scheduled replacement or failure of flight-critical components. Rotor blades are life-limited to ensure that they are replaced before fatigue damage exceeds critical levels, but purchasing new blades is extremely costly. Despite aggressive component replacement times, fatigue failure of rotor blades continues to account for a significant proportion of inflight accidents. Fatigue damage in rotorcraft is unavoidable due to the physics of rotary-wing flight, but new engineering solutions to improve fatigue life in the rotor system could improve rotorcraft operating costs and flight safety simultaneously. Existing rotorcraft design methods treat fatigue life as a consequence, rather than a driver, of design. A literature review of rotorcraft design and fatigue design methods is conducted to identify the relevant strengths and weaknesses of traditional processes. In rotorcraft design, physics-based rotor design frameworks are focused primarily on fundamental performance analysis and do not consider secondary characteristics such as reliability or fatigue life. There is a missing link between comprehensive rotor design frameworks and conceptual design tools that prevents physics-based assessment of RAM-C metrics in the early design stages. Traditional fatigue design methods, such as the safe life methodology, which applies the Miner's rule fatigue life prediction model to rotorcraft components, are hindered by a lack of physics-based capabilities in the early design stages. An accurate fatigue life quantification may not be available until the design is frozen and prototypes are flying. These methods are strongly dependent on extrapolations built on historical fatigue data, and make use of deterministic safety factors based on organizational experience to ensure fatigue reliability, which can lead to over-engineering or unreliable predictions when applied to revolutionary vertical lift aircraft. A new preliminary fatigue design methodology is designed to address these concerns. This methodology is based on the traditional safe life methodology, but replaces several key elements with modern tools, techniques, and models. Three research questions are proposed to investigate, refine, and validate different elements of the methodology. The first research question addresses the need to derive physics-based fatigue load spectra more rapidly than modern comprehensive analysis tools allow. The second investigates the application of different probabilistic reliability solution methods to the fatigue life substantiation problem. The third question tests the ability of the preliminary fatigue design methodology to evaluate the relative impact of common preliminary fatigue design variables on the probability of fatigue failure of a conceptual helicopter's rotor blade. Hypotheses are formulated in response to each research question, and a series of experiments are designed to test those hypotheses. In the first experiment, a multi-disciplinary analysis (MDA) environment combining the rotorcraft performance code NDARC, the comprehensive code RCAS, and the beam analysis program VABS, is developed to provide accurate physics-based predictions of rotor blade stress in arbitrary flight conditions. A conceptual single main rotor transport helicopter based on the UH-60A Black Hawk is implemented within the MDA to serve as a test case. To account for the computational expense of the MDA, surrogate modeling techniques, such as response surface equations, artificial neural networks, and Gaussian process models are used to approximate the stress response across the flight envelope of the transport helicopter. The predictive power and learning rates of various surrogate modeling techniques are compared to determine which is the most suitable for predicting fatigue stress. Ultimately, shallow artificial neural networks are found the provide the best compromise between accuracy, training expense, and uncertainty quantification capabilities. Next, structural reliability solution methods are investigated as a means to produce high-reliability fatigue life estimates without requiring deterministic safety factors. The Miner's sum fatigue life prediction model is reformulated as a structural reliability problem. Analytical solutions (FORM and SORM), sampling solutions (Monte Carlo, quasi-Monte Carlo, Latin hypercube sampling, and directional simulation), and hybrid solutions importance sampling) are compared using a notional fatigue life problem. These results are validated using a realistic helicopter fatigue life problem \jnr{which incorporates the fatigue stress surrogate model and is based on a probabilistic definition of the mission spectrum to account for fleet-wide usage variations. Monte Carlo simulation is found to provide the best performance and accuracy when compared to the exact solution. Finally, the capabilities of the preliminary fatigue design methodology are demonstrated using a series of hypothetical fatigue design exercises. First, the methodology is used to predict the impact of rotor blade box spar web thickness on probability of fatigue failure. Modest increases in web thickness are found to reduce probability of failure, but larger increases cause structural instability of the rotor blade in certain flight regimes which increases the fatigue damage rate. Next, a similar study tests the impact of tail rotor cant angle. Positive tail rotor cant is found to improve fatigue life in cases where the center of gravity (CG) of the vehicle is strongly biased towards the tail, but is detrimental if the CG is closer to the main rotor hub station line. Last, the effect of design mission requirements like rate of climb and cruising airspeed is studied. The methodology is not sensitive enough to predict the subtle impact of changes to rate of climb, but does prove that a slower cruising airspeed will decrease probability of fatigue failure of the main rotor blade. The methodology is proven to be capable of quantifying the influence of \jnr{rotor blade design variables, vehicle layout and configuration, and certain design mission requirements}, paving the way for implementation in a rotorcraft design framework. This thesis ends with suggestions for future work to address the most significant limitations of this research, as well as descriptions of the tasks required to apply the methodology to conventional rotorcraft or conceptual revolutionary vertical lift aircraft.

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A Techno-Economic Approach to the Evaluation of Hybrid-Electric Propulsion Architectures at the Conceptual/Exploratory Design Phase

2022-07-20 , Milios, Konstantinos

A new, revolutionary concept, capable of mitigating the impact of global aviation on the climate, is electrified propulsion-based aircraft configurations. However, the introduction of a new electric powertrain to the existing propulsion system has created a series of challenges. Multiple energy sources are available to meet the system power requirements throughout the flight envelope compared to conventional fuel-only based vehicles. Electrified flight segments (eTaxi, takeoff boost, climb boost, etc.) can lead to large variations in total mission fuel burn depending on the amount and duration of electric power provided. Electrified propulsion systems are radical innovations and as such, entail a high degree of risk in technical and financial performance. Traditional project management methods for new products, such as the stage-gate model, tend to favor more traditional and conventional engine advancements where the associated technologies and economics are better understood, leading to promising novel concepts being discarded during the early design phases. With cost overruns and schedule delays being a common theme among new airplane development programs, it is imperative that the most promising electrified propulsion concepts advance to the later stages of product development. The present work proposes a techno-economic approach for evaluating hybrid-electric propulsion architectures. Technical feasibility and financial viability of notional hybrid-electric concepts is concurrently quantified during the conceptual/exploratory design phase, in combination with uncertainty analysis associated with low maturity technologies and dynamic economic environments. A technical framework was developed based on the Environmental Design Space (EDS) simulation tool capable of performing sizing, mission, and emission analysis of a hybrid-electric aircraft. A comprehensive cost model for hybrid-electric systems was developed and applied for calculating the financial performance of each notional concept. Technical and financial uncertainties associated with hybrid-electric propulsion systems were identified and their impact on the overall business case performance of each concept measured. Finally, the proposed techno-economic framework is demonstrated using a multi-variable scenario-based analysis for determining the impact of external market factors (fuel prices, electricity prices, environmental policies, etc.) on the evaluation of hybrid-electric propulsion systems.

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