Mavris, Dimitri N.

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    A Multidisciplinary Approach to the Evaluation and Selection of Infrastructure Electrification Solutions
    (Georgia Institute of Technology, 2024-01) Zarate Villazon, Angel M. ; Peak, Varick J. ; Duncan, Scott J. ; Fischer, Olivia J. Pinion ; Mavris, Dimitri N.
    Decarbonizing our activities is one of the most important challenges of our generation. The potential consequences of climate change for all life on Earth make these efforts urgently necessary. While a lot of focus has been put on emissions associated with transportation, the International Panel on Climate Change suggests that up to 40% of the total Greenhouse gas emissions come from buildings. As a result, there is a growing need to understand the impact of the different elements around buildings to propose a truly sustainable infrastructure. This impact, however, to be of value, needs to be assessed across multiple perspectives: policy, technological and financial. To that end, this paper studies buildings and infrastructure from a system-of-systems perspective to provide a financially-optimized combination of energy sources that satisfy a specific demand and assesses it across financial, environmental and performance metrics. More specifically, this paper balances the energy impact of building HVAC systems and the demand of electric vehicle charging against the inclusion of photovoltaic and storage systems. To enable such study, a methodology is proposed that leverages surrogate models to integrate a dynamic, Modelica-based model of a building HVAC system with a techno-economic decision support tool developed by the US National Renewable Energy Laboratory. The resulting capability is an integrated analysis of the green infrastructure that considers the multiple aspects of building energy sourcing through renewable and non-renewable sources for a given consumption pattern as a mean to assess the role that technologies and policies have in helping achieve greener, decarbonized buildings and infrastructure.
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    Shock Wave Prediction in Transonic Flow Fields using Domain-Informed Probabilistic Deep Learning
    (Georgia Institute of Technology, 2024-01) Mufti, Bilal ; Bhaduri, Anindya ; Ghosh, Sayan ; Wang, Liping ; Mavris, Dimitri N.
    Transonic flow fields are marked by shock waves of varying strength and location and are crucial for the aerodynamic design and optimization of high-speed transport aircraft. While deep learning methods offer the potential for predicting these fields, their deterministic outputs often lack predictive uncertainty. Moreover, their accuracy, especially near critical shock regions, needs better quantification. In this paper, we introduce a domain-informed probabilistic (DIP) deep learning framework tailored for predicting transonic flow fields with shock waves called DIP-ShockNet. This methodology utilizes Monte Carlo Dropout (MCD) to estimate predictive uncertainty and enhances flow field predictions near the wall region by employing the inverse wall distance function (IWDF) based input representation of the aerodynamic flow field. The obtained results are benchmarked against the signed distance function (SDF) and the geometric mask input representations. The proposed framework further improves prediction accuracy in shock wave areas using a domain-informed loss function. To quantify the accuracy of our shock wave predictions, we developed metrics to assess errors in shock wave strength and location, achieving errors of 6.4% and 1%, respectively. Assessing the generalizability of our method, we tested it on different training sample sizes and compared it against the proper orthogonal decomposition (POD)-based reduced order model (ROM). Our results indicate that DIP-ShockNet outperforms POD-ROM by 60% in predicting the complete transonic flow field.
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    Optimal Deployment Strategies for Cislunar PNT+C Architectures
    (Georgia Institute of Technology, 2024-01) Gabhart, Austin ; Drosendahl, Madilyn ; Robertson, Bradford E. ; Steffens, Michael J. ; Mavris, Dimitri N.
    Cislunar operations are expected to rise dramatically within the next decade, requiring a comparable increase in PNT and communications services. However, current PNT systems are at capacity and need to be augmented to serve a cislunar space domain, specifically in the form of novel cislunar PNT architectures. This paper studies the problem of the deployment of PNT and communications satellites, specifically, the problem of deployment strategies spanning multiple stages over extended periods of time. A set of stage definitions will be determined along with areas of potential user activity. A novel application of the hidden gene genetic algorithm to the constellation optimization problem is presented. A design space exploration is presented with comparisons of circular and elliptical constellations. Optimization results from the first stage are also provided. It is shown that acceptable performance can be achieved with a low number of deployed satellites and that strong trade-offs exist between performance and stability.
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    Framework for Assessment of Technology Maturation Using Uncertainty Quantification
    (Georgia Institute of Technology, 2024-01) Johnston, Hunter B. ; Cox, Adam ; Baker, Adam ; Mavris, Dimitri N.
    The results in this paper come from a project to develop an Uncertainty Quantification (UQ) framework to assist researchers in technology development and maturation. This framework aims to re-frame technology maturation as a process of reducing quantifiable uncertainty instead of completing requirements on a Technology Readiness Level (TRL) scale. The framework provided in this paper uses Bayesian statistics to redefine the technology maturation task as a process of reducing uncertainty in system inputs and outputs. This framework is powered by the calculation of a Variance Reduction Potential (VRP) for each system inputs that relates how much how uncertainty in the system-level outputs are related to the uncertainty in the system inputs. This variance reduction potential can be estimated by simulating the system of interest. This allows for researchers to determine which variables are the most important to test before any testing has actually been done. This framework empowers researchers to gain as much information on their system as possible before spending resources on physical testing rounds, making research and development of new systems more efficient.
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    Air Traffic Flow Identification and Recognition in Terminal Airspace through Machine Learning Approaches
    (Georgia Institute of Technology, 2024-01) Zhang, Wenxin ; Payan, Alexia P. ; Mavris, Dimitri N.
    In modern aviation, a significant amount of data is generated during routine operations and collected using technologies like Automatic Dependent Surveillance-Broadcast (ADS-B). The abundance of such data presents great potential for utilizing emerging data analysis techniques like machine learning to enhance the future of aviation. This paper presents a methodology that leverages clustering and classification models for offline identification and online recognition of air traffic flows. This research utilizes real trajectories in the terminal area of Zurich Airport to train and assess various machine learning models. To prepare the raw trajectory data for analysis, we apply a preprocessing step to clean and resample the data. Clustering is performed using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm, and its performance is compared to Density-Based Spatial Clustering of Applications with Noise (DBSCAN). For classification of the data, we employ two ensemble methods, Random Forest and Extreme Gradient Boosting (XGBoost), and compare their outcomes with those of Long Short-term Memory (LSTM). Our results demonstrate the superior reliability of OPTICS compared to the baseline method for clustering, and the ensemble models perform as effectively as the deep learning model, but with shorter training times due to their relative simplicity. The proposed methodology enhances the understanding of air traffic flows at specific airports and facilitates subsequent trajectory-centric tasks such as anomaly detection, trajectory prediction, and conflict detection, ultimately contributing to the improvement of safety in the terminal airspace.
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    Towards a Framework for Modeling and Simulating Complex Enterprises: The Case of an Academic Research Laboratory
    (Georgia Institute of Technology, 2024-01) Lepez Da Silva Duarte, Noe ; Fischer, Olivia J. Pinion ; Mavris, Dimitri N.
    This paper presents an initial exploration into a comprehensive, reusable, and scalable approach to enterprise architecture modeling and simulation, with a significant emphasis on the use of SysML. Through a case study of a large academic research laboratory, the research delineates the application of UAF and SysML models with diverse simulation methodologies, prioritizing the seamless transfer and enhanced visualization of SysML model data. A key contribution of this study is the incorporation of ontologies and graph databases through Neo4j, offering a robust framework for representing and querying complex SysML data. By juxtaposing traditional tools, such as SimPy, with Neo4j, the research underscores the effectiveness of graph databases in presenting a clearer, more intuitive visualization. While the presented simulation serves primarily as a proof-of-concept, it sheds light on the complexities of enterprise dynamics. The potential for richer, more detailed simulations that harness the full capability of these tools beckons further exploration in subsequent research endeavors.
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    A Reduced Order Modeling Approach to Blunt-Body Aerodynamic Modeling
    (Georgia Institute of Technology, 2024-01) Dean, Hayden V. ; Decker, Kenneth ; Robertson, Bradford E. ; Mavris, Dimitri N.
    Blunt-body entry vehicles display complex flow phenomena that results in dynamic instabilities in the low supersonic to transonic flight regime. Dynamic stability coefficients are typically calculated through parameter identification and trajectory regression techniques using both physical test data and Computational Fluid Dynamics (CFD) simulations. This methodology can generate dynamic stability coefficients, but the resulting data points are limited, and have high degrees of uncertainty due to the nature of data reduction methods. With increased computational capabilities, new methods for dynamic stability quantification have been explored that seek to leverage the high-dimensional aerodynamic data produced from CFD simulations to compute dynamic stability behavior and address the limitations of linearized aerodynamics. The objective of this work is to advance the quantification of dynamic stability behavior of blunt-body entry vehicles by leveraging high-fidelity CFD data through Reduced Order Modeling (ROM). ROMs are capable of leveraging high-fidelity aerodynamic data in a cost effective manner by finding a low-dimensional representation of the Full Order Model (FOM). ROMs based on Proper Orthogonal Decomposition (POD) have shown success in recreating CFD analyses of parametric ROM applications and time-varying ROM applications. Results of this research demonstrated success in constructing two ROMs of a notional blunt-body entry vehicle to recreate heatshield and backshell pressure distributions from forced oscillation trajectories. The ROM was more successful at reconstructing the heatshield pressure distribution, with challenges arising in predicting the chaotic response of backshell latent coordinates.
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    Unsteady Aerodynamic Uncertainty Quantification of a Blunt-Body Entry Vehicle in Free-Flight
    (Georgia Institute of Technology, 2024-01) Willier, Brenton J. ; Hickey, Alexandra M. ; Robertson, Bradford E. ; Mavris, Dimitri N.
    The design process of blunt-body entry vehicles balances atmospheric heating and drag to ensure crucial payloads can safely traverse through entry, descent, and landing. However, the blunt shape leads to a chaotic recirculating wake. Currently, uncertainties in the vehicle design process are captured through scalars and multipliers, and these conservative estimations lead to over-engineered vehicles, reduced payload capacity, and less accurate landings. To supplement the data gathered through physical testing, CFD-in-the-loop free-flight trajectories can be simulated throughout the flight regime. While CFD performance has improved significantly, the number of cases required to produce a meaningful sample for an uncertainty analysis remains computationally intense. Parametric uncertainty can be captured with traditional uncertainty methods like Monte Carlo analysis. However, the non-parametric uncertainty due to the unsteady nature of the chaotic wake has yet to be studied for free-flight analysis. This paper presents and implements an ensemble sampling initialization approach to determine the impact of unsteady wake structures imparted on CFD-in-the-loop data produced using replicated trajectory simulations. To enable this data generation, the Genesis vehicle gridding process is detailed, along with an overview of the free-flight CFD simulation setup for a supersonic flight regime. While running a static unsteady simulation, ten flow fields were saved at various times to capture different instantaneous structures in the wake. After initializing identical free-flight simulations with the ten different flow fields, results of vehicle aerodynamic angles, aerodynamic force and moment coefficients, inertial velocity, and vehicle trajectory in multiple reference frames showed identifiable trends with diverging behavior. The uncertainty on these variables due to unsteady flow is also quantified throughout the motion. It is concluded that this aspect of uncertainty must be carefully considered when CFD-in-the-loop is used to model the flight characteristics of a blunt-body vehicle.
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    Analysis of Infrastructure to Support a Future Space Economy
    (Georgia Institute of Technology, 2024-01) Roohi, Zayn A. ; Robertson, Bradford E. ; Mavris, Dimitri N.
    Beginning with the Artemis-I mission in late 2022, NASA is embarking upon a series of increasingly complex missions to establish a permanent presence on the surface of the Moon, potentially leading to manned Mars missions within the next few decades. Several private companies have also announced that they have begun work on space tourism projects with the goal of launching within this same time-frame. Supporting this expansion will require advanced space logistics and the development of dedicated space-based supply chains in order to reduce cost and increase resiliency. Previous research has focused on studying the impact that a specific technology, vehicle, or type of infrastructure has on supporting a single space campaign or mission; this paper takes a wider view by examining the impact that several types of infrastructure concepts together will have on the entire set of operations that could take place within the next decade. Lunar in-situ resource utilization, space depots, and space tugs are considered as infrastructure concepts, and a Lunar space station, Lunar habitat, Earth space stations, and Mars missions are considered as the operations to support. A time expanded mixed-integer nonlinear programming model is used to solve traditional network flow and supply chain problems, the results of which are used to propose future resupply missions and supply chain architectures.
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    A Trade-off Environment to Support Tabletop Exercises for the Selection of Cislunar Architectures
    (Georgia Institute of Technology, 2024-01) Introne, Stephanie ; Hawkins, Jacob ; Balchanos, Michael ; Mavris, Dimitri N.
    Developing cislunar architectures involves the consideration of many components, and evaluating these architectures requires the consideration of many varied perspectives. The process for making space exploration decisions has become increasingly complicated as interactions amongst participants have become both more impactful and more fraught. There are many additional considerations when conducting this type of analysis, including the context in which the architecture would be operating or the impact of the many assumptions that need to be made. One way to begin to tackle decision-making processes for cislunar architectures is through the development of a tabletop exercise that allows users to see how choices impact the resultant campaign scheduling in real-time, rather than the typical timelines of weeks to months for additional analysis. A tabletop environment brings stakeholder prioritization and interaction to schedule optimization, and contextualizes the decision-making through the use of scenarios. This tabletop tool enables users to compare architectures more quickly than through traditional methods, which could lead to more interactive studies, where different stakeholders are able to participate since new analyses can be run in real-time, or the exploration of more unconventional architectures since there is not the same level of investment needed to obtain results.