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Daniel Guggenheim School of Aerospace Engineering

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

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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

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Publication Search Results

Now showing 1 - 10 of 31

Simultaneous Sizing of a Rocket Family with Embedded Trajectory Optimization

(Georgia Institute of Technology, 2023-12) Jo, Byeongun ; Ho, Koki

This paper presents a sizing procedure for a rocket family capable of fulfilling multiple missions, considering the commonalities between the vehicles. The procedure aims to take full advantage of sharing a common part across multiple rockets whose payload capability differs entirely, ultimately leading to cost savings in designing a rocket family. As the foundation of the proposed rocket family design method, an integrated sizing method with trajectory optimization for a single rocket is first formulated as a single optimal control problem. This formulation can find the optimal sizing along with trajectory results in a tractable manner. Building upon this formulation, the proposed rocket family design method is developed to 1) determine the feasible design space of the rocket family design problem (i.e., commonality check), and 2) if a feasible design space is determined to exist, minimize the cost function within that feasible space by solving an optimization problem in which the optimal control problem is embedded as a subproblem. A case study is carried out on a rocket family composed of expendable and reusable launchers to demonstrate the novelty of the proposed procedure.
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary

(Georgia Institute of Technology, 2023-09-26) Tomita, Kento ; Skinner, Katherine ; Ho, Koki

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.
Optimizing Multi-spacecraft Cislunar Space Domain Awareness Systems via Hidden-Genes Genetic Algorithm

(Georgia Institute of Technology, 2023-07-07) Visonneau, Lois ; Shimane, Yuri ; Ho, Koki

This paper proposes an optimization problem formulation to tackle the challenges of cislunar Space Domain Awareness (SDA) through multi-spacecraft monitoring. Due to the large volume of interest as well as the richness of the dynamical environment, traditional design approaches for Earth-based architectures are known to have challenges in meeting design requirements for the cislunar SDA; thus, there is a growing need to have a multi-spacecraft system in cislunar orbits for SDA. The design of multi-spacecraft-based cislunar SDA architecture results in a complex multi-objective optimization problem, where parameters such as number of spacecraft, observability, and orbit stability must be taken into account simultaneously. Through the use of a multi-objective hidden genes genetic algorithm, this study explores the entirety of the design space associated with the cislunar SDA problem. A demonstration case study shows that our approach can provide architectures optimized for both cost and effectiveness.
Regional Constellation Reconfiguration Problem: Integer Linear Programming Formulation and Lagrangian Heuristic Method

(Georgia Institute of Technology, 2023-07) Lee, Hang Woon ; Ho, Koki

A group of satellites, with either homogeneous or heterogeneous orbital characteristics and/or hardware specifications, can undertake a reconfiguration process due to variations in operations pertaining to Earth observation missions. This paper investigates the problem of optimizing a satellite constellation reconfiguration process against two competing mission objectives: 1) the maximization of the total coverage reward, and 2) the minimization of the total cost of the transfer. The decision variables for the reconfiguration process include the design of the new configuration and the assignment of satellites from one configuration to another. We present a novel biobjective integer linear programming formulation that combines constellation design and transfer problems. The formulation lends itself to the use of generic mixed-integer linear programming (MILP) methods such as the branch-and-bound algorithm for the computation of provably optimal solutions; however, these approaches become computationally prohibitive even for moderately sized instances. In response to this challenge, this paper proposes a Lagrangian relaxation-based heuristic method that leverages the assignment problem structure embedded in the problem. The results from the computational experiments attest to the near-optimality of the Lagrangian heuristic solutions and a significant improvement in the computational runtime as compared to a commercial MILP solver.
A Dynamic Multi-Stage Design Framework for Staged Deployment Optimization of Highly Stochastic Systems

(Georgia Institute of Technology, 2023-06-28) Hamdan, Bayan ; Liu, Zheng ; Ho, Koki ; Buyuktahtakin, Esra ; Wang, Pingfeng

The need for staged design optimization for multidisciplinary systems with strong, cross-system links and complex systems has been acknowledged in various contexts. This is prominent in fields where decisions between subsystems are dependant, as well as in cases where tactical decisions need to be made in uncertain environments. The flexibility gained by incorporating evolutionary design options has been analyzed by discretizing the time-variant uncertainties into scenarios and considering the flexible decision variables in each scenario separately. However, these problems use existing information at the decision time step. This paper presents a dynamic multi-staged design framework to solve problems that dynamically incorporate updated system information and reformulate the problem to account for the updated parameters. The importance of considering staged decisions is studied, and the benefit of the model is evaluated in cases where the stochasticity of the parameters decreases with time. The impact of considering staged deployment for highly stochastic, large-scale systems is investigated through a numerical case study as well as a case study for the IEEE 30 bus system. The case studies presented in this paper investigate multi-disciplinary design problems for large-scale complex systems as well as operational planning for highly stochastic systems. The importance of considering staged deployment for multi-disciplinary systems that have decreasing variability of their parameters with time is highlighted and demonstrated through the results of numerical and engineering case studies.
Rotorcraft takeoff analysis and classification to detect outlier operations that could present a safety risk

(Georgia Institute of Technology. School of Aerospace Engineering, 2023-06) da Silva, Ricardo F. ; Achour, Gabriel N. ; Payan, Alexia P. ; Johnson, Charles ; Mavris, Dimitri N.

Various reports from entities such as the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB) have shown a recent increase in the number of incidents involving helicopters. The versatility of rotorcraft operations makes the establish ment of safety metrics challenging. Yet, flight data monitoring (FDM) programs enable the implementation of data-based models and analyses that can contribute to improving the safety of helicopter operations. Traditionally FDM programs have featured exceedance-based data analyses by defining safety thresholds. However, recent advances in data science, and more particularly in deep learning techniques, have paved the way for a more reliable definition of safety thresholds via the use of outlier detection algorithms. This paper focuses on the implementation of an anomaly detection model for the takeoff phase which represents a large portion of incidents in rotorcraft operations. After generating training data and augmenting the dataset, the takeoff segment is extracted from each flight data record. Then, the type of takeoff performed is identified through a classification algorithm, and finally, a recurrent neural network composed of long short term memory cells is implemented to detect anomalies or outliers in the input takeoff data.
Development of a Parametric Drag Polar Approximation for Conceptual Design

(Georgia Institute of Technology. School of Aerospace Engineering, 2023-06) Sampaio Felix, Barbara ; Perron, Christian ; Ahuja, Jai ; Mavris, Dimitri N.

The present work proposes an efficient parametric approximation of mission drag polars by combining multi-fidelity surrogate models with parametric reduced order modeling techniques. Traditionally, semi-empirical aerodynamic analyses are used to provide drag polars needed for mission analysis during the conceptual design of aircraft. The database needed for these methods is unavailable for unconventional vehicles, and for this reason, many studies rely on higher-fidelity models typical of preliminary design to perform design space exploration for novel vehicle geometries. Due to the high computational cost and evaluation time of these higher-fidelity models, researchers constrain the design space exploration of vehicles by either relying on single discipline optimization or obtaining mission drag polars for a few vehicle geometries within their design loop. The present work demonstrates the application of Hierarchical Kriging surrogate models to obtain mission drag polars for fixed vehicle geometries. Then, the proper orthogonal decomposition reduced order model with Kriging interpolation is used to approximate the coherent structure of mission drag polars. The proposed method is demonstrated on a supersonic commercial aircraft. Experiments showed that both the multi-fidelity surrogate model and the reduced order model are able to emulate vehicle drag polars well for fixed and varying vehicle geometries, respectively.
Formalizing the Decomposition Process Between Elements in the RFLP Framework Using Axiomatic Design Theory

(Georgia Institute of Technology. School of Aerospace Engineering, 2023-06) Omoarebun, Ehiremen Nathaniel ; Cimtalay, Selçuk ; Mavris, Dimitri N.

As interactions and communication between elements in engineered systems continue to increase, the need to manage complexity, mitigate risks and improve human understanding of a system behavior becomes important. Over the years, systems engineering has emerged as a way to address the design of complex systems. However, traditional systems engineering comes with its own limitations, and this has led to the emergence of Model-Based Systems Engineering (MBSE) which aims to provide a better solution of design of complex systems. A lot of MBSE approaches are still based on heuristics and sometimes there is no clear structure on the process of design, especially during product and process decomposition. This paper aims to address that gap through the introduction of a formal structure by integrating concepts from Axiomatic Design Theory with the Requirement, Functional, Logical and Physical (RFLP) framework used in MBSE. The axioms from axiomatic design guide the decision-making process and the zigzagging aspect of the theory aids in the development of a structure during design. This process also aids in the identification and mitigation of potential coupling in design. A coupled spring-mass-damper system will serve as a demonstration to verify the proposed approach
Design Space Reduction using Multi-Fidelity Model-Based Active Subspaces

(Georgia Institute of Technology, 2023-06) Mufti, Bilal ; Perron, Christian ; Gautier, Raphaël ; Mavris, Dimitri N.

The parameterization of aerodynamic design shapes often results in high-dimensional design spaces, creating challenges when constructing surrogate models for aerodynamic coefficients. Active subspaces offer an effective way to reduce the dimensionality of such spaces, but existing approaches often require a substantial number of gradient evaluations, making them computationally expensive. We propose a multi-fidelity, model-based approach to finding an active subspace that relies solely on direct function evaluations. By using both high- and low-fidelity samples, we develop a model-based approximation of the projection matrix of the active subspace. We evaluate the proposed method by assessing its active subspace recovery characteristics and resulting model prediction accuracy for airfoil and wing drag prediction problems. Our results show that the proposed method successfully recovers the active subspace with an acceptable model prediction error. Furthermore, a cost vs. accuracy comparison with the multi-fidelity gradient-based active subspace method demonstrates that our approach offers comparable predictive performance with lower computational costs. Our findings provide strong evidence supporting the usage of the proposed method to reduce the dimensionality of design spaces when gradient samples are unavailable or expensive to obtain.
Multidisciplinary Design Analysis and Optimization of a Hypersonic Inflatable Aerodynamic Decelerator

(Georgia Institute of Technology, 2023-01) Dean, Hayden V. ; Robertson, Bradford E. ; Mavris, Dimitri N.

Human missions to Mars will require advanced entry, descent, and landing (EDL) technology to safely land payloads onto the planet’s surface. With rapidly increasing mass requirements, and stagnant geometry constraints set by current launch vehicles, non-heritage EDL vehicles must be considered to safely land human-scale payloads on Mars. The hypersonic inflatable aerodynamic decelerator (HIAD) is an EDL architecture being evaluated for human-scale payloads to Mars. Parameterization of a HIAD using important geometry variables is generated and used to explore the feasible design space of the entry architecture. The design space is evaluated using GT-Hypersonics, a multidisciplinary design analysis and optimization environment that combines ESP, CBAero, a Dymos-based trajectory optimizer, TPSSizer, and FIAT to perform trajectory, aerodynamic, and aerothermodynamic analysis on a given entry vehicle geometry, and prescribed flight parameters. This analysis is used to size the vehicle’s TPS system, and determine loads experienced by the vehicle during entry. Ranges for geometric inputs were selected and implemented to explore the design space of the HIAD architecture for a use case on Mars using uncrewed and crewed mission constraints. The design spaces for both the uncrewed and crewed missions demonstrated flexibility of inputs, allowing for multiple configurations to be used successfully in a mission to Mars. This study was useful in understanding the future of using the HIAD architecture in space exploration. This study demonstrates the ability to rapidly generate vehicle designs and evaluate their feasibility, a capability that will be useful in the growing space industry.