Organizational Unit:

Aerospace Systems Design Laboratory (ASDL)

Permanent Link

https://hdl.handle.net/1853/70744

Parent Organization

Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1135

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Publication Search Results

Now showing 1 - 10 of 79

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.
Aviation-BERT: A Preliminary Aviation-Specific Natural Language Model

(Georgia Institute of Technology, 2023-06) Chandra, Chetan ; Jing, Xiao ; Bendarkar, Mayank ; Sawant, Kshitij ; Elias, Lidya R. ; Kirby, Michelle ; Mavris, Dimitri N.

Data-driven methods form the frontier of reactive aviation safety analysis. While analysis of quantitative data from flight operations is common, text narratives of accidents and incidents have not been sufficiently mined. Among the many use cases of aviation text-data mining, automatically extracting safety concepts is probably the most important. Bidirectional EncoderRepresentations from Transformers (BERT) is a transformer-based large language model that is openly available and has been adapted to numerous domain-specific tasks. The present work provides a comprehensive methodology to develop domain-specific BERT model starting from the base model. A preliminary aviation domain-specific BERT model is developed in this work. This Aviation-BERT model is pre-trained from the BERT-Base model using accident and incident text narratives from the National Transportation Safety Board (NTSB) and AviationSafety Reporting System (ASRS) using mixed-domain pre-training. Aviation-BERT is shown to outperform BERT when it comes to text-mining tasks on aviation text datasets. It is also expected to be of tremendous value in numerous downstream tasks in the analysis of aviation text corpora.
BERT for Aviation Text Classification

(Georgia Institute of Technology, 2023-06) Jing, Xiao ; Chennakesavan, Akul ; Chandra, Chetan ; Bendarkar, Mayank ; Kirby, Michelle ; Mavris, Dimitri N.

The advent of transformer-based models pre-trained on large-scale text corpora has revolutionized Natural Language Processing (NLP) in recent years. Models such as BERT (Bidirectional Encoder Representations from Transformers) offer powerful tools for understanding contextual information and have achieved impressive results in numerous language understanding tasks. However, their application in the aviation domain remains relatively unexplored. This study discusses the challenges of applying multi-label classification problems on aviation text data. A custom aviation domain specific BERT model (Aviation-BERT) is compared against BERT-base-uncased for anomaly event classification in the Aviation Safety Reporting System (ASRS) data. Aviation-BERT is shown to have superior performance based on multiple metrics. By focusing on the potential of NLP in advancing complex aviation safety report analysis, the present work offers a comprehensive evaluation of BERT on aviation domain datasets and discusses its strengths and weaknesses. This research highlights the significance of domain-specific NLP models in improving the accuracy and efficiency of safety report classification and analysis in the aviation industry.
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.
Development of a Parametric Structural Analysis Environment to Support the Design, Manufacturing, and Production of a Composite UAV Wing

(AIAA, 2023-01) dos Santos, Marcos ; Cox, Adam ; Fischer, Olivia J. Pinion ; Mavris, Dimitri N.

As digital and physical systems become more complex, collect voluminous quantities of data, and move towards greater integration over time, the concept of digital engineering has become of great interest in the engineering world. The integration of digital methods with traditional engineering approaches in product lifecycle management has posed challenges on how techniques such as digital twins can be best used during the design and manufacturing phases of the product lifecycle. To address this need, this research supports the integration of design, manufacturing, and production by assessing the structural integrity of various designs of a parametric UAV wing built with a composite material. A systematic and efficient environment is developed to modify the wing design parameters, develop and analyze the finite element model, obtain structural data, and identify feasible design regions for decision making. The sharing of models, data, and analyses with the manufacturing and production segments of the lifecycle permits integration of the various disciplines in early design phases to allow greater design freedom and avoid great costs during the design of the product. The results indicate that (1) the need for a trade-off analysis between key disciplinary considerations in UAV wing design decision making can be addressed and that (2) the developed capability enables decision makers to choose the configurations to be studied in later design stages after the structural integrity and weight considerations are assessed for multiple wing designs.
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.
Defining and Parameterizing the Design Space for Cislunar PNT Architectures

(Georgia Institute of Technology, 2023-01) Bender, Theresa ; Gabhart, Austin ; Steffens, Michael ; Mavris, Dimitri N.

Operations in cislunar space are expected to greatly increase over the next decade, which will place a heightened demand on position, navigation, and timing (PNT) architectures. Existing PNT systems will be unable to support this growth, evidencing the need for a new cislunar PNT infrastructure. This study defines and parameterizes the design space for cislunar PNT architecture development, with the goal of enabling design space exploration and architecture trade studies. Design choices such as orbit type, architecture symmetry, and preferred design variables and their ranges are discussed. An environment for modeling and evaluating PNT architectures is developed and demonstrated on a subset of the defined design space. Preliminary results are shown to exhibit the type of data and trends to be expected from these studies. A discussion of optimization algorithms that can leverage this environment to fully explore the defined design space and identify optimal designs is presented.
Development of an Open Rotor Propulsion System Model and Power Management Strategy

(Georgia Institute of Technology, 2023-01) Clark, Robert A. ; Perron, Christian ; Tai, Jimmy C. M. ; Airdo, Benjamin ; Mavris, Dimitri N.

The development of an open rotor propulsion system architecture model and fuel burn-minimizing power management strategy is investigated. The open rotor architecture consists of a single-rotor open rotor (SROR) connected to the low speed shaft of a traditional turbojet engine in a puller configuration. The proposed architecture is modeled in the Numerical Propulsion System Simulation (NPSS) tool, and performance is evaluated across a complete flight envelope typical for a narrow body commercial airliner. Rotor performance maps are generated using a custom blade element momentum theory (BEMT) code, while compressor performance maps are created using CMPGEN. The performance of the overall propulsion system is detailed in the context of a notional 150 passenger aircraft mission, and a method for scheduling rotor power across the flight envelope is developed in order to minimize aircraft mission fuel burn. It is demonstrated that the power absorbed by the rotor can be optimized by scheduling rotor blade pitch angle versus fan speed. A power management technique using the optimal blade pitch angle at only six points in the flight envelope was shown to provide significant computational benefits without sacrificing any fuel burn when compared to a method using a schedule generated from data across the complete flight envelope.