Organizational Unit:

Aerospace Systems Design Laboratory (ASDL)

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

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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 - 9 of 9

Identification of Instantaneous Anomalies in General Aviation Operations using Energy Metrics

(Georgia Institute of Technology, 2019-12) Puranik, Tejas G. ; Mavris, Dimitri N.

Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, machine learning techniques have emerged as an important enabler in the data-driven safety enhancement of aviation operations with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft and operations. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis in General Aviation using energy-based metrics is proposed. Each flight data record is processed by a sliding window across the multi-variate time series of evaluated metrics. A Gaussian Mixture Model using energy metrics and their variability within each window is fit in order to predict the probability of any instant during the flight being nominal. Instances during flights that deviate from the nominal are isolated to identify potential increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment such as exceedance detection to highlight the benefits of the developed method. The methodology is demonstrated using flight data records from two representative aircraft for critical phases of flight.
Predicting The Occurrence of Weather And Volume Related Ground Delay Program

(Georgia Institute of Technology, 2019-06) Mangortey, Eugene ; Pinon, Olivia J. ; Puranik, Tejas G. ; Mavris, Dimitri N.

Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) are instituted by traffic management personnel to address and reduce the impacts of constraints in the National Airspace System. Ground Delay Programs are initiated whenever demand is projected to exceed an airport’s acceptance rate over a lengthy period of time. Such instances occur when an airport is affected by conditions such as inclement weather, aircraft congestion, runway-related incidents, equipment failures, and other causes that do not fall in these categories. Over the years, efforts have been made to reduce the impact of Ground Delay Programs on airports and flight operations by predicting their occurrence. However, these efforts have largely focused on weather-related Ground Delay Programs, primarily due to a lack of access to comprehensive Ground Delay Program data. There has also been limited benchmarking of Machine Learning algorithms to predict the occurrence of Ground Delay Programs. Consequently, this research 1)fused data from the Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraged supervised Machine Learning algorithms to develop prediction models as a means to predict the occurrence of weather and volume-related Ground Delay Programs. The Kappa Statistic evaluation metric revealed that Boosting Ensemble was the best suited algorithm for predicting the occurrence of weather and volume-related Ground Delay Programs.
A Framework for General Aviation Aircraft Performance Model Calibration and Validation

(Georgia Institute of Technology, 2018-06) Puranik, Tejas G. ; Harrison, Evan D. ; Mavris, Dimitri N.

A wide range of aircraft performance and safety analyses are greatly facilitated by the development and availability of reliable and accurate aircraft performance models. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraft. However, in reality, this is almost never the case, either owing to underlying simplifications or assumptions or due to the limited fidelity of available or applicable analysis tools. In such cases, model calibration is required in order to fine tune the behavior of available performance models to obtain the desired agreement with the truth model. In the case of point-mass steady-state performance models, challenges arise due to the fact that there is no obvious, unique metric or flight condition at which to assess the accuracy of the model predictions, and since a large number of model parameters may potentially influence model accuracy. This work presents a systematic two- level approach to aircraft performance model calibration that poses the calibration as an optimization problem using the information available. The first level consists of calibrating the performance model using manufacturer-developed performance manuals in a multi objective optimization framework. If data is available from flight testing, these models are further refined using the second level of the calibration framework. The performance models considered in this work consist of aerodynamic and propulsion models (performance curves) that are capable of predicting the non-dimensional lift, drag, thrust, and torque produced by an aircraft at any given point in time. The framework is demonstrated on two popular and representative single-engine naturally-aspirated General Aviation aircraft. The demonstrated approach results in an easily-repeatable process that can be used to calibrate models for a variety of retrospective safety analyses. An example of the safety analyses that can be conducted using such calibrated models is also presented.
Integrated Sizing and Multi-objective Optimization of Aircraft and Subsystem Architectures in Early Design

(Georgia Institute of Technology, 2017-06) Rajaram, Dushhyanth ; Cai, Yu ; Chakraborty, Imon ; Puranik, Tejas G. ; Mavris, Dimitri N.

The aerospace industry's current trend towards novel or More Electric architectures results in some unique challenges for designers due to both a scarcity or absence of historical data and a potentially large combinatorial space of possible architectures. These add to the already existing challenges of attempting to optimize an aircraft design in the presence of multiple possible objective functions while avoiding an overly compartmentalized approach. This paper uses the Integrated Subsystem Sizing and Architecture Assessment Capability to pursue a multi-objective optimization for a Large Twin-aisle Aircraft and a Small Single-aisle Aircraft using the Non-dominated Sorting Genetic Algorithm II with parallel function evaluations. One novelty of the optimization setup is that it explicitly considers the impacts of subsystem architectures in addition to those of traditional aircraft-level design variables. The optimization yielded generations of non-dominated designs in which substantially electrified subsystem architectures were found to predominate. As a first assessment of the impact of epistemic uncertainty on the results obtained, the optimization was re-run with altered sensitivities for the thrust-specific fuel consumption penalties due to shaft-power and bleed air extraction. This analysis demonstrated that the composition of architectures on the Pareto frontier is sensitive to the secondary power extraction penalties, but more so for the Small Single-aisle Aircraft than the Large Twin-aisle Aircraft.
Identifying Instantaneous Anomalies in General Aviation Operations

(Georgia Institute of Technology, 2017-06) Mavris, Dimitri N. ; Puranik, Tejas G.

Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, data mining techniques are emerging as an important enabler in the aviation safety domain with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. There are two types of anomalies typically identified - flight-level (where the entire flight exhibits patterns deviating from nominal operations) and instantaneous (where a subset or few instants of the flight deviate significantly from nominal operations). Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis using energy-based metrics is proposed. Each data record is split by sliding a moving window across the multi-variate series of evaluated energy metrics. A mixture of gaussian models is then used to perform clustering using the values of energy metrics and their variability within each window. The trained models are then used to identify anomalies that may indicate increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment (exceedance detection).
Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations

(Georgia Institute of Technology, 2017-01) Puranik, Tejas G. ; Jimenez, Hernando ; Mavris, Dimitri N.

Among operations in the General Aviation community, one of the most important objectives is to improve safety across all flight regimes. Flight data monitoring or Flight Operations Quality Assurance programs have percolated in the General Aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, we have presented a generic methodology for identifying anomalous flight data records from General Aviation operations using energy based metrics and clustering techniques. The sensitivity of this methodology to various key parameters is quantified using different experiments. A demonstration of this methodology on a set of actual flight data records as well as simulated flight data is presented highlighting its future potential.
Anomaly Detection in General Aviation Operations Using Energy Metrics and Flight Data Records

(Georgia Institute of Technology, 2017) Puranik, Tejas G. ; Mavris, Dimitri N.

Among operations in the General Aviation community, one of the most important objectives is to improve safety across all flight regimes. Flight data monitoring or Flight Operations Quality Assurance programs have percolated in the General Aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, a generic methodology is presented for identifying anomalous flight data records from General Aviation operations in the approach and landing phase. Energy based metrics, identified in previous work, are used to generate feature vectors for each flight data record. Density-based clustering and one-class classification are then used together for anomaly detection using energy-based metrics. A demonstration of this methodology on a set of actual flight data records from routine operations as well as simulated flight data is presented highlighting its potential for retrospective safety analysis. Anomaly detection using energy metrics, specifically, is a novel application presented here.
General Aviation Approach and Landing Analysis using Flight Data Records

(Georgia Institute of Technology, 2016-06) Puranik, Tejas G. ; Harrison, Evan D. ; Min, Sanggyu ; Jimenez, Hernando ; Mavris, Dimitri N.

Ensuring a safe and stabilized approach and landing is one of the important objectives in General Aviation applications. This phase is one of the main phases during which accidents occur. A "nominal" or reference trajectory for General Aviation approach and landing operations is critical for flight instruction and retrospective safety assessments reliant on flight data records captured with on-board systems. While this is a more crisply defined area in commercial aircraft operations, it is not so well-defined in General Aviation. The different aspects that need to be considered in defining a nominal trajectory and provide analyses that can be carried out using flight data records are examined. Various ways of defining this nominal or reference approach trajectory are proposed with the eventual aim of using this in conjunction with energy-based methods and metrics to assess and enhance safety in General Aviation aircraft operations.
Energy-Based Metrics for General Aviation Flight Data Record Analysis

(Georgia Institute of Technology, 2016-06) Puranik, Tejas G. ; Harrison, Evan D. ; Min, Sanggyu ; Jimenez, Hernando ; Mavris, Dimitri N.

Energy management and energy state awareness are important concepts in aircraft safety analysis. Many loss-of-control accidents can be attributed to poor energy management. Energy-based metrics provide a measurable quantity of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. In this work, we have surveyed key energy-based metrics from various domains and identified the challenges of implementing these metrics for General Aviation operations. Modifications to existing metrics and definition of some new energy metrics are proposed. A methodology is developed that can be used to evaluate and visualize the energy metrics. These energy metrics can then be used to understand and enhance General Aviation aircraft safety using retrospective flight data analysis.