Machine Learning Enabled Turbulence Prediction Using Flight Data for Safety Analysis

Presented at the 32nd ICAS Congress, Shanghai, China (2022).The hazards posed by turbulence remain an important issue in commercial aviation safety analysis. Turbulence is among the leading cause of in-flight injury to passengers and flight attendants. Current methods of turbulence detection may suffer from sparse or inaccurate forecast data sets, low spatial and temporal resolution , and lack of in-situ reports. The increased availability of flight data records offers an opportunity to improve the state-of-the-art in turbulence detection. The Eddy Dissipation Rate (EDR) is consistently recognized as a reliable measure of turbulence and is widely used in the aviation industry. In this paper, both classification and regression supervised machine learning models are used in conjunction with flight operations quality assurance (FOQA) data collected from 6,000 routine flights to estimate the EDR (and thereby turbulence severity) in future time horizons. Data from routine airline operations that encountered different levels of turbulence is collected and analyzed for this purpose. Results indicate that the models are able to perform reasonably well in predicting the EDR and turbulence severity around 10 seconds prior to encountering a turbulence event. Continuous deployment of the model enables obtaining a near-continuous prediction of possible future turbulence events and builds the capability towards an early warning system for pilots and flight attendants

Machine Learning Approach for the Evaluation of Degraded Wheel Braking Performance on Contaminated Runways

One of the most challenging issues in the airline community has been the safe operations of aircraft during the landing phase of flight. Several entities agree that runway excursion is the most frequent type of landing incident or accident, and studies on this topic have shown that a significant contributor is the condition of the runway surface at the time of landing, as well as the potential for degraded braking performance

Data-Driven Analysis of Departure Procedures for Aviation Noise Mitigation

13-C-AJFF-GIT-054This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. The paper first appeared in Engineering Proceedings. Citation: Bhanpato, J.; Puranik, T.G.; Mavris, D.N. Data-Driven Analysis of Departure Procedures for Aviation Noise Mitigation. Eng. Proc. 2021, 13, 2. https://doi.org/10.3390/ engproc2021013002The mitigation of aviation environmental effects is one of the key requirements for sustainable aviation growth. Among various mitigation strategies, Noise Abatement Departure Procedures (NADPs) are a popular and effective measure undertaken by several operators. However, a large variation in departure procedures is observed in real operations. This study demonstrates the use of OpenSky ADS-B departure data for comparison and quantification of the differences in trajectories and the resulting community noise impact between real-world operations and NADPs. Trajectory comparison is accomplished in order to gain insights into the similarity between NADPs and real-world procedures. Clustering algorithms are employed to identify representative departure procedures, enabling efficient high-fidelity noise modeling. Finally, noise results are compared in order to quantify the difference in environmental impacts arising from variability in real-world trajectories. The methodology developed enables more efficient and accurate environmental analyses, thereby laying the foundation for future impact assessment and mitigation efforts

Identifying Instantaneous Anomalies in General Aviation Operations

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).Federal Aviation Administratio

Identification of Instantaneous Anomalies in General Aviation Operations using Energy Metrics

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.Federal Aviation Administration (Grant No: 12-C-GA-GIT-018

Anomaly Detection in General Aviation Operations Using Energy Metrics and Flight Data Records

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.Federal Aviation Administration (Grant No. 12-C-GA-GIT-004

Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations

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.Federal Aviation Administratio

A Framework for General Aviation Aircraft Performance Model Calibration and Validation

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

Aircraft Performance Model Calibration and Validation for General Aviation Safety Analysis

© 2020 American Institute of Aeronautics and AstronauticsPerformance models facilitate a wide range of safety analyses in aviation. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraft. However, in reality, this is rarely the case: either owing to underlying simplifications or due to the limited fidelity of applicable tools or data. In such cases, calibration is required to fine-tune the behavior of the performance models. For 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, as well as because a large number of model parameters may potentially influence model accuracy. This work presents a two-level approach to aircraft performance model calibration. The first level consists of using manufacturer-developed performance manuals for calibration, whereas the second level provides additional refinement when flight data are available. 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 at any given point in time. The framework is demonstrated on two representative general aviation aircraft. The demonstrated approach results in models that can predict critical energy-based safety metrics with improved accuracy for use in retrospective safety analyses.Federal Aviation Administration 12-C-GA-GIT-01

Integrated Sizing and Multi-objective Optimization of Aircraft and Subsystem Architectures in Early Design

Presented at AIAA Aviation 2017The 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