Implementataion and Validation of an Internal Combustion Engine and Propeller Model for General Aviation Aircraft Performance Studies

16th AIAA Aviation Technology, Integration, and Operations Conference

Harrison

Min

et al. 2016

Self Cite

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.

Section: B Description Of Performance Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Based Metrics for General Aviation Flight Data Record Analysis

16th AIAA Aviation Technology, Integration, and Operations Conference

Harrison

Min

et al. 2016

Self Cite

“…Many of the decisions made in GA safety analysis depend upon the availability of data. The previous empirical models developed for GA operations ( [8,9]) utilized data from the public domain due to lack of flight data. These models were independently calibrated against available benchmarks.…”

Section: Figure 2 Schematic Of Two-level Calibration Processmentioning

confidence: 99%

“…These models are available in the form of variations of important non-dimensional quantities relevant for aircraft performance, such as lift curve, drag polar, propeller polar, etc. A preliminary implementation of such models using empirical relations and assumptions can be found in Harrison et al [8] and Min et al [9]. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraft.…”

Section: Introductionmentioning

confidence: 99%

A Framework for General Aviation Aircraft Performance Model Calibration and Validation

2018 Aviation Technology, Integration, and Operations Conference

Harrison

Min

et al. 2018

Self Cite

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 finetune 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 multiobjective 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.

“…In GA operations that are the subject of this work (small airplanes weighing less than 12,500 lbs and having seating configuration of 10 or less), [7] a large number of parameters may not be recorded due to limited capability of their data collection hardware. Therefore, using recorded flight data with quantitative aircraft performance models (such as those developed in Harrison et al [8] and Min et al [9]) are a key enabler for retrospective safety analysis in GA. These models are used for evaluating a number of energy based metrics which provide a measurable quantification of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a large fleet of aircraft with minimal amount of recorded parameters.…”

Section: Introductionmentioning

confidence: 99%

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

Journal of Aerospace Information Systems

Mavris

2018

Self Cite

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. Energybased 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.