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Aircraft Performance And Design

NASA concept vehicles and the engineering of advanced air mobility aircraft

In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft.

Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification

A typical aircraft certification process consists of obtaining a type, production, airworthiness, and continued airworthiness certificate. During this process, a type certification plan is created that includes the intended regulatory operating environment, the proposed certification basis, means of compliance, and a list of documentation to show compliance. This paper extends previous work to demonstrate a model-based framework for the management of these certification artifacts for normal category airplanes. The developed framework integrates the regulatory rules and approvedmeans of compliance in a singlemodel while using best-practices found in Model-Based Systems Engineering (MBSE) literature. This framework, developed using SysML in MagicDraw captures not just the textual requirements and verification artifacts, but also their relationships and any inherent meta-data properties via custom defined stereotype profiles. Additionally, a simulation capability that automates the extraction and export of the applicable rules (certification basis) and corresponding means of compliance for any aircraft under consideration at the click of a button has been developed. The framework also provides numerous additional benefits to different stakeholders that have been described in detail with examples where necessary.

A Model-Based Aircraft Certification Framework for Normal Category Airplanes

A key enabler for sustainable growth of aviation is the mitigation of adverse environmental effects. One area of concern is community noise exposure at large hub airports serving growing population centers. Traditionally, community noise exposure is computed using noise contours around airports, which requires knowledge of a large dataset pertaining to the air traffic operations at the airport of interest. Due to the underlying variability in real-world aircraft operations, numerous assumptions need to be made which adversely affect the accuracy of the model. Reduced-Order Modeling (ROM) methods provide a new framework for the retention of a large number of these parameters, thus improving model speed and accuracy. In this work, a proper orthogonal decomposition in conjunction with a response surface methodology-based surrogate model is used to create a rapid noise assessment model. Validation is performed against results obtained from the aviation environmental design tool with quantitative error metrics and visual contour comparisons. Obtained results are encouraging and motivate further work in this area with other ROM methods. ROM based models for noise assessment expand the solution space for noise mitigation strategies which can be evaluated, and therefore can lead to novel solutions which cannot be found with traditional modeling methods.

https://www.mdpi.com/2071-1050/13/3/1120/pdf

Reduced Order Modeling Methods for Aviation Noise Estimation

Aviation noise remains one of the major barriers to sustainable aviation growth. With increasing traffic at major airports and rapid urbanization, community noise exposure is also expected to increase concurrently. New technologies, while effective, take a long time to mature and penetrate the market, and are therefore only suitable as the long-term solution. A near-term solution of noise mitigation is to analyze and modify the ways existing aircraft are flown in current operations. The objective of this paper is to analyze the sensitivities of airport level environmental impacts such as noise and emissions to changes in aircraft takeoff weight, thrust and departure profiles.

Sensitivity Analysis of Airport level Environmental Impacts to Aircraft thrust, weight, and departure procedures

The environmental effects of aviation, particularly community noise exposure, is one of the major barriers to a sustainable growth in passenger air traffic. With an increasing number of aircraft op...

Optimization of Takeoff Departure Procedures for Airport Noise Mitigation

Improved Noise Abatement Departure Procedure Modeling for Aviation Environmental Impact Assessment

Accurate modeling of aircraft fuel consumption, emissions, and noise is crucial in evaluating new air transportation operational procedures and policies to abate negative environmental impacts. The...

Ameya Behere

Papers

Reduced Order Modeling Methods for Aviation Noise Estimation

Sensitivity Analysis of Airport level Environmental Impacts to Aircraft thrust, weight, and departure procedures

Optimization of Takeoff Departure Procedures for Airport Noise Mitigation

Improved Noise Abatement Departure Procedure Modeling for Aviation Environmental Impact Assessment

Development and Analysis of Improved Departure Modeling for Aviation Environmental Impact Assessment