Machine Learning Techniques applied to Flight Test Data Evaluation
This paper proposes an application of machine-learning methods to the analysis of flight test data. A set of training data is used to develop relationships between measurands and generate predicted behavior. These relationships are forecast onto data from the same aircraft model to identify unpredicted measurand behavior. The application of this method may significantly reduce post-test identification time of problematic measurands. Statistical analysis methods are used to determine quality of identified relationships. The importance of a carefully selected training data set and development of robust relationships with low collinearity is emphasized. The developed application demonstrates faster instrumentation failures diagnosis than traditional methods. Areas of continued research include real-time flight safety forecasting, and reduction of required instrumentation.
Low and High-Speed Cryogenic Testing of a Wind Tunnel Model with Remote Control Actuation
This paper discusses the technological advancements made via collaborative cross-company efforts by, an international team to increase the TRL level of remote actuation of wind tunnel model control surfaces in a cryogenic environment through creative use of shape memory alloy-based systems.
AeroTEC Case Study for Test & Certification Opportunities of Electric and Unmanned Aircraft Systems
This document provides an overview of the burgeoning electric, hybrid and unmanned aircraft markets and the associated testing and certification efforts that can make entry to market for these vehicles a reality. Many leading companies, marketing firms and their analyses, along with the multiple of national efforts in the electric and UAS aircraft industries are highlighted with thoughts on potential opportunities for involvement.