Use of Machine Learning for Helicopter Ship Operational Research

The objective of this study is to develop a "smart" approach to support future aircraft clearance activities through a thorough examination of existing flight test and simulation SHOL data. Real-world and simulation datasets are expensive assets that have not been fully examined to understand, analyse and predict aircraft operational limits. This project will develop a new methodology to interrogate existing data and identify sensitivities in the modelling and simulation (M&S) environment to a range of operational conditions. It will generate new workload metrics and best practices for future aircraft clearance activities to improve the efficiency of SHOL work and could potentially improve operational capability of existing and future ship/helicopter combinations. Fulfilling this objective will represent a significant change in best practices and will have potential applications in the clearance processes for future optionally piloted and unmanned platforms.