Use of Machine Learning for Helicopter Ship Operational Research

本研究的目的是通过对现有飞行试验和模拟SHOL数据的彻底检查，开发一种“智能”方法来支持未来的飞机清关活动。真实世界和模拟数据集是昂贵的资产，尚未被充分检查以了解，分析和预测飞机的操作限制。该项目将开发一种新的方法来查询现有数据，并确定建模和模拟（M&S）环境中对一系列操作条件的敏感性。它将为未来的飞机清除活动产生新的工作量指标和最佳实践，以提高SHOL工作的效率，并可能提高现有和未来船舶/直升机组合的作战能力。实现这一目标将代表最佳实践的重大变化，并将在未来可选的有人驾驶和无人驾驶平台的通关过程中具有潜在的应用。

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.