Use of Machine Learning for Helicopter Ship Operational Research

使用机器学习进行直升机船舶运行研究

• 批准号: 2599516
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599516
• 关键词: Use; Machine; Learning; Helicopter; Ship

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.

本研究的目的是通过彻底检查现有的飞行测试和模拟 SHOL 数据，开发一种“智能”方法来支持未来的飞机清关活动。现实世界和模拟数据集是昂贵的资产，尚未经过充分检查以了解、分析和预测飞机的运行限制。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仿真 (M&S) 环境对一系列操作条件的敏感性。它将为未来的飞机清关活动生成新的工作量指标和最佳实践，以提高 SHOL 工作的效率，并有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，并将在未来选择性驾驶和无人驾驶平台的许可流程中具有潜在的应用。