Reducing Environmental Footprint through transformative Multi-scale Aviation Planning (REFMAP)

RefMap的使命是开发一种数字服务，旨在量化客机和无人机系统（UAS）在“多尺度”水平上的空中机动性的环境足迹，其中优化了单轨迹（微观）和多车辆（宏观）的流量，以最大限度地减少其对广泛社区的环境影响。RefMap调查了航空商业模式将如何受到每种类型和航线航空器环境数据的影响，因为这将使该行业制定更严格的循证绿色政策。这将通过开发RefMap分析平台来实现，该平台可以处理环境和天气数据，如U-space和ATM的风、噪音、二氧化碳和非二氧化碳排放。该平台将依赖于许多技术解决方案，包括数值模拟、预测模型和深度学习方法。后者将用于构建准确的非侵入式预测框架，并通过深度强化学习（DRL）优化各种车辆在预测流量条件下的轨迹。这将使新的航空商业模式的发展与欧盟的绿色议程保持一致。索尔福德大学将开发建模框架，以评估和最大限度地减少无人机系统噪音对人类和野生动物的影响。

The mission of RefMap is to develop a digital service aimed at quantifying the environmental footprints of air mobility for airliners and unmanned aircraft systems (UAS) at a "multi-scale" level, where single-trajectories (micro) and the flow traffic of multiple vehicles (macro) are optimised to minimise their environmental impact in a wide range of communities. RefMap investigates how the aviation business models will be affected by the availability of environmental data for each type and route of air vehicle, as this will enable stricter evidence-based Green policy making in the sector. This will be achieved via the development of the RefMap analytics platform processing environmental and weather data such as wind, noise, CO2 and non-CO2 emissions for both U-space and ATM. This platform will rely on a number of technical solutions, including numerical simulation, predictive models, and deep-learning methods. The latter will be used to construct accurate non-intrusive prediction frameworks and to optimize the trajectories of the various vehicles given the predicted flow conditions via deep reinforcement learning (DRL). These will enable the development of a new aviation business models aligned with EU’s Green Agenda.The University of Salford will develop modelling frameworks to assess and minimise the impact of UAS noise on humans and wildlife.