Satellite Radiation Risk Forecasts (Sat-Risk)

卫星辐射风险预测（Sat-Risk）

• 批准号: NE/V002511/1
• 负责人: Michael Balikhin
• 金额: $ 36.3万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV002511%2F1
• 关键词: Satellite; Radiation; Risk; Forecasts; Sat

Our society relies on satellites more than ever before, from the use of mobile phones to broadcasting and Earth observation. The use of GPS navigation, positioning and timing signals has grown considerably and we now use these signals in ways that were never foreseen, for example in agriculture and stock market trading. The modern world has also become more connected and built up dependencies that are no longer clearly identifiable. It is therefore essential that we take every reasonable precaution to ensure that satellites are protected.Space Weather poses one of the most important threats to satellites in orbit, primarily through radiation exposure. Radiation exposure can increase within a few minutes, by a ten thousand fold or more, and remain high for days, even months. For example, in 2003 during a large space weather event known as the Halloween storm radiation levels were highly disturbed and approximately 10% of the entire satellite fleet were affected by satellite anomalies (malfunctions) leading to service interruption and in one case the complete loss of a scientific satellite costing $640 million (Cannon et al., 2013). In 2012 the UK Government recognised the importance of space weather as a low frequency high impact event and included it on the National Risk Register (Cabinet Office, 2012). This was revised in 2017 with a recommendation for more investment into forecasting as a means of mitigating the impact. More recent research suggests that with current forecasting capability the loss of gross domestic product to the UK would be around £2.9 billion but with more investment in enhanced forecasting this could be reduced to £0.9 billion (Oughton et al., 2019). The purpose of this proposal is to provide enhanced forecasting to help reduce the impact on satellites. Currently there are over 2,200 operational satellites in orbit (December 2019).This proposal brings together scientists from across the UK with stakeholders from the UK Met Office. The goal is to develop a real-time system to forecast radiation exposure to satellites for a range of different orbits, and quantify the risk of damage or degradation. We will do this by taking research models of the Earth's radiation belts - regions of high energy electrons and protons trapped by the external geomagnetic field and which circulate around the Earth - and turn them into operational forecasting models. The models will use real-time data from ground and space to forecast radiation exposure up to 24 hours ahead for different orbits, including geostationary orbit, Low Earth orbit and medium Earth orbit. It will also include data on radiation storms and cosmic rays. The particle radiation levels will then be used to calculate the damaging radiation effects on electronic components and solar arrays and compared to design guidelines to assess the risk of damage. The project will also include four research elements which are specifically targeted at reducing the uncertainty in the forecasts. The project will deliver a world leading forecasting capability for the Met Office that will help satellite operators take mitigating action, help satellite designers develop more resilient design and space insurance reduce the risk of loss. It will also support the growth of the satellite industry and the UK National Risk Register.1. Cannon, P, S., et al. (2013), Extreme Space Weather: Impacts on Engineered Systems and Infrastructure, Royal Academy of Engineering, London, SW1A 2WH.2. Cabinet Office, (2012), National risk register of civil emergencies, Whitehall, London SW1A 2WH, www.cabinetoffice.gov.uk3. Oughton et al., (2019), A Risk Assessment Framework for the Socioeconomic Impacts of Electricity Transmission Infrastructure Failure Due to Space Weather: An Application to the United Kingdom, Risk Analysis, https://doi.org/10.1111/risa.13229

从移动电话的使用到广播和地球观测，我们的社会比以往任何时候都更加依赖卫星。GPS导航、定位和定时信号的使用已经大大增加，我们现在以从未预见到的方式使用这些信号，例如在农业和股票市场交易中。现代世界也变得更加紧密相连，建立起了不再清晰可辨的依赖关系。因此，我们必须采取一切合理的预防措施，确保卫星受到保护。空间天气对轨道上的卫星构成了最重要的威胁之一，主要是通过辐射暴露。辐射暴露可以在几分钟内增加一万倍或更多，并保持高水平数天，甚至数月。例如，在2003年被称为万圣节风暴的大型空间天气事件期间，辐射水平受到高度干扰，整个卫星群中约有10%受到卫星异常（故障）的影响，导致服务中断，在一个案例中，一颗科学卫星完全损失，耗资6.4亿美元（Cannon et al., 2013）。2012年，英国政府认识到空间天气作为低频率高影响事件的重要性，并将其列入国家风险登记册（内阁办公室，2012年）。2017年进行了修订，建议增加对预测的投资，以减轻影响。最近的研究表明，以目前的预测能力，英国的国内生产总值损失将约为29亿英镑，但如果在增强预测方面投入更多资金，这一损失可能会减少到9亿英镑（Oughton等人，2019）。这项建议的目的是提供更好的预报，以帮助减少对卫星的影响。目前有2200多颗在轨运行卫星（2019年12月）。这项提议将英国各地的科学家和英国气象局的利益相关者聚集在一起。目标是开发一个实时系统，以预测不同轨道卫星的辐射暴露，并量化损坏或退化的风险。为此，我们将建立地球辐射带的研究模型，并将其转化为可操作的预测模型。地球辐射带是被外部地磁场捕获并围绕地球运行的高能电子和质子区域。这些模型将使用来自地面和太空的实时数据，提前24小时预测不同轨道的辐射暴露情况，包括地球静止轨道、低地球轨道和中地球轨道。它还将包括辐射风暴和宇宙射线的数据。粒子辐射水平将被用来计算对电子元件和太阳能电池阵列的破坏性辐射影响，并与设计指南进行比较，以评估损害的风险。该项目还将包括四个研究要素，专门针对减少预测中的不确定性。该项目将为英国气象局提供世界领先的预测能力，帮助卫星运营商采取缓解措施，帮助卫星设计师开发更具弹性的设计和空间保险，降低损失风险。它还将支持卫星工业和英国国家风险登记册的发展。Cannon， P， S, et .(2013)，极端空间天气：对工程系统和基础设施的影响，皇家工程学院，伦敦，SW1A 2WH.2。内阁府，（2012），国家民事突发事件风险登记册，白厅，伦敦SW1A 2WH， www.cabinetoffice.gov.uk3。Oughton等人，（2019），空间天气导致的输电基础设施故障社会经济影响的风险评估框架：在英国的应用，风险分析，https://doi.org/10.1111/risa.13229

项目成果

A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and Classification

• DOI: 10.1109/icac55051.2022.9911115
• 发表时间: 2022-09
• 期刊: 2022 27th International Conference on Automation and Computing (ICAC)
• 作者: Guoliang Wang;Hua‐Liang Wei

Multi-Task Learning for Time Series Forecasting Using NARMAX-LSTM

• DOI: 10.1109/icac55051.2022.9911071
• 发表时间: 2022-09
• 期刊: 2022 27th International Conference on Automation and Computing (ICAC)
• 作者: N. Nikentari;Hua‐Liang Wei

An effective zero-shot learning approach for intelligent fault detection using 1D CNN

• DOI: 10.1007/s10489-022-04342-1
• 发表时间: 2022-12
• 期刊: Applied Intelligence
• 影响因子: 5.3
• 作者: Siyu Zhang;Hua‐Liang Wei;Jinliang Ding

Structure of a Quasi-parallel Shock Front

• DOI: 10.3847/1538-4357/ad0b71
• 发表时间: 2023-12
• 期刊: The Astrophysical Journal
• 作者: M. Balikhin;M. Gedalin;S. Walker;O. Agapitov;Tielong Zhang

Machine Learning for Medical and Healthcare Data Analysis and Modelling: Case Studies and Performance Comparisons of Different Methods

用于医疗和保健数据分析和建模的机器学习：案例研究和不同方法的性能比较

• DOI: 10.1109/icac55051.2022.9911176
• 发表时间: 2022
• 作者: Sun B