Assessment of small modular reactor core performance using antineutrinos

使用反中微子评估小型模块化反应堆堆芯性能

• 批准号: RGPIN-2020-06715
• 负责人: Atkinson, Kirk
• 金额: $ 2.33万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741378
• 关键词: Assessment; small; modular; reactor; core

The need for electricity is ever growing. Whilst developed nations implement energy conservation measures, developing nations are industrializing. To avert climate change, transition to zero-carbon power sources is essential. To satisfy current and future electricity demands, much effort is being invested in renewable energy technologies such as solar, wind and wave. Whilst existing hydroelectric plants reliably supply baseload energy, renewable energy technologies are inherently intermittent due to weather and season, and hence to provide sufficient baseload electricity, nuclear power is the only viable option in the near-term. Most existing nuclear generating stations are large, each reactor often yielding a gigawatt or more of thermal power. Despite their successful operating histories, safety concerns and economic impediments have led to few new stations of this scale being built in recent years, none of which are in Canada. Instead, 30+ Small Modular Reactor (SMR) designs with enhanced safety features, are in various stages of development. Factory-built, these smaller reactors have thermal powers roughly an order of magnitude lower than existing units. Canada is positioning itself to be a World leader in SMR technologies through progressive policies, with both Canadian Nuclear Laboratories and New Brunswick Power aiming to host demonstration plants. Whilst most SMR designs are based on concepts trialed in the 1960's, they have no relevant operating history and thus performance and safety analysis relies on computer models. Moreover, as SMR designs typically have long-life cores, the ability to assess fuel performance while the plant is operational is important. Unfortunately, in-situ assessment of fuel performance is not possible using existing techniques. A better way of assessing reactor core performance in-situ and through-life is essential. As nuclear reactor fuel is burned, beta decay of resultant fission products typically leads to emission of antineutrinos. Due to their weakly-interacting nature, and because fission product yields vary with reactor composition (core age and fuel type), antineutrinos carry information about reactor core performance through radiation shielding and hence potentially allow direct assessment of core burn-up without needing to shut down or open a reactor. Building on previous work started in the UK, this research program aims to establish a sound computational basis from which use of antineutrinos for through-life reactor monitoring can be predicated. This objective will involve developing methods for the accurate prediction of antineutrino spectra for different reactors and fuels, both static and dynamic; the capability to accurately predict burn up from antineutrino spectra via machine learning; and to be able to localize antineutrino emissions from specific parts of the reactor core or elsewhere in space.

对电力的需求一直在增长。开发的国家实施节能措施，而发展中国家则在工业化。为了避免气候变化，至关重要的是零碳功率来源。为了满足当前和未来的电力需求，在太阳能，风和波浪等可再生能源技术上投入了很多努力。尽管现有的水力发电厂可靠地供应基本电荷能量，但由于天气和季节，可再生能源技术本质上是间歇性的，因此可以提供足够的基本电力电力，而核电是近期唯一可行的选择。大多数现有的核发电站很大，每个反应堆通常会产生Gogawatt或更多的热力。尽管他们成功的经营历史，但安全问题和经济障碍已经导致近年来建立了该规模的新站点，而这些量表却没有在加拿大。取而代之的是，具有增强安全功能的30多个小型模块反应器（SMR）设计处于各种开发阶段。这些较小的反应器具有工厂制造的，其热功率大约比现有单元低的数量级。加拿大通过进步政策将自己定位为SMR技术的世界领导者，加拿大核实验室和新不伦瑞克省的电力旨在举办示威植物。尽管大多数SMR设计基于1960年代试用的概念，但它们没有相关的操作历史记录，因此性能和安全分析依赖于计算机模型。此外，由于SMR设计通常具有长寿核心，因此在植物运行时评估燃料性能的能力很重要。不幸的是，使用现有技术无法对燃料性能进行原位评估。评估反应堆核心绩效的更好方法至关重要。随着核反应堆燃料的燃烧，产生的β衰变通常会导致抗肿瘤的排放。由于其弱相互交互的性质，并且由于裂变产物的产生因反应堆组成（核心年龄和燃料类型）而变化，因此，抗瑞氏杆菌携带有关反应器核心性能通过辐射屏蔽的信息，因此有可能直接评估核心燃烧而无需关闭或打开反应器。在英国以前的工作的基础上，该研究计划旨在建立一个合理的计算基础，可以从中使用抗神经替氏菌对透过生命反应堆监测的使用。该目标将涉及开发方法，以准确预测静态和动态的不同反应堆和燃料的抗肿瘤光谱。通过机器学习准确预测抗肿瘤光谱的能力；并能够从反应堆核心或空间其他地方的特定部位定位抗肿瘤排放。