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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718437
• 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.

对电的需求日益增长。当发达国家实施节能措施时，发展中国家正在工业化。为了避免气候变化，向零碳能源过渡至关重要。为了满足当前和未来的电力需求，正在大力投资于太阳能、风能和波浪能等可再生能源技术。虽然现有的水电站可靠地提供基荷能源，但可再生能源技术本质上会因天气和季节而间歇性，因此为了提供足够的基荷电力，核电是短期内唯一可行的选择。大多数现有的核发电站都很大，每个反应堆通常产生十亿瓦或更多的热能。尽管它们有成功的运营历史，但安全问题和经济障碍导致近年来很少有这种规模的新电站建成，其中没有一个在加拿大。相反，30多个具有增强安全功能的小型模块化反应堆（SMR）设计正处于不同的开发阶段。这些较小的反应堆是工厂建造的，其热功率比现有的反应堆低大约一个数量级。加拿大正在通过渐进的政策将自己定位为SMR技术的世界领导者，加拿大核实验室和新玩法电力公司都希望拥有示范工厂。虽然大多数SMR设计是基于20世纪60年代试验的概念，但它们没有相关的操作历史，因此性能和安全分析依赖于计算机模型。此外，由于SMR设计通常具有长寿命堆芯，因此在工厂运行时评估燃料性能的能力非常重要。遗憾的是，使用现有技术无法对燃料性能进行现场评估。必须有一种更好的方法来评估反应堆堆芯在现场和整个寿命期间的性能。当核反应堆燃料燃烧时，产生的裂变产物的β衰变通常会导致反中微子的发射。由于其弱相互作用的性质，并且由于裂变产物产量随反应堆成分（堆芯年龄和燃料类型）而变化，反中微子通过辐射屏蔽携带有关反应堆堆芯性能的信息，因此可能允许直接评估堆芯燃耗，而无需关闭或打开反应堆。在英国开始的先前工作的基础上，该研究计划旨在建立一个良好的计算基础，从中可以预测反中微子在整个生命周期反应堆监测中的使用。这一目标将涉及开发准确预测不同反应堆和燃料的静态和动态反中微子光谱的方法;通过机器学习准确预测反中微子光谱的燃烧能力;以及能够定位反应堆堆芯特定部分或空间其他地方的反中微子排放。