Computer simulations of molten salts interactions with metallic materials

熔盐与金属材料相互作用的计算机模拟

基本信息

批准号：
573450-2022
负责人：
Béland, LaurentKarimLK
金额：
$ 3.92万
依托单位：
Queen's University
依托单位国家：
加拿大
项目类别：
Alliance Grants
财政年份：
2022
资助国家：
加拿大
起止时间：
2022-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759785
关键词：
Computer simulations molten salts interactions

项目摘要

Small modular reactors have been identified by the Canadian government as a "source of safe, clean, affordable energy [...] for Canada and Canadians." Several of these reactor concepts rely on molten salt heat exchange loops, rather than traditional water-based heat exchangers. A key issue is that molten-salt driven corrosion is much less understood. This is of prime importance for the three main industry partners-the Canadian Nuclear Laboratories, Ontario Power Generation, and Global First Power-which are involved in the design, planning and construction of Canada's small modular reactors. In order to bridge this knowledge gap, we will perform atom-by-atom computer simulations to understand and predict how molten salts interact with metallic structural materials. A challenging aspect of this research is that interatomic interactions are intrinsically quantum in nature. Simulation methods based on quantum mechanics are accurate, but tend to have a prohibitive computational cost. Here, we will use state-of-the-art machine learning algorithms to construct interatomic interaction models with a level of accuracy comparable to that of quantum-mechanics-based methods, but at a tractable computational cost. The research project will involve four industrial internships on the site of the industry partners, helping train the next generation of Canadian nuclear industry scientists.

加拿大政府已将小型模块化反应堆确定为“加拿大和加拿大人的安全，干净，负担得起的能源[...]的来源”。这些反应堆概念中有几个依赖于熔融的盐热交换环，而不是传统的水基热交换器。一个关键问题是，熔融盐驱动的腐蚀知之甚少。这对于三个主要行业合作伙伴 - 加拿大核实验室，安大略省电力公司以及全球第一电力 - 参与加拿大小型模块化反应堆的设计，计划和建设至关重要。为了弥合这一知识差距，我们将执行逐个原子计算机模拟，以了解和预测熔融盐如何与金属结构材料相互作用。这项研究的一个具有挑战性的方面是，原子间相互作用本质上是本质上的量子。基于量子力学的仿真方法是准确的，但往往具有过高的计算成本。在这里，我们将使用最先进的机器学习算法来构建具有与基于量子力学的方法相当的精度级别的原子质交互模型，但以可拖动的计算成本。该研究项目将涉及行业合作伙伴现场的四个工业实习，帮助培训下一代加拿大核工业科学家。