Multiscale, Multi-fidelity and Multiphysics Bayesian Neural Network (BNN) Machine Learning (ML) Surrogate Models for Modelling Design Based Accidents

浮动核电站（NPP）是一个在海上平台上有一个或多个核反应堆的场所。这是一个自主的站点，可以为陆地面积小但地理上靠近海洋的国家提供电力和处理热量。这些类型的核电站还可以通过海水淡化技术向干旱地区提供新鲜清洁的饮用水。它们可以在工厂或造船厂使用现代模块化建造技术建造，从而消除了为其建造和运营建立核许可场地的需要。由于不需要对土地和土地环境进行可行性研究，这类核电站的选址也大大简化。然而，海洋或沿海环境确实需要考虑几个因素。这些因素包括操作人员和设备的进出，以及确保任何放射性物质不会泄漏到海洋中的需要。鉴于大规模、中型和小型模块化压水堆的广泛开发和部署，本博士提案的目的是在本博士提案中重点分析这些类型的核电站。这使新加坡能够在了解压水堆的操作和安全方面获得经验；以及浮动核电站驳船和平台。此外，该提案将使新加坡能够开发关键技能和技术，以模拟和模拟压水堆的运行行为，以及压水堆中的设计基础事故。它还将建立在目前由新加坡国立大学（NUS）核研究与安全计划（SNRSI）资助的博士研究生的基础上，该计划的重点是开发基于贝叶斯神经网络（BNN）的代理建模和仿真（M&S）框架，用于研究浮动核电站后负荷相关的热疲劳问题。

A floating nuclear power plant (NPP) is a site with one or more nuclear reactors located on a platform at sea. It is an autonomous site that can provide electrical power and process heat to countries with a small land mass, but which are geographical close to the sea. These types of NPPs can also provide fresh clean drinking water to dry areas via desalination techniques. They can be built using modern modular construction techniques at a factory or shipyard, eliminating the need to set up a nuclear licensed site for its construction and operation. The location of these types of NPPs is also greatly simplified since it is not necessary to conduct viability studies on the land and land environment. However, the sea or coastal environment does make it necessary to take several factors into account. These factors include the access for operational staff and equipment as well as the need to ensure that any radioactive material cannot leak into the sea. Given the widespread development, and deployment, of large scale, medium scale and small modular PWRs, the aim of this PhD proposal is to focus on the analysis of these types of NPPs within this PhD proposal. This enables Singapore to gain experience in understanding the operational and safety aspects of PWRs; and floating NPPs barges and platforms. In addition, this proposal will enable Singapore to develop key skills and technology for modelling and simulating (M&S) the operational behaviour of PWRs as well as design basis accidents (DBA) in PWRs. It will also build upon the current PhD studentship that is funded by National University of Singapore (NUS) Nuclear Research and Safety Initiative (SNRSI) which is focussed on developed a Bayesian neural network (BNN) based surrogate modelling and simulation (M&S) framework for investigating thermal fatigue issues associated with load following floating NPPs.