Data assimilation and application in radiation physics and shielding

辐射物理与屏蔽中的数据同化及其应用

• 批准号: 2889656
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889656
• 关键词: Data; assimilation; application; radiation; physics

1. Develop and test new, efficient sensitivity techniques in SCONE on high-fidelity problems, demonstrating their speed-up over naïve approaches.2. Apply this sensitivity analysis to a representative problem of interest for Rolls Royce. This may include obtaining sensitivities to material boundaries, for which some work has been performed in the eigenvalue context [3].3. Combine the obtained sensitivities with experimental measurements in a data assimilation framework in order to minimise uncertainties or calibrate uncertain model inputs.4. Generate higher-order sensitivities for the problems of interest, allowing use of more sophisticated data assimilation approaches where, e.g., the covariance of the model inputs isknown. This allows for relaxing the assumption that the response distribution is Gaussian in the input parameters and better characterising the true distribution.5. Explore accelerated weight-window generating through hybrid multi-group and continuous energy calculations.

1. 在 SCONE 中针对高保真问题开发并测试新的、高效的灵敏度技术，证明其相对于简单方法的加速。2. 将这种灵敏度分析应用于劳斯莱斯感兴趣的代表性问题。这可能包括获得对材料的灵敏度。边界，为此在特征值背景下进行了一些工作[3].3。将获得的灵敏度与数据同化框架中的实验测量相结合，以最大限度地减少不确定性或校准不确定性模型。 4. 为感兴趣的问题生成高阶敏感性，允许使用更复杂的数据同化方法，例如，模型输入的协方差是已知的，这允许放宽响应分布为高斯分布的假设。输入参数并更好地表征真实分布。5.通过混合多组和连续能量计算探索加速权重窗口生成。