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A methodology for performing sensitivity analysis in dynamic fuel cycle simulation studies applied to a PWR fleet simulated with the CLASS tool

机译:在动态燃油循环模拟研究中进行敏感性分析的方法,该方法应用于使用CLASS工具模拟的压水堆车队

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Fuel cycle simulators are used worldwide to provide scientific assessment to fuel cycle future strategies. Those tools help understanding the fuel cycle physics and determining the most impacting drivers at the cycle scale. A standard scenario calculation is usually based on a set of operational assumptions, such as reactor Burn-Up, deployment history, cooling time, etc. Scenario output is then the evolution of isotopes mass in the facilities that composes the nuclear fleet. The increase of computing capacities and the use of neutron data fast predictors provide new opportunities in nuclear scenario studies. Indeed, a very high number of calculations is possible, which allows testing a high number of operational assumptions combinations. The global sensitivity analysis (GSA) formalism is specifically well adapted for this kind of problem. In this new framework, a scenario study is based on the sampling of operational data, which become input variables. A first result of a scenario study is the highlight of relations between operational input data and outputs. Input variable subspace that satisfy optimization criteria on an output, such as plutonium incineration or stabilization, can also be determined. In this paper, a focus is made on the methodology based on GSA. This innovative methodology is presented and applied to a simple fleet simulation composed of a PWR-UOx fuel and a PWR-MOx fuel. Calculations are done with the fuel cycle simulator CLASS developed by the CNRS/IN2P3 in collaboration with IRSN. The design of experiment is built from five fuel cycle input sampled variables. Sensitivity indices have been calculated on plutonium and minor actinide (MA) production. It shows that the PWR-UOx Burn-Up and the fraction of PWR-MOx fuel are the most important input variables that explain the plutonium production. For the MA production, main drivers depend strongly on isotopes. Sensitivity analysis also reveals input variable subspace responsible of simulation crash, what led to an important improvement of the model algorithms. An equilibrium condition on the plutonium mass in the stockpile used for building MOx fuel has been applied. The solution is represented as a subspace in the PWR-UOx Burn-Up and PWR-MOx fraction input space. For instance, achieving a plutonium equilibrium in a stockpile fed by a PWR-UOx that operates at 40?GWd/t requires a PWR-MOx fraction between 9 and 14%. This study also provides data related to plutonium incineration induced by the utilization of the MOx.
机译:燃油循环模拟器在全世界范围内用于为燃油循环的未来策略提供科学评估。这些工具有助于了解燃油循环的物理原理,并确定影响最大的驾驶员。标准情景的计算通常基于一组运行假设,例如反应堆的燃尽,部署历史,冷却时间等。情景输出则是组成核反应堆的设施中同位素质量的演变。计算能力的提高和中子数据快速预报器的使用为核情景研究提供了新的机会。实际上,非常大量的计算是可能的,这允许测试大量的操作假设组合。全局敏感性分析(GSA)形式主义特别适合于此类问题。在这个新框架中,情景研究基于操作数据的采样,这些数据成为输入变量。情景研究的第一个结果是突出显示了操作输入数据和输出之间的关系。还可以确定满足输出优化标准的输入变量子空间,例如p焚烧或稳定化。在本文中,重点放在基于GSA的方法论上。提出了这种创新方法,并将其应用于由PWR-UOx燃料和PWR-MOx燃料组成的简单车队模拟。使用CNRS / IN2P3与IRSN合作开发的燃油循环模拟器CLASS进行计算。实验设计是基于五个燃料循环输入采样变量构建的。敏感度指标已根据on和次要act系元素(MA)的产量进行了计算。结果表明,PWR-UOx燃尽和PWR-MOx燃料的比例是解释the生产的最重要的输入变量。对于MA生产,主要驱动力在很大程度上取决于同位素。灵敏度分析还揭示了导致模拟崩溃的输入变量子空间,这导致了模型算法的重要改进。已应用了用于构建MOx燃料的储库中mass质量的平衡条件。该解决方案表示为PWR-UOx Burn-Up和PWR-MOx分数输入空间中的子空间。例如,在以40?GWd / t运行的PWR-UOx进料的储料中要达到equilibrium平衡,则需要PWR-MOx分数在9%至14%之间。这项研究还提供了与利用MOx引起的p焚烧有关的数据。

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