摘要：
随着核热耦合技术的发展，为了考虑多物理场之间的强耦合效应，实现高精度和大规模并行计算，有必要对多物理场守恒方程联立求解，统一处理建模、离散和迭代计算过程。本文基于开源计算流体力学（Computational Fluid Dynamics，CFD）平台OpenFOAM，使用有限体积法（Finite Volume Method，FVM）和高斯定理，对中子扩散和中子输运的控制方程进行离散，实现了对多群中子扩散方程和中子输运方程离散和迭代求解。中子输运方程的离散中使用离散纵标法对空间角度进行离散。为了验证开发程序的有效性，验证计算中采用包括国际原子能机构（International Atomic Energy Agency，IAEA）、TAKEDA和C5G7等在内的多个基准算例对开发的中子扩散和输运程序进行验证。通过对包括非均匀化堆芯基准问题在内的不同基准题中稳态和瞬态结果进行对比验证。验证结果表明：基于有限体积法可以同时实现对均匀化和非均匀化中子物理问题的精确求解，并且具有几何适应性强的特点。为未来实现统一编程框架下的物理与热工多物理场守恒方程的联立求解奠定了基础。 您的浏览器不支持 audio 元素。 AI语音播报 Background
With the development of nuclear-thermal coupling technology, it is essential to consider the strong coupling effects between multiple physics fields and achieve high precision and large-scale parallel computing. Simultaneous solutions to the conservation equations of multiple physics fields need to be pursued, providing a unified approach to modeling, discretization, and iterative computation processes.
Purpose
This study aims to achieve discrete and iterative solutions for multigroup neutron diffusion equations and neutron transport equations, considering the strong coupling between neutronics and thermal-hydraulics.
Methods
Firstly, based on the open-source computational fluid dynamics (CFD) platform OpenFOAM, the finite volume method (FVM) was employed to discretize the control equations for neutron diffusion and neutron transport using the Gauss theorem. Then, the discrete ordinates method was applied to the discretization of the neutron transport equation for spatial angular discretization, and FVM was used to discretize both neutron diffusion and neutron transport equations spatial variables whilst the multigroup method was employed for discretizing energy variables, and implicit Euler method was utilized for discretizing time variables. Finally, neutron diffusion was verified using three benchmark cases, i.e., two-dimensional International Atomic Energy Agency (IAEA), three-dimensional IAEA, and three-dimensional LMW, to validate the effectiveness of the developed program, and neutron transport was verified using various benchmark cases including IAEA, TAKEDA, and C5G7.
Results
The verification results for the two-dimensional IAEA benchmark show excellent agreement, with a maximum error of 1.1% in normalized power. The three-dimensional IAEA benchmark results align closely with reference values, showing a maximum error of 3.4%. For the three-dimensional LMW benchmark, the total power at 20 s is slightly underestimated, with a maximum error below 2%. The IAEA criticality benchmark results show region-averaged flux and effective multiplication factor deviations of 6.9% and 22×10 - ?, respectively. The TAKEDA benchmark confirms the program's accuracy in three-dimensional problems, with effective multiplication factor, neutron flux, and control rod worth matching reference values. The C5G7 benchmark validates the FVM-based transport algorithm's strong geometric adaptability and ability to solve both uniform and non-uniform neutron physics problems accurately.
Conclusions
FVM-based neutron diffusion and transport algorithms developed in this study lay the foundation for the future simultaneous solution of conservation equations for physical and thermal multi-physics fields under a unified programming framework. The integrated verification of neutron diffusion and transport programs underscores the reliability and flexibility of the FVM in accurately solving complex neutron transport and diffusion scenarios, providing a pathway for enhancing precision and computational efficiency in nuclear engineering simulations under a unified programming framework.
作者机构：
[李锦; 邱济平; 杨昌胜; 许刚; 谭志远] College of Agriculture, South China Agricultural University, Guangzhou, 510642, China;[谭德东] School of Resources Environment and Safety Engineering, University of South China, Hengyang, 421001, China
关键词：
托卡马克, 等离子体破裂, 逃逸电流, 大量混合气体注入, 氘氩/氖混合气体, Tokamak, Plasma disruption, Runaway current, Massive gas mixture injection, Deuterium-argon/neon gas mixture
摘要：
托卡马克等离子体破裂会产生逃逸电流，如不进行抑制，其携带的巨大能量将对设备造成严重破坏。本文使用DREAM程序中的流体模型，基于中国环流器二号M（HL-2M）托卡马克装置大等离子体电流放电条件，研究注入氘氩/氖混合气体对破裂逃逸电流的影响。研究表明：注入氘氩/氖混合气体可以抑制最终形成的平台逃逸电流。在讨论的破裂前等离子体电流 I p 范围内，最优条件下氩/氖在混合气体中的含量应在0.50%~0.70%，氘的注入量应在10 20 ~10 21 m -3 。在这个范围外，氘氩/氖混合气体注入对逃逸电流的抑制效果都会减弱，甚至会增大逃逸电流。破裂前等离子体电流 I p 是影响逃逸电流的关键因素。 I p 越大，形成的逃逸电流越大，也需要注入更多的混合气体。在 I p 高达10 MA的聚变堆级托卡马克装置上，注入混合气体的密度需要达到10 22 m -3 ，这是目前大量气体注入（Massive Gas Injection，MGI）技术所不能达到的，通过散裂弹丸注入氘氩/氖混合物将是更加可行的方式。 您的浏览器不支持 audio 元素。 AI语音播报 Background
Tokamak plasma disruption generates a runaway current carrying enormous amounts of energy that, if not suppressed, can cause severe damage to equipment.
Purpose
This study aims to investigate the effects of injecting a deuterium-argon/neon gas mixture on a runaway current during plasma disruption.
Methods
Based on the high plasma current discharge conditions of the HL-2M tokamak device in China, numerical simulations were conducted using a fluid model in the DREAM code. Variations of plasma parameters, such as plasma current ( I p ), ohmic current ( I ohm ), runaway current and the ohmic electric field, with the injected deuterium-argon content and ratio during the disruption process were consistently simulated.
Results
Results show that injecting a deuterium-argon/neon gas mixture suppresses the eventual formation of a platform runaway current, and an optimal content and ratio of the deuterium-argon/neon gas mixture are existed for effective runaway current suppression. Within the range of the pre-disruption plasma current ( I p ) discussed in this study, the amounts of neon/argon and deuterium in the gas mixture should be 0.50%~0.70% and 10 20 ~10 21 m -3 , On fusion-reactor-scale tokamak devices with I p as high as 10 MA, the amount of the injected gas mixture must reach 10 22 m -3 , which cannot be achieved under the current massive gas injection (MGI) technique.
Conclusions
The pre-disruption plasma current ( I p ) is the key factor that influences a runaway current. The larger I p is, the larger is the runaway current that is formed and more amount of the gas mixture must be injected. On fusion-reactor-scale tokamak devices with I p as high as 10 MA, the amount of the injected gas mixture must reach 10 22 m -3 , which cannot be achieved under the current massive gas injection technique. Injecting a deuterium-argon/neon gas mixture through a shattered pellet would be a more viable approach.
摘要：
反应堆在各种工况下堆芯瞬态热工水力参数预测的准确性，直接影响到反应堆的安全性。质量流量和温度作为堆芯热工水力的重要参数，二者常被建模为时间序列预测问题。研究旨在解决瞬时条件下堆芯热工水力参数连续预测的精度问题，检验基于注意力机制的门控循环单元在核心参数预测中的可行性。本文采用1/2中国实验快堆（China Experimental Fast Reactor，CEFR）为研究对象，使用快堆子通道程序SUBCHANFLOW生成瞬态堆芯热工水力参数的时间序列，采用基于软注意力的门控循环单元（Gated Recurrent Unit，GRU）模型预测堆芯的质量流量和温度时间序列。结果表明：相较于自适应径向基（Radial Basis Function，RBF）神经网络，本文使用的软注意力的GRU网络模型预测结果更好，温度在步长为3的情况下平均相对误差不超过0.5%，在15 s内预测效果较好；质量流量在步长为10的情况下平均相对误差不超过5%，且在后续12 s内预测效果较好。本文构建的模型不仅在连续预测过程中表现出更高的预测精度，且能捕捉到动态时间序列中的趋势特点，这对维护反应堆安全，有效防止核电厂事故有极大的用处。基于软注意力的GRU模型能在瞬态反应堆工况下提供一段时间的连续预测，在工程应用中和提高反应堆安全性上具有一定的参考价值。 您的浏览器不支持 audio 元素。 AI语音播报 Background
The accuracy of transient thermal hydraulic parameter prediction of reactor cores under various working conditions directly affects reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which are often modeled as time-series prediction problems.
Purpose
This study aims to solve the accuracy problem of continuous prediction of core thermal hydraulic parameters under instantaneous conditions and to test the feasibility of a gated cycle unit based on the attention mechanism in core parameter prediction.
Methods
The 1/2 full core model of China Experimental Fast Reactor (CEFR) core was taken as the research object. The subchannel SUBCHANFLOW program was employed to generate the time series of transient core thermal hydraulic parameters. The gated recurrent unit (GRU) model based on soft attention was used to predict the mass flow and temperature time series of the core.
Results
The results show that, compared with the adaptive radial basis function (RBF) neural network, the GRU network model with soft attention offers better prediction results. The average relative error of temperature is <0.5% when the step size is 3, and the prediction effect is quite good within 15 s. The average relative error of mass flow rate is <5% when the step size is 10, and fairly good prediction effect is achieved in the subsequent 12 s.
Conclusions
The model constructed in this study not only exhibits higher prediction accuracy in the continuous prediction process but also captures the trend characteristics in the dynamic time series, which is of considerable value for maintaining reactor safety and effectively preventing nuclear power plant accidents. The GRU model based on soft attention can provide continuous prediction for a period of time under transient reactor conditions, providing a reference value in engineering applications and improving reactor safety.
