Affonso Renato R W, Dam Roos S F, Salgado William L, Silva Ademir X da, Salgado César M
Universidade Federal do Rio de Janeiro, COPPE/PEN, P.O. Box 68509, 21941-972, Rio de Janeiro, Brazil.
Instituto de Engenharia Nuclear, CNEN/IEN, P.O. Box 68550, 21945-970, Rio de Janeiro, Brazil.
Appl Radiat Isot. 2020 May;159:109103. doi: 10.1016/j.apradiso.2020.109103. Epub 2020 Feb 25.
Knowledge of the flow regime and the volume fraction in multiphase flow is of fundamental importance in predicting the performance of many systems and processes. This study is based on gamma-ray pulse height distribution pattern recognition by means of an artificial neural network. The detection system uses appropriate one narrow beam geometry, comprising a gamma-ray source and a NaI(Tl) detector. The models for annular and stratified flow regimes were developed using MCNPX code, in order to obtain adequate data sets for training and testing of the artificial neural network. Several experiments were carried out in the stratified flow regime to validate the simulated results. Finally, Ansys-CFX was used as computational fluid dynamics software to simulate two different volume fractions, which were modeled and transformed in voxels and transferred to MCNPX code. The use of computational fluid dynamics is of great importance, because it brings the studies closer to the reality. All flow regimes were correctly recognized and the volume fractions were appropriately predicted with relative errors less than 1.1%.
了解多相流中的流型和体积分数对于预测许多系统和过程的性能至关重要。本研究基于借助人工神经网络的伽马射线脉冲高度分布模式识别。检测系统采用合适的单窄束几何结构，包括一个伽马射线源和一个碘化钠（铊）探测器。为了获得用于训练和测试人工神经网络的足够数据集，使用MCNPX代码开发了环状流和分层流型的模型。在分层流型中进行了几次实验以验证模拟结果。最后，使用Ansys-CFX作为计算流体动力学软件来模拟两种不同的体积分数，将其建模并转换为体素，然后传输到MCNPX代码中。计算流体动力学的应用非常重要，因为它使研究更接近实际情况。所有流型均被正确识别，体积分数得到了适当预测，相对误差小于1.1%。
