Instituto de Engenharia Nuclear, Divisão de Radiofármacos (DIRA/IEN/CNEN), P.O. Box 68550, Rio de Janeiro, RJ, 21941-906, Brazil; Universidade Federal do Rio de Janeiro, Programa de Engenharia Nuclear (PEN/COPPE), P.O. Box 68509, Rio de Janeiro, RJ, 21941-914, Brazil.
Instituto de Engenharia Nuclear, Divisão de Radiofármacos (DIRA/IEN/CNEN), P.O. Box 68550, Rio de Janeiro, RJ, 21941-906, Brazil.
Appl Radiat Isot. 2021 Mar;169:109552. doi: 10.1016/j.apradiso.2020.109552. Epub 2021 Jan 6.
This study presents a method based on gamma-ray densitometry using only one multilayer perceptron artificial neural network (ANN) to identify flow regime and predict volume fraction of gas, water, and oil in multiphase flow, simultaneously, making the prediction independent of the flow regime. Two NaI(Tl) detectors to record the transmission and scattering beams and a source with two gamma-ray energies comprise the detection geometry. The spectra of gamma-ray recorded by both detectors were chosen as ANN input data. Stratified, homogeneous, and annular flow regimes with (5 to 95%) various volume fractions were simulated by the MCNP6 code, in order to obtain an adequate data set for training and assessing the generalization capacity of ANN. All three regimes were correctly distinguished for 98% of the investigated patterns and the volume fraction in multiphase systems was predicted with a relative error of less than 5% for the gas and water phases.
本研究提出了一种基于伽马射线密度计的方法，仅使用一个多层感知器人工神经网络（ANN）来识别流型并同时预测多相流中的气体、水和油的体积分数，使预测与流型无关。两个碘化钠（Tl）探测器用于记录透射和散射光束，而一个具有两种伽马射线能量的源则构成了检测几何形状。两个探测器记录的伽马射线谱被选为 ANN 的输入数据。通过 MCNP6 代码模拟了分层、均匀和环形流动，其中包含（5 到 95%）各种体积分数，以便为 ANN 的训练和评估泛化能力获得足够的数据集。对于 98%的研究模式，所有三种流型都被正确区分，并且对于气体和水相，多相系统中的体积分数的预测误差小于 5%。
