Flow patterns in upstream and downstream straight tubes of sudden-changed areas in a horizontal straight pipe were experimentally examined. Both sudden-expansion cross-section (SECS) and sudden-contraction cross-secti...Flow patterns in upstream and downstream straight tubes of sudden-changed areas in a horizontal straight pipe were experimentally examined. Both sudden-expansion cross-section (SECS) and sudden-contraction cross-section (SCCS) were investigated. The flow pattern maps upstream and downstream were delineated and compared with those in straight tubes with uniform cross-sections. The effects of the SECS and SCCS on flow patterns were discussed and analyzed. Furthermore, flow pattern transition mechanisms resulting in occurrences of different flow patterns were simply discussed and some transition criteria for the flow pattern transitions were deduced by using the non-dimensionlized analysis method.展开更多
The aim of this paper is to obtain relevant sets of collision cross sections of the parent ions in low pressure discharges in argon, oxygen, and nitrogen, i.e., Ar＋ in Ar, O2＋ in O2 and N2＋ in N2. These ion data ar...The aim of this paper is to obtain relevant sets of collision cross sections of the parent ions in low pressure discharges in argon, oxygen, and nitrogen, i.e., Ar＋ in Ar, O2＋ in O2 and N2＋ in N2. These ion data are first discussed and then validated from comparisons between the calculated transport coefficients and those measured in the literature. The elastic momentum transfer collision cross sections are determined from a semi-classical approximation for the phase shift calculation based on a 12-6-4 inter-particle potential while ion transport coefficients are determined versus the reduced electric field from Monte Carlo simulations.展开更多
近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果...近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。展开更多
基金the National Natural Science Foundation (No.59995460)
文摘Flow patterns in upstream and downstream straight tubes of sudden-changed areas in a horizontal straight pipe were experimentally examined. Both sudden-expansion cross-section (SECS) and sudden-contraction cross-section (SCCS) were investigated. The flow pattern maps upstream and downstream were delineated and compared with those in straight tubes with uniform cross-sections. The effects of the SECS and SCCS on flow patterns were discussed and analyzed. Furthermore, flow pattern transition mechanisms resulting in occurrences of different flow patterns were simply discussed and some transition criteria for the flow pattern transitions were deduced by using the non-dimensionlized analysis method.
文摘The aim of this paper is to obtain relevant sets of collision cross sections of the parent ions in low pressure discharges in argon, oxygen, and nitrogen, i.e., Ar＋ in Ar, O2＋ in O2 and N2＋ in N2. These ion data are first discussed and then validated from comparisons between the calculated transport coefficients and those measured in the literature. The elastic momentum transfer collision cross sections are determined from a semi-classical approximation for the phase shift calculation based on a 12-6-4 inter-particle potential while ion transport coefficients are determined versus the reduced electric field from Monte Carlo simulations.
文摘近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。
