Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime
Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime
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引入精确的系统来确定体积百分比，与水垢厚度和流态类型无关
DOI：
10.3390/math10101770
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发表时间：
2022
期刊：
影响因子：
2.4
通讯作者：
Ehsan Nazemi
中科院分区：
文献类型：
--
作者：
A. Mayet;S. M. Alizadeh;Zana Azeez Kakarash;Ali Awadh Al;Abdullah K. Alanazi;Hala H. Alhashimi;E. Eftekhari;Ehsan Nazemi
当流体流入管道时，随着时间的推移，管道中的材料会导致管道内形成沉积物，这对设备的效率和折旧构成威胁。本文提出了一种基于人工智能网络的两相流体积分数检测方法。该方法是非侵入性的，并且以这样的方式工作，即位于管道一侧的检测器吸收已经通过管道另一侧的光子。这些光子由同位素钡-133和铯-137的双源发射到管道中。用蒙特卡罗N粒子程序（MCNP）模拟结构，并从探测器记录的数据中提取小波特征。这些特征被认为是数据处理的分组方法（GMDH）输入。训练神经网络以独立于管道中的水垢厚度的高精度确定体积百分比。在这项研究中，实现一个精确的系统在操作条件下工作，不同的条件下，包括不同的流态和不同的规模厚度值，以及不同的体积百分比，进行了模拟。所提出的系统能够以高精度确定体积百分比，而不管流态的类型和管道内的水垢的量。在所提出的检测系统的实现中使用特征提取技术不仅减少了检测器的数量，降低了成本，简化了系统，而且在很大程度上提高了准确性。
When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.