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TWO-PHASE FLOW PATTERNS RECOGNITION AND PARAMETERS ESTIMATION THROUGH NATURAL CIRCULATION TEST LOOP IMAGE ANALYSIS

机译:通过自然循环测试回路图像分析的两相流模式识别和参数估计

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Visualization of natural circulation test loop cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. Experimental studies on natural circulation flow were originally related to accidents and transient simulations relative to nuclear reactor systems with light water refrigeration. In this regime, fluid circulation is mainly caused by a driving force ("thermal head") which arises from density differences due to temperature gradient. Natural circulation phenomenon has been important to provide residual heat removal in cases of "loss of pump power" or plant shutdown in nuclear power plant accidents. The new generation of compact nuclear reactors includes natural circulation of their refrigerant fluid as a security mechanism in their projects. Two-phase flow patterns have been studied for many decades, and the related instabilities have been object of special attention recently. Experimental facility is an all glass-made cylindrical tubes loop which contains about twelve demineralized water liters, a heat source by an electrical resistor immersion heater controlled by a Variac, and a helicoidal heat exchanger working as cold source. Data is obtained through thermo-pairs distributed over the loop and CCD cameras. Artificial intelligence based algorithms are used to improve (bubble) border detection and patterns recognition, in order to estimate and characterize, phase transitions patterns and correlate them with the periodic static instability (chugging) cycle observed in this circuit. Most of initial results show good agreement with previous numerical studies in this same facility.
机译:自然循环测试回路循环的可视化用于研究与相位瞬变和流动的静态稳定性相关联的两相流动模式。天然循环流动的实验研究最初与核反应堆系统的事故和瞬态仿真有关,具有轻型水冷式制冷。在该制度中,流体循环主要由由温度梯度引起的密度差异产生的驱动力(“热敏头”)引起。自然循环现象在核电站事故中“泵动力损失”或工厂停机情况下提供剩余散热。新一代紧凑型核反应堆包括其制冷剂流体的自然循环,作为其项目的安全机制。已经研究了数十年的两相流程模式,最近相关的不稳定性是特别关注的对象。实验设施是一种全玻璃制成的圆柱管环,其包含大约12个脱矿质的水升,通过变形控制的电阻器浸没式加热器和作为冷源的螺旋热交换器的热源。通过分布在环路和CCD摄像机上的热对获得数据。基于人工智能的算法用于改善(泡沫)边界检测和图案识别,以估计和表征,相变图案并将它们与该电路中观察到的周期性静态不稳定性(Chugging)循环相关联。大多数初始结果表明,在同一设施中与先前的数值研究表明良好的一致。

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