环形通道内液钠的临界热流密度的灰色相关分析和灰色模型的建立(英文)

采用灰色系统理论,对在两台液钠沸腾实验回路上测得的实验数据,进行了影响钠沸腾临界热流密度(CHF)值因素的灰色相关分析,并用GM(1,1)模型对CHF进行了预测。选用GM(1,h)模型对CHF进行了建模。计算及预测结果与实验值符合较好。     

自然循环ONB点灰色理论建模研究

运用灰色系统理论对以氟利昂为工质的自然循环过冷沸腾起始点实验结果进行分析。结果表明,在自然循环中对ONB点起作用因素的大小顺序依次为：加热功率、进口干度、流体温度、流量和压力等。基于实验结果,运用灰色系统理论,建立了GM(1,1)模型和GM(1,4)模型。利用GM(1,1）模型可以得出热功率对ONB点位置的影响。利用GM(1,4)模型得出热功率、进口干度、流体进口温度、流量等因素对ONB点位置的影响。两类模型都可以对实验数据的发展趋势进行预测。     

液膜蒸干模型在液态金属CHF预测中的应用

为对圆管中环状流烧干型沸腾临界进行预测,建立了考虑液滴沉积夹带作用的液膜蒸干模型。沉积率、夹带率等相关关系式采用水等常规流体的已有关系式。在一定参数范围内,将模型预测结果与实验数据和经验关系式进行了比较。结果表明：基于常规流体的液膜蒸干模型大体可应用于液态金属,但在临界热流密度（CHF）较大时可能造成较大偏差;钠、钾两种液态金属在CHF较低时预测准确度区别不大,但在CHF较高时区别较为明显。为更加准确预测液态金属CHF,应开发专门的沉积率、夹带率等相关关系式。     

钠沸腾噪声探测研究

文章叙述了钠沸腾噪声探测研究进展,建立了离线和在线均可进行的高频和低频信号采集和处理系统,引进、开展、改进和编制了信号处理、故障诊断、事故报警和自回归模型分析等软件包。应用这些硬软件对水和钠沸腾噪声进行了探测和分析。结果表明,沸腾噪声信号的自功率谱密度(APSD)的幅值明显大于沸腾时的值,用自回归模型判别因子分析,可实现钠沸腾在线实时诊断和监护。     

钠工艺间钠雾火模型适用性分析

钠冷快堆钠泄漏事故中,泄漏的钠以液滴形式在抛射过程中与空气剧烈反应,发生钠雾火现象。通过桑迪亚国家实验室（Sandia National Laboratories,SNL）的T3钠雾火实验结果,以及原子国际（Atomics International,AI）的J1～J4钠雾火实验结果,对改进的Tsai钠雾火模型适用性进行了评估。针对Tsai模型高估实际工况钠喷雾燃烧速率的问题,将Tsai模型中钠液滴下落模型改为单一速度模型,同时将喷雾燃烧对空间的影响改为瞬发。在T3实验中分析了钠雾火阶段对改进模型适用性的影响,在J1～J4实验中分析了初始氧气浓度对改进模型适用性的影响。结果表明：改进的钠雾火模型能够很好地预测空气中钠喷雾燃烧的升温升压现象以及钠消耗量,可以应用于预测钠工艺间内钠泄漏事故后发生在空气中的钠雾火行为。     

高压工况下圆管内垂直向上流动沸腾CHF关系式比较研究

基于15?MPa至临界压力的圆管内垂直向上流动沸腾临界热流密度（CHF）实验数据，筛选出Katto、Bowring、Hall-Mudawar、Alekseev关系式以及CHF查询表（LUT-2006）进行比较研究，通过对预测值与实验值的误差分析，评价了各个关系式的适用性，得到了15?MPa至临界压力区间内CHF随压力的变化趋势。本研究对高压工况（≥15 MPa），尤其是接近临界区域的CHF预测具有指导意义。      

高压工况下管内垂直向上流动沸腾CHF机理模型研究

针对高压工况下偏离泡核沸腾（DNB）型临界热流密度（CHF）的特点,重新构建了Weisman & Pei模型的本构关系式;针对高压工况下干涸（Dry-out）型CHF,比较分析了Kataoka、Celata以及Hewitt?3个沉积率和夹带率计算关系式的结果。基于以上两类改进的CHF模型,建立了一个适用于高压工况的、结合DNB型和Dry-out型沸腾临界机理的CHF模型。采用高压工况下管内垂直向上流动沸腾CHF实验数据对建立的CHF机理模型进行了验证,分析了热工参数和几何参数的趋势效应。      

内螺纹管临界热流密度预测与数值计算

基于壁面汽泡壅塞理论,针对近临界压力区两相流动沸腾的偏离泡核沸腾(DNB)现象,对垂直上升内螺纹管的DNB型临界热流密度(CHF)进行了数值计算研究。以内螺纹管为分析对象改进已有的汽泡壅塞模型,计算了汽泡层区与主流区的极限传递质量流量、湍流速度分布、汽泡层区临界截面含气率等本构关系,汽泡脱离直径的计算考虑了汽泡接触角的影响。本文模型还根据大量CHF实验数据拟合得到了新的α_b关联式。最后,基于Fortran语言编制了CHF的理论预测数值模型程序,研究分析了压力、质量流速、热平衡干度及进口欠焓对CHF的影响,并根据CHF查表值对本文模型进行评估,同时将实验得到的内螺纹管CHF数据与采用Bowring模型、Katto模型、Shah模型和本文模型计算的CHF进行比较,发现本文模型的误差最小,与实验值吻合结果较好,说明本文模型能较好地对垂直上升内螺纹管DNB型CHF进行预测。     

竖直加热圆管内过冷沸腾及CHF数值模拟

基于欧拉两流体模型和非平衡过冷沸腾模型,完成过冷沸腾数值模型的构建,并通过与Bartolomei单管过冷沸腾实验进行对比,验证模型的正确性。利用该模型计算得到圆管的沸腾曲线,将进入"临界区"后的第一个点作为偏离泡核沸腾(DNB)判定的标准,对高压、高流量下圆管内的DNB型临界热流密度(CHF)进行数值模拟,CHF数据取自最新(2006年)的查询表;计算中考虑质量流量、平衡含汽率和压力对CHF的影响,最终预测值与实际值符合良好,误差在15%以内。预测CHF出现的位置也与实际相符,表明本文提出的方法能够很好地模拟高压、高流量下圆管内的DNB型CHF。     

沸腾传热数值模拟方法及多管耦合应用特性研究

针对直流蒸汽发生器（OTSG）中全流型沸腾传热及一、二次侧耦合换热等复杂物理现象,计算流体动力学（CFD）数值分析普遍面临计算难度大、计算效率低及不确定性大等问题。基于欧拉两流体多相流模型与临界热流密度（CHF）壁面沸腾模型,建立了管内全流型流动沸腾传热数值分析模型,并验证了模型的有效性。基于所验证的模型,开展了数值模型在多管耦合传热下的应用特性研究,明确了该数值模拟方法在多管耦合下的可靠性,并对温度与相分布计算结果对相间作用力模型的敏感性进行了数值分析。研究结果表明：基于欧拉两流体多相流模型与CHF壁面沸腾模型,能够较准确地预测管内水介质由过冷到过热的全流型流动沸腾传热过程,计算的“干涸”点位置及壁面峰值温度与实验值符合较好,最大误差小于10%;基于欧拉两流体多相流模型与CHF壁面沸腾模型的数值方法对多管耦合工况有较好的适用性,计算的二次侧温度与实验结果吻合良好;两相间曳力对壁面温度及空泡份额的计算结果有较明显的影响,但非曳力对壁面温度的影响较小,因此对于大规模工程应用计算,可在分析中不考虑部分相间非曳力的影响。本文研究结果可为OSTG的三维精细化数值分析的模型选择提供有益参考。     

Analysis of critical heat flux during subcooled boiling for finned fuel elements

Experimental and analytical studies were performed to determine the critical heat flux (CHF) during subcooled boiling on finned fuel elements. Tests were conducted in a vertical, concentric-annulus test section consisting of a glass tube containing a finned heater element with either six, eight, or ten longitudinal fins. The phenomena leading to CHF are described and the parametric trends are discussed.A two-dimensional finite-element heat transfer model using the Galerkin method was used to analyse the experimental data to obtain CHF values. A dimensionless correlation was derived to predict the CHF values during subcooled boiling. Over 90% of the predicted CHF values agreed with those obtained from the two-dimensional analysis within ±30%.     

An utilization of liquid sublayer dryout mechanism in predicting critical heat flux under low pressure and low velocity conditions in round tubes

From a theoretical assessment of extensive critical heat flux (CHF) data under low pressure and low velocity (LPLV) conditions, it was found out that lots of CHF data would not be well predicted by a normal annular film dryout (AFD) mechanism, although their flow patterns were identified as annular–mist flow. To predict these CHF data, a liquid sublayer dryout (LSD) mechanism has been newly utilized in developing the mechanistic CHF model based on each identified CHF mechanism. This mechanism postulates that the CHF occurrence is caused by dryout of the thin liquid sublayer resulting from the annular film separation or breaking down due to nucleate boiling in annular film or hydrodynamic fluctuation. In principle, this mechanism well supports the experimental evidence of residual film flow rate at the CHF location, which can not be explained by the AFD mechanism. For a comparative assessment of each mechanism, the CHF model based on the LSD mechanism is developed together with that based on the AFD mechanism. The validation of these models is performed on the 1406 CHF data points ranging over P=0.1–2 MPa, G=4–499 kg m⁻² s⁻¹, L/D=4–402. This model validation shows that 1055 and 231 CHF data are predicted within ±30 error bound by the LSD mechanism and the AFD mechanism, respectively. However, some CHF data whose critical qualities are <0.4 or whose tube length-to-diameter ratios are <70 are considerably overestimated by the CHF model based on the LSD mechanism. These overestimations seem to be caused by an inadequate CHF mechanism classification and an insufficient consideration of the flow instability effect on CHF. Further studies for a new classification criterion screening the CHF data affected by flow instabilities as well as a new bubble detachment model for LPLV conditions, are needed to improve the model accuracy.     

基于加热壁面能量平衡的窄矩形通道内CHF机理模型

窄矩形通道因具有结构紧凑、换热面积大等优点而被广泛应用于各个领域。通过完善窄矩形通道中临界热流密度（CHF）的预测方法,建立CHF机理模型,可以提高反应堆的安全性和经济性。本文对窄矩形通道内竖直向上流动CHF进行了可视化实验研究,在此基础上开发了一种基于加热壁面能量平衡的CHF机理模型,并提供一组本构关系用于封闭所开发的新模型,同时使用实验数据对新模型进行对比评价,对比结果发现,新模型在窄矩形通道中模拟结果良好,偏差基本都在±20%之间。     

Prediction of critical heat flux using ANFIS

The prediction of Critical Heat Flux (CHF) is essential for water cooled nuclear reactors since it is an important parameter for the economic efficiency and safety of nuclear power plants. Therefore, in this study using Adaptive Neuro-Fuzzy Inference System (ANFIS), a new flexible tool is developed to predict CHF. The process of training and testing in this model is done by using a set of available published field data. The CHF values predicted by the ANFIS model are acceptable compared with the other prediction methods. We improve the ANN model that is proposed by Vaziri et al. (2007) to avoid overfitting. The obtained new ANN test errors are compared with ANFIS model test errors, subsequently. It is found that the ANFIS model with root mean square (RMS) test errors of 4.79%, 5.04% and 11.39%, in fixed inlet conditions and local conditions and fixed outlet conditions, respectively, has superior performance in predicting the CHF than the test error obtained from MLP Neural Network in fixed inlet and outlet conditions, however, ANFIS also has acceptable result to predict CHF in fixed local conditions.     

Prediction of Transient Boiling Transition in BWR Fuel Rod Bundles by Subchannel Analysis Program MENUETT

Transient CHF (critical heat flux) tests of a 4 X 4 rod bundle were analyzed by the subchannel analysis program MENUETT. MENUETT is based on a non-equilibrium, five equation, two-phase flow model and is available both for steady state and transient analyses. Turbulent mixing and void drift effects are taken into account to calculate cross flows in fuel rod bundles. The tendency of calculated subchannel mass fluxes and qualities agreed with experimental data. By using a critical quality correlation obtained from steady state CHF data, the position of the earliest boiling transition could be predicted regardless of non-uniform axial heat flux distributions. This transition occurrence time was predicted within a difference of 0.1～0.3 s from the experimental time. MENUETT applicability was confirmed for transient calculations predicting thermalhydraulic behavior in bundles.     

An investigation of flow characteristics and critical heat flux in vertical upward round tube

Prediction of critical heat flux （CHF） in annular flow is important for the safety of once - through steam generator and the reactor core under accident conditions. The dryout in annular flow occurs at the point where the film is depleted due to entrainment, deposition, and evaporation. The film thickness, film mass flow rate along axial distribution, and CHF are calculated in vertical upward round tube on the basis of a separated flow modcl of annular flow. The theoretical CHF values are higher than those derived from experimental data, with error being within 30%.     

A flow boiling burnout correlation for water and Freon-12

An empirical correlation has been developed for calculating critical heat flux (CHF) for vertical upflow in uniformly heated tubes. The correlation is based on parameter groups derived from a dimensional analysis and has been compared with experimental CHF data for Freon-12 and for water. Except for coolant conditions in which (i) mass fluxes are less than 300 kg s⁻¹ m⁻², (ii) dryout qualities are below 10%, or (iii) water pressures are outside the range 3.5 to 12 MPa, the correlation agrees very favourably with the experimental data. The overall mean ratio of calculated to experimental CHF values for 1760 sets of Freon-12 data is 0.992 and the r.m.s. error 3.3%; the corresponding values for 2063 sets of water data are 0.982 and 5.8%. This provides a basis for predicting CHF levels over a wide range of coolant conditions, as required in the analysis of hypothetical loss-of-coolant accidents in water-cooled nuclear reactors.     

基于GM(1,1)模型与灰色马尔可夫GM(1,1)模型的核动力装置趋势预测方法研究

在核动力装置灰色GM(1,1)模型趋势预测的基础上,引入马尔可夫链预测理论,建立核动力装置灰色马尔可夫GM(1,1)趋势预测模型。该模型既考虑了GM(1,1)模型较强的处理单调数列的特性,又考虑了通过状态转移概率矩阵的变换提取数据随机波动响应的特点,灰色马尔可夫GM(1,1)模型将这两种性质有机结合起来,具有严密的科学性,从而拓宽了传统灰色GM(1,1)模型预测的应用范围。实例验证表明：灰色马尔可夫GM(1,1)模型充分利用历史数据给予的信息,不但能对核动力装置运行中的单调数列进行准确的趋势预测,也可实现对波动运行的重要参数进行准确的趋势预测,大幅提高了随机波动较大数据序列的预测精度。