绝热毛细管流量数值计算

根据两相流动的均相流假设 ,建立了绝热毛细管分布参数的稳态数学模型 ,结合制冷工质HFC 134a基于MH状态方程的热力学性质计算模型 ,采用新的基团贡献法计算粘度 ,用熵增判据考虑壅塞流动的影响 ,对绝热毛细管流量进行数值模拟计算。对理论计算结果与相关文献的实验数据进行了比较。针对以HFC 134a为工质的制冷系统 ,编制了一套较为实用的绝热毛细管流量计算软件     

毛细管气体流量特性研究

通过实验研究,得出了气体在毛细管中流动时的沿程能量损失规律,经过数据处理,拟合出了毛细管的气体流量随毛细管内径、长度的变化曲线及其变化率曲线,得出了其韦泊分布函数。拟合出了毛细管的气体流量随压力差变化的曲线及其直线函数。为制冷系统的设计匹配以及毛细管批量生产的质量检验提供了一些非常有益的参考。     

毛细管内R410A两相流摩擦因子关联式研究

准确预测毛细管内两相流制冷剂的压降是提高毛细管分流精度的基础,而高精度的毛细管内制冷剂两相流摩擦因子关联式又是准确预测毛细管压降的关键。本文拟合得到毛细管内R410A两相流摩擦因子关联式,并给出基于近似积分的毛细管压降计算模型。试验验证表明:基于Blasius公式拟合的毛细管内R410A两相流摩擦因子关联式的平均预测误差为±5.3%,95%的数据点的预测误差在±20%以内,而基于本文提出的毛细管内R410A两相流摩擦因子关联式的压降计算结果与试验数据的误差在±12%以内,平均误差在±5%以内,相比Blasius公式具有更高的计算精度。     

浅谈冰箱毛细管流量检测中的几点问题

文章就冰箱毛细管流量检测中的样本、人员、设备、工艺规程、环境几点问题做详细的分析,并提出了解决措施。     

常温下氮气流过毛细管流量特性的实验研究和数学模拟

对常温下氮气流过毛细管的流量特性进行了实验研究,并建立了流动的理论模型,其模拟结果与实验值符合较好,证明可以直接用数学模拟的方法来确定毛细管的流量特性。     

直接式质量流量测量技术

本文综述了近年来的直接式质量流量测量技术，就基于流变介质声阻抗的流量测量技术以及热式质量流量测量技术进行了重点分析研究，并提出了未来质量流量测量技术的发展方向。     

毛细管的进出口状态参数对制冷剂流量的影响

本文建立了绝热毛细管的分布参数仿真模型,分析了毛细管内制冷剂的流动特性,得出了毛细管进口压力、进口过冷度和出口压力对制冷剂流量的影响情况.     

R32毛细管流量特性模型计算和诺模图建立

毛细管是一种重要的节流元件,广泛应用于各种小型制冷制热装置中,R32作为环境友好型制冷剂,在家用空调领域应用愈发广泛。本文首先基于均相流假设及能量守恒、动量守恒、质量守恒方程,建立绝热毛细管的数学模型,并经适量简化,分别从过冷区和两相区对毛细管模型进行分析求解,得到毛细管长度的计算公式。同时,在该模型中引入了壅塞流概念,采用临界流量作为壅塞流是否出现的判断依据,有效提高了算法精度和稳定性。其次,搭建了针对R32的毛细管测试平台,通过实验获得了内径为1 mm,长度为0.5 m和1 m的两根毛细管在冷凝压力为2.4～3.2 MPa、过冷度为5～20℃下的质量流量数据,验证了该仿真模型的精度。最后,基于仿真模型得到的大量数据点,绘制了R32毛细管的诺模图。     

定容式气体微流量标准装置

介绍 工气体微流量标准装置及其性能，讨论了温度变化，气体吸附和系统汛放等干扰效应，分析了标准装置的不确定度。该标准的校准范围为１＊１０＾－３１０Ｐ．ｍ＾３／ｓ，不确定度小于１．２％，用于校准热容式质量流量计。     

相关技术有于毛细管内制冷剂流量的测量

提出一种采用相关技术测量毛细管内制冷剂流量的方法，制冷剂在毛细管内流动存在流动噪声，利用电容传感器测出反映流动噪声的两路随机相关信号，经相关分析，便可求出毛细管内制冷剂的流量。介绍测量原理，流动噪声的提取方法和电容检测电路，并且还建立了相关的理论计算模型。这一方法的实现将解决制冷装置中毛细管内流量测量的技术难题，为制冷系统中制冷量等有关参数的准确测量和系统的最佳匹配提供了可靠的依据。     

Model-based neural network correlation for refrigerant mass flow rates through adiabatic capillary tubes

A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems. A generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes covering both subcooled and two-phase inlet conditions is expected for multiple purposes. Based on the homogeneous equilibrium flow model, a new group of dimensionless parameters has been proposed. To express the nonlinear relationship between the mass flow rate and the associated parameters, the multi-layer perceptron neural network is employed as a universal function approximator. Simulated data from a validated homogeneous equilibrium model are used for the neural network training and testing. A 5-6-1 network trained with the simulated data of R600a and R407C shows good generality in predicting the simulated data of R12, R22, R134a, R290, R410A, and R404A. Also, the deviations between the trained neural network and the experimental data from the open literature fall into ±10%.     

A generalized dimensionless local power-law correlation for refrigerant flow through adiabatic capillary tubes and short tube orifices

This paper presents a new method to obtain generalized dimensionless correlation of refrigerant mass flow rates through adiabatic capillary tubes and short tube orifices. The dimensionless Pi groups were derived from the homogeneous equilibrium model, which is available for different refrigerants entering adiabatic capillary tubes or short tube orifices as the subcooled liquid, two-phase mixture, or supercritical fluid. To mitigate the potential over-fitting risk in neural network, a new “local” power-law correlation reformed from the homogeneous equilibrium model was proposed and compared with the conventional “global” power-law correlation and recently developed neural network model. About 2000 sets of experimental mass flow rate data of R12, R22, R134a, R404A, R407C, R410A, R600a and CO₂ (R744) in the open literature covering capillary and short tube geometries, subcritical and supercritical inlet conditions were collected for the model development. The comparison between the recommended six-coefficient correlation and experimental data reports 0.80% average and 8.98% standard deviations, which is comparable with the previously developed neural network and much better than the “global” power-law correlation.     

A generalized correlation for refrigerant mass flow rate through adiabatic capillary tubes

A capillary tube is a common expansion device used in small sized refrigeration and air-conditioning systems. A generalized correlation for refrigerant flow rate in adiabatic capillary tubes is developed by implementing dimensionless parameters based on extensive experimental data for R-22, R-290, and R-407C measured in this study. Dimensionless parameters are derived from the Buckingham Pi theorem, considering the effects of tube inlet conditions, capillary tube geometry, and refrigerant properties on mass flow rate. The generalized correlation yields good agreement with the present data for R-22, R-290, and R-407C with average and standard deviations of 0.9 and 5.0%, respectively. Approximately 97% of the present data are correlated within a relative deviation of ±10%. Further assessments of the correlation are made by comparing the predictions with measured data for R-12, R-134a, R-152a, R-410A, and R-600a in the open literature. The correlation predicts the data for those five refrigerants with average and standard deviations of −0.73 and 6.16%, respectively.     

Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using artificial neural network

A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and artificial neural network (ANN). Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a three-layer feedforward ANN is served as a universal approximator of the nonlinear multi-input and single-output function. For ANN training and test, measured data for R12, R134a, R22, R290, R407C, R410A, and R600a in the open literature are employed. The trained ANN with just one hidden neuron is good enough for the training data with average and standard deviations of 0.4 and 6.6%, respectively. By comparison, for two test data sets, the trained ANN gives two different results. It is well interpreted by evaluating the outlier with a homogeneous equilibrium model.     

Experimentation and modeling of refrigerant flow through coiled capillary tubes

Air-conditioners use spirally coiled capillary tubes as an expansion device to enhance compactness of the unit. However, most empirical correlations for predicting refrigerant flow rate through capillary tubes were developed for straight capillary tubes without consideration of coiled effects. The objectives of this study are to investigate the flow characteristics of the coiled capillary tubes and to develop a generalized correlation for the mass flow rate through the coiled capillary tubes. The mass flow rate of R22 through the coiled capillary tubes and straight capillary tubes was measured for various operating conditions and tube geometries. The mass flow rates of the coiled capillary tubes decreased by 5–16% more than those of the straight capillary tubes at the same operating conditions. A generalized correlation for predicting refrigerant mass flow rate through coiled capillary tubes was developed by introducing the parameter of capillary equivalent length. The present correlation showed good predictions with the present database for R22, R407C and R410A in the straight and coiled capillary tubes, yielding average and standard deviations of 0.24% and 4.4%, respectively.     

Modified neural network correlation of refrigerant mass flow rates through adiabatic capillary and short tubes: Extension to CO2 transcritical flow

This paper presents a modified dimensionless neural network correlation of refrigerant mass flow rates through adiabatic capillary tubes and short tube orifices. In particular, CO₂ transcritical flow is taken into account. The definition of neural network input and output dimensionless parameters is grounded on the homogeneous equilibrium model and extended to supercritical inlet conditions. 2000 sets of experimental mass flow-rate data of R12, R22, R134a, R404A, R407C, R410A, R600a and CO₂ (R744) in the open literature covering capillary and short tube geometries, subcritical and supercritical inlet conditions are collected for neural network training and testing. The comparison between the trained neural network and experimental data reports 0.65% average and 8.2% standard deviations; 85% data fall into ±10% error band. Particularly for CO₂, the average and standard deviations are −2.5% and 6.0%, respectively. 90% data fall into ±10% error band.     

Algebraic solution of transcritical carbon dioxide flow through adiabatic capillary tubes

This paper outlines an algebraic model for simulating the transcritical expansion of carbon dioxide through adiabatic capillary tubes. The model was put forward based on the analytical solution of the momentum conservation equation assuming an isenthalpic expansion process. The theoretical model predictions were compared with 66 experimental data points covering different operating conditions and tube geometries. A good agreement between the experimental and calculated mass flow rates was achieved, with more than 94% of the data points lying within an error band of ±10%.     

A study of transcritical carbon dioxide flow through adiabatic capillary tubes

This paper advances a study of the transcritical expansion of carbon dioxide (R-744, CO₂) through adiabatic capillary tubes. The influence of both operating conditions (inlet and exit pressures, inlet temperature) and tube geometry (capillary diameter and tube length) on the CO₂ mass flow rate was experimentally evaluated using a purpose-built testing facility with a strict control of the measured variables. A dimensionless correlation to predict the refrigerant mass flow rate as a function of tube geometry and operating conditions was developed. In addition, a theoretical model was put forward based on the mass, energy and momentum conservation principles. The model results were compared with experimental data, when it was found that the model predicts 95% of the measured refrigerant mass flow rate within an error band of ±10%. The model was also employed to advance the knowledge about the transcritical carbon dioxide flow through adiabatic capillary tubes.     

工质为R134a绝热型毛细管数值模拟

根据均相流等多项假设,运用两相流动基本方程建立了绝热毛细管分布参数的稳态数学模型。模型中分别利用了常用的2种计算沿程摩阻系数方法（Churchill关联式、Jung等人推荐工质为R22的摩阻系数关联式）,模拟结果表明用这2种方法来模拟均能获得较高精度,但工质为R134a时优先考虑前者,因此在模拟毛细管时不同工质有不同的最佳摩阻系数关联式。     

A visual study of R-404A/oil flow through adiabatic capillary tubes

The present study explores the potential of using visualization techniques to investigate refrigerant/oil flow through adiabatic capillary tubes. A literature review shows that these techniques have been used before for capillary tube investigations, but none of these studies focused on the refrigerant/oil phenomena. Therefore, the main objective is to investigate the flow of a refrigerant/oil mixture through a glass capillary tube, with special emphasis on the behavior of the vaporization point. The test fluids are R-404A (a near azeotropic blend) and a polyolester-type oil. Experimental data cover oil concentrations ranging from 5.6 to 6.9% (by mass), degrees of subcooling ranging from 6.2 to 21.5 °C (11.2 F to 38.7 F), and a condensing pressure of 1825 kPa (250 psig). The results show trends of mass flow rate, and give some useful insights about the location of the vaporization point for various oil concentrations and operating conditions.