Abstract:This study considers the prediction of the pressure gradient with water-steam two-phase flows through helically coiled steam generator tubes, focusing in particular on the operating conditions of low-medium pressure, low mass flux and low heat flux typical of once-through steam generators with in-tube boiling adopted in small modular nuclear reactor systems. Twenty-five widely used empirical correlations have been tested against an experimental pressure drop databank drawn together in this study containing 980… Show more
“…Furthermore, the HEM method uses a much simpler way to calculate the FPD compared to other methods, such as Lockhart and Martinelli 32 . On the other hand, this method's capability for modeling FPD in coiled tubes has been confirmed by several studies in the literature 29 , 51 , 52 . Accordingly, the aforementioned intelligent methods are used for developing accurate and reliable models for estimating the two-phase friction factor, shown in Eq.…”
Section: Resultsmentioning
confidence: 54%
“…Comparisons of predictions of GP correlation (Eq. ( 20 )) and RBF model with the corresponding experimental values 51 . Figure 9 Effect of saturation temperature on the variation of two-phase FPD versus vapor quality.…”
Section: Resultsmentioning
confidence: 99%
“…Several empirical correlations have been obtained based on data for coiled tube steam generators 28 , 30 , 51 . Santini et al 29 proposed a model based on their experimental data for a steam generator with a 12.53 mm ID and coil diameter of 0.5 m. Based on their empirical model, the FPD was a function of mass flux, specific volume, tube diameter, and vapor quality.…”
Section: Introductionmentioning
confidence: 99%
“…( 11 ) provided a good accuracy for analyzed data and predicted 95% of data with an error of less than 21.9%. The HEM method is also used by Cioncolini et al 51 to estimate the FPD in the steam generators. Their database contained 679 data points for coiled tubes and 321 data for straight tubes, and the developed model showed good agreements with measured values.…”
There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.
“…Furthermore, the HEM method uses a much simpler way to calculate the FPD compared to other methods, such as Lockhart and Martinelli 32 . On the other hand, this method's capability for modeling FPD in coiled tubes has been confirmed by several studies in the literature 29 , 51 , 52 . Accordingly, the aforementioned intelligent methods are used for developing accurate and reliable models for estimating the two-phase friction factor, shown in Eq.…”
Section: Resultsmentioning
confidence: 54%
“…Comparisons of predictions of GP correlation (Eq. ( 20 )) and RBF model with the corresponding experimental values 51 . Figure 9 Effect of saturation temperature on the variation of two-phase FPD versus vapor quality.…”
Section: Resultsmentioning
confidence: 99%
“…Several empirical correlations have been obtained based on data for coiled tube steam generators 28 , 30 , 51 . Santini et al 29 proposed a model based on their experimental data for a steam generator with a 12.53 mm ID and coil diameter of 0.5 m. Based on their empirical model, the FPD was a function of mass flux, specific volume, tube diameter, and vapor quality.…”
Section: Introductionmentioning
confidence: 99%
“…( 11 ) provided a good accuracy for analyzed data and predicted 95% of data with an error of less than 21.9%. The HEM method is also used by Cioncolini et al 51 to estimate the FPD in the steam generators. Their database contained 679 data points for coiled tubes and 321 data for straight tubes, and the developed model showed good agreements with measured values.…”
There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.
“…It is a reliable and effective method and has been used by many scholars to calculate the pressure drop of pipelines containing condensing gas [13] [14]. When the inner diameter of the pipe is 70 mm, the length of the pipe is 1500 mm, the inlet steam pressure P is 8209 Pa and the inlet dryness fraction x is 1, then the pipe temperature is 300K.…”
Condensation characteristics of gas pipelines containing little vapor in different elbows have been studied using a numerical calculation method. This is for the purpose of addressing the problem regarding condensed water corroding the inner wall of pipelines. Based on the condensation model of pipelines with low vapor content, the water vapor condensation process in a natural gas pipeline is simulated by UDF within Fluent software. Pipeline pressure simulated by Fluent is compared with the pressure calculated using the Friedel method. The error is less than 10%, which verifies the accuracy of the numerical simulation results. By analyzing non-uniformity distribution of the flow field caused by secondary circulation in different elbows, the simulation model was established to search for distribution of water vapor condensation in pipelines. The results show that the condensate distributions of water vapor in the elbow are non-uniform and mainly focus on the downstream of the inner elbow. Under the same conditions of flow rate and temperature difference of the inner and outer wall, condensation distribution in liquid phase on the inside elbow was compared at four different elbow angles such as 15°, 30°, 45° and 60°. The results indicate that maximum and minimum condensed vapor are separately generated at 15° and 60° elbow which can provide theoretical references for reducing the corrosion of natural gas pipelines.
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