Prediction Of Pressure Drop Research Articles

A CFD-based surrogate model for predicting slurry pipe flow pressure drops

Slurry pipelines are extensively employed in most mining operations to transport raw materials and tailings. The aim of the paper is twofold: on the one hand, to develop a generalized slurry flow model using computational fluid dynamics (CFD) to develop a better insight into the complexity of slurry pipe flows, and on the other hand, to develop a surrogate model for pressure drop predictions, which can be used in operations simulation and monitoring tools. A two-fluid model based on the Eulerian-Eulerian approach along with the SST k-ω turbulence model was used. The motion of the solid phase was modeled by the kinetic theory of granular flow (KTGF) to account for both particle-particle and particle-wall interactions. The model was validated by comparison with experimental data available in literature. A new two-phase pressure drop correlation is proposed based on the numerical computations of horizontal pipes featuring 525 data points.

Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propagation neural network (BP) pressure drop calculation model and particle swarm optimization for back propagation neuron network (PSO-BP) pressure drop calculation model are established. Two models were used to calculate and compare the measured data, and the average absolute error of the PSO-BP model was the smallest, which was 0.015%. Compared with the BP model, the average relative error is reduced by 13.16%. Therefore, The PSO-BP pressure drop calculation model has high accuracy and is of practical significance for predicting pipeline pressure drop.

Numerical study on pressure drop characteristics for turbulent flow in conical spiral tubes

The study of pressure drop characteristics in a curved tube is important as it has extensive application in coiled tube heat exchangers, hydraulic and pneumatic systems. The literature review suggests that the pressure drop characteristics at the coil level as well as at local level for the curved tubes with varying curvature i. e. conical spiral tubes require further investigations. The objective of the present work is to investigate the flow resistance characteristics of turbulent fluid flow in conical spiral tubes with uniform wall temperature heating with the numerical simulation approach using ANSYS Fluent. The conical spiral tubes with different curvature and cone taper angle ranging from 10° to 60° were modelled and simulated for the Reynolds number range of 104 to 105. The CFD results reveal that the total pressure drop and the local cross-flow pressure loss coefficient increased with Reynolds number, cone taper angle and curvature ratio. The results also show that the friction factor and the cross-flow coefficient increased significantly in the tube at a cone taper angle of 60° due to an enhancement in the secondary flow and hence the pressure loss. For the given curvature radius and tube diameter of the conical spiral tube there exists an optimum Reynolds number at which the dimensionless cross-flow pressure coefficient has an optimum value. A correlation to predict the friction factor as a function of Reynolds number and cone taper angle is proposed for the turbulent flow in conical spiral tubes. For the usual practically applicable curvature ratio of less than 0.1 in curved tubes, the pressure drop prediction using the proposed correlation is found to be within ±10% of the numerical simulation results.

Modelling the minimally fluidized state under reduced pressure

Several aspects of numerically modelling a minimally fluidized gas–solid system have been investigated in this work. The numerical results show that voidage and the resulting pressure drop are not a function of the fluidizing cycle. More interestingly, the pressure drop was not impacted by introducing the lateral axis of gas and solid flow in the 3D models. Under a reduced pressure environment, none of the well-known drag models could capture the effect of the slip flow. A relatively new but not well-known slip flow drag model showed the ability to capture the impact of the slip flow regime. However, improvements in its overall accuracy are desirable. To this extent, the Ergun pressure drop equation was modified to introduce the effect of the slip flow regime. The losses in the slip flow regime were captured by deriving a new correlation using experimental work that predicted a linear relationship between the laminar coefficient and the Knudsen number. The modified Ergun equation showed notable improvement in its pressure drop accuracy. Furthermore, the modified Ergun equation was implemented as a modified Gidaspow drag model. It showed better accuracy in predicting pressure drop and minimum fluidization velocity at reduced pressure for various alumina particle sizes.

Development of CFD based model for sodium flow boiling in narrow channel akin to SFR fuel subchannel towards high void fraction regimes

The Sodium cooled fast reactor (SFR) is one of the Gen IV reactor type meant for the highest nuclear safety standards. Understanding of flow boiling heat transfer characteristics of liquid sodium is essential under certain postulated events of SFR. This study is intended towards the development of computational fluid dynamics (CFD) based multiphase flow model for the sodium boiling in SFR subchannel using the finite volume method. The numerical simulations have been performed for the basic two-dimensional domain that has a resemblance with the flow subchannel of SFR fuel subassembly. The sodium boiling inception, the nucleate flow boiling regime, and further the forced convective vaporization regime characteristics are numerically predicted by the Eulerian-Eulerian model (two-fluid model) developed using flow regime based modeling within the Algebraic Interfacial Area Density (AIAD) framework for the interfacial closure correlations. The improved non-equilibrium wall boiling model is implemented for wall heat flux partitioning. The liquid turbulence is modeled using a Realizable k-ε model with standard wall function. The CFD simulations are performed for the reference experimental cases of liquid sodium flow boiling in narrow annuli and a tubular channel till high void fraction regimes using the model developed. The predictions of void growth behavior along the axial and in the radial direction depict the dominance of annular flow boiling pattern as observed in sodium flow boiling experiments based on the literature. The numerical predictions for the sodium two-phase flow pressure drop are compared with the experimental results and also with the existing empirical correlations. The mechanistic model-based predictions for boiling heat transfer coefficient are obtained from the wall heat balance model and compared with the available empirical correlations. Also based on the flow parameters predicted the possible flow patterns are identified using the available flow regime maps.

New model of pressure drop for the foam trays with dynamic bubbling process analysis

Open-cell foams as the column tray are promising candidates for the purpose of process intensification in the distillation process. In this paper, the real foam porous structure is obtained based on Micro Computed Tomography (µ-CT), and the bubble formation process on pore-scale foam tray is discussed based on the VOF-CSF model (volume of fluid method-continuous surface force). The results show that the wettability (CA), superficial gas velocity (Ug), porous structure, and clear liquid layer height (HCL) all affect a crucial factor of the foam tray - the liquid holdup in the porous channels (LHPC). It determines the effective porosity, which in turn affects the bubbling frequency and pressure drop. Meanwhile, the evolutionary mechanism of instantaneous pressure drop is analyzed based on the flow patterns, force analysis and interfacial phenomena. Moreover, the new model to predict pressure drop is proposed based on LHPC, which shows good consistency with experiments.

Experimental investigation of the pressure-drop characteristics of open-cell metal foam with air-gaps under non-Darcy flow regimes

Multi-layer metal foam with discrete air gaps between neighbored layers can have wide engineering applications, and its pressure drop characteristic is the crucial performance that needs to be predicted precisely. Experimental studies were carried out to investigate the influences of the air gaps and their layouts on the pressure drop characteristics of the multi-layer open-cell metal foam within a wide flow velocity range to develop efficient models for pressure drop predictions. The accumulated metal foam thickness and air gap distance were kept unchanged in the tests, and four sets of test sections with different air gap layouts were tested. The varying airflow speed across the metal foam was realized by adjusting the inlet pressure level of the test section to reveal the relations of the pressure drop versus the mass flow rate. Results indicate that in the multi-layer and multi-gap configurations, the Forchheimer model in the transition regime and bilinear model in the turbulent regime is still applicable for predicting the pressure drop; however, the insertion of air gaps between the metal foam slices leads to an increased pressure drop coefficient. Meanwhile, the first air gap leads to a higher pressure drop compared to the subsequent air gaps and shifts the flow regime transition point towards an even more minor flow rate operating point, while the subsequent air gaps present almost the same influences on the metal foam pressure drop characteristics and transition point. With the experimental study, two models for predicting the pressure drop characteristics of open-cell metal foam with and without air gaps in the transition and turbulent flow regimes with a broadened range are presented.

Prediction of nanofluid heat transfer characteristic and pressure drop in helical coil via artificial neural networks

In this paper, experimental data of Nusselt number and pressure drop utilizing TiO2/water, ZnO/water, and Ag/water nanofluids in helical coils under isothermal conditions were used to design an artificial neural network to predict Nusselt number and pressure drop for experimental and predict data. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) were designed. All predicted results were compared with the original experimental data and the results showed that both networks could be used to predict Nusselt number and pressure drop. Root Mean Square Error (RMSE) was used to measure the accuracy of each neural network. Predicted Nusselt number average RMSE for FFNN and GRNN were 4.84 and 0.004, while expected Nusselt numbers were 4.009 and 0.63. Predicted pressure drop average RMSE for FFNN and GRNN were 6.78 and 0.005 respectively while the average RMSE for expected pressure drop were 6.173 and 0.105 respectively. GRNN showed more accuracy than FFNN. The maximum deviation between trained data and experimental data for FFNN and GRNN were ±15.5% and ±0.02%, respectively. The maximum deviation between predicted data and experimental data for FFNN and GRNN were ±13.1% and ±1.6% respectively. The generated networks to predict non-experimental data showed good and logical behavior and proved that they are sufficient in Nusselt number and pressure drop prediction.

A novel approach for pore-scale study of fines migration mechanism in porous media

The migration of fine particles in oil reservoir rock is the most important cause of formation damage and permeability impairment. The mechanism of this phenomenon includes fines detachment, mobilization, agglomeration, and re-attachment, which lead to throat clogging and/or blocking. Therefore, accurate modeling of this complex mechanism requires a model with high solver efficiency.In this work, based on the physical features of fines migration in pore throat, the conventional DPM model of ANSYS FLUENT software was initially modified by UDF code. Then, predetermined scenarios of suspension injection into a two dimensional (2D) microchannel were simulated for predicting pressure drop as a function of injected pore volume using the modified DPM model. Next, based on these simulation results, a proxy model using the artificial neural network (ANN) was developed for computational cost reduction of fines migration modeling in the microchannel (pore throat). Secondly, a dual pore scale numerical model by combination of proxy model and pore network modeling approach was developed to predict pressure drop as well as permeability impairment in a network of microchannels (porous media). Moreover, for rapid computation of pressure drop in the network model, a meta-heuristic method of rain optimization algorithm (ROA) has been used. Finally, for model validation, a series of laboratory tests including suspension injections into the glass micromodels were conducted.A comparison of simulation results using both proxy model and dual pore scale model with experimental data shows a good agreement and according to the obtained results, a decrease in the particles mean size, and Reynolds number as well as increase in particles concentration lead to increase in pressure drop and permeability reduction. On the other hand, use of a proxy model based on artificial neural network and a meta-heuristic method of rain optimization algorithm lead to significant computational cost reduction, while the accuracy of fines migration modeling is high enough. Hence, using our modeling approach for fines migration study in porous media as an accurate and fast computational tool can help for a better understanding of this phenomenon, which is very important in the petroleum industry.

Experimental Investigation of Two-Phase Flow Boiling Heat Transfer Coefficient and Pressure Drop of R448A inside Multiport Mini-Channel Tube

Regulations and restrictions against high global warming potential (GWP) refrigerants have been introduced to encourage the adoption of environmentally friendly refrigerants and mitigate the environmental impact of the HVAC industry. R448A, a zeotropic blend with a GWP of 1390, has recently been proposed as a drop-in replacement for R404A and R410A in commercial systems. In this study, the heat transfer coefficient and pressure drop characteristics of R448A within a multiport mini-channel tube were experimentally investigated. The experimental ranges of the mass and heat fluxes were 100 to 500 kg/(m2s) and 3–15 kW/m2, respectively. Additionally, the range of quality from 0 to 1 was considered at two fixed saturated temperatures of 3 and 6 °C. The heat transfer coefficient increased with mass flux. Under low mass flux condition, the heat flux increased the heat transfer coefficient, but there was no noticeable effect of the saturated temperature on the heat transfer coefficient. At high mass flux, heat flux had no major effect on heat transfer, while a decrease in the saturated temperature was found to increase the heat transfer coefficient. Moreover, the pressure drop increased with an increase in the mass flux and vapor quality, whereas the heat flux did not affect the pressure drop. The heat transfer coefficient and pressure drop performance of R448A was compared with that of R410A inside the same tube. Finally, correlations for heat transfer coefficient and pressure drop were proposed for the prediction of heat transfer coefficient and pressure drop in practical applications.

Machine-learning-based heat transfer and pressure drop model for internal flow condensation of binary mixtures

Machine learning regression models were developed to predict the heat transfer coefficient and frictional pressure gradient during condensation of three zeotropic mixtures (ethane/propane, R245fa/pentane, and R410A) in micro- and macro-channels (0.76 mm < D < 14.45 mm). 1032 data points were used to train, test, and validate four machine learning models: support vector regression (SVR), random forest regression (RFR), gradient boost (GB), and artificial neural networks (ANN). For data not used for training, SVR predicted the apparent Nusselt number the best, with a mean absolute percent error (MAPE) of 5.0%, while GB predicted the two-phase friction factor the best with a MAPE of 5.5%. To determine the impact of the additional heat and mass transfer resistances in zeotropic condensation, Shapley analysis was performed on the models to weigh the importance of all dimensionless parameters used to predict the heat transfer and pressure drop. Overall, the three most important parameters to predict heat transfer were the Reynolds number of both phases and a dimensionless temperature glide; while the most important parameters to predict pressure drop were the Bond number, Weber number, and vapor-phase Reynolds number. The importance of mass transfer is discussed along with the applicability of other simple mixture models.

Development of pressure gradient correlation for slurry flow using dimensional analysis

Accurate and speedy determination of pressure drop during cuttings transport is an important aspect of a drilling operation. However, the literature suggests that there is a lack of a simple, ready to use, and accurate model that can predict pressure drop for settling solid-liquid flow during the drilling operation. Therefore, this study aims to develop a non-dimensional model that can be used for quick and real-time pressure drop prediction during cuttings transport through the annulus for a wide range of hydrodynamic (viscosity, surface tension, density) and operating (drill pipe rotation, eccentricity, inclination, liquid flow rate) parameters. The model development strategy includes the development of non-dimensional parameters using the Buckingham Pi approach. Experimental data points were extracted from the original experiments and 5 different works of literature for model optimization and validation to increase the range of the model and make it system independent. The results of this study showed that the developed model has a higher accuracy as compared to the SK correlation and Turian model and shows a Mean Absolute Percentage Error (MAPE) of 16.83% for the original experimental data and 20.09% for the entire data set. Model shrinkage was done using power-law minimization which showed that the cuttings transport system needs to have an optimum of 7–8 non-dimensional parameters, which was supported by using statistical analysis. The developed model can be used for real-time hole cleaning monitoring on drilling rigs and can help minimize hole cleaning issues related to pressure loss.

Investigations of nanofluid flow and heat transfer in a rotating microchannel using single- and two-phase approaches

The impacts of single- (SPM) and two-phase models (TPMs), including Euler-Lagrange (ELM) and Eulerian-Eulerian models (EEM), on a laminar forced convection heat transfer and fluid flow of the nanofluid are evaluated in a rotating U-shape microchannel with a square cross-section and constant wall temperature. The effects of Reynolds numbers, rotational speed, volume fraction, Brownian motion, and implementation of no-slip and slip conditions are investigated. The slip velocity and heat transfer increase by increasing the volume fraction and rotational speed. The EEM and ELM provide a higher total Nusselt number than the SPM. The maximum heat transfer augmentations of 53.3%, 45.7%, and 41% are achieved for the EEM, ELM, and SPM by increasing the rotational speed from zero to 600 at ϕ = 5%. The predictions of pressure drop by TPMs are essentially the same but considerably lower than SPM. Unlike the ELM, the nanoparticle concentrations of EEM and SPM are uniform.

Experimental study on prediction model of wet gas pressure drop across single-orifice plate in horizontal pipes in the low gas phase Froude number region

The pressure drop prediction of wet gas across single-orifice plate in horizontal pipes had been solved satisfactorily under an annular-mist flow in the upstream of orifice plates. However, this pressure drop prediction is still not clearly determined when the upstream is in an intermittent flow or stratified flow, which is corresponding to a region of low Fr G (gas phase Froude number) in the flow pattern map of wet gases. In this study, the wet gas pressure drop across a single-orifice plate was experimentally investigated in the low Fr G region. By the experiment, the flow pattern transition in the downstream of single-orifice plates, as well as the effects of Fr G and Fr L (liquid phase Froude number) on Φ G (gas phase multiplier), were determined and compared when the upstream is in the flow pattern transition and the stratified flow region, respectively. Prediction performances were examined on the available pressure drop models. It was found that no model could be capable of jointly predicting the wet gas pressure drop in the low Fr G region with an acceptable accuracy. With a new method of correlating Fr G and Fr L simultaneously, new correlations were proposed for the low Fr G region. Among which the modified Chisholm model shows the best prediction accuracies, with the prediction deviations of Φ G being within 7% and 3% when the upstream is in flow pattern transition and stratified flow region, respectively.

Hydrodynamics of wall-bounded turbulent flows through screens: a numerical study

The hydrodynamic performance of turbulent flows in circular pipes equipped with screen-type static mixers is numerically assessed in this study. A three-dimensional computational fluid dynamics model is used to study the effect of changing the operating conditions and reactor configuration on the flow field. The accuracy of the numerical results is validated by comparing pressure drop predictions to empirical correlations where a maximum relative error of 13.3% is recorded. The macro-mixing performance of screen type static mixers is also assessed using residence time distributions. The study shows that the flow through screens is three-dimensional by nature with secondary flows being prominent near the pipe walls. Moreover, the presence of the screen has a major impact on the turbulent velocity profile both up- and down-stream. The flow field and velocity gradients are interpreted using strain rate and vorticity. These parameters also show that the flow through screens is highly dispersive where 39.3% of the reactor volume has an extensional efficiency value greater than 0.6. This explains their good performance in processing multiphase flows and gives an insight on how to design systems that maximize this dispersive effect in their volume. The residence time distribution study shows that the presence of screens renders the flow closer to plug flow with the effect being more pronounced using finer mesh screens operating at high flow velocities.

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, which is highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications.

Experimental Investigation of Three-Phase Flow Regime Transition in a Large Diameter 90° Bend and the Resultant Pressure Drop Prediction

Experimental Investigation of Three-Phase Flow Regime Transition in a Large Diameter 90° Bend and the Resultant Pressure Drop Prediction

Large-deposit non-linear chemical reactive flows in porous media: Identifiability and observability

This work discusses the use of additional pressure drop measurements in the characterisation of chemical reactive flow in porous media resulting in insoluble solid deposition. Previous studies of this problem have used outlet breakthrough concentration of one species and pressure drop across the sample during the commingled injection of reacting species, to determine model coefficients. In this work, we consider the introduction of additional measurements of pressure drop across subsections of the sample. The recently derived analytical model is compared to laboratory data to demonstrate the validity of both the model and additional measurement data. The effect of measuring different quantities is assessed by considering the identifiability of the model parameters. All parameters exhibit finite confidence intervals, however, datasets neglecting breakthrough concentration result in higher parameter uncertainty. Higher parameter uncertainty is shown to result in uncertainty in deposit profiles and prediction of pressure drop at higher length scales, demonstrating the importance of limiting parameter uncertainty through experimental design.

Prediction of Heat Transfer and Fluid Flow Effects on Entropy Generation in a Monolithic Catalytic Converter Using Large-Eddy Simulation.

In the present work, heat transfer and fluid flow and their effects on entropy generation in a realistic catalytic converter of a Lada Niva 21214 vehicle are studied using large eddy simulation. At first, the pressure drop over the catalytic converter is measured for dry air at constant temperature ( K), different volumetric flow rates, and extrapolated to large volumetric flow rates for dry air ( K) and for the exhaust gas under realistic engine conditions ( K) using the Darcy–Forchheimer relation. Then, coupled heat and fluid flow phenomena inside the catalytic converter are analyzed for nonreacting isothermal conditions and nonreacting conditions with conjugate heat transfer by using the large-eddy simulation. The predicted pressure drop agrees well with the measured and extrapolated data. Based on the obtained numerical results, the characteristic flow features are identified, namely: the impinging flow with stagnation, recirculation, flow separation and laminarization within the fine ducts of the monolith, which depends on the heat transfer through temperature-dependent thermophysical properties of exhaust gas. Moreover, due to high-velocity gradients at the wall of the narrow ducts in the monolith, entropy production by viscous dissipation is observed predominantly in the monolith region. In contrast, entropy production due to heat transport is relatively small in the monolith region, while it overwhelms viscous dissipation effects in the pipe regions.

Statistical analysis of fuidized dense phase conveying of fine particles

Pressure drop in fluidized dense phase pneumatic conveying of fine particles depends upon parameters such as air or solid mass flow rate, pipe diameter or length. For pipeline layouts with definite dimensions, correlations presented by several researchers can be used to predict pressure drop with greater accuracy. However, while estimating pressure drop for an alternative pipeline configuration, majority of these correlations exhibit substantial errors. Straight-line pressure drop was modelled using the response surface methodology in Minitab software under the design of experiment method. On the response straight line pressure drop, a multiple regression model was developed with input parameters such as air mass flow rate, solids mass flow rate, pipeline length, pipeline diameter, particle diameter and solid loading ratio. The experimental data available as input parameters is used to develop a pressure drop correlation and subsequently applied to predict pressure drop for different pipeline configurations ±15% error margin.