Inlet Flow Pattern Research Articles

Numerical analysis on ammonia decomposition for hydrogen production in a membrane reactor assisted by a parabolic trough solar collector

Ammonia (NH3) has emerged as a promising hydrogen storage carrier due to its considerable hydrogen storage density, storage and transportation convenience, and zero carbon emissions during endothermic decomposition. By utilizing a parabolic trough solar collector to provide heat and integrating NH3 decomposition with a membrane reactor, a clean and sustainable method of hydrogen production can be achieved. This paper establishes a comprehensive numerical model for NH3 decomposition within a parabolic trough solar receiver/membrane reactor (PTSMR). The model incorporates heat and mass transfer in porous catalyst beds, membrane permeation, and reaction dynamics to accurately capture the intricate phenomena under actual operating conditions. Special attention is paid to the influences of inlet temperature, flow rate, flow pattern, and reaction pressure on NH3 conversion and hydrogen recovery. The results demonstrate that the temperature distribution within the PTSMR exhibits non-uniformity, and the temperature at the bottom is always higher than that at the top, especially at the outlet, where the temperature difference is more than 100K. The NH3 conversion rate in counterflow is higher than that in parallel flow, while the hydrogen recovery rate in parallel flow is higher due to the reverse permeation of hydrogen in counterflow. For a 2-mm-thickness catalyst bed, the addition of membranes increases NH3 conversion rate by 9% at a reaction pressure of 5 bar, compared to a 3% increase for an 8-mm-thickness catalyst bed. The PTSMR with a 2-mm-thickness catalyst bed obtains the optimum hydrogen yield rate.

Experimental study on leakage characteristics of gas-liquid two-phase flow in a horizontal pipe

Oil and gas pipeline leakage accidents caused by aging and third-party damage have increased, resulting in polluting the ecological environment and jeopardizing life and property safety. A detailed and systematic investigation on the leak characteristics of oil and gas two-phase pipelines is significantly urgent. The leak experiments were performed on a horizontal tube with a diameter of 32 mm. There was a 3 mm round leakage hole at the bottom of the tube. The distance of the leakage hole from the pipe inlet was 4.8 m. Three inlet flow patterns, including stratified flow, slug flow, and annular flow via adjusting the gas superficial velocity (1.73 m/s ≤ USG ≤ 24.19 m/s) and liquid superficial velocity (0.017 m/s ≤ USL ≤ 0.415 m/s), were obtained. The conclusions point out that the type of leakage, whether it is mainly liquid or both gas and liquid, depends on the USG. The degree of phase separation in stratified flow is the greatest among the three inlet flow patterns, with a maximal phase separation efficiency (η) of 0.403. Slug flow has the least phase separation degree. Interestingly, the η is directly proportional to the inlet mass fraction only for slug flow. The harmonic frequency that appeared in the power spectral density curves for the pressure difference of leakage hole in slug flow, indicates that leakage is not a standard periodic event. The liquid velocity loss is primarily related to the gas leakage mechanism. These findings can provide new insights into two-phase pipeline leak detection and accident consequence prediction.

Impact of impeller blade count on inlet flow pattern and energy characteristics in a mixed-flow pump

Mixed-flow pumps, which amalgamate centrifugal and axial-flow attributes, play a pivotal role in various sectors due to their high efficiency and versatility. This paper, utilizing numerical simulation and experimental validation, addresses the critical role of impeller blade count in mixed-flow pump performance. It investigates the effect of the number of impeller blades on the energy dissipation mechanism and inlet flow pattern of a mixed-flow pump. The results reveal that dynamic and static interference effects, along with the separation vortex due to flow separation, are the main sources of energy dissipation in the pump. Under part-load and part-overload conditions, the increase in the number of blades contributes to the improvement of the flow pattern and performance but may induce more intense rotating stall effects under part-load conditions. In overload conditions, the increase in the number of blades significantly amplifies the volume of the inlet vortex structure, consequently deteriorating the inlet conditions of the impeller. This study provides valuable insights for the design and optimization of mixed-flow pumps.

Experimental study on the flow behaviors in a vane-type separator

The axial separator for gas-liquid separation receives amounts of attention due to its compact geometry. Since the inlet flow pattern does a significant impact on the separation performance of the centrifugal separator, it is necessary to figure out the flow behavior inside the separator and give some guidance to the practical application. In this paper, a vane-type separator was used to investigate the flow behaviors inside the separator at different inlet flow patterns. Pre-separation is found in the swirler, which is caused by the curving flow channel. Swirling chain flow, swirling intermittent flow and swirling annular flow inside the separation chamber are observed in the experimental range. Besides, the reason for the formation of swirling chain flow is explained. The phenomenon of bubble accumulation can be eliminated by opening the first light phase outlet. Then the effect of first light phase outlet was studied further. Additionally, a multi-stage vane-type separator was tested to study the flow behavior in the two swirl chambers. From the experimental investigation, it is suggested that the vane-type separator would better be applied in high liquid velocity and low gas void fraction condition.

Identification of Supersonic Inlet Unstarted State and Optimal Sensor Configuration Based on EI-SPEA2

As the key starting component of supersonic flight, identification of the unstarted state of the supersonic inlet is the premise of restarting the inlet and an important link of engine protection control. In order to explore the characteristics of the dynamic pressure inside the inlet, the selection of a pressure sensor configuration scheme, that is, how to obtain more abundant pressure information with fewer sensors, is also an urgent problem to be solved. This paper combines time-frequency analysis and deep learning for flow pattern recognition of a supersonic inlet and proposes a strength-based Pareto evolutionary algorithm combined with improved extinction and migration strategies (EI-SPEA2). The signals of all sensors were preliminarily processed using continuous wavelet transform, and the dynamic signals of all sensors were transformed into two-dimensional (2D) time-frequency spectrograms, which were then used as inputs by convolutional neural network to determine the inlet flow patterns from the dynamic sensor signals. A multiobjective optimization algorithm, EI-SPEA2, was adopted, which takes the minimum classification error, minimum number of selected sensors, and optimal sensor distribution as the optimization objectives. Through the continuous iteration of the population, identification of the unstarted state and optimal configuration of sensors was carried out simultaneously. By introducing the improved EI strategy at the end of iteration, SPEA2 can get rid of the local optimum and achieve convergence faster. A distance index for measuring sensor distribution and two indexes for measuring sensor information differentiation are proposed to improve sensor configuration scheme. The experimental results demonstrated that compared with competitive methods, EI-SPEA2 has better performance on both dual-objective optimization problems and three-objective optimization problems. Moreover, three indexes proposed to measure the distribution of the sensor are beneficial to improving the sensor configuration scheme, and the distance-based index is more suitable.

Investigation on separation characteristics of gas–liquid two-phase flow around the perforated tube

Gas-liquid separator plays an important role in the reactor fission gas removal system. Utilizing the differential pressure formed by flow around the perforated tube, the separator achieves gas–liquid separation, meanwhile having advantages such as simple structure. The evolution characteristics of gas–liquid interface and the variation of parameters such as separation efficiency and pressure drop under plug flow and slug flow are investigated. The void fraction in wake region is only related to the inlet volumetric fraction. The variation of air separation efficiency with inlet volumetric fraction is influenced by the inlet flow pattern, and the separation purity increases with the inlet volumetric fraction. The prediction methods of air separation efficiency, pressure drop and other parameters based on Weber number and liquid phase Reynolds number are obtained in accordance with dimensional analysis and minimum kinetic energy principle with the prediction accuracy within ±20 %, which provides guidance for practical application and optimization.

SCR형 탈질 시스템의 암모니아 분사 노즐의 유량 제어에 대한 CFD기법 적용에 관한 연구

In the present study, computational analysis tools have been applied to improving the performance of the denitrification process by flow control of the ammonia injection nozzles adjusting to the flow distribution of the nitrogen oxides in the inlet flue gas of the selective catalytic reduction system. The uniformity of NH3/NO molar ratio is selected as the performance parameter. Design Xplorer was used to minimize the root mean square of NH₃/NO. Four types of inlet flow pattern were chosen as the simulated parameters, i. e. uniform, parabolic, upper-skewed and random. The flow rate of the sixteen nozzles installed in the ammonia injection grid was adjusted to the inlet conditions. In order to solve the three-dimensional steady, incompressible and viscous flow fields, the commercial software named as ANSYS-Fluent were used with the turbulence model. From the present computational analysis, the denitrification performance was improved between 1.47% and 88.6% according to flow pattern of the inlet flue gas.

Phase separation of two-phase ammonia in horizontal T-junction at low mass velocity

Phase separation of a two-phase working fluid accompanies the thermal-hydraulic process in every system and device, with at least one T-junction used in a two-phase line. Adequate qualitative and quantitative prediction of phase separation in T-junction is critical for successful designing, e.g., refrigeration, thermal control, and fluid transport systems. Three simple T-junctions with different inlet and side diameters were tested in the horizontal position on two-phase ammonia at a saturation level of 40÷60 °C and mass velocities of 25÷150 kg·m−2s−1. Based on test data, it has been revealed that at the relative side branch mass flow rate above 0.6, the phase separation behaviour does not depend on inlet mass velocity, vapour quality, void fraction, flow patterns (stratified, slug and annular) and relative side branch diameter. A simplified empirical correlation has been proposed describing the ammonia test points with an error margin of ±15%.

Numerical study of gas-liquid two-phase flow distribution of refrigerant mixtures in a vertically-upward T-junction

The maldistribution of gas-liquid two-phase flow will decline the efficiency of the refrigeration system as the heat transfer performance of the heat exchangers decreases. The distribution characteristics of gas-liquid two-phase flow of refrigerant mixtures R134a in the vertically-upward T-junction were investigated in this work by 3D numerical simulation using the Eulerian model. The simulations were conducted for inlet quality from 0.005 to 0.8 and inlet mass flux from 100 to 1000 kg·m−2s−1 at a saturated temperature of 272.0 K, respectively. The effect of inlet quality on the two-phase flow distribution was studied when the inlet flow patterns were intermittent flow, annular flow, stratified-wave flow, and mist flow, respectively. The fractions of the gas phase and liquid phase flowing to the branch were used to represent the gas and liquid phase distribution characteristics in the T-junction. Numerical results showed that the fraction of the gas phase flowing through the branch decreased with an increase in inlet quality within the scope of this study. The distribution uniformity of the liquid phase in the vertically-upward T-junction was the best for mist flow and the worst for stratified-wave flow at the inlet. Calculation results indicated that a decrease in the difference in the inertia forces acting on the two phases would promote the uniformity of the gas phase distribution in the vertically-upward T-junction and an increase in pressure difference at the intersection zone would lead to a larger amount of liquid phase diverting to the branch.

Experimental investigation on two-phase flow noise characteristics of R410A through electronic expansion valve of multi-split air conditioner

The refrigerant flowing through an electronic expansion valve (EEV) of a multi-split air conditioner is usually in two-phase state which may generate more severe flow noise than that in sub-cooled state. The purpose of this paper is to experimentally investigate the influences of the two-phase R410A operation conditions on the flow noise characteristics in the EEV, including listening sense forms, frequency distributions and sound pressure levels (SPLs). The experimental conditions cover the refrigerant mass flow rate ranging from 30 to 90 kg h–1 and the refrigerant quality ranging from 0.02 to 0.4. The results show that low mass flow rate facilitates the reduction of noise frequency, and the peak noise frequency was reduced by up to 32% as the mass flow rate decreased. Low refrigerant quality helps to reduce the SPL of flow noise, and the average SPL decreased by 6 dB as the refrigerant quality decreased. The guidance of inlet flow pattern by transforming the churn flow to the annular flow helps to simultaneously reduce the noise frequency and SPL, and the peak noise frequency as well as the averaged SPL decreased by 47% and 4 dB, respectively. Moreover, the hissing noise and the gurgling noise are the only two kinds of listening sense forms of flow noise detected in the present experiment, and the peak frequency and the average SPL of hissing noise are both higher than those of gurgling noise.

Supersonic inlet flow recognition by hybrid-mutation non-dominated sorting genetic algorithm with support vector machines

The recognition of supersonic inlet flow pattern has become a research hotspot in recent years. In this paper, the dual external pressure supersonic inlet is taken as the research object. To explore the flow characteristics of the inlet, time-mean processing on the inlet pressure signal collected by sensors is conducted first, and the features of the inlet pressure data in time domain and frequency domain are extracted, respectively. As feature selection (FS) plays an important role in classification tasks and has been recently studied as a multi-objective optimization problem, two objectives of FS are considered and an improved non-dominated sorting genetic algorithm NSGA2 with hybrid mutation operators using support vector machines (SVM) as classifiers is proposed, aiming to simultaneously select feature subsets and optimize SVMs hyper-parameters. In addition, a way to deal with variation transgression is proposed to make the mutation operator of the single-objective evolution fit well in the multi-objective evolution algorithm. Experimental results on 31 sensor datasets demonstrate that our proposed algorithm can achieve competitive classification accuracy while obtaining a smaller size of feature subset compared with particle swarm optimization algorithm and some multi-objective optimization algorithms using single-objective evolution mutation operators.

Numerical Investigation on Blockage-Related Cavitation Surge and Pressure Gain of a Mixed-Flow Pump With Influence of Blade Leading Edge Shape on Suction Performance

Abstract The blade leading edge is a design variable that can affect the local flow patterns and pressure peaks, implying a direct effect on the cavitation performance. This study was conducted to analyze the effect of the blade leading edge shape on the cavitation and noncavitation states. A total of four sets, including the square shape, were selected under the definition of ellipse ratio, and the main focus was on the cavitation state rather than the noncavitation state. In the noncavitation state, the square set denoted a remarkable negative influence, while the other three sets obtained almost the same performance despite different ellipse ratios. In the cavitation state, the square set obtained a relatively low net positive suction head required, related to the inlet flow pattern with the cloud cavity. The other three sets contained the sheet cavity, and their suction performance tended to improve as the cavity blockage decreased. As a parallel focus, an in-depth analysis of cavitation surge and pressure gain was presented with the head drop slope for the other three ellipse sets. The numerical results included the off-design flow rate points and were validated through an experimental test.

Joint discriminative learning and classification for monitoring flow patterns of supersonic inlet

A supersonic inlet is one of the key components in a supersonic air-breathing propulsion system and is the basis for protection control. The overall system performance can be greatly influenced by inlet flow patterns, so it's necessary to develop methods for monitoring them to ensure stable and safe operation. In this paper, time-frequency analysis and deep learning are combined to determine inlet flow patterns from the dynamic sensor signals. Continuous Wavelet Transform is first used for preliminary signal processing by converting the dynamic sensor signals into the 2-D time-frequency spectrogram, and then the spectrogram is taken as input of Convolutional Neural Network (CNN) for classification. In order to reduce the classification error, Doublet/Triplet Convolutional Neural Network combined with Discriminative Learning (DDL-CNN/TDL-CNN) are proposed that take both the cross entropy loss and discriminative learning of features into account simultaneously. The proposed methods encourage CNN to map the spectrogram into a feature space where different flow patterns become more separable. The experimental results demonstrate that DDL-CNN/TDL-CNN have better performance for monitoring the flow patterns of supersonic inlet. Compared with the baseline CNN, three multi-classification metrics of DDL-CNN/TDL-CNN are higher on most of 31 sensors, and under the statistical comparisons, the performance of DDL-CNN/TDL-CNN is also remarkably better. From the two-dimensional representations obtained by t-sne, different flow patterns become more separable for DDL-CNN/TDL-CNN after taking the constraints of discriminative learning into consideration.

Measurement of high-water-content oil-water two-phase flow by electromagnetic flowmeter and differential pressure based on phase-isolation

When the oil field has been exploited by long-term water-flooding, it will be in high water-content stage of production. However, it is a great challenge for high-water-content measurement due to oil droplets extremely dispersed in the water. In this paper, we developed a phase-isolation based method for high-water-content oil-water two-phase flow measurement. Phase-isolation was realized by axial-flow swirler to concentrate scattered and random oil droplets into the pipe center and change the inlet flow pattern into a particular annular flow before measuring. Owing to the axisymmetric velocity and phase distribution, the electromagnetic flow meter avoided the effect of random distribution of insulating phase, and then had a good measurement performance for total volume flow rate. Furthermore, we respectively studied using axial pressure drop, radial pressure drop and the ratio of the two pressure drops to measure water content. The results showed that the ratio of the two pressure drops not only improves the resolution of oil and water, but also effectively reduces the impact of error transfer. In the dual-parameter measurement experiment, the relative errors of total volume flow rate and water content were almost within ±5%.

A statistical method for flow pattern and efficiency prediction using pressure drop in a two-stage gas liquid cylindrical cyclone

An experimental investigation is conducted in a novel gas-liquid separator called Two-stage Gas Liquid Cylindrical Cyclone (TGLCC) with gas-liquid ratio of 25–600, which enables flow pattern observation and separation efficiency/pressure drop measurement. The experimental results show that three inlet flow patterns are observed in the vertical pipe before the TGLCC inlet, which are continuous flow, intermittent flow and annular flow. The presence of vertical pipe before inlet can affect the evolution process of inlet flow patterns, and both intermittent flow and continuous flow have 3 forms of evolution process. Based on the experimental results, a statistical method called box plot is used to characterize the pressure drop fluctuation for different flow pattern evolutions, including the maximum, minimum, percentiles, mean and median. Based on pressure drop data analysis relating to corresponding flow patterns, a statistical method of predicting flow patterns is proposed using 5 criteria obtained from the box plot analysis. For all experimental data, the prediction accuracy is 98%. Then the separation efficiencies of gas riser and drain pipe are analyzed, which is found to be heavily affected by flow pattern evolutions. Therefore, the statistical method is further used to predict the separation efficiency of TGLCC with accuracy of 93.4%.

Establishment of super sonic inlet flow pattern monitoring system: A workflow

To reduce the negative effects of the buzz flow state of supersonic air inlets, this paper proposes a process in which an online monitoring system for the supersonic inlet flow patterns is established with pressure sensors. The first step in this workflow is to build an experimental system based on the objective air inlet and a large number of pressure sensors. After that, for each pressure sensor, based on Sliding Window Short-Time Fourier Transform (SW-STFT) pre-processing and Deep residual network (ResNet) structure, a method to train a sub classifier (Classifier trained by only one sensor signal) with each pressure sensor is proposed to achieve efficient extraction of highly perturbed signal information and high classification accuracy of different flow patterns. However, the main limitation of this workflow is that too many pressure sensors can increase the complexity of aero engine control systems and make the installation difficult. Therefore, in the third step, a multi-objective optimization model is built to solve this problem and find the best subset of sensors in the experimental system. Compared with previous studies, the flow modal monitoring system established in this paper has the advantages of real-time monitoring, higher classification accuracy, and better performance robustness. At the same time, this workflow allows the construction of individualized flow monitoring systems for different inlets.

Numerical investigation on influences of inlet flow pattern on RP-3 thermal oxidation deposition

The influences of inlet flow pattern on thermal oxidation deposition of RP-3 kerosene were numerically investigated in a horizontal tube under a supercritical pressure of 5 MPa in the temperature range of 300–750 K using a computational fluid dynamics (CFD) model. The developed CFD model was based on a pseudo-detailed chemical kinetics mechanism coupled with fuel flow and heat transfer. The obtained results indicated that reactant starvation and mass diffusion controlled the deposition process. The deposition rate peak was concluded as the criterion. As the inlet Reynolds number increased, the process gradually transformed from a starvation-controlled one to a diffusion-controlled one. The inlet Reynolds number exhibited little effect on the initial deposition position, but the peak position moved downstream by accelerating reactants transfer in axial direction with an increasing inlet Reynolds number. The precursor concentration at outlet increased more than 100 times as the inlet Reynolds number increased from 625 to 12500. For laminar and transition flows at inlet, the total mass of deposition and deposition rate peak values were approximately proportional to mass flow rate, and the surface average deposition rate agreed exponential relationship with inlet Reynolds number. When the inlet flow was fully developed turbulent, the deposition amount and surface average deposition rate increased slightly.

Optimization of the Hollow Rectification Sill in the Forebay of the Pump Station Based on the PSO-GP Collaborative Algorithm

The layout of the pump station is easily affected by topography, site, and other factors, resulting in poor inlet flow patterns in the forebay, which seriously affect the normal operation of the pump station. To optimize its inlet flow pattern, the size of the hollow rectification sill has been continuously improved through physical model tests to meet the requirements of the required pump station inlet flow field. In this paper, particle swarm optimization (PSO) was combined with the Gaussian process (GP) to establish a particle swarm-Gaussian process (PSO-GP) model to predict the velocity uniformity of the inlet sump of pump stations with different hole-to-height ratios, hole-to-width ratios, upper-to-lower sill length ratios, and sill height-to-water table ratios. Finally, the hollow rectification sill with the optimal size was obtained and tested in the physical model to compare the rectification effect with other sizes of hollow sills. The results show that the algorithm model can help the traditional physical experiment quickly predict the velocity uniformity of the inlet sump of the pump station. Through the optimization by the PSO-GP algorithm, we can get the optimal size of the hollow rectification sill. Its hole-to-height ratio is 0.62, its hole-to-width ratio is 0.37, upper-to-lower sill length ratio is 0.63, and sill height-to-water table ratio is 0.23. It shows that this method is practical in the optimization design of the hollow rectification sill and provides a new method for the optimization of the flow field in the forebay of the pump station.

Novel Radial Basis Function Network Based on Dynamic Time Warping and Kalman Filter for Real-Time Monitoring of Supersonic Inlet Flow Patterns

AbstractAs the basis of protection control, supersonic inlet plays an important role in a supersonic air-breathing propulsion system. To reduce the negative effects of buzz flow on the supersonic i...

A novel neural network based on dynamic time warping and Kalman filter for real-time monitoring of supersonic inlet flow patterns

A supersonic inlet is one of the key components in a supersonic air-breathing propulsion system and is the basis for protection control. The overall system performance can be greatly influenced by its flow patterns, so it plays a crucial part and is necessary to develop methods for monitoring its flow patterns to ensure stable and safe operation. This issue can be viewed as a time series classification (TSC) task. Traditionally, several manually-engineered features are extracted as the indicators to evaluate the operation status, but this process can be heavily dependent on the professional experience. In this paper, a novel neural network called DTW-SLFN-KF is proposed, which integrates Dynamic Time Warping (DTW) and Kalman Filter (KF) into a single-hidden-layer neural network (SLFN) architecture to directly determine flow patterns from the dynamic sensor signals. The proposed network first adopts a DTW layer as the feature extractor to automatically extract robust features, and exploits the flexible alignment ability of DTW to keep the temporal continuity and deal with the temporal distortions. Then, these features are fed into an SLFN for classification. After that, to make full use of the extracted features and improve the classification performance of SLFN when the network structure is fixed, KF is applied as a linear post-processing technique to get the predicted output of SLFN closer to the true output. Experimental results demonstrate that the proposed DTW-SLFN-KF network has better comprehensive performance for monitoring the flow patterns of supersonic inlet in terms of monitoring accuracy and real-time performance when compared with other competitive methods.