Gas Viscosity Research Articles

Effect of gas viscosity on the interfacial instability development in a two-phase mixing layer

The interfacial instability in a two-phase mixing layers between parallel gas and liquid streams is important to two-phase atomization. Depending on the inflow conditions and fluid properties, interfacial instability can be convective or absolute. The goal of the present study is to investigate the impact of gas viscosity on the interfacial instability. Both interface-resolved simulations and linear stability analysis (LSA) have been conducted. In LSA, the Orr–Sommerfeld equation is solved to analyze the spatio-temporal viscous modes. When the gas viscosity decreases, the Reynold number (Re) increases accordingly. The LSA demonstrates that when Re is higher than a critical threshold, the instability transitions from the absolute to the convective (A/C) regimes. Such a Re-induced A/C transition is also observed in the numerical simulations, though the critical Re observed in simulations is significantly lower than that predicted by LSA. The LSA results indicate that the temporal growth rate decreases with Re. When the growth rate reaches zero, the A/C transition will occur. The Re-induced A/C transition is observed in both confined and unconfined mixing layers and also in cases with low and high gas-to-liquid density ratios. In the transition from typical absolute and convective regimes, a weak absolute regime is identified in the simulations, for which the spectrograms show both the absolute and convective modes. The dominant frequency in the weak absolute regime can be influenced by the perturbation introduced at the inlet. The simulation results also show that the wave propagation speed can vary in space. In the absolute instability regime, the wave propagation speed agrees well with the absolute mode celerity near the inlet and increases to the Dimotakis speed further downstream.

Effect of Sourdough-Yeast Co-Fermentation on Physicochemical Properties of Corn Fagao Batter.

Fagao is one of China's traditional gluten-free staple foods made with rice or corn flour. Corn Fagao prepared by co-fermentation with sourdough and yeast exhibits better quality and less staling compared to traditional yeast-fermented Fagao. The physicochemical properties of corn Fagao batter during sourdough-yeast co-fermentation were investigated. The results showed that compared with yeast fermentation, the gas production and viscosity of the batter increased with co-fermentation. The co-fermented batter showed a higher hydrolysis of starch and less amylose content. The integrity of starch granules in the co-fermented batter was damaged more seriously, and the crystallinity and short-range ordered structure were less than in the yeast-fermented batter, even though the crystal structure type of starch did not obviously change. The peak viscosity, minimum viscosity, final viscosity, decay value, and recovery value of the corn batter were reduced by co-fermentation, which improved the thermal stability of the batter and slowed down the aging. Co-fermentation also resulted in a more pronounced reduction in protein subunit content than yeast fermentation. The changes in the physicochemical properties of the corn Fagao batter help explain the improvement in quality of corn Fagao made from the co-fermentation method and may provide theoretical references for co-fermentation with sourdough and yeast to other gluten-free foods.

Optimization of cleaning process in semiconductor gas delivery system by computational fluid dynamics simulation

Purging specialty gas delivery systems in semiconductor industries are essential for ensuring worker safety and complying with stringent environmental regulations. The cleaning process impacts production costs due to the required resources and time. This study introduced and evaluated various purging methods, combining vacuum, vacuum with purge, pressurization, and pressure-gradient purge steps. Considering the density and viscosity of various specialty gases used in semiconductor manufacturing, such as gaseous NH3, HCl, WF6, and SiCl4, were selected as the representative specialty gases for the system. Considering the utilization of the semiconductor industries, ultra-high purity nitrogen was selected to clean all components of the gas delivery system, including gas channels, filters, pressure regulator and diaphragm valves. Efficiency was measured by monitoring residual gas concentration changes during different purge-step configurations, the number of purge configuration, and step durations. The dynamic behavior of specialty gases was analyzed based on geometric factors and chemical properties. Findings indicated that cleaning dead-end zones was particularly challenging and impacting overall system efficiency. Frequent purge with a short step time proved more effective in removing residual chemicals than the present conventional vacuum purge process with a continuous N2 supply. Employing a pressure gradient between the inlet and outlet enhanced cleaning efficiency. These insights provide valuable guidelines for optimizing purging processes to reduce cleaning time and resource consumption in semiconductor manufacturing.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

More considerations about the symmetry of the stress tensor of fluids.

Regarding a recent dispute about the symmetry of the stress tensor of fluids, more considerations are presented. The usual proofs of this symmetry are reviewed, and contradictions between this symmetry and the mechanism of gas viscosity are analyzed for simple gas flows. It is emphasized that these proofs depend on the theorem of angular momentum that assumes that internal forces between any two fluid particles are always along the line connecting them. Without this assumption, from Newton's laws of motion one can only obtain the theorem of angular momentum with an additional term. It is proved within classical continuum mechanics that this additional term represents the total moment of internal forces, and its volume density is similar to the body couple introduced in generalized continuum mechanics. When discrete structure of matter is considered, this term corresponds to the total moment of the forces exerted on the nuclei by the internal electrons. This moment of internal forces may lead to nonsymmetry of the stress tensor and is, in general, nonzero as long as shear stress exists. A nonsymmetrical stress tensor suggested in the literature is discussed in terms of its effects in eliminating the contradictions and simplifying the Navier-Stokes equation. The derivation of this stress tensor for ideal gases based on the kinetic theory of gas molecules is presented, and its form in a general orthogonal curvilinear coordinate system is given. Finally, a possible experimental verification of this nonsymmetrical stress tensor is discussed.

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Numerical study of micro–sized bubble deformation and shape oscillation in ultrasonic standing wave fields

To promote the development of science and technology such as underwater explosions, water cleaning, and medical treatment, it is necessary to understand the mechanism of the influence of ultrasonic fields on bubble dynamics. The authors investigated in depth the effects of the dimensionless sound pressure amplitude pa*, the dimensionless wave number k* and the liquid–gas viscosity ratio μl/μg on the micro–sized bubble deformation and shape oscillation using the volume of fluid (VOF) method. It was found that the bubble deformation and oscillation types can be classified into four categories: volume oscillation, shape oscillation, splitting oscillation and other types of oscillation. Both the dimensionless sound pressure amplitude pa* and the dimensionless wave number k* determine the bubble oscillation types. The regime map of predicting the micro–sized bubble deformation and shape oscillation was drawn based on pa* and k*. If the shape oscillation occurs in a micro–sized bubble, the dimensionless time τ*, when the micro–sized bubble experiences the shape oscillation, decreases with an increase in pa*, but increases with an increase in k* and μl/μg. The mode number n of shape oscillation increases with an increase in k* but decreases with an increase in μl/μg, and it is irrelevant to pa*. If the splitting oscillation happens to a micro–sized bubble, τ*, when a micro–sized bubble experiences splitting, decreases with an increase in pa* and increases with an increase in k*, while the liquid–gas viscosity ratio μl/μg has a negligible effect on this phenomenon.

Direct numerical simulations of internal flow inside deformed bubble by phase-field-based lattice Boltzmann method

Internal flow mechanism inside deformed bubble was investigated through direct numerical simulation by Allen-Cahn phase-field-based lattice Boltzmann method and theoretical model developed from Bernoulli equations. The effects of key parameters such as bubble size and gas or liquid physical properties on bubble dynamic behaviors were systematically analyzed. The internal flow that gas flowed through bubble neck from the smaller part to the larger part was well simulated. Lower redistribution was observed at high gas viscosity due to enhanced viscous flow resistance, at low surface tension and high gas density due to the weaken driving force for internal flow, respectively. And, at high liquid viscosity, the time for cutting-off of bubble neck was longer than the time for internal flow due to decreased bubble rising velocity, finally the mother bubble failed to breakup. The internal flow velocity predicted by numerical simulation were in consistent with calculated value by theoretical model, which greatly validated our theoretical model and emphasized the great significance of internal mechanism.

Stagnation point flow of a heated stretching surface boundary layer with temperature dependent viscosity

In this article, we consider the stagnation point two-dimensional flow induced by a linear stretching surface in the streamwise direction. It is noticed that temperature dependent viscosity reduces the base flow velocity profiles and expands the temperature profile, but the gases viscosity nature has the opposite effect in both cases. Under moderate stagnation point flow the base flow profiles are all entrained near to the stretching surface and the effects of variable viscosity decreases. The thermo-physical properties are temperature and concentration dependent. Numerical solution of the governing equations is then calculated by using a RK-five integration scheme alongside Newton-Raphson technique. All the obtained results are presented for the base flow profiles and physical interpretation has been given through figures. We calculate the results quoted by the authors and show excellent agreement with both their asymptotic predication and their results from the linear stretching sheet.

Effects of temperature on gas-solid bubbling fluidization with Geldart A particles characterized by electrical capacitance tomography and pressure drop measurements

The fluidization with Geldart A particles could be usually operated at high temperature which can significantly influence the fluidization behaviors. This paper describes the impacts of temperature on the fluidization of Geldart A particles using electrical capacitance tomography (ECT) and pressure drop measurements. Alumina particles, classified as Geldart A particles, are studied in a fluidized bed across a temperature range of 20 °C to 600 °C. As the superficial gas velocity increases, the minimum fluidization velocity and the minimum bubbling velocity are observed based on the change of solid fraction and pressure drop at high temperatures. As temperature increases, both the minimum fluidization velocity and minimum bubbling velocity decrease, and the solid fraction decreases. The trend agrees well with empirical correlations which consider the change of gas density and viscosity with temperature. Specifically, the increase in temperature leads to a higher gas viscosity, which enhances the hydrodynamic forces and significantly influences the fluidization behaviors. The broad agreement between ECT and pressure drop measurements suggests that ECT can be applied for the characterization of fluidization behaviors at high temperatures.

Gas viscosities from a semiconductor-manufacturing mass-flow controller

We obtain gas viscosities with a pressure-based mass-flow controller used for semi-conductor manufacturing, a rate-of-change approach, and a physics-based calculation. The novelty of this method is that it is used in an industrial process whose main goal is not to measure viscosities. In this way, for pressures of the order of 10 kPa and 25 ∘C and 35 ∘C, we obtain for seven gases viscosities with mean absolute errors with respect to reference viscosities of less than 1%. Using this method, we report viscosities for two semiconductor-manufacturing gases not available in the open literature: hexafluoroisobutene (CAS # 382-10-5) and 1,1,3,3,3-pentafluoropropene (690-27-7).

Liquid droplet entrainment in an annular flow boiling regime—A Bayesian regularization algorithm based study

Accurate prediction of the entrained liquid droplet fraction in an annular two-phase flow regime plays a crucial role for estimating the dryout type critical heat flux to identify the optimized flow characteristics in the thermal systems across different industries. Existing studies have provided different correlations based on the limited experimental data. However, these correlations are applicable to certain operating conditions. Therefore, the present study aims at applying a deep learning method, specifically an artificial neural network (ANN), to enhance the prediction of the entrained liquid droplet fraction. Experimental data from various works on annular flow, covering a wide spectrum of pressure and flow conditions, are utilized for training the ANN model. Eight input variables, viz, superficial gas velocity (JSG), superficial liquid velocity (JSL), gas viscosity (μG), liquid viscosity (μL), gas density (ρG), liquid density (ρL), pipe diameter (d) and liquid surface tension (σLV) are considered as input features. The entrained liquid droplet fraction is the single output feature. The present model employs the Bayesian regularization backpropagation algorithm for training. The present ANN model is compared against the performance of linear regression, decision tree and support vector machine algorithms, and found that the performance of the present Bayesian regularization neural network (BRNN) model is superior within ∼7.5% deviation. Further, the BRNN model is coupled with the film mass flow rate model to obtain the axial variation of the liquid film mass flow rate and good agreement is noticed when compared against the experimental data.

الخصائص الفيزيائية والكيميائية الكاذبة والديناميكية الحرارية للغازات الطبيعية

Physiochemical, pseudocritical, pseudoreduced and thermodynamic properties play an important role for determination natural gas quality and specifications. Nowadays natural gas regard as one of the importance natural source of energy supply due to its high heating value, clean source, no emissions and environmental friendly. In addition, it began to replace the oil as a fuel in various purposes and as essential feed for petrochemical industries. Consequently, the different properties of natural gas mixtures must be investigated to determine their specifications. This study was aiming to distinguish these properties using correlations and mathematical models for assessment. These properties include the physical such as molecular weight (M_w), density(ρ), specific gravity(γ), compressibility factor (z), gas viscosity (μ_g), gas formation volume factor (B_g), gas thermal compressibility (C_g) and water content as well as pseudocritical pressure (p_pc), pseudocritical temperature (T_pc), pseudoreduced pressure (p_pr), pseudoreduced temperature (T_pr). Also, the thermodynamic properties e. g. Gross heating value, thermal conductivity, and specific heat of natural gas mixtures were determined. The study has been conducted on the natural gases of Zelten, Al Ragoba, Al Hotaiba gasfields and Al Braiga terminal to predict and estimate the physical, pseudocritical and thermodynamic properties by using correlation and mathematical models methods. The obtained results revealed that there is a variance between these properties of the natural gas mixtures, which may be attributed to the chemical compositions of components and their behaviors. On the other hand, these characterizations can be used as guide to understand the gases behavior during the different operations and natural gases treatment processes. Keywords: Natural gas, physiochemical, pseudocritical, pseudoreduced thermodynamic properties, and correlations

Impact of gas type on microfluidic drainage experiments and pore network modeling relevant for underground hydrogen storage

Underground hydrogen storage (UHS) in geological reservoirs is proposed as a technically feasible solution to balance mismatch between supply and demand in emerging markets. However, unique hydrogen properties and coupled flow mechanisms require new investigations to fully understand transport and storage of hydrogen in porous media across scales. Here we use microfluidics to investigate the effect of gas type and injection rate on flow patterns, saturation and connectivity of the gas phase. We visually observe that gas flow is characterized by capillary fingering, further confirmed by fractal dimension analysis. At lower injection rates, the gas saturation after drainage appears to increase with gas viscosity, with lower hydrogen saturation compared to methane and nitrogen. The maximum gas saturations (39–46 %) were achieved at higher injection rates, showing no clear correlation to gas type. However, the high-rate injections lead to undesired outcomes in terms of formation of disconnected gas ganglia, mostly pronounced for nitrogen. We identify an optimal injection rate to achieve maximum gas saturation with the least amount of disconnected gas. The experimental results are supported with pore network modeling to derive relative permeability and capillary pressure functions.

Electrical conductivity and shear viscosity of a pion gas in a thermo-magnetic medium

Electrical conductivity and shear viscosity of a pion gas in a thermo-magnetic medium

Study of CO2 Injection into Sumatran Shale Layers to increase Hydrocarbon Gas Productivity of The Shale Gas Reservoir

Injecting CO2 into a reservoir has some important reasons. CO2 injection can enhance oil and gas recovery by reducing the capillary pressure, increasing the pressure gradient, and changing the phase behavior of the fluids. It can reduce greenhouse gas emissions by capturing and storing CO2 underground. It can also create economic benefits by utilizing CO2 as a valuable resource and generating revenue from carbon credits. Therefore, injecting CO2 into a reservoir benefits the environment and the industry. We can inject CO2 in shale gas reservoirs to increase productivity because CO2 has a stronger adsorption capacity on shale surfaces than hydrocarbon gas. When CO2 is injected into shale reservoirs, it can displace the adsorbed CH4 flow out of the micropores and free up more space for gas flow. Injecting CO2 can also reduce the viscosity and density of shale gas, improving its mobility and transport. Moreover, injecting CO2 can provide environmental benefits by reducing carbon emissions and storing CO2 underground. Therefore, CO2 injection is a promising technique for enhancing shale gas recovery and mitigating climate change. We characterized several types of shale from Sumatra using XRD to determine the mineral content. We injected the shale sample with the inert gas and CO2 gas. The characteristics of pressure build up after CO2 injection seem lower than one after inert gas injection. The volumetric of released gas after injection shows the same phenomena as pressure build up’s phenomena which shows clearly that shale rock released less of CO2 gas than the one of inert gas (CH4). These phenomena show that the CH4 can be released easier than the CO2 in the shale rock. Therefore, CO2 can be utilized as material for enhancing the gas recovery in shale reservoir.

Machine learning-driven approach for predicting the condensation heat transfer coefficient (HTC) in the presence of non-condensable gases

This research focuses on developing a predictive model that can effectively estimate the condensation heat transfer coefficient (HTC) on vertical tube surfaces during free-fall. The model takes into consideration the presence of non-condensable gases. The main objective is to assemble an extensive and varied database that encompasses different geometric parameters and operational conditions. The motivation here stems from the urgent need to develop practical models and/or correlations that can accurately predict the condensation heat transfer efficiency for industrial heat exchangers affected by non-condensable gases, with a particular focus on passive containment cooling systems (PCCS) employed in nuclear power plants (NPPs). This innovative cooling system utilizes the force of gravity to condense water steam, effectively removing heat from the containment vessel in the event of an accident. For the development of the model, we employed the neural network toolbox in MATLAB to construct an Artificial Neural Network (ANN) known as a Multi-Layer Perceptron (MLP) network. This model was designed to predict the HTC during the condensation process involving two different types of Non-Condensable Gases (NCG): Air and Nitrogen, along with Helium as a representative light gas. To build the model, we utilized a dataset comprising 1,888 data points sourced from 21 experimental references. The input layer of our model receives a range of parameters, encompassing total pressure (Ptot), subcooling temperature (ΔTsub), mass fraction of non-condensable gases (wnc), wall length (L), hydraulic diameter (Dh), the ratio of the Helium to non-condensable gases (RHe/nc), liquid density (ρl), gas density (ρg), liquid viscosity (μl), gas viscosity (μg), saturation temperature (Tsat), latent heat (hfg), liquid thermal conductivity (kl), and gas specific heat (cp,g). The output data of our model corresponds to the condensation HTC (h). To ensure consistent scaling, the input data was normalized by scaling each feature within the range of 0 to 1. On the other hand, the output data underwent a transformation using the natural logarithm. By utilizing Pearson correlation coefficient analysis and importance analysis for parameter selection, a new correlation for heat transfer was developed and evaluated. The developed machine learning model exhibited outstanding effectiveness in precisely forecasting the condensation HTC with an accuracy range of ± 10 % and ± 20 %. The outcome of this investigation represents the notable progress in analyzing extensive datasets gathered through experiments. Specifically, it addresses the previously unexplored influence of Helium gas by harnessing the capabilities of machine learning.

Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

On the transport behavior of shale gas in nanochannels with fractal roughness

As an efficient and environmentally friendly source of energy, shale gas is abundantly available and continues to contribute to the economy growth because of its huge potential for production. However, accurately predicting the transport behavior of shale gas is still challenging due to the small scale and complexity of nanochannels, which impedes the efficiency of recovery. In this paper, the transport behavior of shale gas in nanochannels with fractal roughness is studied by molecular dynamics simulation and theoretical analysis. It is found that the present work functions well to predict the transport behavior of shale gas in nanochannels with roughness. The introduction of fractal roughness hinders the transport of shale gas and leads to a complex trajectory of methane molecules in nanochannels. Furthermore, it is interesting to find the average gas viscosity increases, while the gas flux decreases with the increase in the inclined angle due to the impediment effect after the deflection. These results are helpful for understanding the migration of shale gas in nanochannels with roughness and guiding the improvement of shale gas recovery in practical applications.

Assessment of dynamic characteristics of fluidized beds via numerical simulations

Euler–Lagrange simulations coupled with the multiphase particle-in-cell (MP-PIC) approach for considering inter-particulate collisions have been performed to simulate a non-reacting fluidized bed at laboratory-scale. The objective of this work is to assess dynamic properties of the fluidized bed in terms of the specific kinetic energy of the bed material kS in J/kg and the bubble frequency fB in Hz, which represent suitable measures for the efficiency of the multiphase momentum exchange and the characteristic timescale of the fluidized bed system. The simulations have reproduced the bubbling fluidization regime observed in the experiments, and the calculated pressure drop Δp in Pa has shown a reasonably good agreement with measured data. While varying the bed inventory mS in kg and the superficial gas velocity uG in m/s, kS increases with uG due to the increased momentum of the gas flow, which leads to a reinforced gas-to-solid momentum transfer. In contrast, fB decreases with mS, which is attributed to the increased bed height hB in m at larger mS. An increased gas temperature TG from 20 to 500 °C has led to an increase in kS by approximately 50%, whereas Δp, hB, and fB are not sensitive to TG. This is due to the increased gas viscosity with TG, which results in an increased drag force exerted by the gas on the solid phase. While up-scaling the reactor to increase the bed inventory, bubble formation is enhanced significantly. This has led to an increased fB, whereas kS, hB, and Δp remain almost unchanged during the scale-up process. The results reveal that the general parameters such as hB and Δp are not sufficient for assessing the hydrodynamic behavior of a fluidized bed while varying the operating temperatures and up-scaling the reactor dimension. In these cases, the dynamic properties kS and fB can be used as more suitable parameters for characterizing the hydrodynamics of fluidized beds.