Fluid Properties Research Articles

Efficient modelling of thermo-hydrodynamic properties in lubricated technical systems using NURBS-based isogeometric analysis

Wet grinding is a process that involves many different subprocesses. In order to model this highly complex process, efficient models of the individual subprocesses are required that are robust enough to be used in multiphysics analyses. To approach the modelling of the macroscopic hydrodynamic effects of the grinding wheel as one component of the analysis, this paper is dedicated to the thermo-hydrodynamic problem and its modelling by expanding the isogeometric analysis (IGA) from hydrodynamic analysis to the thermal consideration. Extended by a consideration of flow effects in areas with high surface gradients and particular attention paid to the necessary stabilization of the governing equations, thermo-hydrodynamic systems with temperature- and pressure-dependent fluid properties are considered. Selected benchmarks feasible for computational fluid dynamics (CFD) simulations are used to demonstrate the good suitability of this modelling strategy. The presented modelling strategies can also be applied to lubricated technical systems in general, beyond wet grinding systems.

Cure State Sensing of Polymethylmethacrylate Using a Vibrating Axial Probe.

A new axially vibrating sensor based on an audio voice coil transducer and a lead zirconate titanate (PZT) piezoelectric disc microphone was developed as a probe for the measurement of in vitro rheological fluid properties, including curing progress for polymethylmethacrylate (PMMA) mixtures with important uses as bone cement in the field of orthopedics. The measurement of the vibrating axial sensor's acoustic spectra in PMMA undergoing curing can be described by a damped harmonic oscillator formalism and resonant frequency (ca. 180 Hz) shift can be used as an indicator of curing progress, with shifts to the blue by as much as 14 Hz. The resonant frequency peak was measured in 19 different 4.0 g PMMA samples to have a rate of shift of 0.0462 ± 0.00624 Hz·s-1 over a period of 400 s while the PMMA was in a dough state and before the PMMA transitioned to a hard-setting phase. This transition is unambiguously indicated by this sensor technology through the generation of a distinct circa 5 kHz high-Q under-damped ring-down response.

Application of mesoporous silica particles as an additive for controlling rheological, thermal, and filtration properties of water-based fluids

Mesoporous silica particles (MSP) have received increasing interest for various applications because of their unique features such as controlled pore size, low density, high chemical and thermal stability, and high surface area. In this study, MSP was applied as an additive in water-based drilling fluids (WBMs). The effect of MSP on the rheological, thermal, filtration, and structural properties of WBMs was investigated. The results were compared with those of analogous fluids containing conventional nonporous silica particles (SSP). Rheological assays showed shear-thinning and viscoelastic behavior, which were more noticeable for fluids including MSP. It was observed that low concentrations of MSP (0.25 %wt) can achieve the same rheological properties as the fluids with higher SSP content (up to 0.5 %wt). The rheological properties of SSP-containing fluids were not significantly affected by the presence of NaCl or aging tests. The theoretical Herschel–Bulkley model represents the rheological behavior of WBMs. The MSP-based WBMs exhibited better filtration properties before aging. The microstructures of the WBMs were analyzed using Scanning Electron Microscopy (SEM). A homogeneous distribution of SSP in the WBMs was observed, while particle agglomeration was observed in WBMs containing MSP. In addition, surface interactions were studied to elucidate the interactions between particles and fluid constituents. The surface interaction, assessed through ζ-potential and FTIR analysis, revealed that the binding affinities of BT, PAC, and XGD with MSP were augmented compared to their individual values. Based on the experimental results, MSP constitutes a promising alternative as an additive for the design of WBMs.

Sensitivity Analysis Nusselt Number Correlation to Thermo Physical and Rheological Properties

We present the results of sensitivity analysis of thermo physical and rheological properties to temperature and concentration and hence sensitivity of Nusselt number to thermo physical and rheological properties. Sodium alginate which is a power law fluid has been used. Thermo physical and rheological properties were first measured experimentally and using RSM models has been developed as a function of temperature and concentration. Sensitivity of these developed models has been studied for temperature and concentration. From dimensional analysis it was found that Nusselt number depends on the thermo physical and rheological properties of power law fluids. Hence sensitivity of Nusselt number model has been studied for these properties. Studied thermo physical properties include density, surface tenson, specific heat and thermal conductivity. Rheological properties like consistency index and power law index have been studied. The sensitivity analysis indicates that properties, density, surface tension and specific heat are sensitive to temperature than concentration. Result for Nusselt number shows that model is very sensitive to specific heat, thermal conductivity and power law index

Plume-scale confinement on thermal convection

Despite the ubiquity of thermal convection in nature and artificial systems, we still lack a unified formulation that integrates the system's geometry, fluid properties, and thermal forcing to characterize the transition from free to confined convective regimes. The latter is broadly relevant to understanding how convection transports energy and drives mixing across a wide range of environments, such as planetary atmospheres/oceans and hydrothermal flows through fractures, as well as engineering heatsinks and microfluidics for the control of mass and heat fluxes. Performing laboratory experiments in Hele-Shaw geometries, we find multiple transitions that are identified as remarkable shifts in flow structures and heat transport scaling, underpinning previous numerical studies. To unveil the mechanisms of the geometrically controlled transition, we focus on the smallest structure of convection, posing the following question: How free is a thermal plume in a closed system? We address this problem by proposing the degree of confinement [Formula: see text]-the ratio of the thermal plume's thickness in an unbounded domain to the lateral extent of the system-as a universal metric encapsulating all the physical parameters. Here, we characterize four convective regimes different in flow dimensionality and time dependency and demonstrate that the transitions across the regimes are well tied with [Formula: see text]. The introduced metric [Formula: see text] offers a unified characterization of convection in closed systems from the plume's standpoint.

Risk factors of postoperative airway obstruction complications in children with oral floor mass.

The aim of the present study was to explore the risk factors of postoperative airway complications in children with oral floor mass. The first choice of auxiliary examination method for children with oral floor mass is also proposed. This retrospective study included 50 children with floor-of-mouth (FOM) masses. Medical records were reviewed, and information on age of onset, functional impacts present, age at consultation, imaging findings, history of preoperative aspiration, pathology findings, properties of biopsied fluid, treatment modality, postoperative outcomes, and operation were recorded. A total of 20 patients exhibited functional impacts such as difficulty in breathing and feeding. Ultrasound examination was performed in 28 cases; and magnetic resonance imaging, in 38 cases. The diagnosis was lymphatic malformation in 12 cases, developmental cyst in 29 cases, and solid mass in 7 cases. There were 28 cases of surgical resection, 9 cases underwent multiple puncture volume reduction followed by surgery, 11 cases treated using sclerotherapy injection, and 1 case treated using sclerotherapy injection and surgical resection. Young age, functional impact, and high grade of lymphatic duct malformation increased the risk of surgical treatment. B-scan ultrasound is the first choice for the diagnosis of FOM masses in children.

Analysis of drop-on-demand printing characteristics and stability driven by inertial forces

As the core technology in the field of microdroplet related applications, researchers have been striving to develop new driving methods and improve the stability of inkjet printing technology to meet the diverse needs of various materials and applications. In this study, a novel, simple, and cost-effective droplet printing method based on inertial force driving is proposed, and its printing characteristics and stability are investigated through experimental and numerical simulation studies. A numerical model was developed to explore the effects of operating parameters and fluid properties on the printing process. The results showed that for a given fluid, it is easier to form satellite droplets when driven from a smaller nozzle with higher voltage and pulse width. The hydrophilic nature of the nozzle can suppress the formation of satellite droplets, but it is prone to retain liquid, thereby affecting the next printing effect. Under certain operating conditions, fluids with lower density, higher viscosity, and higher surface tension are difficult to be driven but can suppress the formation of satellite droplets and promote printing stability. Finally, a parameter space composed of dimensionless numbers Op representing operating parameters and Z representing fluid properties (reciprocal of the Oh number) was established to investigate the comprehensive influence on the printing. The correctness of this parameter space in guiding the selection of parameters for stable droplet printing was validated through experiments.

Physics-Informed Machine Learning Applied to Complex Compositional Model in a Giant Field

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23730,“Physics-Informed Machine-Learning Application to Complex Compositional Model in a Giant Field,” by Guido Bascialla, SPE, ADNOC, and Coriolan Rat and Soham Sheth, SLB, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ Compositional reservoir simulation is a time-intensive activity demanding complex physics. In the complete paper, the authors review the advantages of machine learning (ML) in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure. An ML approach to predict critical temperatures during simulation based on the Heidemann-Khalil method is implemented, resulting in more-accurate results with lower computational cost, outperforming the standard method and improving performance on a giant field model with compositional gradient and miscible gas injection. Field Description The case study refers to a giant offshore carbonate field composed of multiple reservoirs. Production is currently in a rampup phase; crestal miscible hydrocarbon gas injection was implemented soon after startup. The availability of high-potential gas producers as a source of makeup gas and the placement of peripheral water injectors maintains the reservoir pressure above minimum miscibility pressure. All reservoirs show complex variable slope compositional gradients along thick oil columns of hundreds of feet (Fig. 1). To match the fluid behavior and the variation of fluid properties with depth, the equation of state needs at least nine components. The rock quality mainly is controlled by diagenesis. Thirteen rock types were modeled. The permeability can change up to four log cycles for the same porosity. Most of the reservoirs are highly heterogeneous, with features such as high-permeability streaks and baffle zones. A wide range of capillary pressure curves is present; these mainly depend on permeability and lithology. Most development wells were completed with inflow control devices (ICDs) to control gas and water breakthroughs and optimize oil production. The combination of numerous ICDs and long slanted production intervals (i.e., thousands of feet) make wellbore-reservoir coupling critical for proper history matching and forecasting. The model grid size in the horizontal direction is 328 ft, which is considered optimal according to simulated sensitivities. The vertical layering is very fine in order to capture the reservoir heterogeneity; the cell thickness ranges from 1 to 1.5 ft. This results in a model with 3.5 million active cells, which makes the simulation performance and run time very challenging when coupled with compositional gradient and ICDs. Phase Labeling In this section of the complete paper, the authors review why accurate phase labeling is important in compositional simulation and how it can lead to convergence problems, particularly for cases with gas injection. A traditional method is compared with an ML method on a simple model using a complex dummy fluid model to highlight issues that may arise in more-complex simulation models.

Electrofacies-driven 3D-static reservoir modeling of the Late-Cenomanian AbuRoash’G lithounit (Abu-Gharadig Basin, Egypt): Sequence stratigraphic and geomodel constraints for a gas-bearing estuarine system

Estuarine-systems, developed upon transgressive-phases, feature high-quality reservoir-facies, e.g. tidal-bars, that are important stratigraphic-plays critical for hydrocarbon exploration-development. However, capturing their intricate architectural elements (heterogeneity and quality) is still challenging due to the complex stacking-nature and limited-examples. Moreover, defining reservoir-boundaries upon static-modeling of reservoirs cannot be efficient unless it is controlled by stratal-geometries and established depositional-models. To this end, in this study, we performed 3D-static geocellular reservoir-modeling process for the Late-Cenomanian AbuRoash“G” Member (Abu-Gharadig Basin, Egypt) with sequence-stratigraphic and geomodel, relative-geological-time (RGT) model and horizon-stacks, constraints. In this investigation, as an effective-workflow, not only facies-analysis, integrating seismic-stratigraphy and GR-log motifs, was applied for paleo-environment reconstruction, but also machine learning-based electrofacies were applied, through self-organized-maps (SOM), to accurately recognize complex facies-assemblages present. Object-based and pixel-based stochastic-simulation processes were applied upon geocellularly modeling rock and fluid properties, utilizing key-information scales of seismic and well-log data. The results show that three third-order depositional sequences dominate the succession, resting on the Late-Cretaceous unconformity, of which sequence-1 encloses the lowstand and transgressive systems-tracts of the fluvio-estuarine Bahariya and Abu Roash“G” units, respectively. The transgressive phase built AbuRoash“G” lithounit features an estuarine depositional-system encompassing four facies-associations, of which tidal-sand-bars represent significant gas-bearing reservoir-quality facies. The tidal-bar facies’ efficient reservoir quality calls for attention and testing in future development plans and investigation in similar settings. Furthermore, the facies-constrained workflow established in this study, for reservoir modeling, can worldwide help identify the ultimate reservoir-configuration, as long as the 3D-static modeling process is controlled by the stratal and geomodel restraints.

Helmholtz energy models for dipole interactions: Review and comprehensive assessment

Dipolar interactions play an important role for thermodynamic properties of many fluids and accordingly for their modeling. In molecular-based equation of state models, the effect of dipolar interactions is usually described by Helmholtz energy models. There are several Helmholtz energy models for dipolar interactions available in the literature today. In this work, eight dipole contribution models describing the dipole–dipole interactions of fluids were critically assessed by comparing their results with molecular simulation reference data of Stockmayer fluids. Therefore, the dipole contribution models were combined with an accurate Lennard-Jones (LJ) Helmholtz energy model. The following thermodynamic properties were considered in the comparison: vapor pressure, saturated densities, enthalpy of vaporization, surface tension (by using density gradient theory), critical point, second virial coefficient, and thermodynamic properties at homogeneous state points, such as the Helmholtz energy, pressure, chemical potential, internal energy, isochoric heat capacity, isobaric heat capacity, thermal expansion coefficient, isothermal compressibility, thermal pressure coefficient, speed of sound, Joule–Thomson coefficient, and Grüneisen parameter. For the evaluation of the dipole contribution models, molecular simulations for the Stockmayer fluid with the dipole moments of μ2/4πϵ0ɛσ3=0.5,1,2,3,4,5 were carried out. The results indicate, that all considered dipole models exhibit some significant weaknesses. Nevertheless, some dipole contribution models are found to provide a robust description for many properties and state ranges. Overall, the deviations of the dipole contribution models from the Stockmayer simulation data are, in most cases, an order of magnitude higher than the deviations of the LJ EOS from LJ simulation data.

Impact of saliva incorporation on the rheological properties of in vitro gastric contents formulated from sour cream.

Rheological properties of gastric contents depend on the food ingested, and on the volume and composition of secretions from the host, which may vary. This study investigates the impact of saliva regular incorporation in the stomach after a meal on the rheological properties of gastric contents, considering two levels of salivary flow (low = 0.5 and high = 1.5 mL/min). In vitro chymes were obtained by mixing sour cream, simulated gastric fluid, two different volumes of oral fluid (at-rest human saliva, SSF for Simulated Salivary Fluid or water) and adjusting pH at 3. Chymes samples were characterized at 37°C for their particle size and rheological properties. Overall, particle size distribution was not different between samples: incorporating a larger volume of saliva resulted in more heterogeneity, but the surface area moment D[3,2] and volume moment D[4,3] did not differ significantly with the oral fluid type. Shear viscosity of chyme samples was higher when saliva was incorporated, in comparison with water or SSF. In addition, as shown from data extracted at = 20 s-1 the higher the fluid volume the lower the shear viscosity, which is attributed to a dilution effect. However, this dilution effect was attenuated in the case of saliva, most likely due to its composition in organic compounds (e.g., mucins) contributing to the rheological properties of this biological fluid. In these in vitro conditions, both saliva and the salivation rate had a significant but slight impact on the rheological properties of gastric contents (of the order of 1-5 mPa s at = 20 s-1).

Timing of Volatile Degassing From Hydrous Upper‐Crustal Magma Reservoirs With Implications for Porphyry Copper Deposits

AbstractThe timing and duration of volatile generation from crystallizing magma reservoirs and fluid release across the magmatic‐hydrothermal interface depend on complex coupled interactions controlled by non‐linear, dynamic properties of magmas, rocks and fluids. Understanding these mechanisms is essential to explain the rare formation of economic porphyry copper deposits. For this study, we further developed a coupled numerical model that can simultaneously resolve magma and hydrothermal flow by introducing a description of fluid transport within the magma reservoir and volatile release to the host rock. Our simulations use realistic magma properties derived from published experimental and modeling studies and cover different magma compositions and water contents. We show that magma convection at melt‐dominated states leads to homogenization, which delays fluid release and promotes a rapid evolution toward a mush state. The onset of magmatic volatile release can be near‐explosive with a tube‐flow outburst event lasting <100 years for high initial water contents of >3.5 wt% H2O that could result in the formation of hydrothermal breccias and vein stockworks or trigger eruptions. This event can be followed by sustained fluid release at moderate rates by volatile flushing caused by magma convection. Subsequent fluid release from concentric tube rings by radial cooling of non‐convecting magma mush with a volume of ∼100 km3 at ∼5 km depth is limited to remaining water contents of ∼3.1 wt% H2O and lasts 50–100 kyr. Ore formation from hydrous magmas may thus involve distinct phases of volatile release.

Study on the influence of hole shapes on the fluid and mechanical properties of perforated honeycomb sandwich structures

Study on the influence of hole shapes on the fluid and mechanical properties of perforated honeycomb sandwich structures

Industrial and biomedical applications of slippery liquid-infused porous surfaces

Abstract Slippery liquid-infused porous surfaces (SLIPS) are biomimetic interface materials that consist of a porous structure or polymer network substrate with a high specific surface area and a perfused liquid lubricant. This surface has an extremely low coefficient of friction and a high degree of antifouling property, which can effectively repel various liquids, oils, ice, biofilms, etc. The tunable performance of SLIPS endows it with promising application prospects in various fields, thus emphasizing the importance of designing diverse SLIPS materials tailored to specific application scenarios in the future. However, owing to the inherent fluid properties of lubricating oil, the surface may encounter challenges associated with lubricant depletion during practical utilization. This review paper focuses on elucidating the principles, design strategies, and fabrication techniques employed for developing SLIPS materials in anti-ice applications, drag reduction applications, and biomedicine. Furthermore, a comprehensive summary of challenges encountered by SLIPS in different domains is provided along with potential avenues for future research to improve the stability and durability of SLIPS. In conclusion, SLIPS shows significant potential for diverse applications, yet necessitates comprehensive research to address its inherent challenges and extend its versatility and functionality across various domains.

Studying impact of a relative permeability modifier on capillary imbibition process and evaluation of its adverse effect on physical and chemical properties of fracking fluid

Studying impact of a relative permeability modifier on capillary imbibition process and evaluation of its adverse effect on physical and chemical properties of fracking fluid

A hybrid modeling approach: dynamic simulation of two-phase fluid flow in compacting gas condensate reservoirs using potential flow theory

Simulating the flow processes of complex hydrocarbon mixtures, such as gas condensate mixtures and volatile oils, in deep reservoirs is one of the most challenging problems in reservoir hydrodynamics. The challenge lies in meeting three requirements simultaneously when creating a simulator: high accuracy, low computational cost, and high reliability. These metrics determine the performance of the simulation. Meeting all these requirements at the same time necessitates a hybrid approach to solving the problem and optimizing the computational algorithm in terms of CPU time. A new technique has been developed for hybrid modeling of the development of deposits of complex hydrocarbon mixtures. This technique integrates the theory of potential flow, the Binary Flow Model (which accounts for rock compaction, PVT properties of reservoir fluids, phase transformation, and mass transfer between phases), and the material balance equations of the hydrocarbon system using time discretization. In this case, to linearize nonlinear differential equations for the flow of a gascondensate mixture, the method of averaging reservoir pressure along the radial coordinate was used. By introducing the Khrestianovich function, an algorithm was obtained to determine the rate of inflow of the gas-condensate mixture to the well. Using material balance equations for gas and condensate, it was possible to obtain differential equations that describe changes in reservoir pressure and condensate saturation over time. Based on this technique, a simulator for a gas condensate reservoir was created, enabling computer studies to be conducted. The results demonstrated the proposed technique's good accuracy compared with the results of the semi-analytical solution. Keywords: dynamic modeling; simulation; monitoring; streamline; hybrid modeling; binary model.

Shear-Wave Velocity Prediction Based on the CNN-BiGRU Integrated Network with Spatiotemporal Attention Mechanism

Shear wave velocity is one of the important parameters reflecting the lithological and physical properties of reservoirs, and it is widely used in the fields of lithology and fluid property identification, reservoir evaluation, seismic data processing, and interpretation. However, due to the high cost and challenge of obtaining shear wave velocity, only a few key wells are measured. Considering the intricate nonlinear mapping relationship between shear wave velocity and conventional logging data, an integrated network incorporating an attention mechanism, a convolutional neural network, and a bidirectional gated recurrent unit (STACBiN) is proposed for predicting shear wave velocity. The impact of conventional logging data on shear wave velocity is analyzed, thus employing the attention mechanism to focus on data correlated with shear wave velocity, which can enable the prediction results of the method proposed superior to those of conventional methods. Additionally, the prediction results of this method are compared with the prediction results of the two-dimensional convolutional neural network (2DCNN) and bidirectional gated recurrent unit (BiGRU). It is verified that the network proposed can effectively predict the shear wave velocity, with minimal error between predicted and true values.

Numerical and Experimental Investigation of Heat Transfer in the Porous Media of an Additively Manufactured Evaporator of a Two-Phase Mechanically Pumped Loop for Space Applications

Two-phase pumped cooling systems are applied when it is required to maintain a very stable temperature for heat dissipation in a system. A novel additively manufactured evaporator for two-phase thermal control was developed at NASA Jet Propulsion Laboratory (JPL). The Two-Phase Mechanically Pumped Loop (2PMPL) allows to manage the heat transfer with much wider breadth of control authority compared to capillary-based systems, while alleviating the system's sensitivity to pressure drops. The focus of this work is the understanding and capturing the micro-scale evaporation occurring in the porous structure of the evaporator. The Boiling and Phase Change Heat Transfer Laboratory at the University of California, Los Angeles (UCLA) developed an all-encompassing numerical simulation tool to predict the operational thermal behavior of the evaporator considering the effect of the liquid-vapor interface at the wick-to-vapor boundary. The numerical model incorporated the behaviour of the liquid-vapor meniscus at particle level located along the evaporative boundary between the wick structure and the vapor chamber. The numerical model allowed to study the effect of different parameters, such as boundary conditions, geometry, wick and fluid properties. An experimental setup was built at UCLA in order to characterize the heat transfer within an additively manufactured porous sample fabricated at JPL and in particular its evaporative heat load under certain heat inputs. The experimental efforts served as validation for the numerical results and aided in the characterization of the transient phenomena, such as dry-out.

Analysis of APB in vertical wells with evaporite layers: A 1D axisymmetric multilayer thermomechanical model

Temperature increases and creep in saline rocks can cause annular pressure buildup (APB), presenting a significant challenge in oil well operations, particularly in deep and ultra-deep pre-salt fields. This paper proposes a one-dimensional axisymmetric multilayer thermomechanical model that accounts for the influence of evaporite layers and thermomechanical effects on APB. We developed a finite element model that integrates heat transfer and thermomechanical interactions, utilizing a double deformation mechanism as a constitutive model to represent the viscous behavior of the rock formation. Additionally, this model enables the iterative calculation of fluid properties based on temperature and pressure conditions. To validate the model, we conducted case studies using widely adopted commercial finite element software. Its consistent results provide a deeper understanding of the pressure increases caused by the influence of saline rock and thermal effects, offering valuable insights into the safe and efficient operation of oil wells using a model with reduced computational complexity.

Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs

The most crucial aspect in determining field development plans is the oil recovery factor (RF). However, RF has a complex relationship with the reservoir rock and fluid properties. The application of artificial neural networks is able to produce complex correlations between reservoir parameters that affect the recovery factor. This research provides a new approach to improve the accuracy of the ANN model in the form of steps including removing outlier data, selecting input parameters, selecting transferring functions, selecting the number of neurons, and determining hidden layers. By applying these steps, an ANN model was selected with nine input parameters consisting of oil viscosity, water saturation, initial oil formation volume factor, formation thickness, initial pressure, permeability, specific gravity of oil, porosity, and original oil in place. Furthermore, based on the correlation coefficient, a tangent sigmoid transferring function, 30 neurons, and two hidden layers were determined. The proposed ANN correlation gives the best accuracy compared to the previous correlations. This is proved by the highest correlation coefficient of 0.91657.