Influence of Soap Concentration and Oil Viscosity on the Rheology and Microstructure of Lubricating Greases

2006 ◽  
Vol 45 (6) ◽  
pp. 1902-1910 ◽  
Author(s):  
M. A. Delgado ◽  
C. Valencia ◽  
M. C. Sánchez ◽  
J. M. Franco ◽  
C. Gallegos
Keyword(s):  
Oil Viscosity ◽  
Soap Concentration

Analysis of temperature and altitude effects on the Global Energy Balance during WLTC

2021 ◽  
pp. 146808742110342
Author(s):  
Francisco Payri ◽  
Jaime Martín ◽  
Francisco José Arnau ◽  
Sushma Artham
Keyword(s):  
Energy Balance ◽  
Ambient Temperature ◽  
Fuel Consumption ◽  
Experimental Measurements ◽  
Compression Ignition ◽  
Oil Viscosity ◽  
Compression Ignition Engine ◽  
Global Energy ◽  
Global Energy Balance ◽  
L Compression

In this work, the Global Energy Balance (GEB) of a 1.6 L compression ignition engine is analyzed during WLTC using a combination of experimental measurements and simulations, by means of a Virtual Engine. The energy split considers all the relevant energy terms at two starting temperatures (20°C and 7°C) and two altitudes (0 and 1000 m). It is shown that reducing ambient temperature from 20°C to −7°C decreases brake efficiency by 1% and increases fuel consumption by 4%, mainly because of the higher friction due to the higher oil viscosity, while the effect of increasing altitude 1000 m decreases brake efficiency by 0.8% and increases fuel consumption by 2.5% in the WLTC mainly due to the change in pumping. In addition, GEB shows that ambient temperature is affecting exhaust enthalpy by 4.5%, heat rejection to coolant by 2%, and heat accumulated in the block by 2.5%, while altitude does not show any remarkable variations other than pumping and break power.

Dual attenuation peaks revealing mesoscopic and microscopic fluid flow in partially oil-saturated Fontainebleau sandstones

2020 ◽  
Vol 224 (3) ◽  
pp. 1670-1683
Author(s):  
Liming Zhao ◽  
Genyang Tang ◽  
Chao Sun ◽  
Jianguo Zhao ◽  
Shangxu Wang
Keyword(s):  
Fluid Flow ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Confining Pressure ◽  
Time Lapse ◽  
Seismic Attenuation ◽  
Fluid Distribution ◽  
Oil Viscosity ◽  
Measurement Results ◽  
And Shifts

SUMMARY We conducted stress–strain oscillation experiments on dry and partially oil-saturated Fontainebleau sandstone samples over the 1–2000 Hz band at different confining pressures to investigate the wave-induced fluid flow (WIFF) at mesoscopic and microscopic scales and their interaction. Three tested rock samples have similar porosity between 6 and 7 per cent and were partially saturated to different degrees with different oils. The measurement results exhibit a single or two attenuation peaks that are affected by the saturation degree, oil viscosity and confining pressure. One peak, exhibited by all samples, shifts to lower frequencies with increasing pressure, and is mainly attributed to grain contact- or microcrack-related squirt flow based on modelling of its characteristics and comparison with other experiment results for sandstones. The other peak is present at smaller frequencies and shifts to higher frequencies as the confining pressure increases, showing an opposite pressure dependence. This contrast is interpreted as the result of fluid flow patterns at different scales. We developed a dual-scale fluid flow model by incorporating the squirt flow effect into the patchy saturation model, which accounts for the interaction of WIFFs at microscopic and mesoscopic scales. This model provides a reasonable interpretation of the measurement results. Our broad-frequency-band measurements give physical evidence of WIFFs co-existing at two different scales, and combining with modelling results, it suggests that the WIFF mechanisms, related to pore microstructure and fluid distribution, interplay with each other and jointly control seismic attenuation and dispersion at reservoir conditions. These observations and modelling results are useful for quantitative seismic interpretation and reservoir characterization, specifically they have potential applications in time-lapse seismic analysis, fluid prediction and reservoir monitoring.

Experiments and mechanistic modeling of viscosity effect on a multistage ESP performance under viscous fluid flow

2021 ◽  
pp. 095765092110149
Author(s):  
Yi Shi ◽  
Jianjun Zhu ◽  
Haoyu Wang ◽  
Haiwen Zhu ◽  
Jiecheng Zhang ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Viscous Fluid ◽  
Prediction Error ◽  
Flow Direction ◽  
Fluid Viscosity ◽  
Oil Viscosity ◽  
Viscous Fluid Flow ◽  
Viscosity Effect ◽  
High Production Rate ◽  
Pump Head

Assembled in series with multistage, Electrical Submersible Pumps (ESP) are widely used in offshore petroleum production due to the high production rate and efficiency. The hydraulic performance of ESPs is subjected to the fluid viscosity. High oil viscosity leads to the degradation of ESP boosting pressure compared to the catalog curves under water flow. In this paper, the influence of fluid viscosity on the performance of a 14-stage radial-type ESP under varying operational conditions, e.g. rotational speeds 1800–3500 r/min, viscosities 25–520 cP, was investigated. Numerical simulations were conducted on the same ESP model using a commercial Computational Fluid Dynamics (CFD) software. The simulated average pump head is comparable to the corresponding experimental data under different viscosities and rotational speeds with less than ±20% prediction error. A mechanistic model accounting for the viscosity effect on ESP boosting pressure is proposed based on the Euler head in a centrifugal pump. A conceptual best-match flowrate QBM is introduced, at which the impeller outlet flow direction matches the designed flow direction. The recirculation losses caused by the mismatch of velocity triangles and other head losses resulted from the flow direction change, friction loss and leakage flow etc., are included in the model. The comparison of model predicted pump head versus experimental measurements under viscous fluid flow conditions demonstrates good agreement. The overall prediction error is less than ±10%.

Machine learning-based performance analysis of two-axial-groove hydrodynamic journal bearings

2021 ◽  
pp. 135065012199289
Author(s):  
Biswajit Roy ◽  
Sudip Dey
Keyword(s):  
Journal Bearing ◽  
Journal Bearings ◽  
Hydrodynamic Performance ◽  
Support Vector ◽  
Oil Viscosity ◽  
Hydrodynamic Analysis ◽  
Supply Pressure ◽  
Input Parameters ◽  
Precise Prediction ◽  
Output Behavior

The precise prediction of a rotor against instability is needed for avoiding the degradation or failure of the system’s performance due to the parametric variabilities of a bearing system. In general, the design of the journal bearing is framed based on the deterministic theoretical analysis. To map the precise prediction of hydrodynamic performance, it is needed to include the uncertain effect of input parameters on the output behavior of the journal bearing. This paper presents the uncertain hydrodynamic analysis of a two-axial-groove journal bearing including randomness in bearing oil viscosity and supply pressure. To simulate the uncertainty in the input parameters, the Monte Carlo simulation is carried out. A support vector machine is employed as a metamodel to increase the computational efficiency. Both individual and compound effects of uncertainties in the input parameters are studied to quantify their effect on the steady-state and dynamic characteristics of the bearing.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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