scholarly journals Viscosity Measurements of Three Base Oils and One Fully Formulated Lubricant and New Viscosity Correlations for the Calibration Liquid Squalane

2019 ◽  
Vol 124 ◽  
Author(s):  
Arno Laesecke ◽  
Clemens Junker ◽  
Damian S. Lauria
Keyword(s):  
Atmospheric Pressure ◽  
Viscosity Data ◽  
Experimental Uncertainty ◽  
Vibrating Wire ◽  
Data Set ◽  
Base Oils ◽  
Viscosity Range ◽  
Extended Uncertainty ◽  
New Formulation ◽  
Ester Base

The viscosities of three pentaerythritol tetraalkanoate ester base oils and one fully formulated lubricant were measured with an oscillating piston viscometer in the overall temperature range from 275 K to 450 K with pressures up to 137 MPa. The alkanoates were pentanoate, heptanoate, and nonanoate. Three sensing cylinders covering the combined viscosity range from 1 mPa·s to 100 mPa·s were calibrated with squalane. This required a re-correlation of a squalane viscosity data set in the literature that was measured with a vibrating wire viscometer, with an estimated extended uncertainty of 2 %, because the squalane viscosity formulations in the literature did not represent this data set within its experimental uncertainty. In addition, a new formulation for the viscosity of squalane at atmospheric pressure was developed that represents experimental data from 169.5 K to 473 K within their estimated uncertainty over a viscosity range of more than eleven orders of magnitude. The viscosity of squalane was measured over the entire viscometer range, and the results were used together with the squalane correlations to develop accurate calibrating functions for the instrument. The throughput of the instrument was tripled by a custom-developed LabVIEW application. The measured viscosity data for the ester base oils and the fully formulated lubricant were tabulated and compared with literature data. An unpublished viscosity data set for pentaerythritol tetrapentanoate measured in this laboratory in 2006 at atmospheric pressure from 253 K to 373 K agrees with the new data within their experimental uncertainty and confirms the deviations from the literature data. The density data measured in this project for the three base oils deviate from the literature data in a way that is by sign and magnitude consistent with the deviations of the viscosity data. This points to differences in the sample compositions as the most likely cause for the deviations.

TEMPERATURE EFFECT ON DENSITY AND VISCOSITY OF LIGHT, MEDIUM, AND HEAVY CRUDE OILS

2020 ◽  
Author(s):  
D Sagdeev ◽  
◽  
Ch. Isyanov ◽  
I Gabitov ◽  
V Khairutdinov ◽  
...  
Keyword(s):  
Crude Oil ◽  
Atmospheric Pressure ◽  
Oil Field ◽  
Viscosity Data ◽  
Rotational Viscometer ◽  
Correct Operation ◽  
Hydrostatic Weighing ◽  
Weighing Method ◽  
Light Medium ◽  
Measured Density

The density and dynamic viscosity of four light, medium, and heavy (extra-viscous) crude oil samples from Tatarstan Oil Field (Russian Federation) have been measured over the temperature range from (293 to 473) K (for density) and from (293 to 348) K (for viscosity) at atmospheric pressure (101 kPa). The density measurements were made using a new densimeter based on hydrostatic weighing method. The viscosity measurements of the same crude oil samples were made us-ing Brookfield rotational viscometer (DV-II+PRO, LVD-II+PRO). The combined expanded uncertainty of the density, viscosity, atmospheric pressure, and temperature measurements at 0.95 confidence level with a coverage factor of k = 2 is estimated to be 0.16 %, 1.0 %, 1.0 %, and 20 mK, respectively. For validation of the reliability and accuracy of the measured density data and correct operation of the new densimeter, all oil samples were measured using the pycnometric method. The present study showed that the densities measured using the new hydrostatic weighing densimeter (HWD) are agree with the values obtained with pycnometric method within (0.03 to 0.14) %. The measured density and viscosity data were used to develop widerange correlations as a function of temperature and API characteristics. The measured densities were represented using simple function of temperature (polynomial type) with API gravity dependent parameters with an accuracy of AAD within from (0.10 to 0.18) %. The measured viscosity data were also used to develop linear Arrhenius and VTF models. API gravity dependence of the Arrhenius parameters was studied.

scholarly journals STUDY ON BIAS CORRECTION METHODS OF CENTRAL ATMOSPHERIC PRESSURE OF TYPHOON IN A FUTURE CLIMATE DATA SET

2017 ◽  
Vol 73 (2) ◽  
pp. I_1417-I_1422
Author(s):  
Yoshihiko IDE ◽  
Yuji ISSHIKI ◽  
Mitsuyoshi KODAMA ◽  
Noriaki HASHIMOTO ◽  
Masaru YAMASHIRO
Keyword(s):  
Bias Correction ◽  
Atmospheric Pressure ◽  
Future Climate ◽  
Climate Data ◽  
Data Set

scholarly journals Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2390 ◽  
Author(s):  
Olalekan Alade ◽  
Dhafer Al Shehri ◽  
Mohamed Mahmoud ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Atmospheric Pressure ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Viscosity Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

scholarly journals Study of Molecular Interaction for Antibiotic Drug with Sugar Solutions at Different Temperature

2020 ◽  
Vol 10 (01) ◽  
pp. 170-174 ◽  
Author(s):  
Sundus H. Merza ◽  
Nagham H. Abood ◽  
Ahamed M. Abbas
Keyword(s):  
Aqueous Solutions ◽  
Molar Volume ◽  
Atmospheric Pressure ◽  
Apparent Molar Volume ◽  
Viscosity Data ◽  
Free Energy Of Activation ◽  
Ternary Solutions ◽  
Antibiotic Drug ◽  
Solute Interactions ◽  
Molar Expansibility

The interactions of drug amoxicillin with maltose or galactose solutions with a variation of temperature have been discussed by taking in the volumetric and viscometric procedures. Physical properties [densities (ρ) and viscosities (η)] of amoxicillin (AMOX) aqueous solutions and aqueous solutions of two type saccharides (maltose and galactose 0.05m) have been measured at T = (298.15, 303.15 and 308.15) K under atmospheric pressure. The apparent molar volume (ϕv cm3mole-1) has been evaluated from density data and fitted to a Redlich-Mayer equation. The empirical parameters of the Mayer-Redlich equation and apparent molar volume at infinite dilution Ø°v were explicated in terms of interactions from type solute-solvent and solute–solute interactions. Transfer molar volume ΔtraØ°v for AMOX from water to aqueous maltose and galactose solutions were calculated to comprehend different interactions in the ternary solutions. Limiting apparent molar expansibility (Ø°E) and Hepler’s coefficient was also calculated to indicate the structure making ability of AMOX in the ternary solutions. Jones–Dole coefficient B and A have been calculated from viscosity data by employing the Jones–Dole equation. The free energy of activation of viscous flow per mole of the solute (Δμ°2*) and solvent (Δμ°1*) have been explained on the basis of the Eyring and Feakins equation.

scholarly journals Evaluation of Phosphate Ester Base Fire-Resistant Fluids for Gas Turbines

1974 ◽  
Author(s):  
W. C. Young ◽  
R. C. Elwell ◽  
R. B. McBride
Keyword(s):  
Fire Resistance ◽  
Gas Turbines ◽  
Laboratory Testing ◽  
The Other ◽  
Phosphate Ester ◽  
Test Program ◽  
Base Oils ◽  
Simulated Performance ◽  
Industrial Gas Turbines ◽  
Ester Base

Two phosphate ester base fire-resistant fluids were determined in laboratory testing to be superior or equivalent to the conventional petroleum base oils presently used to lubricate industrial gas turbines. This test program included tests of simulated performance, fluid stability, and fire resistance. Some differences were found between the two phosphate ester fluids although neither was judged superior to the other. Limited filed experience with these fluids has been satisfactory to date.

The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data

2013 ◽  
Vol 25 (6) ◽  
pp. 1548-1584 ◽  
Author(s):  
Sascha Klement ◽  
Silke Anders ◽  
Thomas Martinetz
Keyword(s):  
Feature Selection ◽  
Small Sample Size ◽  
Small Sample ◽  
Biological Data ◽  
Support Vector ◽  
Data Sets ◽  
Universal Method ◽  
Data Set ◽  
Separating Hyperplane ◽  
New Formulation

By minimizing the zero-norm of the separating hyperplane, the support feature machine (SFM) finds the smallest subspace (the least number of features) of a data set such that within this subspace, two classes are linearly separable without error. This way, the dimensionality of the data is more efficiently reduced than with support vector–based feature selection, which can be shown both theoretically and empirically. In this letter, we first provide a new formulation of the previously introduced concept of the SFM. With this new formulation, classification of unbalanced and nonseparable data is straightforward, which allows using the SFM for feature selection and classification in a large variety of different scenarios. To illustrate how the SFM can be used to identify both the smallest subset of discriminative features and the total number of informative features in biological data sets we apply repetitive feature selection based on the SFM to a functional magnetic resonance imaging data set. We suggest that these capabilities qualify the SFM as a universal method for feature selection, especially for high-dimensional small-sample-size data sets that often occur in biological and medical applications.

2.5D multifocusing imaging of crooked-line seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Hossein Jodeiri Akbari Fam ◽  
Mostafa Naghizadeh ◽  
Oz Yilmaz
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Line Geometry ◽  
Data Set ◽  
Seismic Surveys ◽  
Seismic Image ◽  
New Formulation ◽  
Optimization Search

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

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