This article deals with thermophysical parameters as: temperature, thermal conductivity, diﬀ usivity and rheologic parameters as: dynamic, kinematic viscosity and ﬂ uidity of milk and acidophilus milk. For thermophysical parameters measurements was used Hot Wire method and for rheologic parameters measurements was used single – spindle viscometer. In the ﬁ rst series of measurements we measured relations between thermophysical and rheologic parameters in temperature range (5– 25) ºC for milk and acidophilus milk. Relations of all physical parameters of milk to the temperature showed inﬂ uence of relative fat content. Eﬀ ect of storage on milk and acidophilus milk is shown in the text. All measured relations for milk and acidophilus milk during temperature stabilisation had linear increasing progress with high coeﬃ cients of determination in the range (0.991–0.998). It was shown that increasing relative fat content has decreasing inﬂ uence on milk thermal conductivity. Relations of rheologic parameters as dynamic and kinematic viscosity to the temperature had decreasing exponential progress, while relation of ﬂ uidity to the temperature had increasing exponential shape with high coeﬃ cients of determination in the range (0.985–0.994).. Mathematical description of the dependencies is summarised by regression equations and all coeﬃ cients are in presented tables. milk, acidophilus milk, thermal conductivity and diﬀ usivity, dynamic and kinematic viscosity, ﬂ uidity, temperature and storing time, fat content
The measured and calculated values of biooils rheological parameters confirmed differences be- tween both samples. The biooil No.1 had higher values of dynamic and kinematic viscosity than the second sample, but the differences were higher at lower temperatures (20–35°C) and almost the same for higher temperature (43–50°C), which is in ac- cordance with hydraulic biooil technical and op- erational requirements. Third analysed parameter – fluidity was higher for the second sample, which was explained in the theoretical part. For biooils density values in the whole temperature range no significant differences were observed. The coeffi- cients of regression equations and coefficients of determination are presented in Table 2.
Food materials are very complex in their composition and in their physical properties. These properties depend on the manipulation, external conditions and other factors, which determine their behaviour. Rheological properties of wines were measured by some authors. Rheologic, thermal properties and density of selected Slovakian red wine Frankovka modrá were measured and analyzed in this paper. Eﬀ ect of temperature and also one week of storing on used red wine was searched. Temperature dependencies of red wine dynamic and kinematic viscosity had decreasing shape and temperature dependencies of ﬂ uidity had increasing shape (Figs. 1–3). For temperature dependencies of rheologic properties were used exponential functions. Thermal properties, such as thermal conductivity and thermal diﬀ usivity characterize heat transfer ability of material, velocity of the temperature equalization and the intensity of the temperature changes in the material. These facts were examined by thermal parameters measurement. From obtained results is clear that thermal conductivity and thermal diﬀ usivity of red wine increases linearly in observed temperature range (Figs. 4 and 5). The temperature dependence of examined wine density had polynomial shape (Fig. 6). All graphical dependencies had very high coeﬃ cients of determination. The results showed that inﬂ uence of one week storing had only small eﬀ ect on the red wine physical parameters.
. The kinematic viscosities of the pure liquids and their mixtures were measured at 303.15K, 308.15K, and 313.15K.The viscometer was filled with liquid mixtures, and its limbs were closed with Teflon caps taking due precaution to reduce evaporation losses. An electronic digital stopwatch with a readability of 0.01s was used for flow time measurements. Experiments were repeated a minimum of four times for all compositions and the results were averaged. The caps of the limbs were removed during the measurement of flow times. The measured values of kinematic viscosity, γ, were converted to dynamic viscosity, η, after multiplication with density. The reproducibility of dynamic viscosity was found to be within 0.003 mpa•s. A thermostatically controlled, well- stirred water bath, whose temperature was controlled to 0.01K was used for all the measurements. Conductivity measurements were carried out in a jacket containing a conductivity cell of cell constant 1.0 cm -1 . Water was circulated in the jacket from thermostat, and the temperature maintained within ± 0.01 K, was used for all the measurements. The kinematic viscosity of solution γ is given by
Kinematic viscosity as a function of temperature of 6 diﬀ erent synthetic and semi–synthetic oils have been considered. Figure 2 shows the overview of ex- perimental data of viscosity–temperature depen- dence. Fig. 3 shows the values of kinematic viscosity as a function of density and temperature. It is ob- vious that density is not the ruling or determinative factor inﬂ uencing viscosity. This eﬀ ect can be ex- plained by partially diﬀ erent chemical composition of individual oils. The viscosity of oil is highly tem- perature dependent and for either dynamic or kine- matic viscosity to be meaningful, the reference tem- perature must be quoted. In ISO 8217 the reference temperature for a residual ﬂ uid is 100 °C. For a dis- tillate ﬂ uid the reference temperature is 40 °C. Table II lists the values of kinematic viscosity at reference temperature of 40 °C and oils density.
Temperature has a dramatic effect on the viscosity of water and a somewhat smaller though significant effect on the density. Viscosity (dynamic) describes the degree to which a fluid particle will be retarded by any lack of synchronized activity of adjacent particles, or the ‘interlaminar stickiness’ (Vogel, 1983). In contrast, kinematic viscosity, which is simply the ratio of viscosity to density, describes the ease with which a fluid flows (i.e. the ‘gooiness’) (Vogel, 1983) and as such is the parameter that is most used when examining the effects of temperature on the fluid mechanics of biological systems. The question of how changes in kinematic viscosity associated with changes in temperature may affect hydrodynamic characteristics and swimming capacity has been discussed previously by a number of authors (e.g. Sidell and Moerland, 1989), but only recently investigated experimentally (Johnson et al. 1993; Podolsky and Emlet, 1993; Fuiman and Batty, 1997). Viscosity was found to have a significant effect on tail-beat frequency and tail-beat amplitude during routine swimming of small herring (Clupea harengus) larvae (Fuiman and Batty, 1997). The latter study questioned the validity of applying hydrodynamic models of inanimate geometric solids to swimming organisms and suggested that viscous forces may be more important than previously expected for animals swimming at higher Reynolds numbers (Re ⭐ 450) (Fuiman and Batty, 1997). The significance of changes in viscosity to the locomotor performance of larger fishes swimming at higher speeds (Re ⭓ 450) is investigated here.
the development of a novel idea for the evaluation of kinetic parameters of winterization, along with obtaining the suitable operation and storage conditions of biodiesel. Therefore, the waste oil of Aurantiochytrium sp. could be developed for biodiesel production and successfully made into a suitable blend diesel. Overall, we acquired the best condition of mixtures and the highly mixed rate of petrodiesel: biodiesel = 80 : 20 (activation energy of winterization 21.32 kJ/mol; onset temperature of winterization -4.15 °C; heat of combustion 43.15 MJ/kg; kinematic viscosity 3.51 mm 2 /s; flash point 67.5 °C), which was an appropriate blend biodiesel from the waste oil’s biodiesel of Aurantiochytrium sp.
Scientific sources indicate that the oil drain intervals at present, for cars are between 10,000 and 50,000 [km] . According to the engine tests by some European manufacturers, engine life is represented as 1,000 hours or 150,000 [km] for the overhaul period at maximum load and revolution conditions. During 1,000 hours of testing, oil drain is every 150 hours or 15,000 [km] [4-6]. It was observed that there is no guideline for the appropriate oil drain interval and that is due to various reasons such as state of the engine, filters type, climate and the design of the road passed through. Oil analysis is a widely used monitoring technique in which information on the performance or condition of a machine is determined from the degree of contamination or degradation of its lubrication oil. Several methods are described in the literature for condition assessment of oil in simulated laboratories [7, 8]. Viscosity, oil insolubles, total acid or base number (TAN/TBN), water content, wear debris and metal particle analysis and spectroscopic methods etc. are some of the test or techniques which have been exploited for health monitoring of the oil systems [6, 9-11]. Timotijevic et al. determined limit values that are set at two levels - the warning and critical (Table 1) . Warning limits define values that can endanger the performance of the motor. The critical limits indicate the real possibility of reduction in motor performance, and in extreme cases, permanent damage.
Viscosity of fluid plays an important roles in fluid characterization, fluid transfer and quality control. This study aim at determination of viscosity of two Newtonian fluids (Glycerine and Engine oil) using falling sphere viscometer. In this method, two steels balls (small and big steels balls) and two Newtonian fluids (Glycerine and Engine oil) were used to analysed the results of the study. The density of both fluids, density of both steels balls, acceleration due to gravity, radius of the steel balls and the velocity of both steels balls are essential in the determination of the dynamic and the kinematic viscosity of both fluids. The density of each steel balls was calculated using the formula, density=mass/volume, the mass of each balls was determined with the aids of a weighing balance and the volume of each steel balls was determined using the formula, V=πr 3 , where r is the
The kinematic viscosity goes on increasing as impurity percentage increased. However Density also shows same changes as impurity percentage is added into the fuel.The Laboratory test based results are compared with statistics of the images. Psnr is basically peak signal to noise ratio. This function computes the degradation. Basically, psnr factor determines the quality of image, the signal to noise ratio. The ratio of psnr is often used as a quality measurement between the original and a compressed image. Higher the PSNR better is the quality of the compressed or reconstructed image. PSNR gives better result than that of Laboratory tests based approach.
So from the above comparison it is clear that the kinematic viscosity of blends is less than the transformer oil. As high kinematic viscosity affects the speed of moving parts such as in power circuit breakers, switch gears etc. Moreover for better insulation property, the viscosity of oil should be low. So our oil different composition blends of castor and madhucaindica oil is suitable as transformer oil from kinematic viscosity point of view.
Specific gravity is the ratio of density of unknown sample to the equal volume of water. This parameter varies with temperature so the temperature must be known at which the density is measured. An increase in the amount of saturated compounds results in decrease of specific gravity while there is rise in gravity as aromatic compounds increase (Isah, 2013). In used oil specific gravity increases due to increasing amount of the solids. Table-2 & Fig.1 show the density (specific gravity) of virgin and waste engine oils are (0.88-0.94). It was noted that the specific gravity of waste oils was greater as compared to fresh oils. This is due to the contamination of high molecular weight components to the used oil samples from inside the engine. 3.2 Kinematic Viscosity
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Viscosity is an important property in determining the lubrication quality of an MWF. It serves as an internal friction within the lubricant against applied shear stress during sliding process, which influences the wear and friction behavior of the two contacting surfaces. Fig. 2 displayed the kinematic viscosity of samples at 40 °C and 100 °C. The viscosity of a lubricant is known to increase with the increasing temperature. It can be observed from both linear graphs that a lower viscous lubricant (SE) at 40 °C has the lowest viscosity value at 100 °C. In fact, the highest viscosity of MRPO2 at 40 °C does not imply the highest viscosity value at 100 °C compared to MRPO1. The kinematic viscosity of SE at both temperatures recorded the lowest compared to RPO and MRPOs. This is reflected to the number of carbon in the oil molecules. The number of carbon in SE is 8 to 14 whilst, 12 to 20 for the number of carbon in RPO. The higher the number of carbon positively affects the oil’s viscosity. Moreover, the kinematic viscosity at both temperatures for all samples of MRPOs has exceeded the kinematic viscosity of SE and RPO. The reason behind it is that, after the chemical modification process of RPO, the large molecular chain and branches of TMP ester is broken and combined with the monoester (FAME) to form a MRPO which has good low-temperature properties .
few values of the dynamic viscosity. The line slopes are close to each other but lower than for the 10% solution. The table 4 shows the shear rates range for which we determined the dependence of the dynamic viscosity natural logarithm on the inverse absolute temperature for the 12% solution of polyethylene-polypropylene copolymer, the parameters A and B derived from the equation (7), the correlation coefficients derived from that equation and the statistically found correlation coefficients.
The Adomian Decomposition Method has been successfully applied to approximate solution of Burgers’ equation. For moderately small kinematic viscosity α≥0.01, the Adomian’s approach provides high accuracy while using a small number of terms and the results are better when compared with Homotopy perturbation method.We have also added to the literatures a numerical result for α=0.001 where neither analytical nor numerical result existed before. We remark that ADM is a powerful mathematical method which solves all kinds of mathematical problem without recourse to perturbation, discretization, transformation or linearization as practiced by other conventional numerical methods.
The effects of four different additive formulations namely 5748, 801, 264 and 261 on the viscosity index of two lubricating oils (base oils) namely 150N and 500N at two temperatures 40 0 C and 100 0 C were investigated. The base oils were blended with the additives in three different proportions of 100/4. 100/8 and 100/12. The results gave a viscosity index of 96 and 98 respectively for 150N and 500N without additives. On the other hand, the addition of 12g of 261 additive formulations to 100cm 3 of both base oils gave about 180% increase in kinematic viscosity at 40 0 C, about 161% increase and 146% increase at 1000C for 150N and 500N respectively. About 60% in viscosity index was achieved by 100/12 blend of 261 additives in 150N. The results revealed that 261 additive formulations gave the highest increase in viscosity in all proportions increasing as the weight of the additive increases. Generally, all the four additive formulations used improved the viscosities of all the blends in all the proportions and at both temperatures. The blends can be classified as very high viscosity index being above 110. This means that they will undergo very little change in viscosity with temperature extremes and so can be considered to have stable viscosity.
Kinematic viscosity of the melts is measured by the torsional vibration method based on measuring the vibration damping factor . The measurement error of kinematic viscosity makes up to 3 %. Investigation of the melt properties was performed using samples selected after melting at 1550 ° and cooling in a flat mould, as well as those in the form of a ribbon of an amorphous structure, which was produced from this alloy by rapid quenching from the melt.
The main goal of this article deals with eﬀ ect of storage temperature on the partially fermented wine must viscosity. Four samples of partially femrented wine must (Pinot Gris) wre observed. Two samples were stored in cold temperature 5 °C and another two samples were stored at the room temperature ~20 °C. The kinematic viscosity has measured for 132 hours from start of stormy fermetation (in the interval about 16 hours). Rotary viscometer with standard spinde and low-volume adapter was used to experimental measured. Results of kinematic viscosity process were mathematically modelled using logarithms function. Created mathematical models were precision because correlation coeﬃ cients and values of signiﬁ cance achieved very high values. Results and trends form this article can be used to the partially fermented wine must viscosity process prediction.
Abstract Biodiesel is a substitute diesel fuel produced from vegetable oils or animal fats by trans-esterification in the presence of a catalyst. Due to its environmental friendliness, its use is becoming necessary in all regions, however, there are problems associated with its fluidity in cold temperatures. This paper examines the cold flow properties as well as the kinematic viscosity of produced biodiesels. The review shows that both properties are affected by the length of the carbon chain and degree of saturation. However, these can be improved by blending either with petro-diesel or less saturated FAME, as well as the use of some additives.
investigated the correlation between the heat flux and first grid size from the wall and found that setting it to 3 mm led to grid independent results using the wall-adapting local eddy-viscosity (WALE) model to resolve the near wall dynamics. Although it is possible to use a wall model to avoid using fine resolutions, this would constitute an additional source of error because of modelling near wall dynamics . Yuan et al.  performed LES of a polyurethane foam (PUF) compartment fire and investigated the effect of ventilation conditions. However, the investigations of Ren et al. [16,19] and Yuan et al.  did not include flame spread, the former focused on wall fires while the later on the combustion process inside the compartment.
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