Search results

Measurement data comprised fuel consumption rate (kg/hr) together with the exhaust emissions like hydrocarbon(HC), oxygen(O2), carbon dioxide(CO2),carbon monoxide (CO), nitrogen oxides (NOx) and smoke opacity by using autocheck 5 channel gas emission analyzer and dragger MSI.
Further analysis and presentation of data is based on the average of measurement.
Measurements data comprised fuel consumption rate (kg/hr) together with the exhaust emissions like hydrocarbon(HC), oxygen(O2), carbon dioxide(CO2),carbon monoxide (CO), nitrogen oxides (NOx) and smoke opacity by using autocheck 5 channel gas emission analyzer and dragger MSI.
Further analysis and presentation of data is based on the average of measurement.
Engine emissions promotes the reduction of NOx and CO2 emission and higher in O2 in the range 5% to 15 % of blends due to more oxygen present during combustion, thus the combustion will become more complete and in oxygenated fuel. 2.

The initial data for calculating heat and power parameters of the building The general descriptions of the building.
The general data of building services systems have been identified.
The data material must be characterized by the availability of raw materials, low energy consumption and low production costs, have the water resistance and frost resistance, mechanical strength, ecological and fire safety.
Gorshkov: The energy efficiency in the field of construction: questions of norms and standarts and solutions for the reduction of energy consumption at buildings (Magazine of Civil Engineering, Russia 2010)
Gorshkov: The energy efficiency in the field of construction: questions of norms and standarts and solutions for the reduction of energy consumption at buildings (Magazine of Civil Engineering, Russia 2010)

It is reported that 35% reduction in engine dimensions and 17% reduction in fuel consumption with a thermal barrier coated engine design in a military tank [4].
The original data is transformed from Taguchi experiments into S/N ratio.S/N ratio for maximum volumetric efficiency, heat transfer rate to coolant and CO emission are computed using the following formula [1].
For larger-the better type of quality characteristic SN ratio= -10log1n i=1n1Yij2 equation 1 The grey grade (Gi) is equivalent to MRPI and is treated as single response problem and MRPI data is analyzed to determine the optimal levels for the factors.
Design of Experiment The experimental results for L9 orthogonal array are given in Table 2.The data has been taken from literature survey.
0.889 1.000 0.602 0.529 0.5 0.624 0.551 7. 0.772 0.652 0 0.228 0.348 1.000 0.814 0.741 0.5 0.685 8. 0.429 0.503 0.165 0.571 0.497 0.835 0.636 0.668 0.544 0.616 9. 0.429 0.166 0.649 0.571 0.834 0.351 0.636 0.545 0.740 0.640 Result and Discussion Table 5.Mean of MRPI A1 0.694 B1 0.697 A2 0.664 B2 0.632 A3 0.647 B3 0.675 Table 6.ANOVA results on MRPI values Source DF Seq SS Adj SS Adj MS F P % contribution A 2 0.0263562 0.0263562 0.0131781 5.30 0.075 44.01 B 2 0.0235769 0.0235769 0.0117884 4.74 0.088 39.37 Error 4 0.0099411 0.0099411 0.0024853  - -   16.62 Total 8 0.0598742  - -  -  -  -  S = 0.04985 R-Sq = 83.40% R-Sq(adj) = 66.79% The figure 1.represents the main effects for Gi due to Load and Speed are shown below the graph Fig.1 Graph for effect on Gi Confirmatory experiments After the optimal level has been selected, it is very essential to perform a confirmation experiment for the parameter design, particularly when less number of data

The dynamic torque sensor measures the rotor torque output and the computer records the relevant data.
Reduction box reduction Measuring equipment DCpower supply Motor Dynamic torque and rotating sensor Coupling Rotor MRF Stator Disc-shaped electromagnet Fig.6 MRF clutch simulator overall view Fig.7 MRF clutch simulator local map Results and discussion In the experiment, where fixed variable method is adopted, the magnetic field strength in the working area should remain constant, and then the data about the motor torque output will be collected through changing the rotating speed of the motor.
Multiple sets of data will be obtained through further altering the strength of the magnetic field.
Shear stress（pa） Shear rate（1/s） 1—20mT 2—50mT 3—70mT 4—90mT 5—110mT 6—130mT 7—150mT 8—170mT 9—180mT 10—190mT Magnetic field strength MT Fig. 8 Experimental data From the Figure8 it can be seen that in the fixed magnetic field, the shear stress goes up as the shear rate rises, yet to just a small degree.

Design and safety assessment of advanced ductile cast iron (DCI) components like wind turbines or transport and storage casks for radioactive materials require appropriate material data in terms of strength and fracture toughness.
A comprehensive compilation of investigations and results concerning mechanical properties, fracture mechanics test methods and material data can be found for instance in [1-3].
Primary goal of these investigations was to establish the experimental data base for development and validation of a key curve single specimen method for dynamic R-curve testing of DCI.
This has to be done with deliberation and profund experimental experience can be seen as a vital prerequisite to be able to provide valuable toughness data of DCI.
Baer, Advanced Fracture Mechanics Testing of DCI - a Key to Valuable Toughness Data, 2013 Keith Millis Symposium on Ductile Cast Iron, Nashville, TN, Oct 15-17, 2013.

As can be seen from Fig. 3 a in rolling contact ethanolamine gave almost no reduction of friction compared to water, however ethylamine and glycol show visible reduction of friction coefficient.
In case of sliding conditions - Fig. 3 b where tests were not reproducible none of additives gave as significant friction reduction.
From the friction data recorded during ball on disc experiments (Fig. 3 b) it can be seen that none of the additives gave significant reduction of the coefficient of friction.
Only ethanolamines gave visible reduction of corrosion and wear on steel specimen, as observed in Fig. 5 a.
Based on surface analysis data of the ethanolamines and ethylamines published in a previous paper of one of the present authors [10], it can be concluded that the adsorption of additives on the iron surface can be explained as proposed on Fig. 6.

This algorithm is very effective to identify natural groups in data from a large data set, thereby allowing concise representation of relationships embedded in the data.
The subtractive clustering method [8] assumes each data point is a potential cluster centre, and calculates a measure of the potential for each data point, based on the density of surrounding data points.
The algorithm selects the data point with the highest potential as the first cluster centre, and then destroys the potential of data points near the first cluster centre.
It is assumed that each point in the data space has equal contribution towards system identification; therefore, the data density determines the grouping of data into clusters.
Assuming that each data point is a potential cluster center, the subtractive clustering algorithm calculates a measure of the potential for each data point based on the density of surrounding data points.

Briefly, ultrapure water (3 μL droplets) were tested at room temperature using Data Physics OCA 15 equipment.
Fig. 3: Antimicrobial log reduction of a) S. aureus, b) E. coli and c) MS2.
All tested samples were subjected to DBD plasma treatment, as non-plasma treated sample did not exhibit any activity (data not shown).
E. coli assays displayed a similar reduction in the “contact” approach (approximately 99 %).
Whereas in “shake flask” almost no reduction was observed for both PES+ALG+AgNPs and PES+ALG+AgNPs+MOR.

Data collected from an iridium anode TXRF were compared to data from a tungsten anode instrument in an evaluation of a wet etch of metal from a hafnium silicate film.
Statistical analysis of this data was performed in JMP version 6.0 (JMP, Version 6.
A gauge study was performed by collecting data for five days, three wafer loads per day, and three repeated measurements per load.
While the median values from the measurements are very close to those found by normal TXRF, Figure 4 shows an example of the spread of the data for the 8E10 atoms/cm 2 wafer for all of the data points with both modes of angle alignment and comparing 10 and 30 second acquisitions.
Figure 4: The spread of the data from the 8E10 atoms/cm 2 copper wafer from different analysis modes with a line connecting the medians.

The main idea of the algorithm is firstly to look the first data as a first cluster center, and further to detect the distance d from remaining data points to the cluster center.
If d is less than a predetermined value T, the data is added to this class.
Otherwise, the data is set as a new cluster center until all the data are classified.
The formation of clusters of data on behalf of a class avoids the unicity of the data in class representation and greatly improves the accuracy of data collection when system retrieve the matching operation by center point of the data set.
Clustering analysis based on attribute reduction[J].