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INTRODUCTION Wind turbines, during operation, need to dissipate a large amount of heat, that, if not properly handled, might generate a temperature rise of the electrical and mechanical components and hence a further reduction of the overall efficiency.
NANOFLUID CHARACTERIZATION In order to acquire significant data on the thermal properties of nanofluids mixtures of base fluid with different commercially available nanoparticles have been tested.
Figure 1, Figure 2 and Figure 3 show experimental data.
Figure 1: Experimental data for nanofluids with water and dispersant and Al2O3 Figure 2: Experimental data for nanofluids with water and dispersant and CuO Figure 3: Experimental data for nanofluids with water and dispersant and ZnO Figure 5: Thermal conductivity of engine oil with carbon nanotubes Cooling system configuration In the present investigation two cooling system options have been considered and each solution is characterized by two steps, as Figure 4 and Figure 5 show.

The extracted results extracted from simulation showed that, in contrast to the dwell time 5o CA, a slightly reduction in peak cylinder pressure by 8.9, 7.8, 6.7, and 9.1% for 10, 15. 20, 25o CA respectively.
Validation To verify the results obtained from the Diesel-RK model tools, the simulated results were compared against the data obtained from an experimental study [16].
Due to this, the ignition process can be started earlier and the combustion process can be completed before TDC, often with a short dwell time, resulting in a reduction in PCP.
Therefore, local fuel reactivity of the fuel mixture reducing and extending the combustion process to be near the TDC with shorter burning period leads a slightly reduction trend.

Data process and statistical analysis 1 mL samples from every flask were collected every two days, and preserved in Lugol’s solution.
All the data of the growth assays in this study were analyzed by ANOVA and Tukey’s test.
Data represent average values (n=4) with corresponding error bars (1 S.
Data represent average values (n=4) with corresponding error bars (1 S.
Data represent average values (n=4) with corresponding error bars (1 S.

Data source.
Data analysis.
The Factor Analysis (FA) allows a considerable reduction in the number of variables and the detection of structure in the relationships between the GAP and other indexes [7].
The first two factors, in which the Eigen values were higher than 1, contribute more than 80% of the total variance in the data, and the high communality estimates suggested that the high portion of variance was explained by the first two factors (Table 2).
The factor analysis result of 2011’s data indicated that the factor 1 explained 84.3% of total variance with a high positive loading from GAP, NEPA, CCF, SAG and SAV.

As more land is converted to urban uses, the question arises as to whether this trend represents a systematic reduction in our ability to produce food by placing our infrastructure on the most productive soil resources.
Data and methods.
The remote sensing data used in this study is a set of Landsat satellite TM images in 1986, 1995, and 2000 (resolution at 30 m), and CBERS-2 data (resolution at 20 m) in 2005.
Tucker, Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988, Science. (1993)1905-1909
Elvidge, A technique for using composite DMSP/OLS “city lights” satellite data to accurately map urban areas, Remote Sensing of Environment. 61(2003)361-370

Overall, the current project evaluation of mathematical methods commonly used are: multi-objective decision making(Chankong and, Haimes, 1983) [5] , data envelopment analysis method and model , AHP (analytic hierarchy process) (Saaty [6], 1980; Liu Bao et al [7], 1984) , Fuzzy Comprehensive Evaluation FCE) methods of mathematical statistics.
Data envelopment analysis by the U.S.
Data envelopment analysis has been advances in technology assessment, evaluation and fields of returns to scale enterprise efficiency evaluation is widely applied.
Assignment using expert scoring method, the experts should be based on project feasibility report, analysis and design documents, construction documents and completion of the inspection reports, audit reports, evaluation of environmental data and survey results of the local government and surrounding residents, and through on-site situ observations are given for each quantitative assignment.
(This analysis using Spss data analysis software, provides Bartlett test of sphericity in Spss, reflected as the correlation matrix test, KMO test.)

Experimental data Through the pipeline system under different flow rate and pressure slurry pipeline to gather data, test data, see table 2 below.
Rheological properties of slime According to Table 2, the test data and the measurement principle of the wall rheology, wall shear stress and shear Department rate values shown in Table 3 Table 3 Rheological characteristics of slurry rheological parameters of 3 Discharge Rate Transmission distance Pressure Shear stress 2.5 7.074 2.45 498 2.875 3.0 8.488 3.18 646 3.034 3.5 9.903 3.8 772 3.176 4.0 11.318 4.8 976 3.303 4.5 12.732 5.64 1146 3.42 5.0 14.147 6.16 1252 3.529 5.5 15.562 7.27 1478 3.63 6.0 16.977 8.31 1689 3.724 6.5 18.391 9.28 1886 3.813 7.0 19.806 10.12 2057 3.898 7.5 21.221 9.85 2002 3.978 Flow index Consistency coefficient Rheological equation Effective viscosity () Viscosity equation Because, as the rheological characteristics of slime dilatant fluid, which is to absolutely avoid transport phenomenon.
When the transmission distance, the fitting formula to calculate the pressure values measured error is greater in individual cases, some even 40%, these data both for the experimental research or engineering is desirable, but can be as a reference for a number of other high concentration of relatively viscous material pipeline.
Material in the pipe due to high pressure, the particle density, viscosity, molecular interaction between the surface charge volume and other basic parameters and morphology of fundamental changes have taken place, the direct performance of the rheological equation for the regular greater reduction of flow patterns change.

Therefore, many methods were proposed for surveillance and reduction of chatter vibration, such as vibrating cutting, control of instantaneous tool-workpiece relative position [3] and varying the spindle speed cutting.
Figure 4 depicts the experimental data.
(a) (b) Fig. 4 Experimental data of x-axis direction: (a) The dynamic cutting force; (b) The relative tool-workpiece vibration The cutting process transfer function was derived from the experimental data with the Fourier Transform (FT) method, as shown in Fig. 5.
A circular curve was fitted with the measured data.
Tobias, PREDICTION OF DYNAMIC CUTTING COEFFICIENTS FROM STEADY STATE CUTTING DATA., Annals of the CIRP 21 (1972) 97-98

Meanwhile, data from the load cell and the linear variable displacement transducer were simultaneously and digitally recorded in a computer by using the National Instrument data-acquisition system.
The input data of the impact energy were digitally recorded, blow by blow, through the data-acquisition system, which was controlled by the LabVIEW software.
In the present work, data were exported to Microsoft Excel for post-analysis.
A drop in flexural strength was observed when the specimens were continuously cured in Nw, and a similar reduction trend for the compressive strength was also detected when the untreated RCA was incorporated into RAC.

Some of these methods will be exemplified on data obtained from long time observation of Bucket Wheel Excavators from Romanian open pit coal mines in order to demonstrate the viability of such new analysis tools in the monitoring and diagnosing mining production systems.
Starting from the main reliability parameters determined on the basis of these recorded data, such as MTBF and MTTR, respectively the exponential distribution associated parameters, the rate of failure l and the rate of repair m , using the Monte Carlo simulation method, we simulated the operating cycles during one month.
The inherent production fluctuation The simulated variability of the production system, with above data, considering breakdown-safe operation is given in figure 3.
Diagram of simulated hourly production during 1 month If we realize a number high enough of iterations, by averaging, we obtain in the average results near to start input data considered.
Now, we can study different scenarios changing the input parameters, as reduction of the average repair time, or reducing the fluctuation of the production rate.