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At first, the designers look for files and design data, compare the designing reducer and the existing reducers, then give out a design scheme combined with design experiences, and check the components.
Two methods usually are used to exchange data between Pro/E and ANSYS.
One is IGES, and the other is the data change interface in ANSYS [5].
Wu: Research on model data conversion based on Pro/E and ANSYS, Manufacturing Automation, Vol. 33(2011), pp.119-122 [6] J.
Amit: Trade-off Analysis in Minimum Volume Design of multi-stage Spur Gear Reduction Units, Mechanism and Machine Theory, Vol.35(2000), pp.609-627

The MCS of a measured data point set is determined by a small number of critical data points according to geometric criteria.
If it is determined by two data points, shift to Step 2; If it is determined by three data points, shift to Step 3; And four data points, shift to Step 4; • Step 6 Search out the nearest data point pf from the center of the MCS.
For the Set 1, only 7 data points of 26 data points are used to obtain the MCS.
For the Set 2, only 8 data points of 40 data points are used to obtain the MCS.
Only 12 data points is used to compute MCS.

Magnesium and to limited extent, titanium, alloys are candidate materials for weight and emission reduction in the automotive industry.
The combination of experimental data and predictive models developed in this work are intended to characterize the forming behaviour of various lightweight sheet alloys without the requirement for excessive experimental data, and to ultimately aid in the selection of lightweight sheet metals for multi-material vehicle design.

Experimental data of stress-controlled CF test on P92 steel were employed to verify the precision of the two prediction methods.
The reduction of fatigue lifetime due to holding periods is a well known phenomenon which has already been investigated at various temperature [8-10].
Creep-fatigue life prediction models for P92 steel In this paragraph the CF tests data were employed to regress the parameters in the life prediction models described above.
The values of temperature dependent material parameters A and β were obtained by regression of CF test data according to Eq. 3 and Eq. 4,and the life prediction model and its simplified expression are describe as below
(8) (9) According to Eq. 1, Eq. 2 and Eq. 7, the parameters of the modified-AMWD model which were obtained by regression of CF test data are given in Table 3.

In the process of carbon footprint calculation and analysis, the data is used such as the consumption, economic, population data of Jiangsu Province and so on from statistical yearbook of Jiangsu Province in 2009.
Carbon footprint growth index was at its lowest level in 2000, it is the only time that the data is in the negative, is -0.002, at its highest level in 2005, is 0,805.
The trend of GDP growth index has been stable, from 2000 to 2004, the data was from 0.105 to 0.226, then back down to 0.106 in 2008.
The decoupling index reduction in the growth amplitude is largest, the lowest point appeared in 2000, it is the only time that the data is in the negative, was -0.002, the peak appeared in 2005, was 4.74, then down to the lowest point, it was 0.119 in 2008, indicating that from 2000 to 2009.
Conclusion This article takes Jiangsu Province with rapid economic development as an example, using the energy consumption data and its social and economic statistical data from 2000 to 2009, to analyze the changing rule of energy consumption carbon footprint; using the trend correlation method, to analyze qualitatively the factors that affect energy consumption carbon footprint, including technology development, economic and population; using the decoupling index, to analyze the mutual relationship between the economic growth and energy consumption carbon footprint, the conclusion as follows: In 2000, the energy consumption carbon footprint in Jiangsu Province reached the minimum, was 0.506 hm2/person, and it reached the peak in 2009, was 3.508 hm2/person, during the ten years, the average growth rate was 13.6%.

The joint probability distribution of the local wind speed and direction information was obtained using the raw meteorological data.
By CFD numerical simulation, the ratio (rM) of the measuring point wind speed (Uz) to the standard wind speed in meteorological data (U10) can be obtained.
In this study, according to the meteorological data, sixteen wind directions are used.
To make the results representative, according to wind speed data provided by the meteorological data [9], the exceeding probability of wind speed of 5m/s is around 10%.
[9] Thermal environment dedicated meteorological data sets, China Building Industry Press (2005).

In order to compare with the experimental data, here the flow speed is also set to 60km/h.
The result is shown in the table 2, which also includes the experiment data for comparison.
The DES result is smaller than the experimental data, but more close to the experimental data.
Compared to the experiment data, the relative errors of RANS and DES are 9.75% and -5.1% respectively.
There is no big difference between RANS and DES, although DES gets a little more accurate result, such as Cd coefficient, compared to experimental data.

In the presence of oil, the effect of mobility reduction of foam on gas or water is weakened and the propagation of foam in the porous media is delayed [16-18].
Data for foam with different types of porous media, surfactant formulations and over a range of flow velocities prove this behavior to be general.
Kim et al. [26] collected previous data for CO2 foam studies, and found either the high-quality regime or the low-quality regime appeared in one study and the two regimes didn’t appear simultaneously.
The understanding of the trapped gas fraction is crucial in interpreting gas mobility reduction and foam modeling in porous media.
When the parameters are too many, for example ten in Kovscek’ model [34], data fitting becomes difficult.

Though culture determines economic and social effects, here it is explained only its economic consequences, both inside of cultural productive system and as multiplier effect in related sectors, using data from the Symbola - Unioncamere report [35].
The sector employs 1,371 million workers or 5,7 % of total employment in the country; the trend of the employed in the sector is in contrast with national data.
Although, the important economic value produced by culture, a weakening of the competitiveness of Italian model has occurred, with repercussions on the tourist market which indicates a lower ability to attract and a consequent reduction of flows so that the country has been overcome by countries such as Spain and China.
The situation has worsened in 2012-13: although the resources for the Ministry of Culture have increased, there have been reductions in financing of regions and local authorities.
These data indicate of Italy’s inability to adapt to the extreme dynamism of the sector and to the levels of growth of the competitor countries. 3.

The outside air temperature was recorded by employing data logger and thermocouple system on hourly basis.
The maximum temperature of a particular day was obtained from the temperature data logger.
The outside temperature, indoor air temperature and wall surface temperature of both these rooms were recorded by employing data logger and thermocouple system.
For system 2, both the temperature reduction and energy saving increased to 12.73% and 3.90C respectively against the untreated rooms due to the coating (Weathercoat Kool & Seal) applied on the roof.
However, it is to be noted here that if the comparison of the performance of Weathercoat Heat Insulating Paint is made against an unpainted building, higher saving and temperature reduction may have been achieved.