Search results

The MpCCI software enables the exchange of data between meshes of simulation codes (FLUENT and ABAQUS) in the coupling region.
The CFD results are compared with the published data from Abbot et al. [10] in order to validate the current work.
Fig.2 shows the comparison of these data.
As can be seen in Fig. 2, a good agreement between the CFD results and published data is achieved.
L.S., (1945), Summary of Airfoil data, National Advisory Committee For Aeronautics, Report No. 824, Washington 25, D.C, p. 193

The emissivity was determined by experimentally and compared with the emissivity data of various sources.
The data was used for developing PMT prediction model as follow ; PMT = const + a1pelec + a2vline + a3tsub + a12pelecvline + a13pelectsub + a23vlinetsub
Optimization and Systemization of the Model The regression coefficients of PMT setup model was tuned by using the temperature data from the field's oven.
The strip temperature data were collected for the various conditions such as paint color, electric power, line speed and substrate thickness.
The quality data were collected in time and coil basis, and stored as long-term archive file.

Currently, the main preparation approaches of silicon nitride powders include carbothermal reduction, gas phase reaction, thermal decomposition, and direct nitridation of silicon powder, et al [3-6].
Table 1 Parameters of the variables in Eq. 5 (g·cm-3) (cm) b (g·mol-1) (Pa) (J·mol-1·K-1) (mol/cm3) 2.329 1.4×10-4 1.5 28 1.01×105 8.314 7.5×10-6 (at 1623 K) Through the values in table 1, the Eq. 6 can be derived: (6) Use Eq.6 to fit the experimental data at 1623K in Fig. 2, it finds that the simulation results are in good agreement with experimental data when ks1 is 3.62×10-5 cm/s, and its calculated curve is shown in Fig. 2.
(2) When the nitridation temperature is 1723K, 1773K and 1823K, due to relatively higher temperature, the reaction rate increased accordingly, fr and fs can not be ignored, the relationship between reaction time and the conversion rate of silicon is determined by the Eq. 7: (7) Using Eq. 7 to fit the experimental data at 1773K in Fig. 2, it finds that the simulation results are in good agreement with experimental data when De is 6.2×10-8 cm2/s and ks2 is 1.03×10-3 cm/s, and its calculated curve at 1773K is shown in Fig. 2.
Therefore, the reaction rate constant ks at any temperature can be calculated from Eq. 8: (8) Assumed the effective diffusion coefficient of nitrogen De does not change with temperature, it uses ks, De and other data into the Eq. 7, the results at 1723K and 1823K can be calculated, and show that the simulation results are in good agreement with experimental data; the curves are shown in Fig. 2.

According to the trend of measured data, phenomenological model associating the geometrical information to electrical performance is proposed.
All the equations have been verified with 40nm technology silicon measured data.
Verification The proposed model is verified with 40nm low leakage process data.
Medium values of the mapping data of above electrical characters are used for model parameter extraction.
Simulated and measured data are well consistent.

The main idea of this systematic energy-saving for an intensive energy enterprise is to analyze and mine the possible energy-saving potential by the data mining from the historical production data which stored in the database of a Distributed Control System(DCS) or in the database of Central Production Dispatch System(CPDS) or the central control center of a big continuous production enterprise, and the material flow data from the database of a Manufacturing Execution System(MES), and the database of other corresponding information application systems.
All the dynamic production data are selected or filtered and then stored into the real-time database (PI for example) from the DCS , PLC,CCS, and the CPDS by the factory/enterprise intranet network.
Based on the accumulating production data in both the real-time database and the relational database, the systematic energy-saving can be implemented in the factory/enterprise level with the data integration of the device production and energy consumption, and will be studied further in the following section.
All the status data of running devices are sampled into DCS,PLC,CCS,CPDS, and then controlled by them in the real production field.
A systematical, feasible and reasonable energy-saving software system should consist of the basic functional modules as: data interface module, the data warehouse module, the energy-saving potential analysis module, etc.

From Fig. 3 a)-h) and table 1, it can be seen that the key structure information of a retinal image mainly exists in the green channel data.
So, only the green channel data were used for Optimization and bottom cap transformation.
The result is identical that either optimization transformation of a retinal image is done ahead and then the green channel data of optimized retinal image are separated or the green channel data of a retinal image are separated ahead and then Optimization of the data of the separated green channel are done, as the data were shown in the two rows in the bottom of table 1 and the pictures were shown in Fig. 3 k) and m).
of retinal image For a good focusing retinal image, such as Fig. 3 a), to use the data of green channel of retinal image still can obtain the better result, as shown in Fig. 3 g). 3.4 Optimization for bottom cup transformation Comparing optimization of bottom cap transformation of green channel data(middle in Fig. 4) with direct optimization of green channel data(bottom in Fig. 4), it can be concluded that the visual quality of the former group are always better than the latter group, seeing corresponding U-IQAF values.
Conclusion The visual qualities of the resulting images for optimizing bottom cap transformation data of the green channel data of the retinal images are always better than for directly optimizing the green channel data of the retinal images, especially for defocusing.

The presented data ensure the prognosis of tube steels properties and shift of the critical embrittlement temperature for 60 years resource.
The available data referring to the TA at 350 °C during 10000 hours, are equivalent, according to the formula (1), to the approx. 50000 hours of TA at the operating pipelines temperature about 300°C. 60 years resource is equal to 420000 effective hours. 
Thus, the extrapolation of data for TA at 350°C during 10000 hours is well founded for 60 years resource at the operating temperature.
But the average values of relative elongation (A) and percentage reduction (Z) decrease at both temperatures. 

The obtained data have been analysed and the strength of each joint modelled.
In particular in multipass friction stir welding the main effect is represented by the reduction of the holes due to the improved material flow [5].
The obtained data have been analysed and the strength of each joint modelled.
The obtained data have been analysed and the strength of each joint modelled.

At the start of every cycle, random input parameters of meeting specific distribution can be obtained according to some specific sampling method .To avoid simulation cycle repeated problem caused by data points concentration in direct sampling, this paper adopts Latin hypercube sampling method (LHS)[7], to sample random parameters.
Measured data showed that the stress distribution of the stress area is: the maximal horizontal stress is greater than the minimal horizontal stress stress, simultaneously greater than vertical stress (that is SH>Sh>Sv).
Table 1 Load of all kind surrounding rock during construction surrounding rock types largest burry depth external water head (m) external water head reduction factor b lateral pressure (MPa) 0.5 reduction factor of grouting pressure(MPa) Ⅱ 122.43 96.63 0.3 4.1 0.2 Ⅲ 51.45 32.5 0.5 2.5 0.2 Ⅳ 35.44 21.78 0.7 2.4 0.2 Ⅴ 33 21.87 0.85 0.9 0.2 Random parameters and their distribution.
Based on project geology exploration data, the statistical characteristics of material parameters are represented as Table 2.
Because of lacking of overbreak and underbreak statistical data, random factors considered in this paper are finite.

Forming of high-tensile steel-sheets is one major technology to obtain weight reduction in automotive applications.
The data was used to calculate the position of the dressing wheel in order to compensate for mounting errors by transforming the coordinate system into the position and rotation of the dressing spindle during the dressing process.
Therefore it was necessary to process the measured data in a special way.
The data from the optical 3D microscope had been analysed by use of the software tool "winSAM" that was developed at the Chair of Manufacturing Technology of the University of Erlangen.
The simulation of the grinding process will also be used to verify the process strategies and NC-data for shape grinding.