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Based on the experimental data and regression analysis method, the mathematical model of the critical strain of Cr4 steel is constructed.
The critical strain model provides data support to predict the dynamic recrystallization during Cr4 heavy supporting roller hot forging process.
From the data of the table 1, it can be seen that at a certain strain rate, the peak strain decreases with the increase of deformation temperature.
Base on the thermo-simulation experiment data which listed in table 1, the constants,,can be calculated by the regression analysis method using the data analysis and graphing software OriginTM.
（3）Based on the experimental data and regression analysis method, the mathematical model of the critical strain of Cr4 steel is constructed.

A critical comparison between experimental data and results of a 3D CFD analysis of the combustor is discussed.
The results of numerical analysis are in good agreement with experimental data as concerns CO emissions, while overestimate NO production in the diffusive flame region at 50% load.
A critical comparison between experimental data and results of the CFD analysis of the combustor is discussed.
The detailed facility layout, the gas turbine control and monitoring system, the data transfer system and the diagnostic measurement equipments are described in [2].
Fig. 4, Gaseous emissions experimental measurements and CFD results at different loads Figure 4 summarizes the comparison between the experimental data and CFD results concerning the main gaseous species at the exhaust at the different loads.

Plane182 unit has Many unit technology that overcome shear locking and volumetric locking, such as the enhanced strain formula, the choose cut integration ,the unanimous reduction integration and the mixed U-P etc, powerful, has a lot of advantages. 3.1.2 The choice of unit formula The traditional unit formula is called the complete integral, at the bending problem, the complete integral low order unit occurs shear locking for” too hard"; when the Poisson's ratio approaches to 0.5, volumetric locking happens by numerical integral difficulty, so the traditional unit formula due to the shear locking and volumetric locking is rarely used.
The unanimous reduction integration, uses the low first-order integral formula that the accurate numerical integration required, Volume and deviation use cut integration, can help to eliminate the volumetric locking and shear locking problem.
In the choice of unit, to ensure that the selected unit support the plastic of the concern, If it is the dominant issues in bent model, adopt appropriate unit formula to ensure that shear locking is not a problem, for big strain, should use unanimous reduction integral (URI) unit.
Here to choose unanimous reduction integration unit formula. 3.2 The treatment of Contact and friction In the finite element method, first of all, finite element discrete for the possible contact boundary, namely, establish a layer of virtual contact unit between these boundaries, after finding the contact point, calculate contact force according to the object motion law, the constraint method of the contact force calculation is penalty function method and Lagrange multiplier method.
The exact shape of the formation spline can be gained by the extraction, analysis and calculations of the result datas according to calculate for the model.

However, experimental data concerning CO2 diffusivity in heavy oils due to the tedious nature of diffusivity measurements are relatively rare in the open literature.
Figure 4 - Measured pressure data versus time for CO2– Paydar-e-Shargh heavy oil in porous media system at different temperature.
The results of all experimental data are presented in tabular form as well, in Table 1.
Figure 5 - Measured pressure data versus time plot for CO2–Paydar-e-Shargh heavy oil in porous media and bulk system behaviors at different temperatures.
The molecular diffusion coefficients were determined without consideration of data belonging to incubation time period of the first one hour interval [4].

(b) Data availability.
This research used target progressive method for data standardization; and used historical comparative method to amend the assessment results and to repair the lack of policy target value [4].
During the data standardization process, the data of target progressive method adopted broken line type nondimensionalization; the data of historical comparative method adopted extremum method of linear type nondimensionalization [7]
The data is shown in Table 1.
Secondly, some index datum are not complete.

This set of weight is realized by adding all the pxiels that have the same distance to be one input data of BPNN.
Section 3 describes the organization of training data and BPNN structure used in the proposed fast deblurring method.
The organization of training data and BPNN structure The training data of input and output samples should be organized suitably.
In figure 2(b) we propose another effective data organization and mapping method.
Obviously, this mapping method will largely reduce the input data.

The following room temperature data characterise the steel: lower yield strength ReL = 570 MPa, ultimate tensile strength Rm = 880 MPa, total elongation A5 = 21 %, and reduction of area at fracture Z = 56 %.
Data for fracture toughness determination were obtained by using standard compact tension (CT) specimens with thickness of 30 mm.
The gap in fracture toughness data of PC specimens between 60 and 90 MPa.m1/2 is related to the Lüders' flow effect.
Good correspondence of data obtained from CT and PC specimens can be also seen from Fig. 1.
For the pearlitic R7T steel a gap in fracture toughness data of PC specimens between the value of 60 and 90 MPam1/2 is possible to observe.

Meanwhile，the data to be calculated is in huge size and is accumulating rapidly every day.
Meanwhile, there is plenty of data to be processed in real-time and the data is accumulating in drama speed.
One of the methods is to divide the data into parts and computing them in parallel, because the size of remote sensing image data is usually very big and cannot be accommodated and computed efficiently by a single computer [2].
Because the remote sensing image is very huge, there are large amount of data to be addressed.
In the experiments, we use two types of images from experimental data.

Moreover, using two potential suppliers’ information and data tested the model.
This situation cannot provide effective data support for the later supplier admittance evaluation.
Basic information index data were dimensional, and evaluation standard cannot be universal in different purchasing plate’s varieties.
Other dimensionless indicators which were by collecting ratio data could be in common use, and evaluation standard setting was obtained by modified Delphi method.
Furthermore, two potential suppliers’ information and data were used to verify the model effectiveness.

Using linear acoustic array in through-transition method, a set of data from different angles is obtained.
Using this data set and the back projection method, the test area imaging is obtained, which is represented by a set of small local areas.
Using this data set, three-dimensional reconstruction of the internal structure of the tested object can be carried out.
The amplifier, in block 7, has a system for the time corrected gain to compensate for the reduction of the signal amplitude at the receiver due to the different distances.
[11] D.Potts and al., Fast Fourier transforms for nonequispaced data: a tutorial in modern sampling theory: mathematics and applications, J.J Benedetto and P.J.S.G.Ferreira (Eds.), Applied and Numerical Harmonic Analysis Series.