Search results

Data Preparation.
In this study, the basic data required to develop the models included bathymetry, hydrology, meteorological data.
Hydrology data were obtained from Bureau of Hydrology, MWR.
Meteorological data were downloaded from the website maintained by the China meteorological data sharing service system (http://www.climate.cq.cn/).
Obviously there is a good agreement between the simulated data and observed data.

Because of the particular grain morphology the EBSD data were collected on the surface ND plane of the sheet, i.e. in the plane of rolling near the surface of the sheet cf.
Relative grain boundary energy values were obtained by combining crystallographic and geometrical data at triple junctions following the procedure described in [5].
For this reason the electrolitically pure iron data are not presented here.
Hence, the present data appear to reflect the known grain boundary energy reduction of the <110> 26.5° Σ9 misorientation.
Although the present conclusion is somehow preliminary because of poor data statistics it appears that a very strong preference for a specific (low-energy) crystal plane may be excluded.

As today’s ever-competitive business environment demands reductions in product development time and cost, the need for faster turn-around times and more efficient means of producing prototype and short-run tooling has increased.
In the preprocessing process, data should be import, simulation models should be built, and simulation conditions need to be set; in the solving process, the simulation results will be obtained; in the post-processing process, the simulation results will be analyzed and evaluated.
Geomagic Qualify is the most widely used one of CAI software.The general process of Geomagic Qualify is: First, CAD data and the data needs to be detected are imported and aligned; and then the data should be compared; finally, the data error are evaluated by comparing 3D comparison, 2D comparison, GD&T Measuring etc.朗读 Geomagic Qualify can create a variety of benchmarks and features which is helpful for data; Geomagic Qualify has many ways for alignment: the alignment based on benchmarks and features, RPS (Reference Point System), 3-2-1 alignment, the best fitting and other manual alignment; Geomagic Qualify can also be used for flatness, parallelism, perpendicularity, angle, cylindrical, bit Configuration, surface profile and total eccentricity G D & T Measuring and Comparing; 3D Compare creates a full color deviation map comparing the reference to the as-built part — aiding the designer’s understanding of the problems and enabling quicker turnaround time for design changes; Additional
We use the intermediate format such as STL or IGS for Dynaform and Geomagic Qualify as a bridge between them, to ensure that they exchange and share data between.
The barrier to data exchange between CAE and CAI is fundamentally solved, and ties of cooperation between analysis departments and inspection department can be established.

Closed squares and circles indicate data using homoepitaxial film and type-IIa substrates, respectively.
Closed triangles are reference data.
Except for these data points, n-type conduction was not observed clearly.
Open squares are reference data.
From Hall effect measurements, obvious n-type conduction was not clearly observed both on P or S implanted samples, except for several data points.

Fig. 5: Schematic of low-speed open circuit wind tunnel (left), and specialized design balance (right) Data Acquisition.
The analog-digital converter can acquire 200 kilo data samples per second simultaneously.
The output data can be obtained and saved in the desired file automatically.
The measured strains were converted to force data based on the balance calibration data.
To confirm that the measured data are reasonable and acceptable, two reference airfoils were selected for comparison.

The data collection process was carried out by conducting tensile testing and microstructural testing with two specimens each.
The test results will be analyzed using tensile strength data and visual microstructure analysis.
From the results of the analysis of tensile and microstructural test data, it can be concluded that only the 7075 aluminum specimen with a thickness of 1.4 mm shows the greatest decrease in tensile strength and spread of Mg-Zn and Fe-Al particles, when compared to specimen 7075 with a thickness of 0.6 mm which on the other hand, undergo the separation of Mg-Zn and Fe-Al particles.
Microstructure Test Result Data Microstructure Microstructure tests were carried out on the specimens before and after heat treatment, with the results shown in Figure 1: Figure 1.
The tensile strength value of aluminum 7075 after heat treatment of solution heat treatment has decreased, where the most reduction occurred in specimens with a thickness of 0.6 mm when compared to specimens with a thickness of 1.4 mm and 2.5 mm.

Superimposed on the frequency histograms in Fig. 3 are the lognormal and Rayleigh probability distributions showing that the former gives best fit of the data.
A more rigorous comparison of the experimental data with Eqs (1) and (2) was achieved by plotting, for a given grain size class, iD , the residue of the expected and actual frequency, i.e
This parameter is plotted in Fig. 4 for all grain size classes after annealing for 1h at 400 to 500 °C (for clarity, data of other annealing conditions are not given).
At longer annealing times (3h) at 500 °C, the data are more dispersed at the largest grain diameters indicating some deviation from invariant coarsening behaviour.
Superimposed on the histograms is the best fit of the data for the lognormal and Rayleigh probability distribution (see text).

The general functions of CMMS program are designed to organize the data, evaluate the data, and report the information via the internet.
Especially, for the plastic injection machine, the program cannot be added, deleted or edited the data immediately.
The PM plan is derived from the collecting data in every month and evaluating the results into the fixing plan.
The CMMS program divides the users into three groups for the convenience and security in viewing and managing the data.
The developed database is easy to add, delete or edit the data quickly whenever the machine is breakdown.

Definite the ratio of the high, medium and low pressure cylinder’s ideal enthalpy drop and each of their ideal enthalpy drop as the enthalpy drop factor , with the benchmark of units’ designed condition data, and constant condition is regarded as invariable; definite the ratio of the high, medium and low pressure cylinder’s relative internal efficiency and steam turbine’s relative internal efficiency as efficiency factor , so the steam turbine’s internal efficiency is as follows: (7) If other conditions remain unchanged when the actual operation, the decreased efficiency of high pressure cylinder is , so: (8) (9) Actually, because of the reduction of cylinder efficiency, the inlet flow needs to be increased to maintain the same power of unit.
The designed data in THA condition is shown in table 1: Table 1 The main parameters of 1000MW units in THA condition Item Unit Data Turbine power MW 1000 Main steam pressure MPa 25 Main steam temperature ℃ 600 HP cylinder exhaust steam pressure MPa 4.73 Reheat steam inlet pressure MPa 4.25 Reheat steam inlet temperature ℃ 600 Main steam flow t/h 2733.434 Exhaust pressure kPa 5.1 Calculate with conventional heat method, the heat economic indicators of units are shown in Table 2: Table 2 The heat economic indicators of 1000MW units in THA condition Item Unit Data Ideal enthalpy drop of HP cylinder kJ/kg 492.38 Ideal enthalpy drop of IP cylinder kJ/kg 459.85 Ideal enthalpy drop of LP cylinder kJ/kg 988.39 The internal efficiency of HP cylinder % 87.73 The internal efficiency of IP cylinder % 92.24 The internal efficiency of LP cylinder % 92.57 Turbine internal efficiency % 91.26 heat rate kJ/kWh 7355.6 boiler thermal efficiency % 93.5 Coal consumption rate g/kWh 271.5 Quantitative
analysis of changes in 1000MW turbine cylinder efficiency According to the data in Table 2 and the definition in this paper, the cylinder’s enthalpy drop factor and efficiency factor in THA condition are shown in Table 3, and the impacts of changes in cylinder’s efficiency on units’ heat rate and coal consumption rate are shown in Table 4, Table 5 and Table 6: Table 3 Enthalpy drop factor and efficiency factor of each cylinder of 1000MW units Item Data Item Data Enthalpy drop factor of HP 0.312 Efficiency factor of HP 0.961 Enthalpy drop factor of IP 0.252 Efficiency factor of IP 1.011 Enthalpy drop factor of LP 0.436 Efficiency factor of LP 1.014 Table 4 The impact of changes in high-pressure cylinder efficiency of 1000MW turbine on units heat economic indicators Item Unit Data Absolute decline in HP cylinder’s efficiency % 1 2 3 4 5 6 7 8 9 10 Absolute decline in turbine efficiency % 0.254 0.508 0.762 1.016 1.269 1.523 1.778 2.031 2.285 2.539 Absolute increase of turbine
heat rate kJ/kWh 20.51 41.2 61.94 82.79 103.76 124.85 146.13 167.46 188.91 210.49 Absolute increase in units coal consumption rate g/kWh 0.76 1.52 2.29 3.06 3.83 4.61 5.39 6.18 6.97 7.77 Table 5 The impact of changes in intermediate-pressure cylinder’ efficiency of 1000MW turbine on units heat economic indicators Item Unit Data Absolute decline of IP cylinder’s efficiency % 1 2 3 4 5 6 7 8 9 10 Absolute decline in turbine efficiency % 0.252 0.504 0.756 1.008 1.26 1.512 1.764 2.016 2.268 2.52 Absolute increase of turbine heat rate kJ/kWh 19.17 38.37 57.75 77.23 96.81 116.49 136.2 156.09 176.09 196.2 Absolute increase in units coal consumption rate g/kWh 0.71 1.42 2.13 2.85 3.57 4.3 5.03 5.76 6.5 7.24 Table 6 The impact of changes in low-pressure cylinder efficiency of 1000MW turbine on units heat economic indicators Item Unit Data Absolute decline of LP cylinder’s efficiency % 1 2 3 4 5 6 7 8 9 10 Absolute decline in turbine efficiency % 0.508 1.018 1.527 2.035 2.544 3.054 3.562
(4) The data in the table and graph shows that, changes in cylinder efficiency have linear relationship with units’ heat economic indicators.

Through using experimental data to determine the number and constant of model, thus obtained the stress relaxation equation
(3).Mathematical experimental data collation and analysis of the stress relaxation to obtain empirical formula.
Method3:this is the data processing method, although the theoretical value is low, but the research and analysis for the project has a good guide.
Through the finite element method, we analyzed the flange connection on the oil pipeline, and compared with the experimental data of PVRC.
And the simulation data is generally higher than the experimental data, which also shows the actual conditions of creep relaxation occurred more dramatic effect.