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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.

The degradation reduction is similar.
We use multiple interpolation method to data processing of equal interval.
Table 1 A engine EGTM degradation data tables NO. cycle number original value /℃ NO.
Average Life Prediction for Aero-Engine Fleet Based on Performance Degradation Data. 2010 PHM2010 Macau
Based on the performance degradation the reliability of data modeling and applied research，[Ph.D.

Grey Relational Analysis Grey data processing must be performed before grey correlation coefficients can be calculated.
Usually; every series is normalized by dividing the data in the original series by their average.
Data pre-processing converts the original sequence to a comparable sequence.
Several methodologies of pre-processing data can be used in the grey relational analysis, depending on the characteristic of the original sequence.
Calculation of Grey relation coefficient and grey relational grades Following the data pre-processing, a Grey relational coefficient can be calculated using the pre-processed sequences.

In addition, Nc is the total area of the landscape of the study area represented by the number of squares, the landscape has not used the data matrix grid represents the total number of square grid, but with minimal study area to remove plaque area to get the total area, which is used the minimum size of each plaque area as a grid, this approach is to reduce the data changes caused by different grid scale, so that the index remained stable at a given study area classification system.
Figure.1 Relationship between environmental features and land use amount Reduction of arable land is mainly converted to unused land, water and land for construction, which increased mainly by the water, pasture and woodland into one.
Digital ecological data stored in the database with a multi-source, multi-dimensional, tense and data volume (mass) characteristics.
Multi-source data refers to a variety of data sources, data formats are not the same, can be remote sensing images, vector, attribute data, text data, multimedia data.
To a lot of digital eco-temporal data, based on indicators of ecological simulation and demonstration, to evaluate and predict the ecological conditions, provide the basis for management decisions ecological environment.

Methods Experimental data were measured using a Hysitron TI 950 nanoindenter.
As the tip was not perfectly aligned in the holder, the data were corrected for this tilt.
In this case the data were fitted by a general parabola as this was found to give a satisfactory fit.
A critical examination of the fundamental relations used in the analysis of nanoindentation data.
Higher accuracy analysis of instrumented indentation data obtained with pointed indenters.

In this paper, a principal component analysis was used to extract feature from original data.
In a linear subspace, the algorithm can provide a way to compress data without losing much information and simplifying the representation.
The resulting combination may be used as a linear classifier, or , more commonly , for dimensionality reduction before later classification.
LDA is also closely related to principle component analysis(PCA) and factor analysis in that they both look for linear combinations of variables which beat explain the data[3] LDA attempts to model the difference between the classes of data.
In nonlinear case, the train data are mapped into a high dimensional linear feature space through nonlinear tranformation .

Contact resistivities in the range of 10 -4 Ω-cm2 were routinely achieved, but data was typically scattered over a wide range.
The data clearly showed that the target layer thicknesses and compositional structure were achieved.
The data clearly illustrates the benefits obtained when contacts are formed on the smooth photoresist capped surfaces.
As before, all of the data reflect the inclusion of purge gas purification during anneals and optimized W thickness layers.
Ti/Ni/Al compounds were observed, but there is not yet enough compelling data to indicate that these compounds had a profound effect.

Fig. 1: Overall design process It is an xml-based (extensible markup language) database with hierarchical data structure allowing the share of arbitrary levels of detail between different partners.
It furthermore offers a validation schema which reduces the probability of incorrect or inconsistent data.
A CPACS file stores the geometric data of the structure, profiles, materials but also tool-specific data used to control numerical solvers.
Due to its parametric approach data may be changed quickly and easily.
As all relevant data is stored in one single file the whole data deck is clearly arranged.

The data is first imported from the physical model.
If missing data is detected when the completeness is checked, this data is collected manually.
If the data is complete, the evaluation follows.
The data needed for the simulation in the physical model and the resulting data needed for the realization of the changes have to be determined.
Moreover, the handling with this data has to be described.