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The proposed approach was used to calculate the storage capacity of geothermal resources in Gaoling Formation of Xi’an Depression, in which data of 57 existing geothermal wells were available.
Reduction in the error through finer division of blocks requires in-depth exploration.
Large exploitation amounts and rich geothermal geological data have been accumulated.
The 11 parameters of each node were substituted into the calculation procedure to calculate and output the corresponding data.
The sums of each type of data in the 102 elements were listed in Column 4, while the data for the western half were listed in Column 5.

The result of this modeling was compared with experimental data taken from literature and good agreement was observed.
For validation, all the modeling result is compared by experimental data from literature [12]. 2.
The result of modeling was compared with the data taken from literature and observed good agreement which testify the model validation.
As the gas velocity increases, the removal efficiency decreases due to reduction in resident time.

The research result shows the fatigue design data at different temperature may be corrected by elastic modulus with room temperature curve.
Table 1 Tensile test result at room temperature and 380℃ 0.2% yield strength[MPa] ultimate tensile strength [MPa] Total elongation [%] Percent reduction in area [%] Specimen No.
Table 2 and Table 3 show test data and the number of cycle to failure.
The fatigue data used to construct the design curve s are plotted using room temperature E values.

According to survey data, the paper calculated the probability of the top event, as well as the probability importance degree coefficients and critical importance degree coefficients of basic events, and quantitatively analyzed the cause of accidents.
The basic events X3, X4, X5, X17, and X18, namely poor safety awareness, weak sense of responsibility, high control office noise, unsound rules and regulations, and inadequate enforcement of rules and regulations are the main reasons for the accident; the basic events X21 and X22, namely low precision of radar and poor stability of radar easily lead to the accident; other basic events can cause accidents too, but not seriously. 5 Quantitative Analysis of the Fault Tree According to the survey data, this paper used the Delphi technique to get the ratio and weight of each basic event.
Taking into account the uncertainty of the event and the fuzziness of the expert’s estimation, the fuzzy evaluation of survey data was completed to get the probability of each basic event, as shown in Table 2. 5.1 Calculation of the Top Event Probability.
At the same time, with the reduction of their critical importance degree, the influence of these basic events becomes smaller and smaller gradually. 2) The basic events X21, X22, X8, X13, X11, X7, X10, X16, andX20, namely low precision of radar, poor stability of radar, poor cognitive ability, poor memory, lack of relevant control training, poor language expression ability, high similarity degree of flight number, poor sense of space, and lack of coordination awareness, whose critical importance degree coefficients are relatively bigger, can also lead to the accident easily.

Simulations of the Lankford parameters were carried out with the help of the viscoplastic self-consistent scheme (based on the global texture) and compared with the experimental data.
The strain was measured with the optical system Aramis (GOM mbH, Germany) yielding the elongation dεx and the width reduction dεy simultaneously.
All data given for the initial material B are taken from [5].
However this requires precise data on the local texture in each layer in order to get an exact separation of the global texture.

By calculating the information entropy for the scale of construction of energy transportation channels with the data of annual investment in the fixed assets from 1986 to 2008, we analyzed the relation between transportation channels and the information entropy combined with the maximum entropy methods.
When Ki is normalized, the formula for pi is, els as, (4) According to the data of annual investment for conventional energy in fixed assets in China from 1986 to 2008 and utilizing the formula noted above, the information entropy Si for the four kinds of the construction scale of energy transportation channels can be achieved.
In the present work, we further study the formula (2) combined with our data and it is found that the base value is pi=0.33, i.e., when pi>0.33, pi decreases with Si, but, when pi<0.33, pi increases with Si.
According to the calculated date from 1986 to 2008, as shown in figure 1 and figure 2, it can be summarized as follows, With regard to rail transportation, between 1986 and 1996, the ratio of annual investment to the total investment is kept above 50% and p1 tends to total reduction.

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, China a hydrowang@163.com, b lubaohong@126.com, c hanwenzhan05015134@163.com, d 1176445330@qq.com, Key words: Hebei province, Precipitation, Trend, Abrupt change point, Periodic variation Abstract: Based on the precipitation data observed monthly of 19 weather stations in Hebei province from 1960 to 2011, three methods, linear trend estimation, Mann-Kendall test as well as Morlet wavelet transformation, were adopted to analyze the characteristics of precipitation trend, abrupt change points and cyclical variations under the circumstance of multi-time scales in the past 52 years.
Data series and methodology In the study, 52 years’ precipitation data in Hebei province was chosen from National Weather Centre, which were in great representative and distributed in towns or cities of Zhangbei, Weixian, Shijiazhuang, Xingtai, Fengning, Weichang, Zhangjiakou, Huailai, Chengde, Zunhua, Qinlong, Qinhuangdao, Langfang, Tangshan, Leting, Baoding, Raoyang, Huanghua as well as Nangong.
The reduction amplitude was 86.0mm, accounting for 24.08% of summer precipitation.

As a result the simulation computational solution is compared with experimental data in this work, both of which show the same characteristics of data distribution.
Simulating data of weight loss are compared with experimental data obtained in 3.5%NaCl as shown in Fig.3.
Fig.3 Comparison of weight loss between experimental data and simulation data for 4 weeks of carbon steel The phenomenon of data distribution is investigated from the theories used in modeling and the real corrosion process.
This is the main reason for the experimental data increases slowly comparing with simulating data.
Computation outcome indicates that there is certain similarity with experimental data and simulation and test data represent same characteristics of data distribution.

It is shown that increasing of alloying elements’ content, in comparison to austenitic steels, improves the strength characteristics while improving corrosion resistance and resulting in a dramatic reduction in machinability.
Regarding this issue there is a considerable amount of experimental data accumulated by various researchers.
Curves 2 and 5 are based on literature data, with power-law approximation (in contrast to the quadratic polynomials employed in [7]).

The followings are the arithmetic procedure of PCA: (1) standardizing the data of original variables Since the measurement units of different indicators are various, the original data need to be standardized firstly to eliminate the effects caused by different magnitudes and dimensions.
Supposing there are n samples and P indicators, the data matrix can be obtained, in which means j th index value in the i th sample.
The matrix can be used to standardize data
() (1) in which: () ,() is the data after standardizing
Firstly the original data are standardized to get the relevant matrix; then the characteristic values, their contribution rates and the cumulative contribution rates can be calculated.