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Transformer Design and Experiment Data There is a RCT, whose core is made of a ultrachrystallite material, shown in Fig. 5 with an outer diameter D1, internal diameter D2, height h0.
It can be seen from Fig. 6, in lower frequencies, the theoretical values better fit experimental data; as frequency increases, higher than 8 MHz, the experimental data gradually deviates from the theoretical value.
At higher frequencies of about 24 MHz, the theoretical value and the actual data come to a minimum value at the same time.
The 3 dB passbands of the theoretical values and experimental data overlap approximately.
Fig. 9 is the data of the RCT comparison experiment containing uniformly coil winded CT without shielding box, uniformly coil winded CT with the shield box and specially coil winded CT without shielding box .Through comparison: ① the CT with a shield box is significantly less impacted by the rise of frequency than the one without shield box.

The applicability of the second_order equation was proved by the linear dependence of the experimental data in the ln(1/θs–1/θ0)/T2–1/Т coordinates, where θ0 is the degree of the complete coverage of the surface.
The data in analog mode representation was obtained (MS plotted).
The decrease in intensity is connected with the reduction of copper oxides included in the catalyst.
DME desorbs in the temperature range 473-673 K (Тmax=553 К), which is also consistent with our data obtained in the catalytic experiments to determine the effect of temperature, which showed a maximal yield of DME at 553 K.
These data provide the basis to develop the technological standards to produce DME from CO and H2 combining two reactions in a single reactor.

From the test data, the regular curves of the shear strength discounting ratio were determined for the soft clay under different dynamic and static combinations.
Based on the experimental data and spot investigation, the numerical analysis model was built to analyze the stability of inland waterway slope under effect of ship wave.
The driving system is pneumatic, mainly consisting of the pneumatic power device, the testing machine, the data acquisition system and the computer.
Fig.1 The dynamic triaxial testing system, model for DCS This system equips with a patent dynamic ambient pressure balance device, which could really ensure the stability of the ambient pressure under the axial dynamic loading in a high frequency, and a real-time data acquisition and the man-machine dialogue system known as the software GDSLAB which is able to display and switch the real-time test data dynamically in the experiment.
Also, the slope stability before and after the action of ship wave were analyzed contrastively by strength reduction method.

The optimizing software tool is an important constituent of PRO2 TM, since it couples the models that are part of the process chain and organises the flow of data between individual models.
The arrows in the figure show the sequence of the through process simulations which are run in several iterations, where the optimiser tool selects the input data for each new simulation based on the results from the previous.
FE based casting programs [5]) or they can be based on experience data from measurements on similar alloys, as in the present work.
Input data.
Due to limitations in space, the paper was focusing on simulations without any verification of the output data.

The simulation experiments verify the model of the logistic route selection and the profits and it has very litter errors. 1 Introduction With the rapid socio-economic development, the rational management in the logistics industry plays a more and more significant role on enterprise efficiency improvement, operation cost reduction and financial income increase which is now the third profits source for the enterprises.
Because the 8 clients are distributed in the different areas, the contents of the data selection are the amount of the material distribution, distances among the clients and distances among the clients and the distribution center.
The data of the amount of material distribution of each client and the distance among the clients and the distribution center is shown in table 1.
Table 1 The relative data of the amount of material distribution and the distances among clients amount of material distribution 0.9 12 1.3 10 5 1 9 10 9 0.8 6 20 10 13 1.6 8 18 17 14 6 0.8 6 13 10 15 13 8 1.4 10 8 13 16 14 13 6 0.6 8 5 9 8 9 12 9 4 First the clients’ different positions are not concerned.
According to the data in the table, the routes among the clients are as follows.

Finally, scenario reduction techniques can be employed to reduce the number of scenarios, to eliminate a scenario with very low probability and scenarios that are very similar [4].
Thermal power unit parameters are shown in[6], the wind and load forecast data is shown in figure 3 and figure 4 respectively.
Fig.3 Wind power forecast data Fig.4 Load forecast data In order to verify the effectiveness and superiority of the proposed MOIPSO algorithm for the EED problem, it is applied on the 6-unit system.
Michalewicz.Genetic Algorithm + Data Structure = Evalution Program,Comput.New York: Springer-Verlag,1996

After the accident, the accident point reduction in capacity, if the upstream traffic demand exceeds the capacity, bottlenecks will appear the returning wave, when the accident exclusion, the "wave start" while there follow the vehicle reaches the tail, returning wave is lasting, both exist and are in motion backwards.
From the literature 3, the relationship between actual capacity and basic capacity is: .So (7) Put (7) into (4), (5), (6), we obtain the relationship between vehicle queue length and the actual capacity of cross-section, accidents duration，section of the upstream trafficis: (8) In which is the average of 120m after the accident on the road congested traffic density, the specific data shown in Table 1: Table 1 120m after the accident on a crowded road traffic density sampling table Sampling points 1 2 3 4 5 6 The average of the last four sets Motor coach （pcu/km） 1 1 0 2 0 1 0.75 Car （pcu/km） 8 10 22 21 21 22 21.50 Electromobile（pcu/km） 3 1 0 1 2 2 1.25 After conversion（pcu/km） 13 12 22 23.5 24 25.5 23.75 Since the beginning of accidents, the bottleneck link has not yet reached congestion status of the queue, the traffic density in the first three sets of data is relatively
We take traffic density basically stable the last four sets of data to estimate the vehicle queuing traffic density = 23.75/0.12 = 197.9pcu/km.
And because the relationship between traffic flow and traffic density, speed is, combined with Greenshields model, the relationship between the upstream traffic density and road traffic can be deduced as follows: (9) Put data above and (9) into (8) to obtain the road vehicles queuing models as for video 1 in the accessory: (10) Among When ， Analysing trafficwavemodel By equation(10), we can analyze the relationship between queue lengthand the actual capacity at accident segment, the duration ofthe accident, the upstream traffic flow.

When controlling the displacement, the loading speed is set for 1.0 mm/min, and the data acquisition captured 1 point for each 0.02 second.
Analysis of Experiment Result Sliding load caused by reduction in axial bolt clamping force was measured and evaluated by administering static test on the joint areas of high tension bolt in H-beam applied with pure bending.
Clamping force 100% Clamping force 75% Clamping force 50% Clamping Force 25% Experiment data 463.1 376.21 254.54 119.19 The axial bolt clamping force(kN) Specifications 517.79 388.34 258.89 129.44 Experiment data 89 97 98 92 Percentage of Clamping force in design(%) Specifications 100 100 100 100 Experiment data 388.64 275.16 252.16 222.61 Sliding load(kN) Specifications 243.67 243.67 243.67 243.67 safety factor for the standard of specifications (ρ/ aρ ) 1.59 1.13 1.03 0.91 Conclusions Static test was administered by creating a test sample for the joint areas of high tension bolt in Hbeam applied with pure bending.

Introduction Statistical data on different Undergrounds of the Russian Federation shows a variation in values of specific energy consumption per traction [1].
Petersburg Nizhny Novgorod Samara Yekaterinburg Kazan Values of specific energy consumption for Underground vehicle traction [Wh/t·km] 54,81 48,08 58,6 65,5 63,1 52 Such a variation in values necessitates the research aimed at finding feasible ways of the reduction of the Underground rolling stock energy consumption per traction.
At the first stage of researchthe time (Тх) of the vehiclestation-to-station block traction was measured.Experimental data for each station-to-station block on Leninskaya line of the Novosibirsk Underground is givenin Table 2.
Lenina”– “Krasniy Prospect” 0,125 0,132 “Krasniy Prospect”- “Gagarinskaya” 0,25 0,28 “Gagarinskaya” – “Zayelcovskaya” 0,069 0,08 Based on the experimental data the following conclusion is made: the optimal traction mode is not supported.
Results adequacy check up During the experimental tests, running resistance data was obtainedfor the vehicle, series 81-717/714.

There were three specimens for each age of the cubes to obtain the average data for more accurate results.
The 28 days compressive strength of eggshell concrete was used, and the result of each set of data was compared with the experimental data obtained in this study.
From Figure 5, the experimental data had a similar trend when plotted with other sets of data from design codes.
The value of experimental data had significant differences with the China and India code, but was accurately represented by the expression from EC-2.
However, an unusual sharp drop was observed for 15% ESC compared to other sets of data.