Search results

A moderate amount of Bi element resulted in the microstructural refinement and melting temperature reduction of Sn-0.2Ag-0.7Cu solder.
Each datum represents an average of at least five measurements.
It is known that the addition of Bi leads to the reduction of solidification temperature and compositional super-cooling [6], and influences the growth behavior of Cu6Sn5.
As shown in Fig. 4 and Table 1, all of TS, TL and the peak temperature has been decreasing with the increment of Bi, indicating that the addition of Bi plays a positive role in the reduction of melting temperature.

Experimental data (points) are compared to the theoretical MH model (dashes) and to the quenched MH model (solid lines).
Experimental data (points) are compared to the theoretical MH model (dashes) and to the proposed model (solid lines).
Surface diffusivity coefficient of crystal silicon (100) obtained from the rapid thermal annealing (RTA) experiment compared with literature data [4,6,9-11].
It shows an excellent agreement with most of the literature data obtained from the measurements of the transformation of the shape of periodic superficial silicon structures.
Their experimental data are obtained on the structures with high aspect ratio (high local curvature) where MH approximation could give significant errors (see numerical analysis of the limits of MH approximation in [10]).

To illustrate differences and similarities, some characteristic data are shown in table I.
The data might be too rough an approach, but they indicate some clear hint that there is not much room for luxury in case of the PV manufacturing processes.
The data are a rough indicator for strict constraints in the PV cleaning processes.
A significant reduction of the initial Fe and Cu contamination seems to be possible.
Some data indicate that these levels may be in the range of 10 ppb to some 10 ppb.

The COE data are in the range of 12.43 x 10-6 - 14.35 x 10-6 °C-1 which is consistent with another soft silicate glass [11].
All samples were also analyzed by IR spectroscopy and the data were analyzed according to the previous literatures [12-13].
The quantity of Cu+ and Cu2+ in the glass sample can be calculated using the empirical edge shift (data from Table 3) as shown in Eq.1 and Eq.2 [15]: % of Cu+ = (1) % of Cu2+ = (2) While the average oxidation state of copper ion in the glass matrix can be determined as follows: Copper oxidation state = + (3) From the calculations, valence state of Cu according to the edge energy shift position for each glass sample was obtained as data in Table 4.
Table 4 Calculated valence state of Cu according to the edge energy shift positions Sample ∆E of sample (eV) ∆E of Cu2+ and Cu+ (eV) Cu oxidation state %Cu+ %Cu2+ Cu(1) 1.28 3.63 1.35 64.74 35.26 Cu(2) 1.19 3.63 1.33 67.22 32.78 Cu(3) 0.99 3.63 1.27 72.73 27.27 Cu(4) 0.98 3.63 1.27 73.00 27.00 The appearance of Cu+ in Cu(1) might come from the oxide in the melt causing the reduction of Cu2+ to Cu+ in Cu(1) as presented in Eq. 4 [16]. 4Cu2+ + 2O2- 4Cu+ + O2 (4) While in the case of Cu(2), Cu(3) and Cu(4), the reduction by oxide as in Eq. 4 and by aluminum as in Eq. 5 might occur. 3Cu2+ + Al 3Cu+ + Al3+ (5) However aluminum can be reacted with oxygen from the air or in the melt as in Eq. 6 [17]. 4Al + 3O2 2Al2O3 (6)
Newville, ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT, J.

(2)regulating cost of AGC and, :unite it's regulating cost in period j; :unit is quoted price of AGC market capacity in period j;:frequency regulation of AGC causes the increase of cost; Therefore, the objective function of AGC dispatching model is: Except the above data, other data explain as follows: I,m,n: correspond to the numbers of the conventional generation, the micro-gas turbine and gas generator;,0-2 variation, it depends on whether unit i, m, n participate AGC service;respectively correspond to AGC upward regulating capacity of unit m,n;respectively correspond to AGC upward regulating capacity of unit I,m,n. 1.2.2 Constraints The concrete expression is as follows:
Except the above data other data explain as follows:the total demand of AGC in period j;AGC response time; respectively correspond to AGC regulating speed of unit I,m,n;the base load capacity of unit i in period j; respectively correspond to the upper limit of AGC regulating capacity of unit i,m,n; lower limit of AGC regulating capacity of unit i. 1.3 Accounting Examples 1.3.1 the relevant data This chapter takes the micro-grid system which consists of DG and thermal generation units as the Studying system.
Figure 1.2, whether that system is a stand-alone or networked mode, the system frequency deviation, non-plan exchange of power in under the control of DG units to participate in a faster reduction in amplitude and in a networked way, DG to participate in AGC system to a larger reduction in oscillation frequency offset rate.

There is not enough data about the chip formation during the machining of composite materials produced by powder extrusion method.
First, the friction angle β is found by substituting the experimental data for the cutting force () and the axial force () in Eq. 2.
Consequently, the experimental cutting forces data and the cutting forces that are calculated according to Merchant and Lee-Shafer models are compared with each other. 3-1-1.
This means that the data of these models compared with the experimental data are underestimated.
Third reason for reduction of cutting forces can be the thermal softening of the aluminum matrix, which occurs with increasing cutting temperature.

The fit, ( ) ( )[ ] 18 *10 1371.2 ln / 255 / 1.2924 DN RGB t = − − to the data in Fig. 3a may be used for calibration, where RGB and t are the transmission intensity with a scanner 0.57γ= and the thickness of the wafer, respectively.
While -1 0 2.546 cm α = compares quite well with data from Wellman et al., ,0 18 -3 7.122*10 cm DN − = is approximately 20X smaller [9].
The samples were provided by two vendors, A and B with the corresponding data presented in Fig. 3 b) and c), respectively.
Presented as open boxes in Fig. 3b are data recorded for a moderately doped 4H-SiC substrate following the growth of a ~100 µm n- epitaxial film.
The open boxes represent data from a sample with a 100 µm epitaxial film.

As a result, variation in the calculated roughness value was more or less different from the measured data produced by them.
By comparison between empirical data and computed values, the validity of the proposed formulation was proved.
The material in the filament is melted in a specially designed head, which extrudes a layer according to generated section data from a prepared 3D CAD model.
Gaps between empirical data and computed values are within 3 micrometers.
This is also due to reduction of layer thickness by the equation (7).

The point is that fatigue crack growth data on the 15-5PH-type steel are not widespread in literature, especially in relation with ageing.
The secant method was used to analyse the fatigue crack growth rate (FCGR) data.
Repeated tests were conducted for each given testing condition and only small scattering of the experimental data was observed.
However, a reduction of this domain is observed with ageing time and temperature.
This data will be used to describe the crack tip behaviour which is going to be implemented in a model that could account for the observed phenomena [9, 10].

Based on the principle of cost reduction, we designed and manufactured motor prototype, and simulated it with the finite element method for electromagnetic field, and had no load and load test to verify the legitimacy of motor design parameters.
We made a system test of fan-driven by a prototype, and the measured data showed that the external-rotor brushless DC motor had good performance and lower cost and had the advantages for industrial production.