Search results

Comparative Analysis on Emission Between hybrid and Conventional Vehicles WANG yong-ding1a, MA chen-qi1b, PAN hai1b 1 College of Engineering Science & Technology, Shanghai Ocean University, Shanghai, China aydwang@shou.edu.com bsmilemuch@sina.com Keywords: HEV; Energy saving; Emission reduction; parameter match; advisor.
Data shown in Figure 1and figure 2: Fig. 1 Honda Insight petrol engine data map Fig. 2 Toyota Prius engine data map The motor selection.
Data shown in Figure 4 and figure 5: Figure 4 Honda insight motor data graph Figure 5 Toyota prius motor data graph Determine the type and parameters of the battery pack.
Conclusions By using the vehicle simulation software ADVISOR we can match the hybrid cars of all key parts of the parameters, and ADVISOR can do the second development and modular design [11],Can also add more variables are calculated which is more close to the real data.

Finally, by using the non-delayed effects and stability of the earthquake magnitude prediction data, the state-transition matrix is obtained through the Markov chain, and the state interval and corresponding probability of the GEP model prediction are obtained.
It will have important significance to earthquake prediction, earthquake prevention and disaster reduction to collect partial geological parameters from a certain location with modern instruments and seek the relation between these parameters and the earthquake magnitude of this area through scientific analysis.
Earthquake magnitude prediction based on the improved GEP algorithm The experimental data came from Reference[4] , which represent 6 representative indices of earthquake on the aspects of time, space and intensity: frequency, creep, energy, B value, lack-earthquake and n value, as well as the earthquake magnitude of the next year.This paper has selected samples of No. 1-20 as the training samples and samples of No. 21-27 as the test samples.
(iii) Comparison between the GEP model and the Neural Network model Reference[4] used the Elman Neural Network to conduct prediction of the data in this paper, and Table 1 shows the comparison between the GEP model and the Neural Network model on the prediction of test samples.
Therefore, in accordance with the Markov theory[5], this paper has converted the earthquake magnitude data obtained by the GEP model to the change intervals and obtain corresponding probabilities, so that the prediction result of the GEP model will have more values.

The paper deals with experimental data regarding the effect of internal and external factors on the corrosion decay of Zr1Nb alloy fuel elements.
To determine the change in the fuel element wall thickness under normal operating conditions, a mathematical calculation based on the experimental and reference data was theoretically carried out.
The following data were used and designation introduced when calculating: The experimental value of gain: ZrO2 VZrcorr= 240 mg/dm2 for 5500 hours (230 days).
Calculation of the total reduction in the fuel element wall thickness due to corrosion.
Conclusion Reduce in the fuel element wall, which equals 7 µm that corresponds to losses up to 1% of fuel elements wall thickness, was for the first time calculated based on the experimental data array concerning the nuclear reactor zirconium fuel elements corrosion.

Simulation and Prediction of Signal Processing in Communication Terminal Based on Grey Algorithm Guangfei Wang Student Department, Yancheng Teachers University, 224002, Yancheng, China e-mail: wang_guangfei@126.com Keywords: Grey Model; Simulation Algorithm; Data Simulation; Communication Equipment; Terminal Signal; Distortion and Noise Abstract.
Collecting relevant materials and analyzing data, we could summarize that impoverished university students’ consciousness in the protection of rights and interests was very optimistic, security interests’ rate in 2009 rose to74% from 49.7% in 2008, as shown in Figure 2.
This paper uses the DPS software for grey system forecasting model GM (1, 1) to analyze[10]. ①Do cumulative generation of data sequence (1), get formula (2)
Chinese impoverished university students’ rights protection standards from 2007 to 2010 Use the data in Table 1to establish the gray prediction model, doing a long-term prediction on our university students' rights protection standards and the dynamic development trends of poor university students’ number.
Through calculation and analysis, we obtained the data in following Table 2 and Table 3.

This methodology compares experimental data with the numerically obtained results.
Therefore, an effective parameter identification strategy based on stress-strain input data should be considered.
The experimental data used in this type of optimization problem is composed by discrete values representing a set of stress-strain measured points, as depicted in Fig. 2.
In addition, the stress-strain numerical results are defined through a curve and interpolations for each experimental point are performed to compare both data.
The hydraulic bulge test results and the experimental (reference) data are in excellent agreement for polar displacements smaller than 35 mm, with a deviation for larger displacements.

(3) With this method, the wear rates can be calculated with the inclination and with the interception of the linear regression for the data plotted in a graph of SN/Vc against Vs/Vc.
A reduction of 26 % was measured for the cryogenically treated specimens.
The specimens coated with CrN show a wear rate reduction of 29 %, compared to the AR specimens.
A wear rate reduction of 29 % could be reached due to the coating with CrN.
Hutchings, Methods of data analysis for the micro-scale abrasion test on coated substrates, Surface and Coatings Technology 183 (2004) 312-327.

And then, the HVAC models are modified throughout the comparison between the energy consumption patterns and the real-time monitoring in-field data.
This building was built in the 1990’s, so BIM data for this building was not available.
For Building energy performance simulation, the BIM data for the target building was constructed using Revit.
Fig. 4 Convertion process of BIM data In this study, the periode of HVAC control time schedule is 3 days from January 14 to January 16.
Fig. 6 HVAC control time schedule on supply air temperature Conclusion In this study, BIM data for the proposed building was made using Revit.

Based on the cluster approach, applying the perturbation procedure similar to that in Ref. [5, 9], the improved perturbation formulas of the g factors and the hyperfine structure parameters for the tetragonal 3d9 center can be established as follows: g||=gs+8kςdE1+kςd2E22+4kςd2E1E2-gsςd21E12-12E22+kςd34E1E22-1E23-2kςd32E12E2-1E1E22+gsςd31E1E22-12E23 g⊥=gs+2kςdE1-4kςd2E1E2+2gsςd2E12+kςd22E1E2-1E22+kςd32E1-1E212E22+1E1E2-gsςd312E2E12-12E1E22+12E23 (1) A||=P-κ-4N27+g||-gs+3g⊥-gs7 A⊥=P-κ-2N27+11g⊥-gs14 In the above formulas, gs ≈ 2.0023 is the g value of free-ion. k is the orbital reduction factor, which is equivalent to the covalency factor N, characteristic of the covalency between the central ion and the ligands.
Taking into account the covalency effect (characterized by the covalence reduction factor N), the spin–orbit coupling parameter ζ and dipolar hyperfine constant P can be given as [15, 16] Σ = Nς0, P = NP0 (2) so, the spin-orbit coupling coefficient ζ and the dipolar hyperfine structure parameter P can be acquired for the studied systems by using the free-ion data ζ0 (≈ 829 cm-1 [17]) and P0 (≈ 388 ×10-4 cm-1 [18]) for Cu2+ ion.
Therefore, only the relative tetragonal elongation ΔZCu is unknown in the formulas of the spin Hamiltonian parameters, which can be regarded as an adjustable parameter by matching the calculated spin Hamiltonian parameters to the experimental data.
Table 1 The calculated and experimental spin Hamiltonian parameters g factors and the hyperfine structure constants (in 10-4cm-1) for the doped Cu2+ centers in different alkali barium glasses Borate glass g|| g⊥ A|| A⊥ Li-Ba-B Calculation 2.283 2.054 -130.1 -24.5 Li-Ba-B Experimenta 2.284 2.053 131 25 Na-Ba-B Calculation 2.263 2.049 -137.1 -25.6 Na-Ba-B Experimenta 2.262 2.049 137 24 K-Ba-B Calculation 2.257 2.048 -139.1 -25.9 K-Ba-B Experimenta 2.259 2.048 140 24 a The experimental data for the Cu2+ center in alkali barium Borate glasses [7].
Discussion Table 1 reveals that the calculated spin Hamiltonian parameters under the relative tetragonal elongations ΔZCu in Eq. (5) agree well with the experimental data.

The stress and strain-life relations are obtained from basic fatigue data and the linear rule of damage summation is assumed.
The same load ratios used in the experiments were adopted in order to generate crack growth data from an initial crack size a0 = 8.0 mm to a final crack af = 15.0 mm.
By employing the linear Miner rule, the value given by Eq. 2 is obtained, where f(s,R) is a function of life reduction for the material
(2) Results and Discussion Figures 1 (a, b and c) show the experimental crack growth data of the alloys AA6005, AA6063 and AA6351 for different stress ratios (R = -0.5, R = 0.1 and R = 0.5) described by the relationship between crack growth rate, da/dN, and the applied stress intensity factor range, DK, plotted in the log-log form.
Model predictions versus experimental data for AA6005 alloy.

In multilayer constructions which include even contemporary timbre structures the occurrence of condensation is more dangerous and can result in degradation of the materials from a structural point of view, because of potential fungal attack of the wood. [5, 6] Further implications of the occurrence of water vapours in a multilayer construction could even be a significant reduction in effectiveness of the heat-insulating layer [7, Skramlik et al.]: "Moisture in building structures influences the physical properties of materials and can cause their degradation.
Temperature, moisture and pressure dataloggers Voltcraft DL181THP [40 to +70 °C, resolution 0.1 (%, °C, hPa), accuracy: ±1°C, ±3.5 %, ±2.5 hPa] were used for the measurements and data collection.
Exterior datalogger at the object B (source: author's archive) Obtained data.
Each experimental object provided two sets of data.
Partial results of the analysis Due to the large amount of measured data, only the most interesting results will be presented.