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Results of numerical simulation are compared with experimental data and a suitable turbulence model for resistance prediction is proposed for the typical Myring shape for AUVs.
Figure 3 shows the comparison between the numerical results and the experimental data.
As can be observed in Fig. 3, the hull resistance calculated by the Standard k-ε turbulence model greatly differs from the experimental data.
Simulation results by the RNG k-ε model, the Realizable k-ε model, the SST k-ω model, and the RSM model are comparatively close to the experimental data.
(m/s) ΔDαv /Eαv Figure 3 Comparison between the simulation results and the experimental data: where ∆Dαv/Eαv denotes the error in percentage, Eαv denotes the experimental data, and Dαv denotes the simulation results.

From the PVT data generated from the three Weyburn reservoir fluid-CO2 mixtures it was showed that viscosity reduction and oil swelling by CO2 contributed to oil recovery.
From the measured P-V data, the BPP with CO2 concentrations were calculated and shown in Fig. 3.
R.K Srivastava et al. [2]noted that for the three Weyburn reservoir fluids, if using the relative BPP(BPP of mixture - BPP of reservoir fluid), the data points of the relative BPP with CO2 concentration nearly collapse to a single curve.
Then the description of the heavy componet C+ is: cnBMA cn eZ   （1）   0 7 7    CN C BM C Cn CneMM （2）         CN C BM C Cne ZA 7 7 lnln （3） (2) Kij : The Kij can be gotten from the original data given by PREOS.
If it is not be given for expand componets, the Kij can be extrapolated from original data

The structure shown in Figure 2, Image data are sorted to find the median in Filter_5X5 after buffering in Filter_buf.
Filter_buf Module Implementation This module buffer the serial data using Fifo, the structure is shown in Figure 3.
Filter5X5 Module Implementation When the serial data went through the Filter_buf module, it becomes the output with 5 lines.
Sort4 structure The major operation of median filter is to sort the data in the window.
The sort module of two data shown in Figure 7 is the basic unit.

Parameters The numerical Vehicle Priuse Kerb mass(kg) 1265 Full of quality(kg) 1530 Drag coefficient 0.3 Windward area(m2) 1.746 Rolling resistance coefficient 0.0135 Front axle load distribution 0.6 The centroid height(m) 0.569 Wheelbase(m) 2.55 Power assembly Insight Insight(ISG) -- -- The engine Saturn 1.9L SOHCSI Maximum engine power(kW/rpm) 90 Gearbox 5 speed manual transmission Ratio 3.25,1.81,1.21,0.86,0.64 The main reduction ratio 4.06 Battery NIMH6 Battery group number 20 Simulation of vehicle model I.
Simulation of II vehicle model application of thermoelectric generator are summarized, and the whole vehicle model I contrast data as shown in table 1-2 Table 1-2 Comparison of dynamic and economic parameters of vehicle model I and II (NEDC) Parameters vehicle model I vehicle model II Fuel consumption per 100 kilometers 7.4 6.7 Emission of HC(g/km) 0.124 0.117 Emission of CO(g/km) 0.465 0.443 Emission of NOx(g/km) 0.071 0.064 0-100Km/h Acceleration time(s) 8.5 8.5 60-100Km/h Acceleration time(s) 4.5 4.5 0-140Km/h Acceleration time(s) 15.9 15.8 acceleration time The maximum acceleration(m/s2) 4.8 4.8 The maximum speed(km/h) 225.8 225.8 Maximum gradability(%) 14.1 14.1 Comparison of the data of vehicle model I, including power, fuel economy and emissions of the vehicle, the discovery and application of thermoelectric generator, fuel economy can be improved effectively, 100 km fuel consumption increased by nearly 9.46%, emissions have also been improved.
Comparative data dynamic and economic parameters of vehicle model I, II and III as shown in table 3, the main component of vehicle model II and III average efficiency comparison data such as shown in table 7.
Vehicle model II Vehicle model III Fuel consumption per 100 kilometers 7.4 6.7 7 Emission of HC(g/km) 0.124 0.117 0.120 Emission of CO(g/km) 0.465 0.443 0.451 Emission of NOx(g/km) 0.071 0.064 0.069 0～100Km/h Acceleration time(s) 8.5 8.5 8.7 60～100Km/h Acceleration time(s) 4.5 4.5 4.6 0～140Km/h Acceleration time(s) 15.9 15.8 16.2 acceleration time The maximum acceleration(m/s2) 4.8 4.8 4.8 The maximum speed(km/h) 225.8 225.8 225.8 Maximum gradability(%) 14.1 14.1 14.1 Table 4 The main components of vehicle models II and III average efficiency (NEDC) Parameters vehicle model II vehicle model III Engine average efficiency (%) 17.75 18.60 Transmission system average efficiency (%) 93.3 93.1 Motor for electric average efficiency (%) 47.23 83.94 Motor power average efficiency (%) 50.53 80.82 Battery discharge average efficiency (%) 96.95 98.3 Battery charging average efficiency (%) 89.62 88.09 Battery charging and discharging efficiency (%) 86.89 86.6 Table 1-3 Comparison of three sets of data
, thermoelectric applications to a certain extent, improve vehicle fuel economy and lower emissions; compare two sets of data in Table 1-4 in the motor vehicle model III, the average efficiency has improved, remaining at 80% level, to ensure the efficient operation of the motor, the engine's efficiency is also improved by nearly 1%.

The experimental data consists of 15 numbers of experiments.
In general, the S/N ratio is a quality index that represent the quality characteristics for the measured data.
The computational steps of multi objective Taguchi method can be expressed as: Step 1: Compute the quality loss (MSD) for each measured data.
Step 2: Calculate the normalized MSD (NMSD) for each measured data
Step 4: Compute the multiple S/N ratio (MSNR) at each jth experimental data

The possible reasons are some difficulties in preparing high quality samples and controlling oxygen content which make it difficult to obtain good and consistent research data.
For sample with x = 0.11, 0.13 and 0.19, no trace superconductivity was observed from dc magnetic-susceptibility data.
For the non-superconducting samples, all susceptibility data were analyzed using Eq. 1.
Extrapolation on this data indicates that the value of θ was a negative.
The gradient value of all data can be extracted to find the Curie constant (C) and magnetic moment per atom per unit volume.

Multi-sensor information fusion technology is a new emerging research area which combines data from multiple sensors to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone[1-2].
Therefore, multiple welding states were activated and the experimental data could be thought as covering all the penetration situations.
Comparative experiment For the purpose of validating, the data set is randomly split into 730 training and 252 test samples.
McMullen: Mathematical Techniques in Multisensor Data Fusion ( Artech House.
Klein: Sensor and Data Fusion Concepts and Applications (Society of Photo-Optical Instrumentation Engineers (SPIE) Bellingham, WA, USA.1999) [3] B.

The allowable stress is calculated with the data collected on the site according to the ASTM standard.
The time domain data of the acceleration response from the elbow of the small bore pipe and the socket weld root at the connection position with the main pipe were collected.
The acceleration data of the elbow is recorded as the response of the vibration system as shown in Fig.2 while that of the connection point is recorded as the excitation of the system.
Fig.1 Complicated small bore pipe system layout Fig.2 Acceleration data of the time domain in 3 directions on the elbow position 0.0 5.0k 10.0k 15.0k 20.0k 25.0k 30.0k 40 60 80 100 120 140 Acceleration( m/s2) Frequency(Hz) 0 5k 10k 15k 20k 25k 30k 0 10 20 30 40 50 60 70 Acceleration( m/s2) Frequency(Hz) 0.0 5.0k 10.0k 15.0k 20.0k 25.0k 30.0k 0 50 100 150 200 250 Acceleration( m/s2) Frequency(Hz) Fig.3 Acceleration data of the frequency domain in 3 directions The vibration velocity spectrum is considered as the function of the bending stress, the natural frequency, the damping coefficient, Young's modulus and pipe geometry [1].
The velocity spectrum at the elbow of the small bore pipe in the time domain can be calculated with integrating the acceleration data and high-pass filter, according to which, the effective velocity value can be obtained with the result of 8.59mm/s.

Based on analyzing the testing data, the change law of the shearing parameters of the soils with water content is studied.
The soil samples are dried and grinded and prepared by layer-compacting method in order to obtain the optimum testing data.
Fig.2 Relations of cohesions and water content w－wp / % c / kPa It is shown in the Fig.2 that the fitting curve coincides with the testing data.
Through analyzing the testing data of sliding-zone and sliding-body soils, the variation law of internal friction angle f with water content approximately conforms to the following linear relation through normalizing method
Fig.3 Relations of internal friction angle and water content w－wp / % f / fp The Fig.3 shows that the fitting curve coincides with the testing data.

BIM data can be viewed as a 3D (three dimensional) model, and can be integrated with other programs for construction estimating, scheduling, and project management, then the applications of this use throughout the planning, design, construction and facility operation processes.
When simulating or coordinating constructions sequencing by integrating schedule data with the model data can be virtually viewed as 4D (four dimensional) model, and when using the model data to quantify materials and apply cost information can be virtually viewed as 5D (five dimensional) model.
The digital model includes data components that represent building elements and characteristics, such as materials, weight, thermal resistance, and other physical properties that contribute to building performance.
Project team can also incorporate local weather and electric grid data to estimate building energy consumption and carbon emissions, and renovate buildings to reduce water usage or to utilize more reclaimed water, and select recycled or renewable materials or finishes during building renovations.
Providing BIM models to building product manufacturers to prefabricate building elements off-site offers many green benefits, including time saving, cost saving and the reduction of waste produced by onsite fabrication.