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Introduction With the rapid development of modern railway construction in our country, the pros and cons of locomotive diesel engine performance, people's environmental awareness, the requirement of energy conservation and emissions reduction, the challenge of comprehensive performance of locomotive diesel engine is becoming more and more big.
In this article, taking 16V265H diesel engine as the research object, after using UG software to build entity model, put the entity model of the nozzle imported into GAMBIT, meshing and set the import and export of the fluid, then, using FLUENT, considering the characteristics of nozzle and the test data to determine the reasonable boundary conditions, doing a data simulation of porous nozzle fuel three-dimensional flow field and proving the validity of the results.
The entity model According to the entity drawing of 16V265H locomotive diesel engine nozzle and related data, using UG to build the entity model of nozzle.
The following is generating the assembly drawing of nozzle and the distance between the top of the needle valve and the top of the needle valve body is 6 mm, then according to the related data of nozzle, establishing the physical model of nozzle flow channel, as shown in Fig.3.
If we have the actual test data, we could directly calculate the average mass flow of fuel.

Thus, thermal conductivity reductions provide the only mechanism for increasing the figure of merit.
The variation of power factor with boron content calculated from the electrical conductivity and seebeck coefficient data of Fig. 1 and Fig. 2 at elevated temperature is shown Fig. 3.
Electronic thermal conductivity increase with boron content, but it exhibits lower data values than that of others over 1.0wt% boron content.
Z value with Boron content calculated from the power factor and thermal conductivity data is shown in Fig. 7.
The electrical conductivity increases with boron content.Because the excessive dopant concentration affected the lattice structure and the scattering center, analyzed data containing over 1.0wt% boron shows lower electrical conductivity. 2 The power factor has the largest calculated data value at 0.5wt% boron content. 3.

Cutting force data comparison: (a) Simulation results (b) Experimental results.
Chip measurement data comparison: (a) Simulation results (b) Experimental results.
However, the decreasing proportion predicted by the model is slightly larger than the experimental data.
Chip morphology was predicted and compared with the experimental data as well.
Cook, A constitutive model and data for metals, in: 7th International Symposium on Ballistics. 1983

But the reduction of metal or iron parts of landmines and the disturbance of metal or iron chips in minefield have increased the false alarm of electromagnetic induction detection or magnetic detection.
Data acquisition and control system could complete the amplification and the reading of the output signal from detector, the delay system control, voltage source control and so on.
Data processing and analysis system could accomplish software processing and data analysis[9-11].
Results and discussion By using the transmission testing system, the data sampling interval is 0.004ps, the record number is 1805.
The absorption spectrum have confirmed the correctness and reliability of the testing data, which compared with those of other organizations are basically identical[7][12].

The important guarantee of control performance are the choice of bearing calibration,the realization of soft measurement data processing in a DCS(Distributed Control System),the calculation of the model and the correction of module,and soft measurement model generalization ability of ascension.
Fig.2 A typical calculation process of genetic algorithm K - means algorithm combining with literature[8] research proposed an anomaly detection algorithm based on the nearest neighbor clustering algorithm and genetic algorithm.It has carried on the clustering analysis of historical data for sewage treatment and successfully found out the abnormal data.It has established the fault rules according to the clustering results.It also has a certain practical reference value for the establishment of the fault diagnosis system in sewage treatment technology.
Stetp2:The soft measurement technology based on ANN in wastewater treatment ANN take a simple nonlinear neuron as a processing unit.And it is a nonlinear dynamic system with large-scale distributed parallel processing ability through extensive connection form; The characteristics of self-organizing, self-learning and distributed associative memory, and the nonlinear approximation have attracted wide attention in control group[9].Under the condition of object does not have prior knowledge,the soft measurement method based on ANN can directly establish model and has strong ability of on-line correction according to the I/O data of objects[15].It take primary variables as network input, BOD as network output, to solve the problem of the soft measurement in the wastewater quality through the training of all kinds of learning algorithm.A kind of typical BOD neural network soft measurement hierarchy is shown in fig.3.
Fig.3 A kind of typical BOD neural network soft measurement hierarchy From the optimization of network structure and the ascension of real-time data processing ability,in recent years, there is a based on PCA(Principal Component Analysis) of the artificial neural network soft measurement method.And it is used in sewage treatment system.
General expression is: (1) The basic idea of SVM is to limited training samples from the input space nonlinear mapped to a high-dimensional feature space, and obtained by solving the quadratic convex programming problem globally unique optimal solution.The method solves the general method of study that is difficult to solve the problem,such as easily trapped in local minimum problem, the structure learning method, type selection to rely too much on experience and so on,to improve the generalization ability of the model.Combined with parameter characteristic analysis, punish the optimization of parameters and kernel function or methods of knowledge reduction,it ensure the accuracy and real-time water quality soft measurement in the process of sewage treatment.

Separate data of the optical, TSC and TSL investigation of sapphire are reported in [14].
These data closely correspond to each other.
Experimental data on VS of Cr Kα1 in complex garnets were published in [16,17].
Separate data of the ab initio theoretical calculation for Cr ions in garnet crystals are presented in Table 2.
Similar data for ruby are given in [2,14].

The diffraction data for title complex were collected at 293 K on a Bruker Smart CCD diffractometer with Mo-Ka radiation (l=0.071073 nm), and the data reduction was performed using Bruker SAINT.[14] The structure was solved using a direct method, which yielded the positions of all or most of the non-hydrogen atoms.
All calculations were performed using the SHELXTL programs.[15] The crystallographic data are summarized in Table 1.
CCDC-798273 Table 1 Crystallographic data and structure refinement for title complex.
Empirical formula C90H66I6N24O2Zn3 α [º] 103.364(2) Formula weight 2473.2 β [º] 91.508(2) Temperature [K] 293(2) γ [º] 101.476(2) Crystal system triclinic V [nm3] 4.5412(8) Space group P-1 Dc [g·cm-3] 1.809 a [nm] 0.99234(10) m [cm1] 2.888 b [nm] 2.1284(2) S on F2 0.997 c [nm] 2.2615(2) Z 2 R1a, wR2b [I>2σ (I)] 0.0788, 0.1861 R1a, wR2b [all data] 0.1543, 0.2268 R1a = å||Fo|-|Fc||/å|Fo|, wR2b = [åw(Fo2-Fc2)2/åw(Fo2)2]1/2.

Those data are of great importance for large scale numerical simulation of the explosion effects from pyrotechnic mixtures.
With those known data, the new set of mole numbers xi can be found from Eqs. 25 26.
Their molar standard thermochemical data are obtained from JANAF databank [8].
From these data as initial state, the explosion effects can be estimated via hydrodynamic code to for the calculation of shock wave propagation.
[8] Data from http://kinetics.nist.gov/janaf.

Combining theoretical considerations, including extensive ray tracing, and experimental data, a new pentaprism adjustment procedure was developed [6].
The resulting calibration data are used to correct a given angle reading of the autocollimator to achieve an accurate and traceable angle measurement by means of the device.
The measurements were performed by applying an optimized shear combination of 4 & 35 data points (=140 data points per scan, physical shears of 2.3 mm & 20.0 mm for the scan length of 80 mm).
The measurement uncertainty (calculated in accordance with [12]) for the ESAD topography scans is a function of the position of the data point in the scan.
Summary The following points summarize the basic features of ESAD shearing deflectometry in general and the measuring capabilities of the ESAD device at PTB: • Absolute measurement method (basis: straight propagation of light) • Standard measurement uncertainty of topography < 1 nm up to 500 mm scan length • Optimized measurand (angle and length) traceability to SI units • Near-constant measuring conditions independent of surface dimensions • No basic limitation of the dimensions of the surface under test to be measured • Optimized adjustment (automated and in situ) of pentaprism and surface under test • Reduction of influences of prism guiding errors on deflection angle (by factor 1:10000) • Elimination of any whole-body tilting of the surface (due to shifting of the specimen) Acknowledgement The author would like to thank Dipl.

Modern neural networks are non-linear statistical data modeling tools.
They are usually used to model complex relationships between inputs and outputs or to find patterns in data.
Clustering analysis is a technique to generate groups of subsets of data called clusters within which the distance between subsets is small, whereas a distance between clusters is greater in that two subsets from different clusters are distant.
The measurement data of the T1, T7, T25, T32 are used to train the prediction model, the output of the model is the Y axis thermal error and Z axis thermal error.
Reduction and Compensation of Thermal Errors in Machine Tools.