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A novel method to apply artificial neural network (ANN) for both chemical kinetics reduction and source term evaluation is introduced and tested in direct numerical simulation (DNS) and large eddy simulation (LES) of reactive flows. To gather turbulence affected flame data for ANN training, a new computation-economical method, called 1D pseudo-velocity disturbed flame (PVDF), is developed and used to generate thermo-chemical states independent of the modeled flame. Then a back-propagation ANN is trained using scaled conjugate gradient algorithm to memorize the sample states with reduced orders. The new method is employed in DNS and LES modeling of H 2 /air and C 3 H 8 /air premixed flames experiencing various levels of turbulence. The test result shows that compared to traditional computation with full mechanism and direct integration, this method can obtain quite large speed-ups with adequate prediction accuracy. [7,8] are often used, while ANN method, a extremely new systemic technique, is seldom seen in previous work. The procedure of using QSS assumption has been shown in Figure 1. However, QSS species concentrations should be first resolved which includes a large amount of algebraic iterations. Therefore, the net efficiency could be undermined [9], and also calculation may be failed if iterations do not converge. ANN, kinetics reduction, LES, DNSAs for the second respect, several approaches to accelerate chemical sources evaluation have already been presented, such as look-up table (LUT) [10] and in situ-adaptive tabulation (ISAT) [11]. However, they are both based on tabulation technique which needs huge memory and a large number of check-up and interpolation operations. Artificial neural network (ANN) method, although it was proposed previously to handle above drawbacks, has got great progress through Sen and Menon's work [12,13]. It has been successfully applied to account for chemical kinetics in LES modeling of turbulent premixed flame with speed-ups even more than 10.In this paper, we tend to extend the application of ANN method, using it to not only calculate chemical sources but also reduce the detailed mechanism. The initial idea is to construct the direct mapping between non-QSS species concentrations and their reaction rates at plenty of thermal

This document analyses the surface of piston in two aspects, discrete points of axial section profile are fitted by cubic spline tool of MATLAB, interpolate curve after fitting with method of precision linear approximation. Equipartition of cross section profile are done,and through subdivision of points of equipartition,to approach theory contour. the number of divide points must satisfy the request was made, with the principle of the relative movement，in the processing of cross section in an ellipse，detailed analysis the motion of line motor，pointed out how changes the speed and acceleration of workbench.and must satisfy the basic conditions of the line motor to respond to the turning of cross section in an ellipse. On this basis, design a lathe machining CNC system of piston with structure of upper and lower computer, moulding of surface of piston are completed by upper computer, feed drive of X, Z axis and spindle are completed by lower computer, surface of middle-convex and varying piston are processed finally.

In materials science, the relationship between the material internal structure and its associated macroscale properties can be used to guide the design of materials. In this study, we constructed an interpretative machine learning (ML) model to capture the structure-property relationship and predict the solid solubility in binary alloy systems. To do this, we used a dataset containing about 1843 binary alloys and corresponding experiment values of solid solubility. We designed a common function to represent the relationship between individual descriptor and solid solubility, and a deep neural network to integrate the multiple functions. The resulting model can correctly predict the solid solubility value than other ML models. What is more, based on this model, it is feasible to analyze the effect of structures on target property.