No abstract

In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approximation of functions and prognosis. Neural networks include analyzing subject areas and weight coefficients of neurons, detecting conflict samples and outliers, normalizing data, determining the number of samples required for teaching a neural network and increasing the learning quality when their number is insufficient, as well as selecting the neural network type and decomposition based on the number of input neurons. We consider the technology of initial data processing and selecting the optimal neural network structure that allows to significantly reduce modeling errors in comparison with neural networks created with unprepared source data. Our accumulated experience of working with neural networks has demonstrated encouraging results, which indicates the prospects of this area, especially when describing processes with large amounts of variables. In order to verify the resulting neural network model, we have carried out a computational experiment, which showed the possibility of applying scientific results in practice.

The paper presents the results of mathematical simulation of the characteristics of a vane diffuser of a centrifugal compressor intermediate stage, such as the loss coefficient and the deviation angle versus the outlet vane angle of the diffuser. The simulation of these characteristics was made on the basis of processing the results of studies performed by the Research Laboratory “Gas Dynamics of Turbomachines” of Peter the Great St.Petersburg Polytechnic University at the model characteristics of vane diffusers. Given the almost complete absence of recommendations in the literature, the paper describes the technology for constructing neural network models, which includes preparing a sample of input data and determining the optimal structure of the neural network. Based on the obtained mathematical models, a computational experiment was carried out in order to determine the influence of the main geometric and gas-dynamic parameters on the efficiency of vane diffusers. The results of the computational experiment on neural models of the efficiency of a vane diffuser are analyzed according to the existing ideas about the physics of the processes of energy conversion in a vane diffuser.

The paper presents the results of CFD-calculations of a centrifugal compressor stage with a high-pressure 3D impeller and a vaneless diffuser. The stage was designed by Prof. A. M. Simonov in the Problem Laboratory of Compressor LPI according to the following design parameters: flow rate coefficient 0.080, loading factor 0.74, and the relative Mach number 0.78. Two design grids were used: 2.4 and 4.4 million cells for the sector with one blade. The entire stage was calculated with a sparser grid. Special “Stage” interface conditions are used to interface the gas-dynamic parameters at the boundary regions. The SST turbulence model was used in the calculations. The results of efficiency characteristics and work coefficient comparison showed the following: in design flow rate all three variants of the calculation overstate the loading factor by 14.3%; the calculated characteristics of polytrophic work coefficient in the staging of 360 degrees are closest to the experimental characteristics, but the absolute value is greater than 12% at a flow rate coefficient of 0.085; the maximum calculated efficiency of a stage (the circle of 360 degrees) is almost equal to the measured maximum efficiency.

Optimal design of centrifugal compressor stages needs special computational and experimental methods, both of them could be costly enough. So new advanced design methods which can provide optimal solution faster are needed. Authors developed the set of mathematical models – Universal modeling method - for describing compressor stages characteristics. Its models are being widened and improved. One the most advanced approaches of model building is based on machine learning. A neural network based method for predicting centrifugal compressor vane diffuser characteristics was developed. Input data for network training was obtained from CFD simulations. The resulting model for diffuser loss coefficient shows good approximation quality and can be used for improvement of VD model in Universal modeling method.