Neural-network-based method of calibration and measurand reconstruction for a high-pressure measuring system

1998 ◽  
Vol 47 (2) ◽  
pp. 362-370 ◽  
Author(s):  
D. Massicotte ◽  
S. Legendre ◽  
A. Barwicz
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Measuring System

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

scholarly journals Parametric diagnostics of the condition of a dual-flow turbojet engine using neural network simulation of the operating process

2018 ◽  
Vol 224 ◽  
pp. 02057
Author(s):  
Anas S. Gishvarov ◽  
Julien Celestin Raherinjatovo
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Combustion Chamber ◽  
Early Stage ◽  
Low Pressure ◽  
Similarity Criteria ◽  
Ann Model ◽  
Turbojet Engine ◽  
Operating Process ◽  
Pressure Compressor

The article presents a method of parametric diagnostics of the condition of a dual-flow turbojet engine (DFTE). The method is based on the identification (determination) of the condition of the DFTE components (the compressor, combustion chamber, turbine) with application of a mathematical model of the operating process which is presented as an artificial neural network (ANN) model. This model describes the relation between the monitored parameters of the DFTE (the air temperatures (Tlpc*, Thpc*) beyond the low pressure compressor (LPC) and the high pressure compressor (HPC), the pressure beyond the LPC (Plpc), the fuel consumption rate (Gf), the gas temperatures (Thpt*, Tlpt*) beyond the high pressure turbine (HPT) and the low pressure turbine (LPT)) and the parameters of the condition of its components (the efficiencies of the LPC and the HPC (ηlpc*, ηhpc*), the stagnation pressure recovery factor in the combustion chamber (σcc), the efficiencies of the HPT and the LPT (ηhpt*, ηlpt*)). The parameters of the condition of the engine components (ηlpc*, ηhpc*, σcc, ηhpt*, ηlpt*) are the similarity criteria (integral criteria) which enable to identify the condition of the DFTE components to a high degree of reliability. Such analysis enables to detect defects at an early stage, even if the values of the monitored parameters (Тlpc*, Тhpc*, Plpc, Gf, Тhpt*, Тlpt*) are within the permissible limits. We provide the sequence for development of the ANN model and the results of its performance study during the parametric diagnostics of the condition of the DFTE.

Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

Metrological analysis of a neural network measuring system for medical purposes

2021 ◽  
pp. 65-80
Author(s):  
O.A. Avdeyuk ◽  
Yu.P. Mukha ◽  
D.N. Avdeyuk ◽  
M.G. Skvortsov ◽  
Z. Omiotek ◽  
...  
Keyword(s):  
Neural Network ◽  
Measuring System ◽  
Metrological Analysis

Dynamic Inverse Model for Multi-Sensor Measurement System Based on Wavelet Neural Network

2012 ◽  
Vol 499 ◽  
pp. 335-339
Author(s):  
Dong Zhi Zhang ◽  
Bo Kai Xia ◽  
Kai Wang ◽  
Jun Tong ◽  
Nian Zhen Yang
Keyword(s):  
Neural Network ◽  
Measuring Method ◽  
Wavelet Neural Network ◽  
Inverse Model ◽  
Measuring System ◽  
Water Mixture ◽  
Accuracy Measurement ◽  
Sensing Technology ◽  
Dynamic Inverse ◽  
Digital Oilfield

As traditional measuring method based on dielectric coefficients shows cross-sensitivity for multi-factor in the measurement of oil/water mixture, it can not meet the requirements of digital oilfield construction. Therefore, this paper presents an inverse model of wavelet neural network (WNN) combining with multi-sensing technology for achieving high-accuracy measurement of water content in crude oil. The simulation and experimental results demonstrate that the proposed method is available to eliminate the cross-coupling effects of multi-factors. The method has higher measurement accuracy and stronger generalization than the model built by BP-NN, and opens a versatile approach in nonlinear error calibration for multi-factors measuring system.

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