Deterministic behavior of temperature field in turboprop engine via shallow neural networks

2021 ◽  
Author(s):  
Ivo Bukovsky
Keyword(s):  
Neural Networks ◽  
Temperature Field ◽  
Turboprop Engine ◽  
Deterministic Behavior

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

2012 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim ◽  
Keonwoo Kim
Keyword(s):  
Neural Networks ◽  
Diagnostic System ◽  
Engine Performance ◽  
Performance Model ◽  
Performance Data ◽  
Turboprop Engine ◽  
The Neural Networks ◽  
Fault Diagnostic ◽  
Intelligent Methods ◽  
Learning Data

Recently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks, Fuzzy Logic and Genetic Algorithms have been studied to improve the model based engine diagnostic methods. Among them the Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if only use of the Neural Networks. In addition, it has a very complex structure due to finding effectively faults of single type faults and multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP(Feed Forward Back Propagation) is used. In order to user’s friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB. The proposed program is verified by application of several case studies having the arbitrary implanted engine component faults as well as real engine performance data.

Prediction of compressor nominal characteristics of a turboprop engine using artificial neural networks for build standard assessment

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
C. Jagadish Babu ◽  
Mathews P. Samuel ◽  
Antonio Davis ◽  
R. K. Mishra
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Transfer Functions ◽  
Power Lines ◽  
Feed Forward Neural Network ◽  
Constant Power ◽  
Standard Assessment ◽  
Turboprop Engine ◽  
Novel Method ◽  
Accuracy Of Prediction

AbstractCompressor characteristics of a single spool turboprop engine have been studied in this paper. It has been brought outhow constant power lines in the compressor characteristics of these compressors make them different from others. Constant speed lines and constant power lines have also been highlighted. A novel method of modeling of compressorof a single spool turboprop engine has also been studied in this paper. Application of neural networks in prediction of compressor characteristics has been investigated. Multilayer Perceptron feed forward neural network has been considered with different transfer functions to assess the potential capability of network in extrapolation and interpolation. Effectiveness of prediction with and without engine bleed valve open and anti-ice valve open situations have been assessed. Network Predictionshas been compared with engine test data to assess the accuracy of prediction and to quantify the build variation in the manufacture of engines. Capability of network with limited test data to predict the complete performance has also been assessed and presented in this paper.

Model of Predicting Polymer Melt Temperature Field by BP Neural Networks

2012 ◽  
Vol 531-532 ◽  
pp. 425-428
Author(s):  
Meng Shan Li ◽  
Bing Xiang Liu ◽  
Yan Wu
Keyword(s):  
Neural Networks ◽  
Temperature Field ◽  
Wall Temperature ◽  
Polymer Melt ◽  
Measuring Method ◽  
Bp Neural Networks ◽  
Molding Process ◽  
Melt Temperature ◽  
Continuous Extrusion ◽  
High Degree

Temperature of the polymer melt is one of the most important parameters for the polymer continuous extrusion molding process. There are many factors influence the distribution of the melt temperature, these factors have the coupling and nonlinear relationship which is difficult to measure accurately by the traditional measuring method. In this study, a BP neural networks-based model approach is presented in which the effects of the die wall temperature and screw speed and the wall temperature of the transition section and the measurement section in the continuous extrusion molding are investigated. Comparison of the BP neural networks model predictions with the experimental data yields very good agreement and demonstrates that the BP neural networks model can predict the polymer melt temperature field with a high degree of precision (the mean square error within 0.03)

scholarly journals Turbine Blade Temperature Field Prediction Using the Numerical Methods

2021 ◽  
Vol 11 (6) ◽  
pp. 2870
Author(s):  
Miroslav Spodniak ◽  
Karol Semrád ◽  
Katarína Draganová
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Temperature Field ◽  
Numerical Methods ◽  
Turbine Blade ◽  
Temperature Fields ◽  
Jet Engine ◽  
Artificial Neural ◽  
Temperature Field Prediction

Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article’s main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.

Prediction of compressor nominal characteristics of a turboprop engine using artificial neural networks for build standard assessment

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
C. Jagadish Babu ◽  
Mathews P. Samuel ◽  
Antonio Davis ◽  
R. K. Mishra
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Transfer Functions ◽  
Power Lines ◽  
Feed Forward Neural Network ◽  
Constant Power ◽  
Standard Assessment ◽  
Turboprop Engine ◽  
Novel Method ◽  
Accuracy Of Prediction

Abstract Compressor characteristics of a single spool turboprop engine have been studied in this paper. It has been brought outhow constant power lines in the compressor characteristics of these compressors make them different from others. Constant speed lines and constant power lines have also been highlighted. A novel method of modeling of compressorof a single spool turboprop engine has also been studied in this paper. Application of neural networks in prediction of compressor characteristics has been investigated. Multilayer Perceptron feed forward neural network has been considered with different transfer functions to assess the potential capability of network in extrapolation and interpolation. Effectiveness of prediction with and without engine bleed valve open and anti-ice valve open situations have been assessed. Network Predictionshas been compared with engine test data to assess the accuracy of prediction and to quantify the build variation in the manufacture of engines. Capability of network with limited test data to predict the complete performance has also been assessed and presented in this paper.

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