Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Krzysztof Kosowski ◽  
Karol Tucki ◽  
Adrian Kosowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Turbine ◽  
High Performance ◽  
Cross Sectional ◽  
Complementary Method ◽  
Numerical Tests ◽  
Flow Parameters ◽  
Turbine Cascades ◽  
Artificial Neural

We present the results of numerical tests of artificial neural networks (ANNs) applied in the investigations of flows in steam turbine cascades. Typical constant cross-sectional blades, as well as high-performance blades, were both considered. The obtained results indicate that ANNs may be used for estimating the spatial distribution of flow parameters, such as enthalpy, entropy, pressure, velocity, and energy losses, in the flow channel. Finally, we remark on the application of ANNs in the design process of turbine flow parts, as an extremely fast complementary method for many 3D computational fluid dynamics calculations. By using ANNs combined with evolutionary algorithms, it is possible to reduce by several orders of magnitude the time of design optimization for cascades, stages, and groups of stages.

scholarly journals Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

2017 ◽  
Author(s):  
◽  
D. Flores
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Artificial Neural Networks ◽  
High Performance ◽  
Digoxin Concentration ◽  
Computational Method ◽  
Specific Concentration ◽  
Filling Time ◽  
Artificial Neural

Artificial neural networks (ANN) are a computational method that has been widely used to solve complex problems and carry out predictions on nonlinear systems. Multilayer perceptron artificial neural networks were used to predict the physiological response that would be obtained by adding a specific concentration of digoxin to Tivela stultorum hearts, this organism is a model for testing cardiac drugs that pretends to be used in humans. The MLPANN inputs were weight, volume, length, and width of the heart, digoxin concentration and volume used for diluting digoxin, and maximum contraction, minimum contraction, filling time, and heart rate before adding digoxin, and the outputs were the maximum contraction, minimum contraction, filling time, and heart rate that would be obtained after adding digoxin to the heart. ANNs were trained, validated, and tested with the results obtained from the in vivo experiments. To choose the optimal network, the smallest square mean error value was used. Perceptrons obtained a high performance and correlation between predicted and calculated values, except in the case of the filling time output. Accurate predictions of the T. stultorum clams cardioactivity were obtained when a specific concentration of digoxin was added using ANNs with one hidden layer; this could be useful as a tool to facilitate laboratory experiments to test digoxin effects.

scholarly journals Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ayse Gokcen Kavaz ◽  
Burak Barutcu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Wind Turbine ◽  
High Performance ◽  
Detection Algorithm ◽  
Sensor Calibration ◽  
Isolation Technique ◽  
Measurement Validation ◽  
Artificial Neural

This paper proposes a method for sensor validation and fault detection in wind turbines. Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system’s state of health. Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect. Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed. Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim. Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm. Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments. The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.

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