The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Lars E. Bakken - One of the best experts on this subject based on the ideXlab platform.
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Axial Compressor Deterioration Caused by Saltwater Ingestion
Journal of Turbomachinery, 2007Co-Authors: Elisabet Syverud, Olaf Brekke, Lars E. BakkenAbstract:Gas turbine performance Deterioration can be a major economic factor. An example is within offshore installations where a degradation of gas turbine performance can mean a reduction of oil and gas production. This paper describes the test results from a series of accelerated Deterioration tests on a General Electric J85-13 jet engine. The axial compressor was deteriorated by spraying atomized droplets of saltwater into the engine intake. The paper presents the overall engine performance Deterioration as well as deteriorated stage characteristics. The results of laboratory analysis of the salt deposits are presented, providing insight into the increased surface roughness and the deposit thickness and distribution. The test data show good agreement with published stage characteristics and give valuable information regarding stage-by-stage performance Deterioration.
M. Wasfi - One of the best experts on this subject based on the ideXlab platform.
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A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Mohamed Talaat, M. H. Gobran, M. WasfiAbstract:In this paper, the diagnosis system of power plant gas turbine has been developed to detect the Deterioration of engine performance. This system can be analyzed the gas path measurement to predict the Deterioration of engine main component by using artificial neural network. The Deterioration performance data of gas turbine was generated by using the thermodynamic model. So, the artificial neural network model was built to predict the deteriorated characteristics of gas turbine. Thermodynamic model was used to simulate gas turbine performance as well as the Deterioration of engine components (compressor, combustion chamber and turbine) which were represented by changing component characteristic parameters (efficiency and flow capacity). On one hand, the probability of these deteriorated components was simulated to generate deteriorated data (measurement parameters and Deterioration degree of each component). On the other hand, the neural network was trained with Deterioration data and the best structure of neural network (number of hidden layers, number of neurons in hidden layer and transfer function) was selected based on the minimum value of the mean square error. The different Deterioration data (testing data) was generated in thermodynamic model to test the effectiveness of the neural network. The comparison between the mean square error value of single and multi-neural network output parameters at training and testing data were achieved. In final, the testing with the real engine data were achieved.
Elisabet Syverud - One of the best experts on this subject based on the ideXlab platform.
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Axial Compressor Deterioration Caused by Saltwater Ingestion
Journal of Turbomachinery, 2007Co-Authors: Elisabet Syverud, Olaf Brekke, Lars E. BakkenAbstract:Gas turbine performance Deterioration can be a major economic factor. An example is within offshore installations where a degradation of gas turbine performance can mean a reduction of oil and gas production. This paper describes the test results from a series of accelerated Deterioration tests on a General Electric J85-13 jet engine. The axial compressor was deteriorated by spraying atomized droplets of saltwater into the engine intake. The paper presents the overall engine performance Deterioration as well as deteriorated stage characteristics. The results of laboratory analysis of the salt deposits are presented, providing insight into the increased surface roughness and the deposit thickness and distribution. The test data show good agreement with published stage characteristics and give valuable information regarding stage-by-stage performance Deterioration.
Mohamed Talaat - One of the best experts on this subject based on the ideXlab platform.
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A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Mohamed Talaat, M. H. Gobran, M. WasfiAbstract:In this paper, the diagnosis system of power plant gas turbine has been developed to detect the Deterioration of engine performance. This system can be analyzed the gas path measurement to predict the Deterioration of engine main component by using artificial neural network. The Deterioration performance data of gas turbine was generated by using the thermodynamic model. So, the artificial neural network model was built to predict the deteriorated characteristics of gas turbine. Thermodynamic model was used to simulate gas turbine performance as well as the Deterioration of engine components (compressor, combustion chamber and turbine) which were represented by changing component characteristic parameters (efficiency and flow capacity). On one hand, the probability of these deteriorated components was simulated to generate deteriorated data (measurement parameters and Deterioration degree of each component). On the other hand, the neural network was trained with Deterioration data and the best structure of neural network (number of hidden layers, number of neurons in hidden layer and transfer function) was selected based on the minimum value of the mean square error. The different Deterioration data (testing data) was generated in thermodynamic model to test the effectiveness of the neural network. The comparison between the mean square error value of single and multi-neural network output parameters at training and testing data were achieved. In final, the testing with the real engine data were achieved.
Lith Choummanivong - One of the best experts on this subject based on the ideXlab platform.
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Interim road Deterioration models during accelerated Deterioration
2015Co-Authors: Tim Martin, Lith ChoummanivongAbstract:The accelerated Deterioration of pavements subject to increased axle loading needs the development of interim road Deterioration (RD) models so that the cost of accelerated Deterioration can be estimated. These RD models were based on selected assemblies of road condition data in the rapid Deterioration phase. These data selections produced a wide range of data across a time series to enable the development of the RD models for cracking, and cumulative rutting and roughness. The cumulative rutting and roughness RD models have incorporated an RD model for cumulative cracking due to a lack of surface maintenance. These RD models are expected to be a useful tool for the prediction of the consequences of reduced maintenance on pavement conditions and increased axle mass loading.
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Interim road Deterioration cracking model during accelerated Deterioration
2014Co-Authors: Lith Choummanivong, Tim MartinAbstract:This report documents the process of assembling and reviewing the RMS NSW cracking data and the analysis of this data and its associated variables of climate, traffic load and pavement strength which has led to the development of a potential road Deterioration cracking model under accelerated traffic load and Deterioration conditions. A road Deterioration cracking model is fully documented and compared with the current model which was based on the DPTI SA cracking data measured by the RoadCrack device.
