Numerical study on turbulent mixed convection in inclined plane channels,from 15° to 90° (vertical),was carried out to examine the effect of inclination on fluid flow and heat transfer distributions.The turb...Numerical study on turbulent mixed convection in inclined plane channels,from 15° to 90° (vertical),was carried out to examine the effect of inclination on fluid flow and heat transfer distributions.The turbulent air flows upward or downward into the duct with one wall heated from bottom.Calculation results with several kinds of k-εtype turbulence models were used to compare the experimental data with those in literatures to determine suitable model.The dependents of Nusselt number on the inclination angle of both the buoyancy-aided and buoyancy-opposed flow are discussed.展开更多
The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.展开更多
A geometrically similar scaling was made from small-scale specimen to full-scale stiffened panels and then their collapse behaviour is investigated. It is considered that the stiffened panel compressive ultimate stren...A geometrically similar scaling was made from small-scale specimen to full-scale stiffened panels and then their collapse behaviour is investigated. It is considered that the stiffened panel compressive ultimate strength test was designed according to geometrical scaling laws so that the output of the test could be used as representative of the stiffened panels of the compressive zone of a tanker hull subjected to vertical bending moment. The ultimate strength of a tanker hull is analysed by a FE analysis using the experimentally developed master stress-strain curves which are obtained by the beam tension test and the compressive test of the stiffened panel, and are then compared with the result achieved by the progressive collapse method.展开更多
Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal ...Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.展开更多
A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ...A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.展开更多
Isotopic neutron-source logging plays a great role in oil field exploration and development.Several isotopic neutron logging methods which detect gamma ray and neutron are summarized.It's introduced that the isoto...Isotopic neutron-source logging plays a great role in oil field exploration and development.Several isotopic neutron logging methods which detect gamma ray and neutron are summarized.It's introduced that the isotopic neutron logging tool import,independently develop,numerical simulate,data process and apply in China.Therefore,the prospect of neutron logging technology in future was pointed out.展开更多
Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely us...Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.展开更多
文摘Numerical study on turbulent mixed convection in inclined plane channels,from 15° to 90° (vertical),was carried out to examine the effect of inclination on fluid flow and heat transfer distributions.The turbulent air flows upward or downward into the duct with one wall heated from bottom.Calculation results with several kinds of k-εtype turbulence models were used to compare the experimental data with those in literatures to determine suitable model.The dependents of Nusselt number on the inclination angle of both the buoyancy-aided and buoyancy-opposed flow are discussed.
基金Supported by the 973 project of China （2013CB733600）, the National Natural Science Foundation （21176073）, the Doctoral Fund of Ministry of Education （20090074110005）, the New Century Excellent Talents in University （NCET-09-0346）, ＂Shu Guang＂ project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
基金financed by the Portuguese Foundation for Science and Technology (Fundao para a Ciência e Tecnologia), under contract SFRH/BD/65120/2009
文摘A geometrically similar scaling was made from small-scale specimen to full-scale stiffened panels and then their collapse behaviour is investigated. It is considered that the stiffened panel compressive ultimate strength test was designed according to geometrical scaling laws so that the output of the test could be used as representative of the stiffened panels of the compressive zone of a tanker hull subjected to vertical bending moment. The ultimate strength of a tanker hull is analysed by a FE analysis using the experimentally developed master stress-strain curves which are obtained by the beam tension test and the compressive test of the stiffened panel, and are then compared with the result achieved by the progressive collapse method.
基金Project(1390/2)supported by Khuzestan Gas Company,Iran
文摘Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.
文摘A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.
文摘Isotopic neutron-source logging plays a great role in oil field exploration and development.Several isotopic neutron logging methods which detect gamma ray and neutron are summarized.It's introduced that the isotopic neutron logging tool import,independently develop,numerical simulate,data process and apply in China.Therefore,the prospect of neutron logging technology in future was pointed out.
基金supported by the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2011AA040202)the National Natural Science Foundation of China(Grant No.40976114)
文摘Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.
