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A Surrogate Model for a CAES Radial Inflow Turbine with Test Data-Based mlp neural network Algorithm

Journal of Thermal Science 2023年第6期32卷 2081-2092页

作者： WANG Xing ZHU Yangli LI Wen ZUO Zhitao CHEN Haisheng National Energy Large Scale Physical Energy Storage Technologies R&D Center(Bijie) Bijie551712China Institute of Engineering Thermophysics Chinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing101408China

It is usually to conduct a full-scale three-dimensional flow analysis for a radial turbine to find a way to increase the efficiency of a Compressed Air Energy Storage(CAES)***,long solving time and huge consumption of computing resources become a major obstacle to the ***,in present study,a surrogate model with test data-based multi-layer perceptron(mlp)neural network is proposed to overcome the *** of complex flow field solving process,it provides reliable turbine aerodynamic performance and flow field distribution characteristics in a short solution time by“learning the measurement results”.The validation results illustrated that the predicted maximum relative errors of isentropic efficiency,corrected mass flow rate and corrected power are only 0.03%,0.22%and 0.26%*** predicted flow distribution parameters in chamber,shroud cavity and outlet region of rotor are also basically consistent with the experimental *** the chamber,it can be found that a pressure stagnation point is observed at circumferential angle of 270°when total pressure ratio is *** the shroud cavity,obvious pressure variation is found near outlet of shroud cavity which although labyrinth seals *** outlet of rotor,obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade *** the same time,obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height and this is because the influence of upper passage vortex,lower passage vortex and end wall secondary *** present study can provide further reference for the dynamic performance evaluation of CAES radial inflow turbine.

关键词： CAES surrogate model radial inflow turbine mlp neural network

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Mapping salt diapirs and salt diapir-affected areas using mlp neural network model and ASTER data

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International Journal of Digital Earth 2013年第2期6卷 143-157页

作者： Mohammad H.Tayebi Majid H.Tangestani Hasan Roosta Department of Earth Sciences Shiraz UniversityShirazIran Department of Civil Engineering Islamic Azad UniversityLarestanIran

This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron(mlp)in the Zagros Folded Belt,Iran,and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact *** neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data,and the results were compared using confusion *** most appropriate classification image for L1B input to mlp was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%.The mlp result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs,*** observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified *** is concluded that mlp is an efficient approach for mapping salt diapirs and salt-affected areas.

关键词： remote sensing digital earth digital image classification mlp neural network lithological mapping salt diapir ASTER Zagros Iran Geology image processing

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Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation

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Computers, Materials & Continua 2022年第5期71卷 4033-4050页

作者： Walid Aydi Fuad S.Alduais Department of Computer Science College of Humanities and Science in Al AflajPrince Sattam Bin Abdulaziz UniversityAl-AflajSaudi Arabia Department of Mathematics College of Humanities and Science in Al AflajPrince Sattam Bin Abdulaziz UniversityAl-AflajSaudi Arabia Laboratory of Electronics&Information Technologies Sfax UniversitySfaxTunisia Department of Administration Administrative Science CollegeThamar UniversityYemen

The Weibull distribution is regarded as among the finest in the family of failure *** of the most commonly used parameters of the Weibull distribution(WD)is the ordinary least squares(OLS)technique,which is useful in reliability and lifetime *** this study,we propose an approach based on the ordinary least squares and the multilayer perceptron(mlp)neural network called the OLSmlp that is based on the resilience of the OLS *** mlp solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers,and eases the difficulty of determining weights in case of the weighted least square(WLS).Another method is proposed by incorporating a weight into the general entropy(GE)loss function to estimate the parameters of the WD to obtain a modified loss function(WGE).Furthermore,a Monte Carlo simulation is performed to examine the performance of the proposed OLSmlp method in comparison with approximate Bayesian estimation(BLWGE)by using a weighted GE loss *** results of the simulation showed that the two proposed methods produced good estimates even for small sample *** addition,the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods,in terms of the mean squared error and requirements related to time.

关键词： Weibull distribution maximum likelihood ordinary least squares mlp neural network weighted general entropy loss function

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Application of neural network in modeling of activated sludge wastewater treatment process

Application of neural network in modeling of activated sludg...

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第36届中国控制会议

作者： Li Jun Zhang Neng Li Jing Jing Zhang Xi Yong Tian School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

This paper present a prediction model for three different objection(the airflow rate,the carbonaceous biochemical oxygen demand（CBOD） of the effluent,and the total suspend solids（TSS） of the *** model is built by the mlp neural *** accurancy of the prediction result of mlp neural network is compared with the accurancy of the result of trational stational autoregressive model（AR）.The conclution is that the percentage error prediction model of the mlp neural network（PE）,the fractional deviation（FB）,normalized mean square error（NMSE）,the mean absolute error（MAE） and mean square error（MSE） prediction model of evaluation index is better than the AR *** other words,the prediction model based on mlp neural network provides a reliable basis for reducing the energy consumption in the activated sludge process of industrial waste water treatment and further improving its effect on the treatment of industrial waste water.

关键词： Industrial waste water treatment Activated sludge process mlp neural network Data mining

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Chebyshev neural network Observer Based RBF neural network Terminal Sliding Mode Controller for a Class of Nonlinear System

Chebyshev Neural Network Observer Based RBF Neural Network T...

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第30届中国控制与决策会议

作者： Hadjer Sioud Amin Sharafin Karam Eliker Weidong Zhang Department of Automation Shanghai Jiao Tong University School of Mechatronic Engineering and Automation Shanghai University

This paper focuses on an observer-based controller for a class of nonlinear *** order to solve the problem of unknown dynamic in practical systems,two different neural networks（NNs） for observer and controller parts are *** the observer part,a Multi-Layer Perceptron（mlp） neural network based on error back propagation algorithm is applied to update the weights of neural network that will be used to estimate the system *** the controller part,Radial Basis Function neural network（RBFNN） with terminal sliding mode controller,based on a new sliding surface,is combined to achieve a finite time stability and fast convergence *** update law in the controller part is related to the proposed sliding *** stability of the closed loop system is analyzed by Lyapunov stability *** simulation,the proposed technique is applied to a chaotic system to obtain the effectiveness of the proposed approach.

关键词： mlp neural network RBF neural network Terminal Sliding Mode Sliding Surface State Estimation Observer

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