Thermoelectric effect is the most efficient way to convert electric energy directly from the temperature gradient. Thermoelectric effect-based power generation, cooling and heating devices are solid-stated, environmen...Thermoelectric effect is the most efficient way to convert electric energy directly from the temperature gradient. Thermoelectric effect-based power generation, cooling and heating devices are solid-stated, environmentally friendly, reliable, long-lived, easily maintainable, and easy to achieve miniaturization and integration. So they have unparalleled advantages in the aerospace, vehicle industry, waste heat recovery, electronic cooling, etc. This paper reviews the progress in thermodynamic analyses and optimizations for single- and multiple-element, single- and multiple-stage, and combined thermoelectric generators, thermoelectric refrigerators and thermoelectric heat pumps, especially in the aspects of non-equilibrium thermodynamics and finite time thermodynamics. It also discusses the developing trends of thermoelectric devices, such as the heat sources of thermoelectric generators, multi-stage thermoelectric devices, combined thermoelectric devices, and heat transfer enhancement of thermoelectric devices.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
In order to measure the axial flowing velocity of carbon particle suspension with particle diameter of tens of micrometers, the photoacoustic Doppler(PAD) frequency shift is calculated based on a series of individual ...In order to measure the axial flowing velocity of carbon particle suspension with particle diameter of tens of micrometers, the photoacoustic Doppler(PAD) frequency shift is calculated based on a series of individual A scans using an autocorrelation method. A 532 nm pulsed laser with repetition rate of 20 Hz is used as a pumping source to generate photoacoustic signal. The photoacoustic signals are detected using a focused piezoelectric(PZT) ultrasound transducer with central frequency of 5 MHz. The suspension of carbon particles is driven by a syringe pump. The complex photoacoustic signal is calculated by the Hilbert transformation from time-domain photoacoustic signal, and then it is autocorrelated to calculate the Doppler frequency shift. The photoacoustic Doppler frequency shift is calculated by averaging the autocorrelation results of some individual A scans. The advantage of the autocorrelation method is that the time delay in autocorrelation can be defined by user, and the requirement of high pulse repetition rate is avoided. The feasibility of the proposed autocorrelation method is preliminarily demonstrated by quantifying the motion of a carbon particle suspension with flow velocity from 5 mm/s to 60 mm/s. The experimental results show that there is an approximately linear relation between the autocorrelation result and the setting velocity.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.11305266&51576207)the National Basic Research Program of China("973"Project)(Grant No.2012CB720405)
文摘Thermoelectric effect is the most efficient way to convert electric energy directly from the temperature gradient. Thermoelectric effect-based power generation, cooling and heating devices are solid-stated, environmentally friendly, reliable, long-lived, easily maintainable, and easy to achieve miniaturization and integration. So they have unparalleled advantages in the aerospace, vehicle industry, waste heat recovery, electronic cooling, etc. This paper reviews the progress in thermodynamic analyses and optimizations for single- and multiple-element, single- and multiple-stage, and combined thermoelectric generators, thermoelectric refrigerators and thermoelectric heat pumps, especially in the aspects of non-equilibrium thermodynamics and finite time thermodynamics. It also discusses the developing trends of thermoelectric devices, such as the heat sources of thermoelectric generators, multi-stage thermoelectric devices, combined thermoelectric devices, and heat transfer enhancement of thermoelectric devices.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金supported by the Joint Funds of the National Natural Science Foundation of China(No.U1204612)Natural Science Foundation of He’nan Educational Committee(No.13A416180)
文摘In order to measure the axial flowing velocity of carbon particle suspension with particle diameter of tens of micrometers, the photoacoustic Doppler(PAD) frequency shift is calculated based on a series of individual A scans using an autocorrelation method. A 532 nm pulsed laser with repetition rate of 20 Hz is used as a pumping source to generate photoacoustic signal. The photoacoustic signals are detected using a focused piezoelectric(PZT) ultrasound transducer with central frequency of 5 MHz. The suspension of carbon particles is driven by a syringe pump. The complex photoacoustic signal is calculated by the Hilbert transformation from time-domain photoacoustic signal, and then it is autocorrelated to calculate the Doppler frequency shift. The photoacoustic Doppler frequency shift is calculated by averaging the autocorrelation results of some individual A scans. The advantage of the autocorrelation method is that the time delay in autocorrelation can be defined by user, and the requirement of high pulse repetition rate is avoided. The feasibility of the proposed autocorrelation method is preliminarily demonstrated by quantifying the motion of a carbon particle suspension with flow velocity from 5 mm/s to 60 mm/s. The experimental results show that there is an approximately linear relation between the autocorrelation result and the setting velocity.
