The identification of magnetohydrodynamic(MHD)modes is a crucial issue in the control of magnetically confined plasmas.This paper proposes a novel method for identifying the evolution of MHD modes from a signal with a...The identification of magnetohydrodynamic(MHD)modes is a crucial issue in the control of magnetically confined plasmas.This paper proposes a novel method for identifying the evolution of MHD modes from a signal with a low signal-to-noise ratio.The proposed method generates a weighted directed graph from the time-frequency spectrum and calculates the evolution of the mode frequency by solving the shortest path.This method addresses the limitations posed by the lack of data channels and the disturbance of noise in the estimation of mode frequency and yields much better results compared to traditional methods.It is demonstrated that,using this method,the evolution of an unlocked tearing mode was more accurately calculated on the J-TEXT tokamak.This method remains feasible even with a low signal-to-noise ratio of 0.5,as shown by its uncertainty.Furthermore,with appropriate parameters,this method can be applied to not only signals with MHD modes,but also to general signals with continuous modes.展开更多
Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current d...Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem.The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other tokamaks.Based on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning method.Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.展开更多
The phase difference Δξ between locked islands(2/1 and 3/1) has been found to influence the heat transport on the thermal quench during disruptions by numerical modeling [Hu Q et al 2019Nucl.Fusion 59,016005].To ver...The phase difference Δξ between locked islands(2/1 and 3/1) has been found to influence the heat transport on the thermal quench during disruptions by numerical modeling [Hu Q et al 2019Nucl.Fusion 59,016005].To verify this experimentally,a set of resonant magnetic perturbation(RMP) coils is required to excite coupled magnetic islands with different Δξ.The spectrum analysis shows that the current RMP coils on J-TEXT can only produce sufficient 2/1 and 3/1RMP fields with a limited phase difference of Δξ∈[-75°,75°].In order to broaden the adjustable range of Δξ,a set of coils on the high field side(HFS) is proposed to generate 2/1 and 3/1 RMP fields with Δξ=180°.As a result,RMPs with adjustable Δξ∈[-180°,180°] and sufficient amplitudes could be achieved by applying the HFS coils and the low field side(LFS)coils.This work provides a feasible solution for flexible adjustment of the phase difference between m and m+1 RMP,which might facilitate the study of major disruptions and their control.展开更多
The spectrum effect on the penetration of resonant magnetic perturbation(RMP) is studied with upgraded in-vessel RMP coils on J-TEXT.The poloidal spectrum of the RMP field,especially the amplitudes of 2/1 and 3/1 comp...The spectrum effect on the penetration of resonant magnetic perturbation(RMP) is studied with upgraded in-vessel RMP coils on J-TEXT.The poloidal spectrum of the RMP field,especially the amplitudes of 2/1 and 3/1 components,can be varied by the phase difference between the upper and lower coil rows,ΔΦ=Φ_(top)-Φ_(bottom),where Φ_(top)and Φ_(bottom)are the toroidal phases of the n=1 field of each coil row.The type of RMP penetration is found to be related to ΔΦ,including the RMP penetration of either 2/1 or 3/1 RMP and the successive penetrations of 3/1 RMP followed by the 2/1 RMP.For cases with penetration of only one RMP component,the penetration thresholds measured by the corresponding resonant component are close for variousΔΦ.However,the 2/1 RMP penetration threshold is significantly reduced if the 3/1 locked island is formed in advance.The changes in the rotation profile due to 3/1 locked island formation could partially contribute to the reduction of the 2/1 thresholds.展开更多
The J-TEXT three-wave polarimeter-interferometer system(POLARIS),which measures timespace distribution of electron density and current density,has been optimized with both the optical system and the equilibrium recons...The J-TEXT three-wave polarimeter-interferometer system(POLARIS),which measures timespace distribution of electron density and current density,has been optimized with both the optical system and the equilibrium reconstruction method.The phase resolution of a Faraday rotation angle has been improved from 0.1 to 0.06 degree in chords from–0.18 to 0.18 m(plasma minor radius),and the sawtooth oscillation behavior has been detected by Faraday rotation angle measurement.By combining the POLARIS measured data and the equilibrium and fitting code(EFIT),an upgraded equilibrium reconstruction method has been developed,which provides a more accurate temporal and spatial distribution of current density and electron density.By means of the optimized POLARIS and improved equilibrium reconstruction,variations of profiles with increasing density have been carried out,under both Ohmic and electron cyclotron resonance heating discharges.展开更多
Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT.Since 2013,various kinds of traditional machine learning,as well as deep learning methods have been applie...Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT.Since 2013,various kinds of traditional machine learning,as well as deep learning methods have been applied to fusion plasma experiments.Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods.For disruption prediction,we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system.After years of study,nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time.Furthermore,cross-device disruption prediction methods have obtained promising results.Interpretable analysis of the models are studied.For diagnostics data processing,efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system.Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments.The models have been cooperating with the plasma control system and other systems,to make joint decisions to further support the experiments.展开更多
The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor.When some diagnostics fail to detect information about the plasma status,such as electron tempe...The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor.When some diagnostics fail to detect information about the plasma status,such as electron temperature,they can also be obtained by another method:fitted by other diagnostic signals through machine learning.The paper herein is based on a machine learning method to predict electron temperature,in case the diagnostic systems fail to detect plasma temperature.The fully-connected neural network,utilizing back propagation with two hidden layers,is utilized to estimate plasma electron temperature approximately on the J-TEXT.The input parameters consist of soft x-ray emission intensity,electron density,plasma current,loop voltage,and toroidal magnetic field,while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer.Therefore,the temperature profile is reconstructed by other diagnostic signals,and the average errors are within 5%.In addition,generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy.Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.展开更多
Magnetohydrodynamic(MHD)instabilities are widely observed during tokamak plasma operation.Magnetic diagnostics provide important information which supports the understanding and control of MHD instabilities.This paper...Magnetohydrodynamic(MHD)instabilities are widely observed during tokamak plasma operation.Magnetic diagnostics provide important information which supports the understanding and control of MHD instabilities.This paper presents the current status of the magnetic diagnostics dedicated to measuring MHD instabilities at the J-TEXT tokamak;the diagnostics consist of five Mirnov probe arrays for measuring high-frequency magnetic perturbations and two saddle-loop arrays for low-frequency magnetic perturbations,such as the locked mode.In recent years,several changes have been made to these arrays.The structure of the probes in the poloidal Mirnov arrays has been optimized to improve their mechanical strength,and the number of in-vessel saddle loops has also been improved to support better spatial resolution.Due to the installation of high-field-side(HFS)divertor targets in early 2019,some of the probes were removed,but an HFS Mirnov array was designed and installed behind the targets.Owing to its excellent toroidal symmetry,the HFS Mirnov array has,for the first time at J-TEXT,provided valuable new information about the locked mode and the quasi-static mode(QSM)in the HFS.Besides,various groups of magnetic diagnostics at different poloidal locations have been systematically used to measure the QSM,which confirmed the poloidal mode number m and the helical structure of the QSM.By including the HFS information,the 2/1 resonant magnetic perturbation(RMP)-induced locked mode was measured to have a poloidal mode number m of~2.展开更多
基金supported by the Hubei Provincial Natural Science Foundation of China(No.BZQ22006)National Natural Science Foundation of China(Nos.51977221 and 51821005)。
文摘The identification of magnetohydrodynamic(MHD)modes is a crucial issue in the control of magnetically confined plasmas.This paper proposes a novel method for identifying the evolution of MHD modes from a signal with a low signal-to-noise ratio.The proposed method generates a weighted directed graph from the time-frequency spectrum and calculates the evolution of the mode frequency by solving the shortest path.This method addresses the limitations posed by the lack of data channels and the disturbance of noise in the estimation of mode frequency and yields much better results compared to traditional methods.It is demonstrated that,using this method,the evolution of an unlocked tearing mode was more accurately calculated on the J-TEXT tokamak.This method remains feasible even with a low signal-to-noise ratio of 0.5,as shown by its uncertainty.Furthermore,with appropriate parameters,this method can be applied to not only signals with MHD modes,but also to general signals with continuous modes.
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFE03040004)the National Natural Science Foundation of China (Grant No. 51821005)
文摘Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem.The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other tokamaks.Based on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning method.Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.
基金supported by the National Magnetic Confinement Fusion Energy R & D Program of China (Nos. 2018YFE0309102 and 2019YFE03010004)National Natural Science Foundation of China (Nos.12075096, 11905078,and 51821005)
文摘The phase difference Δξ between locked islands(2/1 and 3/1) has been found to influence the heat transport on the thermal quench during disruptions by numerical modeling [Hu Q et al 2019Nucl.Fusion 59,016005].To verify this experimentally,a set of resonant magnetic perturbation(RMP) coils is required to excite coupled magnetic islands with different Δξ.The spectrum analysis shows that the current RMP coils on J-TEXT can only produce sufficient 2/1 and 3/1RMP fields with a limited phase difference of Δξ∈[-75°,75°].In order to broaden the adjustable range of Δξ,a set of coils on the high field side(HFS) is proposed to generate 2/1 and 3/1 RMP fields with Δξ=180°.As a result,RMPs with adjustable Δξ∈[-180°,180°] and sufficient amplitudes could be achieved by applying the HFS coils and the low field side(LFS)coils.This work provides a feasible solution for flexible adjustment of the phase difference between m and m+1 RMP,which might facilitate the study of major disruptions and their control.
基金supported by the National Magnetic Confinement Fusion Energy R&D Program of China(Nos.2019YFE03010004,2018YFE0309100)the National Key R&D Program of China(No.2017YFE0301100)National Natural Science Foundation of China(Nos.11905078,12075096 and 51821005)
文摘The spectrum effect on the penetration of resonant magnetic perturbation(RMP) is studied with upgraded in-vessel RMP coils on J-TEXT.The poloidal spectrum of the RMP field,especially the amplitudes of 2/1 and 3/1 components,can be varied by the phase difference between the upper and lower coil rows,ΔΦ=Φ_(top)-Φ_(bottom),where Φ_(top)and Φ_(bottom)are the toroidal phases of the n=1 field of each coil row.The type of RMP penetration is found to be related to ΔΦ,including the RMP penetration of either 2/1 or 3/1 RMP and the successive penetrations of 3/1 RMP followed by the 2/1 RMP.For cases with penetration of only one RMP component,the penetration thresholds measured by the corresponding resonant component are close for variousΔΦ.However,the 2/1 RMP penetration threshold is significantly reduced if the 3/1 locked island is formed in advance.The changes in the rotation profile due to 3/1 locked island formation could partially contribute to the reduction of the 2/1 thresholds.
基金the National MCF Energy R&D Program of China(No.2018YFE0310300)National Natural Science Foundation of China(Nos.11905080 and 51821005)。
文摘The J-TEXT three-wave polarimeter-interferometer system(POLARIS),which measures timespace distribution of electron density and current density,has been optimized with both the optical system and the equilibrium reconstruction method.The phase resolution of a Faraday rotation angle has been improved from 0.1 to 0.06 degree in chords from–0.18 to 0.18 m(plasma minor radius),and the sawtooth oscillation behavior has been detected by Faraday rotation angle measurement.By combining the POLARIS measured data and the equilibrium and fitting code(EFIT),an upgraded equilibrium reconstruction method has been developed,which provides a more accurate temporal and spatial distribution of current density and electron density.By means of the optimized POLARIS and improved equilibrium reconstruction,variations of profiles with increasing density have been carried out,under both Ohmic and electron cyclotron resonance heating discharges.
基金supported by the National Key R&D Program of China(No.2022YFE03040004)National Natural Science Foundation of China(No.51821005)
文摘Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT.Since 2013,various kinds of traditional machine learning,as well as deep learning methods have been applied to fusion plasma experiments.Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods.For disruption prediction,we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system.After years of study,nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time.Furthermore,cross-device disruption prediction methods have obtained promising results.Interpretable analysis of the models are studied.For diagnostics data processing,efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system.Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments.The models have been cooperating with the plasma control system and other systems,to make joint decisions to further support the experiments.
基金supported by the National Magnetic Confinement Fusion Science Program(Nos.2018YFE0301104,2018YFE0301100)State Key Laboratory of Advanced Electromagnetic Engineering and Technology(No.AEET2020KF001)National Natural Science Foundation of China(Nos.12075096,51821005)。
文摘The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion reactor.When some diagnostics fail to detect information about the plasma status,such as electron temperature,they can also be obtained by another method:fitted by other diagnostic signals through machine learning.The paper herein is based on a machine learning method to predict electron temperature,in case the diagnostic systems fail to detect plasma temperature.The fully-connected neural network,utilizing back propagation with two hidden layers,is utilized to estimate plasma electron temperature approximately on the J-TEXT.The input parameters consist of soft x-ray emission intensity,electron density,plasma current,loop voltage,and toroidal magnetic field,while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal spectrometer.Therefore,the temperature profile is reconstructed by other diagnostic signals,and the average errors are within 5%.In addition,generalized regression neural network can also achieve this function to estimate the temperature profile with similar accuracy.Predicting electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.
基金supported by the National MCF Energy R&D Program of China(No.2018YFE0309100)National Natural Science Foundation of China(NSFC)(No.11905078)‘the Fundamental Research Funds for the Central Universities’(No.2020kfy XJJS003)。
文摘Magnetohydrodynamic(MHD)instabilities are widely observed during tokamak plasma operation.Magnetic diagnostics provide important information which supports the understanding and control of MHD instabilities.This paper presents the current status of the magnetic diagnostics dedicated to measuring MHD instabilities at the J-TEXT tokamak;the diagnostics consist of five Mirnov probe arrays for measuring high-frequency magnetic perturbations and two saddle-loop arrays for low-frequency magnetic perturbations,such as the locked mode.In recent years,several changes have been made to these arrays.The structure of the probes in the poloidal Mirnov arrays has been optimized to improve their mechanical strength,and the number of in-vessel saddle loops has also been improved to support better spatial resolution.Due to the installation of high-field-side(HFS)divertor targets in early 2019,some of the probes were removed,but an HFS Mirnov array was designed and installed behind the targets.Owing to its excellent toroidal symmetry,the HFS Mirnov array has,for the first time at J-TEXT,provided valuable new information about the locked mode and the quasi-static mode(QSM)in the HFS.Besides,various groups of magnetic diagnostics at different poloidal locations have been systematically used to measure the QSM,which confirmed the poloidal mode number m and the helical structure of the QSM.By including the HFS information,the 2/1 resonant magnetic perturbation(RMP)-induced locked mode was measured to have a poloidal mode number m of~2.
