In 2021,EAST realized a steady-state long pulse with a duration over 100 s and a core electron temperature over 10 keV.This is an integrated operation that resolves several key issues,including active control of wall ...In 2021,EAST realized a steady-state long pulse with a duration over 100 s and a core electron temperature over 10 keV.This is an integrated operation that resolves several key issues,including active control of wall conditioning,long-lasting fully noninductive current and divertor heat/particle flux.The fully noninductive current is driven by pure radio frequency(RF)waves with a lower hybrid current drive power of 2.5 MW and electron cyclotron resonance heating of 1.4 MW.This is an excellent experimental platform on the timescale of hundreds of seconds for studying multiscale instabilities,electron-dominant transport and particle recycling(plasma-wall interactions)under weak collisionality.展开更多
Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE...Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.展开更多
基金the National Key R&D Program of China(No.2022YFE03010003)National Natural Science Foundation of China(No.12275309).
文摘In 2021,EAST realized a steady-state long pulse with a duration over 100 s and a core electron temperature over 10 keV.This is an integrated operation that resolves several key issues,including active control of wall conditioning,long-lasting fully noninductive current and divertor heat/particle flux.The fully noninductive current is driven by pure radio frequency(RF)waves with a lower hybrid current drive power of 2.5 MW and electron cyclotron resonance heating of 1.4 MW.This is an excellent experimental platform on the timescale of hundreds of seconds for studying multiscale instabilities,electron-dominant transport and particle recycling(plasma-wall interactions)under weak collisionality.
基金This work is supported by the National MCF Energy R&D Program of China(Grant Nos.2018YFE0302100 and 2019YFE03010003)the National Natural Science Foundation of China(Grant Nos.12005264,12105322,and 12075285)+3 种基金the National Magnetic Confinement Fusion Science Program of China(Grant No.2022YFE03100003)the Natural Science Foundation of Anhui Province of China(Grant No.2108085QA38)the Chinese Postdoctoral Science Found(Grant No.2021000278)the Presidential Foundation of Hefei institutes of Physical Science(Grant No.YZJJ2021QN12).
文摘Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.
