{"id":7512,"date":"2020-12-01T11:50:26","date_gmt":"2020-12-01T11:50:26","guid":{"rendered":"https:\/\/www.istp.cnr.it\/?post_type=product&#038;p=7512"},"modified":"2022-11-10T23:08:51","modified_gmt":"2022-11-10T23:08:51","slug":"alternative-detection-of-n1-modes-slowing-down-on-asdex-upgrade","status":"publish","type":"product","link":"https:\/\/www.istp.cnr.it\/it\/research-product\/alternative-detection-of-n1-modes-slowing-down-on-asdex-upgrade\/","title":{"rendered":"Alternative Detection of n=1 Modes Slowing Down on ASDEX Upgrade"},"content":{"rendered":"<p>Disruptions in tokamaks are very often associated with the slowing down of magneto-hydrodynamic (MHD) instabilities and their subsequent locking to the wall. To improve the understanding of the chain of events ending with a disruption, a statistically robust and physically based criterion has been devised to track the slowing down of modes with toroidal mode numbers n = 1 and mostly poloidal mode number m = 2, providing an alternative and earlier detection tool compared to simple threshold based indicators. A database of 370 discharges of axially symmetric divertor experiment&#8211;upgrade (AUG) has been studied and results compared with other indicators used in real time. The estimator is based on a weighted average value of the fast Fourier transform of the perturbed radial n = 1 magnetic field, caused by the rotation of the modes. The use of a carrier sinusoidal wave helps alleviating the spurious influence of non-sinusoidal magnetic perturbations induced by other instabilities like Edge localized modes (ELMs). The indicator constitutes a good candidate for further studies including machine learning approaches for mitigation and avoidance since, by deploying it systematically to evaluate the time instance for the expected locking, multi-machine databases can be populated. Furthermore, it can be thought as a contribution to a wider approach to dynamically tracking the chain of events leading to disruptions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Peluso, Emmanuele; Rossi, Riccardo; Murari, Andrea; Gaudio, Pasqualino; Gelfusa, Michela<\/p>\n","protected":false},"featured_media":1294,"comment_status":"closed","ping_status":"open","template":"","meta":[],"product_cat":[574],"product_tag":[1184,2068,2402,2700,2701],"class_list":["post-7512","product","type-product","status-publish","has-post-thumbnail","hentry","product_cat-journal-articles","product_tag-mhd-instabilities","product_tag-magnetic-islands","product_tag-disruptions","product_tag-disruption-avoidance","product_tag-mode-locking","prodpage-style2"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product\/7512","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/comments?post=7512"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media\/1294"}],"wp:attachment":[{"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/media?parent=7512"}],"wp:term":[{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_cat?post=7512"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.istp.cnr.it\/it\/wp-json\/wp\/v2\/product_tag?post=7512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}