2021
DOI: 10.1088/2631-8695/ac3fa1
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Experimental study of thermal limits with one-side heated smooth channel for plasma facing component safety

Abstract: In order to stably operate the equipment inside the tokamak, which is loaded with a heat flux of several MW/m2 under the one-side heating condition, it is necessary to thoroughly prepare for various thermal engineering limits that may occur under the high heat flux load condition. In this study, we have experimentally explored critical heat flux (CHF) and onset of flow instability (OFI), which are considered potential threats in a DEMO fusion power plant. Specifically, the effect of system parameters on CHF wa… Show more

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Cited by 14 publications
(2 citation statements)
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“…To address this issue, the RANdom SAMple consensus (RANSAC) algorithm was used in our regression analysis. RANSAC has the characteristic of completely ignoring data above a certain threshold, allowing for the mitigation of the influence of outliers and the derivation of an optimal model supported by the bulk of the data [53,54]. The RANSAC algorithm operates under the following assumptions: (1) that the dataset contains outliers; (2) that the dataset can be expressed using mathematical model factors; (3) that inlier data can be incorrectly determined to be outliers as a result of extreme values, incorrect measurement of data, or incorrect assumptions about data interpretation; and (4) that there is an algorithm that can predict the model parameters or fit the data in a given set of inliers.…”
Section: Python Code Developmentmentioning
confidence: 99%
“…To address this issue, the RANdom SAMple consensus (RANSAC) algorithm was used in our regression analysis. RANSAC has the characteristic of completely ignoring data above a certain threshold, allowing for the mitigation of the influence of outliers and the derivation of an optimal model supported by the bulk of the data [53,54]. The RANSAC algorithm operates under the following assumptions: (1) that the dataset contains outliers; (2) that the dataset can be expressed using mathematical model factors; (3) that inlier data can be incorrectly determined to be outliers as a result of extreme values, incorrect measurement of data, or incorrect assumptions about data interpretation; and (4) that there is an algorithm that can predict the model parameters or fit the data in a given set of inliers.…”
Section: Python Code Developmentmentioning
confidence: 99%
“…To overcome this limitation, the random sample consensus (RANSAC) algorithm has been added to the linear regression. Because this algorithm adopts a model supported by a large amount of data as the final product, it is possible to minimize the influence of outliers [54,55]. Finally, the Python code is designed to go through an iteration process so that the code can learn through machine learning through the linear regression analysis process evaluated in the previous step.…”
Section: Python Code With Ransac Algorithmmentioning
confidence: 99%