M. Sertoli, A. Alieva, P. F. Buxton, Aleksei Dnestrovskii, Michael Gemmell, Hazel Lowe, Thomas O'Gorman, Dmitry Osin, A. Sladkomedova, J. Varje, H. Willett, Jonathan Wood, B. Lomanowski, Ephrem Delabie, Oleksandr Marchuk, E. Litherland-Smith, Kingsley Collie, Sanket Gadgil
{"title":"从最小可行产品到完整模型:逐步发展诊断前向模型,以支持 ST40 托卡马克的设计、分析和建模工作","authors":"M. Sertoli, A. Alieva, P. F. Buxton, Aleksei Dnestrovskii, Michael Gemmell, Hazel Lowe, Thomas O'Gorman, Dmitry Osin, A. Sladkomedova, J. Varje, H. Willett, Jonathan Wood, B. Lomanowski, Ephrem Delabie, Oleksandr Marchuk, E. Litherland-Smith, Kingsley Collie, Sanket Gadgil","doi":"10.1088/1361-6587/ad668f","DOIUrl":null,"url":null,"abstract":"\n Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products (MVP) are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021-22 experimental campaign.","PeriodicalId":510623,"journal":{"name":"Plasma Physics and Controlled Fusion","volume":"124 42","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak\",\"authors\":\"M. Sertoli, A. Alieva, P. F. Buxton, Aleksei Dnestrovskii, Michael Gemmell, Hazel Lowe, Thomas O'Gorman, Dmitry Osin, A. Sladkomedova, J. Varje, H. Willett, Jonathan Wood, B. Lomanowski, Ephrem Delabie, Oleksandr Marchuk, E. Litherland-Smith, Kingsley Collie, Sanket Gadgil\",\"doi\":\"10.1088/1361-6587/ad668f\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products (MVP) are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021-22 experimental campaign.\",\"PeriodicalId\":510623,\"journal\":{\"name\":\"Plasma Physics and Controlled Fusion\",\"volume\":\"124 42\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plasma Physics and Controlled Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6587/ad668f\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plasma Physics and Controlled Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1361-6587/ad668f","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak
Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products (MVP) are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021-22 experimental campaign.