Matches in SemOpenAlex for { <https://semopenalex.org/work/W2973000350> ?p ?o ?g. }
- W2973000350 endingPage "014021" @default.
- W2973000350 startingPage "014021" @default.
- W2973000350 abstract "Fusion performance in tokamaks depends on the core and edge regions as well as on their non-linear feedbacks. The achievable degree of edge confinement under the constraints of power handling in presence of a metallic wall is still an open question. Therefore, any improvement in the core temperature and density peaking is crucial for achieving target performance. This has motivated further progress in understanding core turbulent transport mechanisms, to help scenario development in present devices and improve predictive tools for ITER operations. In the last two decades, detailed experiments and their interpretation via the gyrokinetic theory of turbulent transport have led to a satisfactory level of understanding of the heat, particle, and momentum transport channels and of their mutual interactions. This paper presents some highlights of the progress, which stems from joint work of several devices and theory groups, in Europe and worldwide within the ITPA (International Tokamak Physics Activities) frame-work. On the other hand, the achievement of predictive capabilities of plasma profiles via integrated modeling, which also accounts for the nonlinear interactions inherent to the multi-channel nature of transport, is a priority in view of ITER. This requires using faster, reduced models, and the extent to which they capture the complex physics described by nonlinear gyrokinetics must be carefully evaluated. Present quasi-linear models match well experiments in baseline scenarios, and thus offer reliable predictions for the ITER reference scenario, but have issues in advanced scenarios. Some of these challenges are examined and discussed. In the longer term, advances in high performance computing will continue to drive physics discovery through increasingly complex gyrokinetic simulations, allowing also further development of reduced models. The development of neural network surrogate models is another recent advance that bridges the gap towards physics-based fast models for optimisation and control applications." @default.
- W2973000350 created "2019-09-19" @default.
- W2973000350 creator A5015061883 @default.
- W2973000350 creator A5019766649 @default.
- W2973000350 creator A5033471033 @default.
- W2973000350 creator A5034362339 @default.
- W2973000350 creator A5048055685 @default.
- W2973000350 creator A5058508661 @default.
- W2973000350 creator A5063142899 @default.
- W2973000350 creator A5066056395 @default.
- W2973000350 creator A5078712021 @default.
- W2973000350 creator A5081608139 @default.
- W2973000350 date "2019-12-06" @default.
- W2973000350 modified "2023-10-01" @default.
- W2973000350 title "Progress and challenges in understanding core transport in tokamaks in support to ITER operations" @default.
- W2973000350 cites W1001888662 @default.
- W2973000350 cites W1597745259 @default.
- W2973000350 cites W1968704136 @default.
- W2973000350 cites W1969610810 @default.
- W2973000350 cites W1973896528 @default.
- W2973000350 cites W1979687371 @default.
- W2973000350 cites W1985682406 @default.
- W2973000350 cites W1986626506 @default.
- W2973000350 cites W1990665076 @default.
- W2973000350 cites W1992288426 @default.
- W2973000350 cites W1994835364 @default.
- W2973000350 cites W1997666441 @default.
- W2973000350 cites W2004434804 @default.
- W2973000350 cites W2009537451 @default.
- W2973000350 cites W2010600479 @default.
- W2973000350 cites W2011522146 @default.
- W2973000350 cites W2012242246 @default.
- W2973000350 cites W2028113377 @default.
- W2973000350 cites W2029169867 @default.
- W2973000350 cites W2032498212 @default.
- W2973000350 cites W2040916517 @default.
- W2973000350 cites W2041317506 @default.
- W2973000350 cites W2049817842 @default.
- W2973000350 cites W2052581806 @default.
- W2973000350 cites W2054249504 @default.
- W2973000350 cites W2054637546 @default.
- W2973000350 cites W2060261254 @default.
- W2973000350 cites W2081466567 @default.
- W2973000350 cites W2082361162 @default.
- W2973000350 cites W2085221569 @default.
- W2973000350 cites W2087430571 @default.
- W2973000350 cites W2088290263 @default.
- W2973000350 cites W2092337037 @default.
- W2973000350 cites W2100355352 @default.
- W2973000350 cites W2107069653 @default.
- W2973000350 cites W2124149128 @default.
- W2973000350 cites W2130879942 @default.
- W2973000350 cites W2138030864 @default.
- W2973000350 cites W2150787345 @default.
- W2973000350 cites W2162630125 @default.
- W2973000350 cites W2171536225 @default.
- W2973000350 cites W2197182578 @default.
- W2973000350 cites W2199212310 @default.
- W2973000350 cites W2279214691 @default.
- W2973000350 cites W2472169734 @default.
- W2973000350 cites W2473291359 @default.
- W2973000350 cites W2555567076 @default.
- W2973000350 cites W2575874086 @default.
- W2973000350 cites W2593529124 @default.
- W2973000350 cites W2605889225 @default.
- W2973000350 cites W2609523672 @default.
- W2973000350 cites W2622651081 @default.
- W2973000350 cites W2766426540 @default.
- W2973000350 cites W2767805767 @default.
- W2973000350 cites W2785247716 @default.
- W2973000350 cites W2787059193 @default.
- W2973000350 cites W2791558413 @default.
- W2973000350 cites W2793042843 @default.
- W2973000350 cites W2793691831 @default.
- W2973000350 cites W2801783822 @default.
- W2973000350 cites W2805843691 @default.
- W2973000350 cites W2890263589 @default.
- W2973000350 cites W2899729140 @default.
- W2973000350 cites W2904922941 @default.
- W2973000350 cites W2916945678 @default.
- W2973000350 cites W2969400576 @default.
- W2973000350 cites W2971741794 @default.
- W2973000350 cites W3101819494 @default.
- W2973000350 cites W3103997951 @default.
- W2973000350 cites W4211151981 @default.
- W2973000350 cites W4229793109 @default.
- W2973000350 cites W581154177 @default.
- W2973000350 doi "https://doi.org/10.1088/1361-6587/ab5ae1" @default.
- W2973000350 hasPublicationYear "2019" @default.
- W2973000350 type Work @default.
- W2973000350 sameAs 2973000350 @default.
- W2973000350 citedByCount "24" @default.
- W2973000350 countsByYear W29730003502020 @default.
- W2973000350 countsByYear W29730003502021 @default.
- W2973000350 countsByYear W29730003502022 @default.
- W2973000350 countsByYear W29730003502023 @default.
- W2973000350 crossrefType "journal-article" @default.
- W2973000350 hasAuthorship W2973000350A5015061883 @default.