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Panoramica delle principali modifiche delle ordinanze bio e dei marchi bio con spiegazioni.

Tous les changements des directives bio entrent en vigueur dès le 1er janvier 2022.

Die Publikation enthält die Änderungen mit Erläuterungen der Anforderungen ab 1. Januar 2022 für Schweizer Biobetriebe.

Panoramica delle principali modifiche delle ordinanze bio e dei marchi bio con spiegazioni. Il riassunto (senza spiegazioni) è inserito in forma stampata al centro di Bioattualità 10/2020.

Die Publikation enthält die Änderungen mit Erläuterungen der Anforderungen ab 1. Januar 2021 für Schweizer Biobetriebe.

Tous les changements des directives bio entrent en vigueur dès le 1er janvier 2021. La version courte (sans les explications) se trouve sous forme imprimée au milieu du Bioactualités 10/2020.

Turbulent convection flows in nature are often organized in regular large-scale patterns, which evolve slowly relative to to the typical convective timescale, and are arranged on spatial scales that are much larger than the layer height. Prominent examples are cloud streets in the atmosphere and granulation patterns in solar convection. This order in a fully developed turbulent flow is sometimes called turbulent superstructure in convection. Large-scale structure formation in turbulent Rayleigh-Benard convection recently became accessible in direct numerical simulations, which resolve all relevant scales of turbulence in horizontally extended domains with a large aspect ratio. Using DNS output we apply the dynamic Laplacian approach [Froyland, 2015] to identify these turbulent superstructures as finite-time coherent sets in both quasi-2D and fully three-dimensional settings. A modest number of trajectories are meshed and a finite-element based method is employed to numerically estimate the dynamic Laplacian [Froyland & Junge, 2018]. The coherent sets are encoded in the leading eigenvectors of the dynamic Laplacian, and the individual coherent sets are ``decoded'' using the recently developed Sparse Eigenbasis Approximation (SEBA) algorithm [Froyland, Rock, & Sakellariou, 2019] Extraction of nite-time coherent sets in 3D Rayleigh-BenardConvection using the dynamic LaplacianGARY FROYLAND, University ofNew South Wales, ANNA KLUENKER, KATHRIN PADBERG-GEHLE, CHRISTIANE SCHNEIDE, Leuphana University, JOERG SCHUMACHER, TechnicalUniversity Ilmenau | Turbulent convection ows in nature are often organized inregular large-scale patterns, which evolve slowly relative to to the typical convectivetimescale, and are arranged on spatial scales that are much larger than the layerheight. Prominent examples are cloud streets in the atmosphere and granulationpatterns in solar convection. This order in a fully developed turbulent ow is some-times called turbulent superstructure in convection. Large-scale structure formationin turbulent Rayleigh-Benard convection recently became accessible in direct numerical simulations, which resolve all relevant scales of turbulence in horizontally extended domains with a large aspect ratio. Using DNS output we apply the dynamicLaplacian approach [Froyland, 2015] to identify these turbulent superstructures as nite-time coherent sets in both quasi-2D and fully three-dimensional settings. Amodest number of trajectories are meshed and a nite-element based method isemployed to numerically estimate the dynamic Laplacian [Froyland Junge, 2018].The coherent sets are encoded in the leading eigenvectors of the dynamic Laplacian,and the individual coherent sets are \decoded" using the recently developed SparseEigenbasis Approximation (SEBA) algorithm [Froyland, Rock, Sakellariou, 2019]

The analysis of the turbulent heat transfer in thermal convection flow is mostly done in the Eulerian frame of reference. Here, we use an ensemble of more than half a million individual Lagrangian tracer trajectories, which are advected together with the evolving turbulent flow, to identify subsets that contribute least to the heat transfer. These trajectories probe Lagrangian Coherent Sets — sets that remain spatially connected for a finite time and evolve in time. The analysis is based on three-dimensional direct numerical simulations at three different Prandtl numbers. Our Lagrangian analysis results provide a complementary view on the transport and are found to be consistent with the standard Eulerian results. Lagrangian Coherent Sets as transport barriers in convection1JOERG SCHUMACHER, PHILIPP VIEWEG, TU Ilmenau, CHRISTIANESCHNEIDE, KATHRIN PADBERG-GEHLE, Leuphana University Lueneburg —The analysis of the turbulent heat transfer in thermal convection flow is mostlydone in the Eulerian frame of reference. Here, we use an ensemble of more thanhalf a million individual Lagrangian tracer trajectories, which are advected togetherwith the evolving turbulent flow, to identify subsets that contribute least to theheat transfer. These trajectories probe Lagrangian Coherent Sets sets that remainspatially connected for a finite time and evolve in time. The analysis is based onthree-dimensional direct numerical simulations at three different Prandtl numbers.Our Lagrangian analysis results provide a complementary view on the transport andare found to be consistent with the standard Eulerian results.1 The work is supported by the Priority Programme DFG-SPP 1881 of the DeutscheForschungsgemeinschaft.

Turbulent convection flows are ubiquitous in natural systems such as in the atmosphere or in stellar interiors as well as in technological applications such as cooling or energy storage devices. Their physical complexity and vast number of degrees of freedom prevents often an access by direct numerical simulations that resolve all flow scales from the smallest to the largest plumes and vortices in the system and requires a simplified modelling of the flow itself and the resulting turbulent transport behaviour. The following article summarises some examples that aim at a reduction of the flow complexity and thus of the number of degrees of freedom of convective turbulence by machine learning approaches. We therefore apply unsupervised and supervised machine learning methods to direct numerical simulation data of a Rayleigh-Bénard convection flow which serves as a paradigm of the examples mentioned at the beginning.