Деятельность трудовой народно-социалистической партии в Украине в период открытого противостояния большевикам

In this paper, we propose the concepts of conditional climate resilience and conditional climate sensitivity as measures of the nonlinear response of a non-stationary background climate state to arbitrary perturbations. Based on the theory of nonlinear stability, we formulate both sensitivity and resilience in terms of a conditional nonlinear optimization problem. As illustrated by results of a zero-dimensional energy balance model, the new measures provide useful information of sensitivity and resilience of the climate system in the presence of bifurcations and under non-stationary external forcing

Wirbel-getriebene Transporte im Antarktischen Zirkumpolarstrom System

This PhD thesis consists of three research papers. The first research paper addresses crucial issues of the present climate change debate, namely the response of the meridional overturning circulation (MOC) of the Southern Ocean (SO) to decadal-scale trends in wind stress forcing, and the ability of up-to-date meso-scale eddy parameterisations to represent the corresponding changes in the eddy field in climate models. The results show that it is indispensable to incorporate the correct sensitivity of eddy field into climate models in order to reproduce the correct sensitivity of the MOC to wind stress and that up-to-date meso-scale eddy parameterisations are only partially successful. The second and third research papers are guided by a more conceptual perspective and focus on one of the most common diagnostics of the MOC: the concept of the MOC streamfunction. The second research paper clarifies the question: Is it possible to define a MOC streamfunction completely void of standing eddies? It is shown that the construction of a MOC streamfunction with an exactly vanishing standing eddy part has to be performed by zonal integration along depth-dependent horizontal isolines of time-mean density. The third research paper considers the two most common approaches to calculating MOC streamfunctions directly in Eulerian space, i.e. the series expansion of the residual-mean eddy streamfunction and the series expansion of the quasi-Stokes streamfunction, and discusses their limitations.Diese Dissertation besteht aus drei Forschungsartikeln. Der erste Forschungsartikel behandelt entscheidende Fragen der gegenwärtigen Klimawandel-Diskussion: Wie reagiert die meridionale Umwälzzirkulation (MOC) des Südlichen Ozeans (SO) auf dekadisch-skalige Trends im Windstress-Antrieb? Inwieweit ist es den gegenwärtigen Parameterisierungen von meso-skaligen Wirbeln möglich, die entsprechenden Veränderungen im Wirbelfeld in Klimamodellen wiederzugeben? Die Ergebnisse zeigen: Um die richtige Abhängigkeit der MOC vom Windstress zu reproduzieren, ist es notwendig, die korrekte Sensitivität des Wirbelfeldes in Klimamodelle einzubeziehen. Den aktuellen Parameterisierungen von meso-skaligen Wirbeln gelingt dies jedoch nur ansatzweise. Die Forschungsartikel zwei und drei stehen in einer konzeptionelleren Perspektive und konzentrieren sich auf eine der verbreitetsten Diagnostiken der MOC: das Konzept der MOC Stromfunktion. Der zweite Forschungsartikel klärt die Frage: Ist es möglich, eine MOC Stromfunktion zu definieren, welche gänzlich frei von stehenden Wirbeln ist? Es wird gezeigt, dass eine MOC Stromfunktion mit einem exakt verschwindenden stehenden Wirbel-Anteil dadurch erhalten wird, dass die zonale Integration entlang von tiefenabhängigen horizontalen Isolinien der zeitlich gemittelten Dichte ausgeführt wird. Im dritten Forschungsartikel werden die zwei gebräuchlichsten Ansätze zur Berechnung von MOC Stromfunktionen direkt im Euler-Raum - die Reihenentwicklung der residuellen Wirbelstromfunktion und die Reihenentwicklung der quasi-Stokes Stromfunktion - betrachtet und deren Grenzen aufgezeigt

Towards a turbulence closure based on energy modes

A new approach to parameterizing subgrid-scale processes is proposed: The impact of the unresolved dynamics on the resolved dynamics (i.e., the eddy forcing) is represented by a series expansion in dynamical spatial modes that stem from the energy budget of the resolved dynamics. It is demonstrated that the convergence in these so-called energy modes is faster by orders of magnitude than the convergence in Fourier-type modes. Moreover, a novel way to test parameterizations in models is explored. The resolved dynamics and the corresponding instantaneous eddy forcing are defined via spatial filtering that accounts for the representation error of the equations of motion on the low-resolution model grid. In this way, closures can be tested within the high-resolution model, and the effects of different parameterizations related to different energy pathways can be isolated. In this study, the focus is on parameterizations of the baroclinic energy pathway. The corresponding standard closure in ocean models, the Gent-McWilliams (GM) parameterization, is also tested, and it is found that the GM field acts like a stabilizing direction in phase space. The GM field does not project well on the eddy forcing and hence fails to excite the model's intrinsic low-frequency variability, but it is able to stabilize the model

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching actions to control power flows on the grid, and is trained on only a single well-chosen scenario. The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios. The type and variability of topologies suggested by the agent are analysed across the test scenarios, demonstrating efficient and diverse agent behaviour

Charged Rotating Black Holes in Odd Dimensions

We consider charged rotating black holes in $D=2N+1$ dimensions, $D \ge 5$. While these black holes generically possess $N$ independent angular momenta, associated with $N$ distinct planes of rotation, we here focus on black holes with equal-magnitude angular momenta. The angular dependence can then be treated explicitly, and a system of 5 $D$-dependent ordinary differential equations is obtained. We solve these equations numerically for Einstein-Maxwell theory in D=5, 7 and 9 dimensions. We discuss the global and horizon properties of these black holes, as well as their extremal limits.Comment: 11 pages, 10 figure

Learning to run a Power Network Challenge: a Retrospective Analysis

Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators when optimizing electricity transportation while avoiding blackouts. Motivated to investigate the potential of Artificial Intelligence methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks. The NeurIPS 2020 competition was well received by the international community attracting over 300 participants worldwide. The main contribution of this challenge is our proposed comprehensive Grid2Op framework, and associated benchmark, which plays realistic sequential network operations scenarios. The framework is open-sourced and easily re-usable to define new environments with its companion GridAlive ecosystem. It relies on existing non-linear physical simulators and let us create a series of perturbations and challenges that are representative of two important problems: a) the uncertainty resulting from the increased use of unpredictable renewable energy sources, and b) the robustness required with contingent line disconnections. In this paper, we provide details about the competition highlights. We present the benchmark suite and analyse the winning solutions of the challenge, observing one super-human performance demonstration by the best agent. We propose our organizational insights for a successful competition and conclude on open research avenues. We expect our work will foster research to create more sustainable solutions for power network operations

Learning to run a Power Network Challenge: a Retrospective Analysis

Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators when optimizing electricity transportation while avoiding blackouts. Motivated to investigate the potential of Artificial Intelligence methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks. The NeurIPS 2020 competition was well received by the international community attracting over 300 participants worldwide. The main contribution of this challenge is our proposed comprehensive ’Grid2Op’ framework, and associated benchmark, which plays realistic sequential network operations scenarios. The Grid2Op framework, which is open-source and easily re-usable, allows users to define new environments with its companion GridAlive ecosystem. Grid2Op relies on existing non-linear physical power network simulators and let users create a series of perturbations and challenges that are representative of two important problems: a) the uncertainty resulting from the increased use of unpredictable renewable energy sources, and b) the robustness required with contingent line disconnections. In this paper, we give the highlights of the NeurIPS 2020 competition. We present the benchmark suite and analyse the winning solutions, including one super-human performance demonstration. We propose our organizational insights for a successful competition and conclude on open research avenues. Given the challenge success, we expect our work will foster research to create more sustainable solutions for power network operations

The Oceanographic Multipurpose Software Environment (OMUSE v1.0)

In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model