@article{KramerKreisbeckRihaetal.2016, author = {Kramer, Tobias and Kreisbeck, Christoph and Riha, Christian and Chiatti, Olivio and Buchholz, Sven and Wieck, Andreas and Reuter, Dirk and Fischer, Saskia}, title = {Thermal energy and charge currents in multi-terminal nanorings}, volume = {6}, journal = {AIP Advances}, doi = {10.1063/1.4953812}, pages = {065306}, year = {2016}, abstract = {We study in experiment and theory thermal energy and charge transfer close to the quantum limit in a ballistic nanodevice, consisting of multiply connected one-dimensional electron waveguides. The fabricated device is based on an AlGaAs/GaAs heterostructure and is covered by a global top-gate to steer the thermal energy and charge transfer in the presence of a temperature gradient, which is established by a heating current. The estimate of the heat transfer by means of thermal noise measurements shows the device acting as a switch for charge and thermal energy transfer. The wave-packet simulations are based on the multi-terminal Landauer-B{\"u}ttiker approach and confirm the experimental finding of a mode-dependent redistribution of the thermal energy current, if a scatterer breaks the device symmetry.}, language = {en} } @article{HeinzeDipankarHenkenetal.2017, author = {Heinze, Rieke and Dipankar, Anurag and Henken, Cintia Carbajal and Moseley, Christopher and Sourdeval, Odran and Tr{\"o}mel, Silke and Xie, Xinxin and Adamidis, Panos and Ament, Felix and Baars, Holger and Barthlott, Christian and Behrendt, Andreas and Blahak, Ulrich and Bley, Sebastian and Brdar, Slavko and Brueck, Matthias and Crewell, Susanne and Deneke, Hartwig and Di Girolamo, Paolo and Evaristo, Raquel and Fischer, J{\"u}rgen and Frank, Christopher and Friederichs, Petra and G{\"o}cke, Tobias and Gorges, Ksenia and Hande, Luke and Hanke, Moritz and Hansen, Akio and Hege, Hans-Christian and Hose, Corinna and Jahns, Thomas and Kalthoff, Norbert and Klocke, Daniel and Kneifel, Stefan and Knippertz, Peter and Kuhn, Alexander and van Laar, Thriza and Macke, Andreas and Maurer, Vera and Mayer, Bernhard and Meyer, Catrin I. and Muppa, Shravan K. and Neggers, Roeland A. J. and Orlandi, Emiliano and Pantillon, Florian and Pospichal, Bernhard and R{\"o}ber, Niklas and Scheck, Leonhard and Seifert, Axel and Seifert, Patric and Senf, Fabian and Siligam, Pavan and Simmer, Clemens and Steinke, Sandra and Stevens, Bjorn and Wapler, Kathrin and Weniger, Michael and Wulfmeyer, Volker and Z{\"a}ngl, G{\"u}nther and Zhang, Dan and Quaas, Johannes}, title = {Large-eddy simulations over Germany using ICON: a comprehensive evaluation}, volume = {143}, journal = {Quarterly Journal of the Royal Meteorological Society}, number = {702}, doi = {10.1002/qj.2947}, pages = {69 -- 100}, year = {2017}, abstract = {Large-eddy simulations (LES) with the new ICOsahedral Non-hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small-scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary-layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small- to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small- to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.}, language = {en} } @misc{MaherFischerGallyetal.2017, author = {Maher, Stephen J. and Fischer, Tobias and Gally, Tristan and Gamrath, Gerald and Gleixner, Ambros and Gottwald, Robert Lion and Hendel, Gregor and Koch, Thorsten and L{\"u}bbecke, Marco and Miltenberger, Matthias and M{\"u}ller, Benjamin and Pfetsch, Marc and Puchert, Christian and Rehfeldt, Daniel and Schenker, Sebastian and Schwarz, Robert and Serrano, Felipe and Shinano, Yuji and Weninger, Dieter and Witt, Jonas T. and Witzig, Jakob}, title = {The SCIP Optimization Suite 4.0}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-62170}, year = {2017}, abstract = {The SCIP Optimization Suite is a powerful collection of optimization software that consists of the branch-cut-and-price framework and mixed-integer programming solver SCIP, the linear programming solver SoPlex, the modeling language Zimpl, the parallelization framework UG, and the generic branch-cut-and-price solver GCG. Additionally, it features the extensions SCIP-Jack for solving Steiner tree problems, PolySCIP for solving multi-objective problems, and SCIP-SDP for solving mixed-integer semidefinite programs. The SCIP Optimization Suite has been continuously developed and has now reached version 4.0. The goal of this report is to present the recent changes to the collection. We not only describe the theoretical basis, but focus on implementation aspects and their computational consequences.}, language = {en} } @misc{GamrathFischerGallyetal.2016, author = {Gamrath, Gerald and Fischer, Tobias and Gally, Tristan and Gleixner, Ambros and Hendel, Gregor and Koch, Thorsten and Maher, Stephen J. and Miltenberger, Matthias and M{\"u}ller, Benjamin and Pfetsch, Marc and Puchert, Christian and Rehfeldt, Daniel and Schenker, Sebastian and Schwarz, Robert and Serrano, Felipe and Shinano, Yuji and Vigerske, Stefan and Weninger, Dieter and Winkler, Michael and Witt, Jonas T. and Witzig, Jakob}, title = {The SCIP Optimization Suite 3.2}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-57675}, year = {2016}, abstract = {The SCIP Optimization Suite is a software toolbox for generating and solving various classes of mathematical optimization problems. Its major components are the modeling language ZIMPL, the linear programming solver SoPlex, the constraint integer programming framework and mixed-integer linear and nonlinear programming solver SCIP, the UG framework for parallelization of branch-and-bound-based solvers, and the generic branch-cut-and-price solver GCG. It has been used in many applications from both academia and industry and is one of the leading non-commercial solvers. This paper highlights the new features of version 3.2 of the SCIP Optimization Suite. Version 3.2 was released in July 2015. This release comes with new presolving steps, primal heuristics, and branching rules within SCIP. In addition, version 3.2 includes a reoptimization feature and improved handling of quadratic constraints and special ordered sets. SoPlex can now solve LPs exactly over the rational number and performance improvements have been achieved by exploiting sparsity in more situations. UG has been tested successfully on 80,000 cores. A major new feature of UG is the functionality to parallelize a customized SCIP solver. GCG has been enhanced with a new separator, new primal heuristics, and improved column management. Finally, new and improved extensions of SCIP are presented, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs.}, language = {en} }