Urban flood modeling using shallow water equations with depth-dependent anisotropic porosity

The shallow water model with anisotropic porosity conceptually takes into account the unresolved subgrid-scale features, e.g. microtopography or buildings. This enables computationally efficient simulations that can be run on coarser grids, whereas reasonable accuracy is maintained via the introduction of porosity. This article presents a novel numerical model for the depth-averaged equations with anisotropic porosity. The porosity is calculated using the probability mass function of the subgrid-scale features in each cell and updated in each time step. The model is tested in a one-dimensional theoretical benchmark before being evaluated against measurements and high-resolution predictions in three case studies: a dam-break over a triangular bottom sill, a dam-break through an idealized city and a rainfall-runoff event in an idealized urban catchment. The physical processes could be approximated relatively well with the anisotropic porosity shallow water model. The computational resolution influences the porosities calculated at the cell edges and therefore has a large influence on the quality of the solution. The computational time decreased significantly, on average three orders of magnitude, in comparison to the classical high-resolution shallow water model simulation.Chinese Scholarship Counci

A depth-averaged non-cohesive sediment transport model with improved discretization of flux and source terms

This paper presents novel flux and source term treatments within a Godunov-type finite volume framework for predicting the depth-averaged shallow water fl ow and sediment transport with enhanced the accuracy and stability. The suspended load ratio is introduced to differentiate between the advection of the suspended load and the advection of water. A modified Harten, Lax and van Leer Riemann solver with the contact wave restored (HLLC) is derived for the fl ux calculation based on the new wave pattern involving the suspended load ratio. The source term calculation is enhanced by means of a novel splitting-point implicit discretization. The slope effect is introduced by modifying the critical shear stress, with two treatments being discussed. The numerical scheme is tested in five examples that comprise both fixed and movable beds. The model predictions show good agreement with measurement, except for cases where local three-dimensional effects dominate.China Scholarship Counci

Влияние интенсивной пластической деформации методом кручения под квазигидростатическим давлением на структуру и фазовый состав высокоазотистой аустенитной стали Х20АГ20Ф2

We investigate the microstructure and microhardness of high-nitrogen austenitic steel Fe-20Cr-20Mn-2.6V-0.3C-0.8N (in wt. %) after upset and high-pressure torsion (HPT) (6GPa) for ?, ?, and 1 revolutions at room temperature. As the result of deformation, steel microhardness increases by 1.5 times after HPT. Slip, twinning, formation of localized deformation microbands, and precipitation hardening are the main deformation mechanisms under HPT, and the level of solid solution strengthening of steel remains high after deformation

Von der Rechenmaschine zum "neurotischen Computer"?

Die Autoren verstehen Computer als vom Menschen geschaffene, veräußerlichte Denkmodelle. Während bei der klassischen Maschine Inhalt und Gestalt zusammenfallen, existiert der Computer zunächst inhaltsleer. Der Computer ist 'implementierte Theorie'. Insofern seine Programme aus Algorithmen bestehen, die am Verhalten der Maschine orientiert sind, realisiert er programmspezifische Handlungstheorien. 'Zwischen Denken und Handeln gibt es beim Computer keinen Hiatus mehr.' Vor diesem Hintergrund werden zwei theoretische Modelle künstlicher Intelligenz (KI) untersucht: die informationsverarbeitende KI (Symbolverarbeitung) und die wachsende KI (Neokonnektionismus). Während Computermodelle nach Art der Symbolverarbeitung an der zweiwertigen Logik mit deduktiver Programmanwendung orientiert sind, basieren die theoretischen Grundlagen des Neokonnektionismusmodells auf der Stochastik. Computer dieses Modells folgern induktiv und lernen aus Beispielen. Im Anschluß an Überlegungen von S. Turkle diskutieren die Autoren die Beziehung von KI-Forschung und Psychologie. Im neokonnektionistischen Modell wird der Versuch gesehen, die entscheidenden Komponenten problemlösenden Denkens (Regelanwendung und -generierung) zu kopieren. Schließlich wird das implizite Subjektverständnis der symbolverarbeitenden und der neokonnektionistischen KI anhand deren Aussagen zum Phänomen der 'Fehlleistungen' (Freud) herausgearbeitet. Hier werden insbesondere D. Normans Überlegungen zu Fehlleistungen einer kritischen Bewertung unterzogen. (ICD

Improved multislope MUSCL reconstruction on unstructured grids for shallow water equations

In shallow water flow and transport modeling, the monotone upstream-centered scheme for conservation laws (MUSCL) is widely used to extend the original Godunov scheme to second-order accuracy. The most important step in MUSCL-type schemes is the MUSCL reconstruction, which calculate extrapolates the values of independent variables from the cell center to the edge. The monotonicity of the scheme is preserved with the help of slope limiters that prevent the occurrence of new extrema during the reconstruction. On structured grids, the calculation of the slope is straightforward and usually based on a two-point stencil that uses the cell centers of the neighbor cell and the so-called far-neighbor cell of the edge under consideration. On unstructured grids, the correct choice for the upwind slope becomes non-trivial. In this work, two novel TVD schemes are developed based on different techniques for calculating the upwind slope and downwind slope. An additional treatment that stabilizes the scheme is discussed. The proposed techniques are compared to two existing MUSCL reconstruction techniques and a detailed discussion of the results is given. It is shown that the proposed MUSCL reconstruction schemes obtain more accurate results with less numerical diffusion and higher efficiency

Signatures of polaronic excitations in quasi-one-dimensional LaTiO3.41_{3.41}

The optical properties of quasi-one-dimensional metallic LaTiO$_{3.41}$ are studied for the polarization along the $a$ and $b$ axes. With decreasing temperature modes appear along both directions suggestive for a phase transition. The broadness of these modes along the conducting axis might be due to the coupling of the phonons to low-energy electronic excitations across an energy gap. We observe a pronounced midinfrared band with a temperature dependence consistent with (interacting) polaron models. The polaronic picture is corroborated by the presence of strong electron-phonon coupling and the temperature dependence of the dc conductivity.Comment: 5 pages, 5 figure

Characterization of Reachable Attractors Using Petri Net Unfoldings

International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net

Reports of the AAAI 2019 spring symposium series

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates