Geometric parameters influence on Piano Key Weir hydraulic performances

The Piano Key Weir is a recent evolution of the traditional labyrinth weir. Thanks to a reduced foot print, this nonlinear weir can be placed on the top of gravity dams. The Piano Key Weir geometry involves a large number of geometric parameters. Several experimental studies have been carried out to investigate the main geometric parameters influencing the weir hydraulic efficiency and to define their optimal value. In this paper, the experimental data gathered at the University of Liege are re-examined to show how the weir height, the keys widths and the overhangs positions influence, for a given crest length magnification ratio, the weir discharge capacity. The theoretical rating curve of a standard linear weir is considered for comparison. The analysis highlights that the keys widths and overhangs lengths ratios influence significantly the Piano Key Weir efficiency, but less than the weir height. Considering the above mentioned results, a cost efficient design proposed in the literature is also proved to be close to the hydraulic optimum

prediction of mean and turbulent kinetic energy in rectangular shallow reservoirs

AbstractShallow rectangular reservoirs are common structures in urban hydraulics and river engineering. Despite their simple geometries, complex symmetric and asymmetric flow fields develop in such reservoirs, depending on their expansion ratio and length-to-width ratio. The original contribution of this study is the analysis of the kinetic energy content of the mean flow, based on UVP velocity measurements carried throughout the reservoir in eleven different geometric configurations. A new relationship is derived between the specific mean kinetic energy and the reservoir shape factor. For most considered geometric configurations, leading to four different flow patterns, the experimentally observed flow fields and mean kinetic energy contents are successfully reproduced by an operational numerical model based on the depth-averaged flow equations and a two-length-scale k- turbulence closure. The analysis also highlights the better performance of this depth-averaged k- model compared to an algebraic turbu..

Physical Modeling of an Aerating Stepped Spillway

To mitigate the negative effects on the water quality in the downstream river of a projected large dam, and in particular to increase the dissolved oxygen concentration during low flow periods within the first 10 years of dam operation, an aerating weir has been designed and tested on a physical model at the Laboratory of Engineering Hydraulics (HECE) of the Liege University. The design of the structure has been done considering data from the literature. The selected solution is a 3 m high stepped spillway designed to operate in nappe flow conditions within the range of design discharges (25 – 100 m³/s). To validate the design, a physical model representing a section of the weir at a 1:1 scale has been built and operated in the laboratory. Chemical dissolved oxygen removal technique has been applied upstream of the model to be able to measure the weir aerating efficiency. The physical model results show that the proposed structure is able to maintain, in the range of discharge in the river from 25 to 100 m³/s, a minimum 5 mg/l oxygen concentration downstream, whatever the upstream oxygen concentration. The paper presents the design process of the weir, the scale model features and the results of the validation tests on the physical model. The prototype construction will take place in 2017 and the water quality will be monitored

Inferring hidden states in Langevin dynamics on large networks: Average case performance

We present average performance results for dynamical inference problems in large networks, where a set of nodes is hidden while the time trajectories of the others are observed. Examples of this scenario can occur in signal transduction and gene regulation networks. We focus on the linear stochastic dynamics of continuous variables interacting via random Gaussian couplings of generic symmetry. We analyze the inference error, given by the variance of the posterior distribution over hidden paths, in the thermodynamic limit and as a function of the system parameters and the ratio {\alpha} between the number of hidden and observed nodes. By applying Kalman filter recursions we find that the posterior dynamics is governed by an "effective" drift that incorporates the effect of the observations. We present two approaches for characterizing the posterior variance that allow us to tackle, respectively, equilibrium and nonequilibrium dynamics. The first appeals to Random Matrix Theory and reveals average spectral properties of the inference error and typical posterior relaxation times, the second is based on dynamical functionals and yields the inference error as the solution of an algebraic equation.Comment: 20 pages, 5 figure

Upgrading from Gaussian Processes to Student's-T Processes

Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the associated sample values. Student's-T processes are a generalization of Gaussian processes, founded on the Student's-T distribution instead of the Gaussian distribution. Student's-T processes maintain the primary advantages of Gaussian processes (kernel function, analytic update rule) with additional benefits beyond Gaussian processes. The Student's-T distribution has higher Kurtosis than a Gaussian distribution and so outliers are much more likely, and the posterior variance increases or decreases depending on the variance of observed data sample values. Here, we describe Student's-T processes, and discuss their advantages in the context of aerospace optimization. We show how to construct a Student's-T process using a kernel function and how to update the process given new samples. We provide a clear derivation of optimization-relevant quantities such as expected improvement, and contrast with the related computations for Gaussian processes. Finally, we compare the performance of Student's-T processes against Gaussian process on canonical test problems in Bayesian optimization, and apply the Student's-T process to the optimization of an aerostructural design problem.Comment: 2018 AIAA Non-Deterministic Approaches Conferenc

Technical note: Laboratory modelling of urban flooding: strengths and challenges of distorted scale models

Laboratory experiments are a viable approach for improving process understanding and generating data for the validation of computational models. However, laboratory-scale models of urban flooding in street networks are often distorted, i.e. different scale factors are used in the horizontal and vertical directions. This may result in artefacts when transposing the laboratory observations to the prototype scale (e.g. alteration of secondary currents or of the relative importance of frictional resistance). The magnitude of such artefacts was not studied in the past for the specific case of urban flooding. Here, we present a preliminary assessment of these artefacts based on the reanalysis of two recent experimental datasets related to flooding of a group of buildings and of an entire urban district, respectively. The results reveal that, in the tested configurations, the influence of model distortion on the upscaled values of water depths and discharges are both of the order of 10&thinsp;%. This research contributes to the advancement of our knowledge of small-scale physical processes involved in urban flooding, which are either explicitly modelled or parametrized in urban hydrology models.</p

Exchange between drainage systems and surface flows during urban flooding: Quasi-steady and dynamic modelling in unsteady flow conditions

The accurate modelling of urban flooding constitutes an integral part of flood risk assessment and management in residential and industrial areas. Interactions between drainage networks and surface runoff flows are commonly modelled based on weir/orifice equations; however, this approach has not been satisfactorily validated in unsteady flow conditions due to uncertainties in estimating the discharge coefficients and associated head losses. This study utilises experimental data of flow exchange between the sewer flow and the floodplain through a manhole without a lid to develop two alternate approaches that simulate this interaction and describe the associated exchange flow. A quasi-steady model links the exchange flow to the total head in the sewer pipe and the head losses in the sewer and the manhole, whilst a dynamic model takes also into account the evolution of the water level within the manhole at discrete time steps. The developed numerical models are subsequently validated against large-scale experimental data for unsteady sewer flow conditions, featuring variable exchange to the surface. Results confirmed that both models can accurately replicate experimental conditions, with improved performance when compared to existing methodologies based only on weir or orifice equations

Testing two-step models of negative quantification using a novel machine learning analysis of EEG

The sentences “More than half of the students passed the exam” and “Fewer than half of the students failed the exam” describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. “Fewer than half of the dots are blue”) that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with fewer than half. We provide an alternative interpretation of our results in line with an expectation-based pragmatic account