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..

Side-channel based intrusion detection for industrial control systems

Industrial Control Systems are under increased scrutiny. Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated with state-of-the-art security measures. We propose a system that uses electromagnetic side-channel measurements to detect behavioural changes of the software running on industrial control systems. To demonstrate the feasibility of this method, we show it is possible to profile and distinguish between even small changes in programs on Siemens S7-317 PLCs, using methods from cryptographic side-channel analysis.Comment: 12 pages, 7 figures. For associated code, see https://polvanaubel.com/research/em-ics/code

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

Natural and sail-displaced doubly-symmetric Lagrange point orbits for polar coverage

This paper proposes the use of doubly-symmetric, eight-shaped orbits in the circular restricted three-body problem for continuous coverage of the high-latitude regions of the Earth. These orbits, for a range of amplitudes, spend a large fraction of their period above either pole of the Earth. It is shown that they complement Sun-synchronous polar and highly eccentric Molniya orbits, and present a possible alternative to low thrust pole-sitter orbits. Both natural and solar-sail displaced orbits are considered. Continuation methods are described and used to generate families of these orbits. Starting from ballistic orbits, other families are created either by increasing the sail lightness number, varying the period or changing the sail attitude. Some representative orbits are then chosen to demonstrate the visibility of high-latitude regions throughout the year. A stability analysis is also performed, revealing that the orbits are unstable: it is found that for particular orbits, a solar sail can reduce their instability. A preliminary design of a linear quadratic regulator is presented as a solution to stabilize the system by using the solar sail only. Finally, invariant manifolds are exploited to identify orbits that present the opportunity of a ballistic transfer directly from low Earth orbit

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

Infinite factorization of multiple non-parametric views

Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale of classical canonical correlation analysis into a flexible, generative and non-parametric clustering setting, by introducing a novel non-parametric hierarchical mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the views. The commonalities between the sources are modeled by an infinite block model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views. Cluster analysis of co-expression is a standard simple way of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation