Optimal design of pipes in series: An explicit approximation

This paper introduces a new methodology for the optimum design of pipes in series, named Optimum Hydraulic Grade Line (OHGL). This methodology is explicit and is based on the knowledge of the series topology and the geometrical distribution of water demands on nodes, i.e. the way in which the pipe in series delivers water mass as function of the distance from the entrance. OHGL consists in the pre-determination of that hydraulic grade line which gives the minimum construction cost, in an explicit way. Once this line has been established, calculation of the pipe’s continuous diameters is direct; after a round up to commercial diameters is developed. To validate the proposed methodology, several pipes in series were designed both using GA and OHGL. Four hundred series were used in total, each with different topological characteristics and demands. Keywords: Pipe in series, optimum design, genetic algorithms, optimum hydraulic grade line

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation.</p> <p style='line-height: 20px;'><b>Keywords: </b>neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series.</p>

Stochastic evaluation of sewer inlet capacity on urban pluvial flooding

In this paper we present an innovative methodology to stochastically assess the impact of sewer inlet conditions on urban pluvial flooding. The results showed that sewer inlet capacity can have a large impact on the occurrence of urban pluvial flooding. The methodology is a useful tool for dealing with uncertainties in sewer inlet operational conditions and contribute to comprehensive assessment of urban pluvial risk assessment

Observation of Muon Neutrino Disappearance with the MINOS Detectors in the NuMI Neutrino Beam

This Letter reports results from the MINOS experiment based on its initial exposure to neutrinos from the Fermilab NuMI beam. The rates and energy spectra of charged current ν_μ interactions are compared in two detectors located along the beam axis at distances of 1 and 735 km. With 1.27×10^(20) 120 GeV protons incident on the NuMI target, 215 events with energies below 30 GeV are observed at the Far Detector, compared to an expectation of 336±14 events. The data are consistent with ν_μ disappearance via oscillations with Δm_(32)^2|=2.74_(-0.26)^(+0.44)×10^(-3)  eV^2 and sin^2(2θ_(23))>0.87 (68% C.L.)

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

International audienceA model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series.