Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Impact of wind field horizontal resolution on sea waves hindcast around Calabrian coasts

We investigated the impact of wind field enhanced horizontal resolution on sea wind-wave hindcast around the Calabrian coasts, which lie at the southernmost tip of the Italian peninsula. Simulations have been performed using WAM (WAve Model), a third-generation state of the art wave-model. In order to study this topic, we shall discuss two simulations sets. The first set forces WAM by ECMWF (European Centre for Medium-Range Weather Forecasts) surface wind field analysis, used in this paper with a resolution of 0.5◦; whilefor these cond simulation set RAMS (Regional Atmospheric Modelling System) surface wind field forcesWAM. Initial and dynamic boundary conditions for RAMS simulations, which have a 20 km horizontal resolution, are derived from ECMWF analysis. To obtain a reliable statistical data set, integrations have been performed over six months from 1 October 2003 to 31 March 2004. We have evaluated performance comparing the WAM modelled wave heights and directions against data of Wave measuring Buoys (WBs) moored off Cetraro and Crotone. Statistical tests are performed to assess differences between modelled data and measurements and between modelled data sets. Results show better performance for wave height fields when RAMS forces WAM. The best results are obtained for Crotone but differences between simulated and measured wave height distributions are significant at a 99% statistical level. Simulated wave directions are generally good for the model set-up used in this paper and the differences between modelled data sets are minor

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Implementation of an acoustic stall detection system using near-field diy pressure sensors

In this paper the authors propose the use of an unconventional instrumentation, based on a DIY transducer, to measure the pressure instabilities in a low speed industrial axial fan, with the purpose of rotating stall detection. Rotating stall is an aerodynamic instability with a frequency typically half the rotor frequency, and in slow turbomachines such as industrial fans this frequency has a value even lower than 10 Hz. The authors carried out the pressure measurements using a dynamic transducer and a piezoelectric sensor to provide the measurement base-line. In turbomachinery standard methods, time-resolved pressure measurements use piezoelectric sensors such as microphones in the far field and pressure transducers in the near field. Other classes of sensors, such as electret microphones, May be not suited for pressure measurements, especially in the infrasound region since their cut-off frequency is about 20 Hz. In the present study, the authors compare a low cost and DIY technology to a high precision piezoelectric sensor as alternative technology to stall detection. They implemented and set-up a measurement chain that is the basis of a stall warning system able to identify the rotating stall typical pattern in low speed axial fans. The results have been validated respect to the state of the art of the acoustic control techniques described in literature. The signals acquired using the two technologies are discussed combining spectral analysis and time-domain reconstruction of phase space portraits. The acoustic patterns obtained through the phase space reconstruction shows that the DIY dynamic sensor is a good candidate solution for the rotating stall acoustic analysis

A procedure to model and design elastomeric-based isolation systems for the seismic protection of rocking art objects

This paper investigates the rocking behavior of rigid bodies seismically protected by means of lead rubber bearings and high damping rubber bearings, which are the most popular kinds of elastomeric isolators in the market. The complex nonlinear force–displacement relationship displayed by such devices is predicted by a phenomenological model based on a small set of parameters having a clear mechanical meaning. The algebraic nature of the proposed hysteretic model makes it suitable for a design procedure, using an energy-based approach, that allows one to obtain the hysteretic model parameters on the basis of the mass and the isolation period. Overturning spectra are evaluated and discussed to illustrate the effect of the isolation devices on the rigid bodies' rocking behavior. Furthermore, nonlinear time history analyses associated with six real earthquakes are carried out on six of Michelangelo's sculptures, located in the Galleria dei Prigioni at the Accademia Gallery of Florence, in order to examine their actual behavior under real strong ground motions