Paolo Maria Congedo

Modeling and Analysis of Natural Convection Heat Transfer in Nanofluids

Nanofluids are engineered colloids made of a base fluid and nanoparticles (1–100 nm). The presence of nanoparticles causes a dramatic enhancement of thermal conductivity, an increase of convective heat transfer coefficient as well as of viscosity. These features make nanofluids suitable for the most common industrial cooling and heat transportation applications, for example in the heat exchanger whose performances can be dramatically improved. In the nanofluid literature it is not really evident the mechanism inside the unusual heat transport properties. Several studies concerning nanofluids were carried out to provide experimental data for different configurations and to find models suitable with these experiments. Unfortunately measurements available in literature seem to be affected by a significant dispersion so that some experimental data are not coherent with the others. The issue is that the properties of nanofluid are influenced by many factors such as the nature of the components, the nanoparticle size, shape and concentration, the temperature, the pH of the solution, the presence of surfactants (used to stabilize suspensions), and the charge state of the particle in suspension. Not all of these quantities are usually measured in an experimental campaign and then sometimes it is not possible to make a comparison between different experimental data available in literature. For this reason, several models proposed to validate experimental measurement work well only within a small range of validity, in terms of temperature or concentration interval or nanoparticle type. In this paper we consider always the nanofluid as a single phase and we compared different models presented in literature for the following properties: density, specific heat, viscosity and thermal conductivity. (All this properties depend, at least, on the nanoparticles concentration in the base fluid). The water-Al2 O3 nanofluid is considered since several models and experimental data are available for this kind of fluid. The numerical simulations have been made by using the CFD code Fluent (release 6.3), where the models have been implemented by using external routines. The natural convection in a horizontal tube heat exchanger has been simulated in a wide region of conditions for which experimental data are available. Different models proposed in literature for viscosity and thermal conductivity have been considered, and compared to empirical models obtained by means a regression from experimental data. Aim of this work is to set suitable models which allows reproducing nanofluid behavior with a good accuracy in a wide region of different conditions.Copyright

Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

An Integrated Tool to Monitor Renewable Energy Flows and Optimize the Recharge of a Fleet of Plug-In Electric Vehicles in the Campus of the University of Salento: Preliminary Results

A tool has been developed to integrate electric vehicles into a general systems for the energy management and optimization of energy from renewable sources in the Campus of the University of Salento. The tool is designed to monitor the status of plug-in vehicles and recharging station and manage the recharging on the basis of the prediction of power from the photovoltaic roofs and usage of electricity in three buildings used by the Department of engineering. The tool will allow the surplus of electricity from photovoltaic to be used for the recharge of the plug-in vehicles. In the present investigation, the benefits in terms of CO2 and costs of the scheduled recharge with respect to free recharge are evaluated on the basis of the preliminary data acquired in the first stage of the experimental campaign.

Photovoltaic forecast based on hybrid PCA-LSSVM using dimensionality reducted data

The power forecasting plays a significant role in the electrical systems. Furthermore the high-dimensional data reduction without losing essential information represents an important advantage in the forecasting models. Low computational costs and short execution time together with high predicted performance are the main goals to be reached in the development of a prediction method. In this paper a hybrid method based on an active selection of the support vectors, using the quadratic Renyi entropy criteria in combination with the principal component analysis (PCA), is shown to dimensionally reduce the training data in the forecasting models. The reduced data have been used to implement the Least Squares Support Vector Machines (LS-SVM) in order to predict the photovoltaic (PV) power in the day-ahead time horizon. The model has been validated using historical data of a PV system in the Mediterranean climate. Additionally the weather variations have been taken into account to evaluate the outcome of the sunny and cloudy condition in the PV forecasting models. The proposed technique gives fulfill results. A training data size same as 30% original dimension allows to improve the forecasting accuracy and reduces the computational time of 70% respect to an implementation without dimensionality reduction data. A dimensionally reduction technique, based on the Renyi entropy and PCA is shown.LS-SVM models are applied to predict the PV output power in a one-day head frame.Photovoltaic forecasting model is performed using the historical PV power data.The predicted method considers the weather conditions as sunny and cloudy days.The accuracy and executive time of the hybrid method has been investigated.

High performance solutions and data for nZEBs offices located in warm climates.

This data article contains eleven tables supporting the research article entitled: Cost-Optimal Design For Nearly Zero Energy Office Buildings Located In Warm Climates [1]. The data explain the procedure of minimum energy performance requirements presented by the European Directive (EPBD) [2] to establish several variants of energy efficiency measures with the integration of renewable energy sources in order to reach nZEBs (nearly zero energy buildings) by 2020. This files include the application of comparative methodological framework and give the cost-optimal solutions for non-residential building located in Southern Italy. The data describe office sector in which direct the current European policies and investments [3], [4]. In particular, the localization of the building, geometrical features, thermal properties of the envelope and technical systems for HVAC are reported in the first sections. Energy efficiency measures related to orientation, walls, windows, heating, cooling, dhw and RES are given in the second part of the group; this data article provides 256 combinations for a financial and macroeconomic analysis.

Data on photovoltaic power forecasting models for Mediterranean climate

The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in Supplementary material File 1, were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in Supplementary material File 2.

Data of cost-optimality and technical solutions for high energy performance buildings in warm climate

The data reported in this article refers to input and output information related to the research articles entitled Assessment of cost-optimality and technical solutions in high performance multi-residential buildings in the Mediterranean area by Zacà et al. (Assessment of cost-optimality and technical solutions in high performance multi-residential buildings in the Mediterranean area, in press.) and related to the research article Cost-optimal analysis and technical comparison between standard and high efficient mono residential buildings in a warm climate by Baglivo et al. (Energy, 2015, 10.1016/j.energy.2015.02.062, in press).

Characterization of Polyurethane Foam Added with Synthesized Acetic and Oleic-modified TiO2Nanocrystals

In this work, the effects due to the addition of nanoparticles in polyurethane foams on thermo-physical and mechanical properties have been evaluated. Two types of nanoparticles were used, acetic and oleic-modified titania nanocrystals TiO2. The nanoparticles were first dispersed in a polyol component via the use of sonication; then, the doped polyol was mixed with isocyanate. The different characterization techniques describe the state of the dispersion of fillers in foam. The effects of these additions in foam were evaluated according to UNI EN 826-UNI EN 12087- UNI EN 13165, in terms of thermo-physical and mechanical properties, i.e., diffusivity, conductivity, compressive strength and water uptake. The microstructure of the foam was analysed using scanning electron microscopy (SEM). The foam obtained with nanoadditives presented improved mechanical characteristics compared to neat foam, presumably due to the different shape of the nanoparticles. The addition of nanoparticles favoured the formation of nucleation centres; this effect was likely due to the size, shape and distribution of particles and due to their surface treatment.

Data of high performance precast external walls for warm climate.

The data given in the following paper are related to input and output information of the paper entitled Design method of high performance precast external walls for warm climate by multi-objective optimization analysis by Baglivo et al. [1]. Previous studies demonstrate that the superficial mass and the internal areal heat capacity are necessary to reach the best performances for the envelope of the Zero Energy Buildings located in a warm climate [2–4]. The results show that it is possible to achieve high performance precast walls also with light and ultra-thin solutions. A multi-criteria optimization has been performed in terms of steady and dynamic thermal behavior, eco sustainability score and costs. The modeFRONTIER optimization tool, with the use of computational procedures developed in Matlab, has been used to assess the thermal dynamics of building components. A large set of the best configurations of precast external walls for warm climate with their physical and thermal properties have been reported in the data article.

Properties of nanocrystals-formulated aluminosilicate bricks

In the present work, seven different types of nanocrystals were studied as additives in the formulation of aluminosilicate bricks. The considered nanocrystals consisted of anatase titanium dioxide (two differently shaped types), boron modified anatase, calcium carbonate (in calcite phase), aluminium hydroxide and silicon carbide (of two diverse sizes), which were prepared using different methods. Syntheses aim to give a good control over a particles size and shape. Anatase titania nanocrystals, together with the nano-aluminium hydroxide ones, were synthesized via microwave-assisted procedures, with the use of different additives and without the final calcination steps. The silicon carbide nanoparticles were prepared via laser pyrolysis. The nano-calcium carbonate was prepared via a spray drying technique. All of the nanocrystals were tested as fillers (in 0.5, 1 and 2 wt. % amounts) in a commercial aluminosilicate refractory (55 % Al2O3, 42 % SiO2). They were used to prepare bricks that were thermally treated at 1300 °C for 24 hours, according to the international norms. The differently synthesized nanocrystals were added for the preparation of the bricks, with the aim to improve their heat-insulating and/or mechanical properties. The nanocrystals-modified refractories showed variations in properties, with respect to the untreated aluminosilicate reference in heat-insulating performances (thermal diffusivities were measured by the “hot disk” technique). In general, they also showed improvements in mechanical compression resistance for all of the samples at 2 wt. %. The best heat insulation was obtained with the addition of nano-aluminium hydroxide at 2 wt. %, while the highest mechanical compression breaking resistance was found with nano-CaCO3 at 2 wt. %. These outcomes were investigated with complementary techniques, like mercury porosimetry for porosity, and Archimedes methods to measure physical properties like the bulk and apparent densities, apparent porosities and water absorption. The results show that the nano-aluminium hydroxide modified bricks were the most porous, which could explain the best heat-insulating performances. There is a less straightforward explanation for the mechanical resistance results, as they may have relations with the characteristics of the pores. Furthermore, the nanoparticles may have possible reactions with the matrix during the heat treatments.