Mariano Pierantozzi

Equation for the Thermal Conductivity of Liquids and an Artificial Neural Network

This work presents a literature survey of the available experimental data regarding the thermal conductivity of organic liquids. Experimental data are regressed with the most reliable semiempirical correlating methods existing in the literature, and a set of 5010 data are finally selected, belonging to 164 compounds in the following families: bromide derivatives, chlorine derivatives, condensed rings, fluorine+chlorine+bromide derivatives, F-derivatives, hydrocarbon chains, monocyclic compounds, carboxylic acids, cycloalkanes, cycloalkenes, esters, and ketones. A new correlation to represent the thermal conductivity of pure liquids is presented. A factor analysis is performed for the data selected in order to select the physical parameters to adopt. Optimal coefficients and a different version of the recently proposed equation are presented. The correlation is very simple and is able to predict the thermal conductivity with very low deviation for all families studied. The correlation reproduces the select...

Assessment of outdoor thermal comfort and its relation to urban geometry

Microclimate conditions in urban open spaces are directly linked to the configuration of street axes and building heights and their attributes. Within street canyons, public places, and open spaces, the local microclimate depends directly on the physical properties of the surrounding surfaces and objects, producing well-known effects that can decrease or increase thermal loads. All of these phenomena can greatly influence the comfort of a city and the thermal comfort of pedestrians. Thermal comfort is an indicator that cannot be easily converted into physical parameters. However, it may be defined more qualitatively as the range of climatic conditions in which most people feel comfortable. One well-recognized thermal comfort index used to measure comfort levels inside a space is the predicted mean vote (PMV). Fanger’s PMV index has been widely used in the last ten years. It is based on six factors: air temperature, air speed, humidity, mean radiant temperature, metabolic rate, and clothing levels. The comfort equation establishes relationships among the abovementioned environmental variables, clothing type, and metabolic rate. The authors present results of PMV simulations using a multi-objective optimization tool (i.e., modeFrontier). ModeFRONTIER is an integration platform used to optimize and arrange PMV algorithms linked to urban geometry parameters (e.g., the height-to-width (H/W) ratio of urban streets). The optimization process employs given constraints, custom procedural algorithms, and genetic algorithms to examine a wide urban space and identify interesting relationships among the variables considered. Urban geometry, meteorological data, and latent influences are examined and negotiated quantitatively to improve outdoor thermal comfort.

Liquid thermal conductivity prediction for alkanes, ketones and silanes

ABSTRACT The values of thermal conductivity at different temperatures for organic and inorganic compounds in the liquid phase is required in the study of several processes, but experimental data are often not available with acceptable reliability or not available at all; since rigorous theoretical or semi-theoretical models of the liquid state are usually of poor practical use for engineering purposes (the errors can be very high and the mathematical difficulties generally lead to excessive simplifications) empirical or semi-empirical methods are used to estimate with reasonable accuracy. In this work a simple equation already proposed by the authors for the estimation of the liquid thermal conductivity of alcohols is generalized in order to allow the calculation of for the compounds belonging to the families of n-alkanes, ketones and silanes. The proposed equation requires for each compound the knowledge of the critical temperature Tc and the molecular weight M and contains three parameters: the ‘golden ratio’ , a factor h characteristic of the investigated organic family and an exponent a depending on the molecular structure. The families of n-alkanes, ketones and silanes were chosen to verify the general reliability of the method when used in large temperature ranges for very different organic families, above all the silanes (compounds containing silicon), whose liquid thermal conductivity is experimentally investigated in very few cases. The comparison between estimated and experimental values was developed taking into account the database due to ‘The DIPPR Information and Data Evaluation Manager’. The general equation appears to be successful: in the reduced temperature range 0.30–0.80, along or near the saturation line, the average absolute deviations between calculated and experimental thermal conductivity data are usually <4% and the maximum absolute ones usually <8%. A comparison is developed between the proposed equation and several correlations appeared in scientific and technical literature. The final goal to be reached is to cover by the proposed estimation method in general the different families of the organic compounds.

Assessing the representativeness of thermal comfort in outdoor spaces

This paper presents preliminary findings of an outdoor thermal comfort study conducted in an urban area to evaluate the representative Predicted Mean Vote index. Thermal comfort in outdoor urban spaces is often faced with the task of using large amounts of data that yields meaningful information concerning the thermal sensation. It is essential to interpret correctly meteorological and thermal comfort data. In particular, it is important to interpret data using an appropriate statistical analysis, and the analysis of thermal comfort presupposes a synthesis of information derived from a series of temporal data. It is indispensable to deal with realistic data and an actual day should be considered, but the widely used average day is not an actual day. On the contrary, the representative day is made of the actual data of the day, in the period considered, where the sum of the mean-square differences among its monitored quantities, averaged within each hour, and the same quantities for all other days at the same hour, is minimised. The goal of this research is to assess the representativeness of the thermal comfort indices provided using a representative day technique. Specifically, a new tool has been developed using a powerful and useful environment for symbolic and numerical computing and data visualization such as Wolfram MathematicaTM, aiming at linking information computed by a bio-climate model to the representative day technique. The possibility of assessing the diurnal variation of PMV thermal comfort index by introducing the Representative Day technique has been evaluated in order to gather information on the correlation between thermal comfort and meteorological parameters. A case study has been analysed in order to improve the microclimate in an outdoor space located in a typical Mediterranean area and a comparison with CFD code, namely ENVI-MET, has been reported. This technique can prove to be a very

Artificial neural network modelling of liquid thermal conductivity for alcohols

ABSTRACT This study investigates the applicability of artificial neural networks (ANNs) as an efficient tool for the description of thermal conductivity of liquid alcohols for a broad range of temperatures. The proposed multilayer perceptron has 1 hidden layer with 43 neurons, determined according to the constructive approach. The model developed was trained and validated on the set of data gathered, showing that the accuracy of the ANN model is higher than that of other approaches proposed in the literature. The experimental or experimental and predicted thermal conductivity data of alcohols were taken from the database due to the ‘DIPPR Database’. The ability of the ANN method to reproduce the original data was tested for 26 alcohols in the liquid phase at reduced temperatures ranging from 0.30 to 0.90. The maximum absolute deviations between experimental and calculated thermal conductivity data points are generally less than 0.0110%, while the average absolute deviations are usually less than 0.0016%. This study shows that the model used is a good alternative to estimating thermal conductivity of alcohols with excellent precision.

The QLandQLife Tool

In recent decades, the city and the broader concept of the territory have experienced a metamorphosis: from usable physical resources and controllable, designable space to a new interpretation of the urban system. This system is complex, so the inadequacy of linear planning becomes clear when faced with an increasingly strong need for multiple intelligible responses. The ideal of the city as a “single element” has been substituted by the concept of “system city”, going beyond the model of a city that can be decomposed and simplified to attain an interpretation of the system as a “complex unit”.

Artificial neural network for the second virial coefficient of organic and inorganic compounds: An ANN for B of organic and inorganic compounds

ABSTRACT An artificial neural network (ANN) to estimate the second virial coefficient, valid for organic and inorganic compounds, is presented. First, we statistically analyzed 6,531 experimental points, belonging to 234 fluids, collected from literature. The data were investigated with a factor analysis approach to identify the most significant parameters that influence the second virial coefficient. The factor analysis, combined with physical considerations, allowed to find four (Tr, Tc, Pc, ω) or five (μr) parameters as input variables for the ANN, according to the specific chemical family. The architecture of the proposed multi-layers perceptron (MLP) neural network consists of one input layer with five input variables (Tr, Tc, Pc, ω, μr), one output layer with one neuron (B) and two-hidden-layers with 19 neurons each. We trained, validated and tested several configurations of the neural network to obtain this network topology that minimizes the deviations between experimental and calculated points. Results show that the ANN is able to calculate the second virial coefficient with greater accuracy (RMSE = 29.38 cm3/mol) than that of correlations available in literature. To identify the outliers and applicability domain of the proposed MLP neural network, an outlier diagnosis based on the Leverage approach was performed. This analysis shows that the model is statistically valid.

Experimental characterization of a solar cooker with thermal energy storage based on solar salt

High temperature solar cooking allows to cook food fast and with good efficiency. An unavoidable drawback of this technology is that it requires nearly clear-sky conditions. In addition, evening cooking is difficult to be accomplished, particularly on the winter season during which solar radiation availability is limited to a few hours in the afternoon in most of countries. These restrictions could be overcome using a cooker thermal storage unit (TSU). In this work, a TSU based on solar salt was studied. The unit consists of two metal concentric cylindrical vessels, connected together to form a double-walled vessel. The volume between walls was filled with a certain amount of nitrate based phase change material (solar salt). In order to characterize the TSU, a test bench used to assess solar cooker performance was adopted. Experimental load tests with the TSU were carried out to evaluate the cooker performance. The obtained preliminary results show that the adoption of the solar salt TSU seems to allow both the opportunity of evening cooking and the possibility to better stabilize the cooker temperature when sky conditions are variable.