1,175 research outputs found
Mathematical modeling for Thermodynamics: Thermophysical Properties and Equation of State
No full textNelle moderne società multiculturali e multidisciplinari, sempre di più si devono adottare delle prospettive più ampie possibili.
In questa tesi, si è tentato di adottare un metodo multidisciplinare che coinvolgesse non solo la matematica e la fisica, ma anche la chimica, la statistica, e più in generale l’ingegneria.
Gli aspetti toccati sono quelli delle proprietà termofisiche della materia e delle equazioni di stato dei gas (EOS). Le proprietà termofisiche analizzate sono: tensione superficiale, conduttività termica, viscosità, dei liquidi e dei gas ed il secondo coefficiente del viriale. Dopo la raccolta dei dati sperimentali, essi sono stati analizzati con varie tecniche statistiche che trasformassero i dati grezzi in dati più attendibili. Dopo lo studio delle equazioni della letteratura si è proceduto con uno studio di sensibilità dei dati per vedere quali proprietà fisiche avessero maggiore impatto sulle proprietà studiate. Infine si è cercata un’equazione che potesse rappresentare nel migliore modo possibile i dati sperimentali.
Si sono sempre preferite equazioni scalate ad equazioni puramente empiriche, in modo da avere non solo l’aderenza ai dati sperimentali, ma anche il rispetto dell’aspetto chimico-fisico. Dall’analisi dei residui, confrontandoci con le migliori equazioni in letteratura, i nostri risultati sono sempre stati migliori, tanto che hanno avuto dignità di pubblicazione nelle maggiori riviste del settore.
Discorso a parte per le EOS.
Analizzando la letteratura, ciò che subito è saltato all’occhio è che cercare la migliore equazione possibile è impossibile! Oppure come dice Martin parafrasando una frase della favola Biancaneve: “Specchio specchio delle mie brame, qual è la più bella del reame?”
Abbiamo scelto la modifica dell’equazione Carnahan-Starling-De Santis. Tramite tecniche di minimizzazione multi obiettivo si sono migliorate le performance di tal equazione proprio intorno al punto critico.
Questi sono gli aspetti principali toccati in questo lavoro di tesi, che di là dai risultati, pur buoni ottenuti, mi ha aperto il mondo della ricerca.Abstract
In the modern multicultural and multidisciplinary society, always adopting more and more wider prospective than before.
In this thesis, we try to adopt a multidisciplinary method, which involves Mathematics, Physics, but also Chemistry, Statistics, and in general the scientific engineering.
The aspects explained are thermo physical properties, and Equations of State (EOS) of gases.
Regarding thermo physical properties have been analysed Surface Tension, Thermal Conductivity, Viscosity, and the second virial coefficient.
On this arguments, the work had been subdivided between the gathering of experimental data, the analysing of data with statistical techniques transforming them to more reliable data than row.
The second step was to collect the equations of literature. Then we went ahead studying the sensibility of data to find out which physical properties could have bigger impact to property examined.
At the end, we looked for an equation that could represent experimental data in a better way. We always preferred the scaled equations that respect chemical and physical aspects, to the empirical ones.
Comparing our results with better equations in literature, our results are always better, in fact all of the have been published in the best international journals on this subject.
A separate discussion is that of EOS.
Analyzing the previous literature, the first thing that came to our minds was that to find the best possible equation is impossible. Or as Martin wrote copying words of the famous fables Snow White: “Mirror mirror on the wall, who is the fairest of them all?”.
We choose to modify The Carnahan-Starling-De Santis (CSD) equation of state, a parametrich equation with good results in the calculation of Vapor Liquid Equilibrium.
Due to multi objective minimization techniques the performance of CSD has been improved.
These are the principals aspect brought to light in this research, which apart from the results, with good results has opened to me the world of research
Modeling thermal conductivity in refrigerants through neural networks
No full textThe thermal conductivity value for a material measures its attitude to transfer heat, though, not many values coming from experimental measurements of the thermal conductivity of different materials are available to the scientific community, which needs accurate model to predict such value from other observations. In this work, we trained and evaluated a Multi-Layered Perceptron architecture for a regression task in which the thermal conductivity for a set of families of refrigerants at the liquid state is predicted from their acentric factor, critical pressure, reduced temperature, and dipole moment, at atmospheric pressure condition. Such model has been proven capable to capture deep regularities over the whole data set and also across different families of refrigerants. Compared to other well-known equations from the literature for the same task, our model significantly outperformed all of them
Surface tension prediction for refrigerant binary systems
No full textThis work presents a literature survey of the available data of the experimental surface tension data for refrigerant binary systems. The experimental data were collected for the following binary systems: R32-R227ea, R32-R125, R32-R134a, R290-R32, R125-R152a, R290-R152a, R290-R600a, RE170-R290, R143a-R134a, R125-R134a, R125-R143a, R134a-R152a and R143a + R227ea. Two recently proposed equations for the prediction of the surface tension of pure fluids were evaluated for their abilities to predict the surface tension of binary systems. A new equation for the prediction of the surface tension of refrigerant binary systems based on the Corresponding States Principle theory is proposed. © 2012 Elsevier Ltd and IIR. All rights reserved
Surface tension correlation of carboxylic acids from liquid viscosity data
No full textThis study presents a method to calculate the surface tension of a wide variety of organic carboxylic-based acids molecules starting from the experimental data of the liquid viscosity. The first step of this research was a data collection and then an accurate selection of experimental sources for the calculation of surface tension. Besides, in order to build two different but comparable databanks, the experimental points were regressed with simple equations at the same reduced temperature range, spanning from 0.35 to 0.75. As a final step, the original relationship of Pelofsky between the natural logarithm of surface tension and the inverse of viscosity was changed to better describe the property under investigation. The simple modification of adding the reduced temperature allowed to achieve an AAD of 1.24% for the whole dataset that contains 22 fluids with 170 surface tension data and 379 liquid viscosity experimental data
Modeling investigation on the viscosity of pure refrigerants and their liquid mixtures by using the Patel-Teja viscosity equation of state
No full textModeling equilibrium and non-equilibrium thermophysical properties of liquid lubricants using semi-empirical approaches and neural network
No full textThis study explored the capability of semi-empirical and neural network approaches for correlating and predicting some equilibrium and non-equilibrium thermophysical properties of liquid lubricants. The equilibrium properties, including the densities and several thermodynamic coefficients for 12 liquid lubricants, were correlated and predicted through a perturbed hard-chain equation of state (PHC EoS) by an attractive term of Yukawa tail. The molecular parameters of PHC EoS were obtained by correlating them with 935 data points for the densities and isothermal compressibilities of studied systems in the 278-353 K range and pressure up to 70 MPa with the average absolute relative deviations (AARDs) of 0.36 % and 5.25 %, respectively. Then, that EoS was employed to predict the densities of other literature sources (with an AARD of 0.81 %) along with several thermodynamic coefficients, including isobaric expansivities (with an AARD of 12.92 %), thermal pressure coefficients (with the AARD of 12.93 %), and internal pressure (with the AARD of 13.67 %), for which the reference values were obtained from Tait-type equations and available in literature. Apart from the equilibrium mentioned above properties, the PHC EoS was combined with a rough hard-sphere-chain (RHSC) model to correlate and predict the 548 data points for the viscosities of 7 selected liquefied lubricants in 283-353 K range and pressures up to 100 MPa with the AARD of 11.85 %. The accuracy of the results from the RHSC-based model has also been compared with an empirical P eta T equation of Tammann-Tait type and an artificial neural network (ANN), both of which were developed in this work. The ANN of one hidden layer and 13 neurons was trained using the back-propagation algorithm. The results acquired from this approach were very promising and demonstrated the potential of the ANN approach for predicting the viscosity of lubricants, reaching an AARD of 0.81 % for the entire dataset
Modeling surface tension of ten binary cryogenic mixtures with a thermodynamic method and artificial neural network
No full textThe phase equilibrium calculations between the liquid and surface phase are conducted to predict the surface tension and interfacial mole fractions of the components for ten binary cryogenic systems. This thermodynamic model is combined with the perturbed chain statistical association fluid theory equation of state to determine the fugacity coefficients and molar volumes of the components. Based on the application of molar or partial molar volumes, 4 different strategies are applied to the molar surface area of this model. The results of the thermodynamic model indicate that the first strategy has the best predictions for most cases. Then an artificial neural network has been applied to the surface tension of these ten mixtures. This model contains four input parameters and 9 neurons with a single layer. The overall good predictive capability of the artificial neural network model is proved with an R2 of 0.999 and an AADγ% of 0.94 for the entire dataset
Environmental Quality Index In Hospital Patient’s Rooms. Rapid assessment tool for the verification and design
No full textThe research proposes an environmental quality system assessment in hospital patient's rooms, based on a series of spreadsheets that define a global index, resulting from measurement of spatial, technological and perceptive parameters
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