The object of the work presented in this thesis was to obtain quantitative values of the thermodynamic changes in water upon adsorption by silica. Two methods were used. The first was a determination of the differential entropy and heat of adsorption independent of any theory of adsorption. The second method included a new interpretation of the "c" constant of the Brunauer, Emmett, and Teller Theory in which c was expressed in terms of the standard free energy of adsorption. The experimental data gave satisfactory results in determining the differential heat and entropy of adsorption. The results obtained in determining the standard entropy and heat of adsoption of use of the B.E.T. equation were questionable. The standard entropy of adsorption as determined by the B.E.T. equation was less negative than the corresponding entropy of condensation. It is unlikely that water molecules adsorbed on silica would have more freedom than water molecules in the liquid state. The adsorption of water on silica appears to be a van der Waals type of adsorption, the heats of adsoption being only 1-4 k. cal. greater than the heat of condensation of water.

A new isothermal titration calorimeter has been designed to facilitate the study of heats of reaction and heats of solution at constant temperature. With this calorimeter, endothermic or exothermic processes can be studied at temperatures constant to ±0.0002°C. Heat effects are compensated by balancing the heat effect of the process taking place in the calorimeter against a variable heat and a constant cooling Peltier device. The calorimeter was tested by measuring the heat of ionization of water and the heat of dilution of aqueous HClO_4 solutions. For these systems heats were measured to an accuracy of ±0.02 calorie. Log K, ΔH°, ΔS° and ΔCp° values valid at zero ionic strength have been reported for the interaction of CN^- with Ni^2+ , Zn^2+ , Cd^2+ and Hg^2+ in aqueous solution at 10, 25 and 40°C. The results were compared to electrostatic predictions and it was found that electrostatic considerations do not predict the metal-ligand behavior in solution. The effect of temperature upon the behavior of ΔG, ΔH and ΔS is predicted using the ΔCp values determined in this study. Log K, ΔH°, ΔS° and ΔCp° values were determined for the glycinate complexes of bivalent Mn, Fe, Co, Ni, Cu, Zn and Cd. The results indicate that solvent effects for the various metal complexes are similar (based on ΔCp values) and that the temperature dependence of the thermodynamics of the metal glycinate complexes are similar for each of the metal glycinate systems.

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<p>The first two chapters make use of simple, simulated model systems to break down the unique solvation thermodynamics of solutes at the vapor-liquid interface of water and of aggregation processes in the bulk. In particular, attention is paid to the direct solute-solvent energetic and entropic components that dictate the chemical potential. This proves a fruitful approach to understanding the counter-intuitive adsorbtion of ions to the interface. Additionally, the validity of linear response theory is tested in the interfacial region. Further, the contribution of ion surface pinning to the total adsorbtion thermodynamics is explored.</p> <p><br></p> <p>The third chapter studies the solvent-solvent interaction energy in response to changes in solute-solvent interactions. A solvent-solvent coupling scheme is developed to the relationship of solute-solvent interactions and solvent-solvent interactions and allows for more statistically stable access to solvent restructuring energies</p> <p><br></p> <p>The fourth chapter looks at ions of a range of sizes, but with a focus on those smaller than sodium. There smaller cations show a counter-intuitive trend of showing more attraction to the interface, when they are predicted to be more repelled than the larger ions. This is investigated with thermodynamic and hydration structure tools.</p> <p><br></p>

The ever-increasing focus of policy-makers on environmental issues are pushing the combustion community towards making combustion cleaner by optimizing the combustion equipment in order to reduce emissions, improve efficiency and satisfy the increasing energy demand. A major part of this involves advancing modelling capabilities of these complex combustion systems, which is a combination of computational fluid dynamics with detailed chemical kinetic models. A chemical kinetic model comprises of a series of elementary reactions with corresponding kinetic rate parameters and species thermodynamic and transport data. The predictive capability of these models depends on the accuracy to which individual chemical reaction rates, thermodynamic and transport parameters are known. A minor fraction of the rate constants and thermodynamic properties in the widely used kinetic mechanisms are experimentally derived or theoretically calculated. The remaining are approximated using rate rules and group additivity methods respectively for rate constants and thermodynamic properties. Recent works have highlighted the need for error checking when preparing such models using the approximations, but a useful community tool to perform such analysis is missing. In the initial part of this work, we developed a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. Furthermore, issues related to unphysically fast time scales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also presented a procedure to screen ultra-fast reaction time scales using computational singular perturbation (CSP). The screening of kinetic models is a necessary condition, however, not a sufficient one. Therefore, exploring new approaches for the simulation of complex chemically reacting systems are needed. This work focuses on developing new methods for estimating thermodynamic data efficiently and accurately, thereby increasing the compliance of forth-mentioned screening. Machine Learning (ML) has been increasingly becoming a tool of choice for regression, replacing traditional function fittings. Group additivity incorporates simple functions and derive constants with a certain existing data and use these functions to estimate the unknown values. ML algorithms does the same without fixing a specific function there by letting algorithm to learn the non-linearity from the training data itself. With the new data coming in with time, ML algorithms learn better and improves over time, whereas this need not necessarily happen with traditional methods. In the first part of the study, data for standard enthalpy is collected from the literature sources and ML models are built on these databases. Two different models were built and studied for a straight-chain species and cyclic species dataset. Molecular descriptors are used as the datasets collected from literature are small for using any sparse representations as input. As expected, we observed a good improvement above group additivity method for these ML models. The improvement is observed to be more significant for cyclic species. With the motivation of ML models showing benefit over the group additivity method, a step further was taken. A homogenous and accurate dataset is necessary for building a ML model that can be used for generating the thermodynamic data for kinetic models. With this in mind, an accurate database for thermodynamic data is built from ab-intio calculations. The species in the dataset are taken from a detailed and well established mechanism to cover all the species in a typical kinetic mechanism. The calculations are performed at a high level of accuracy, in comparison to other similar datasets in literature. In the later part of this work, the dataset developed using ab-inito calculations is used for developing ML models. Unlike the ML models built from the literature datasets, this database consists of all the thermodynamic data required for kinetic models viz. standard enthalpy and standard entropy and heat capacity at 300 K and higher temperatures. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not always accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. Therefore, we propose to use machine-learning algorithms directly to achieve better predictions, circumventing the kinetic models. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency. Measured species profiles of methane, ethylene, propylene, hydrogen, carbon monoxide and carbon dioxide are used for machine-learning model development. The model considers both chemical effects and physical conditions. Chemical effects are described as different functional groups, viz. primary, secondary, tertiary, and quaternary carbons in molecular structures, and physical conditions as temperature. Both the Machine-learning models used in this study showed a good prediction accuracy. By expanding the experimental database, machine-learning models can be further applied to many other hydrocarbons in future work, for the direct predictions.

<p>Solidification of Nb-microalloyed HSLA steels may result in the precipitation of niobium carbonitrides, which is hardly surprising in view of their extreme thermodynamic stability. Recently, it was proposed in literature that coarse Nb-rich particles found along the centerline of continuously cast HSLA steels originated from ferroniobium additions during ladle metallurgy. In particular, it was hypothesized that thermally stable phases formed during manufacturing of ferroniobium were released into the melt once the ferroniobium had partially fused. In this contribution, Scheil–Gulliver formalism is employed to predict the phase portrait of ferroniobium in an attempt to simulate the manufacturing process of ferroniobium.</p> <p>To corroborate the predictions, the microstructure of ferroniobium is characterized to determine if thermally stable particles exist in ferroniobium. Further, a model is developed to predict the dissolution rate of thermally stable phases that were observed in ferroniobium as well as in the centerline region of as-cast HSLA steel. Finally, a sample near the centreline region of a Nb-microalloyed HSLA steel is characterized and centreline compositions are measured. Based on experimental evidences, an alternative explanation to the origin of thermally stable particles found near the centreline of HSLA steels is proposed.</p> / Master of Materials Science and Engineering (MMatSE)

This thesis investigates and determines the design considerations to be addressed when designing switched reluctance machines (SRMs) operating at high surface-speeds and high-loads. A new method is introduced to the traditional machine design procedure that captures all of the mechanical, thermal and electro-magnetic considerations for such electric machines. This method is applicable to any motor design; however, is most suitable for machines with rotors that sustain mechanical stresses near the rotor core material limits. The method begins by using common application specifications to identify the maximum diameter and length of a rotor through a series of structural analyses. Maximizing rotor diameter and axial length enables designers to evaluate a machine's theoretical mechanical and electro-magnetic performance limits. The design method is structured such that the designer must use theoretical limits as a constraint for assessing future design decisions which ultimately influence machine cost and performance. The proposed design method is applied to a case study example typical of a large electric vehicle traction machine, a 22,000rpm, 150 kW switched reluctance machine, while attempting to adhere with design practices commensurate with automotive mass manufacturing. To achieve this, a parallel connected 12/8 pole topology was finally developed. The thesis research suggest that a 440 MPa yield strength, 0.27mm thickness lamination with 30 turn stator coils is sufficient to meet the specification requirements within a prescribed power electronic converter voltage and current constraints, while satisfying material mechanical and thermal considerations. Detailed analysis of AC effects, performance characteristics, thermodynamics, noise and vibration is presented to simultaneously demonstrate and validate the proposed machine design and design method. / Thesis / Doctor of Philosophy (PhD)