This dissertation studies how nanoadditives influence burning rates through the development and use of a model to conduct parametric studies on nanoadditive interaction and to formulate theories. Decades of research have yet to determine the specific mechanisms for additive influence and the theories remain diverse and fragmented. It has been theorized that additives catalyze the combustion and thermal decomposition of AP, influence the condensed phases, and enhance the pyrolysis and regression of the binder. The main focus of the thesis was to approximate the enhanced boratory using spray-dried, spray-dried/heat-treated, and premixed TiO2 nanoadditives with ammonium perchlorate (AP) / hydroxyl-terminated polybutadiene (HTPB) composite propellants. The model is based on the classic Beckstead-Derr-Price (BDP) and Cohen-Strand models and contains a component that determines the pressure changes within the strand burner during a test. The model accurately predicts measured burning rates for baseline propellants without additives over a range of 500 - 3000 psi within 10%. The strand burner component of the model predicts the experimental pressure trace accurately. Further, the strand burner component determines an average burning rate over time and predicts a transient burning rate if provided a pressure trace. A parametric study with the model parameters determined that the nanoadditives appear to be increasing the AP condensed phase reaction rate. This conclusion was drawn because only changes in AP condensed-phase reaction rate would adequately and realistically replicate burning rate enhancements seen in laboratory experiments. Parametric studies with binder kinetics, binder regression rate, AP surface kinetics, and primary flame kinetics produced burning rate behavior that did not match that seen in experiments with the additives. The model was further used to develop a theory for how the nanoadditive affects the AP condensed phase, and a new parameter, (Omega)c, that influences the AP condensed phase reaction rate was created that replicates spray-dried, spray-dried/heat-treated, and premixed TiO2 nanoadditive experimental burning rates. Finally, the model was used to develop a first approximation of predicting anomalous burning rate trends such as a negative pressure dependence and extinguishment. A new term, Mc, that modifies the ratio of binder mass flux to oxidizer mass flux is used in tandem with (Omega)c to develop a negative burning rate trend that is close to the experimental result.

Highly repellent surfaces are constantly being sought in a number of industrial sectors, where accumulation of unwanted material (ice, debris, insects etc…) can cause seriously detrimental effects on these function. The chemistry and physics of such surfaces is relatively well-understood, yet their industrial adoption is still very limited, due to their poor durability. Emerging technologies for nanostructured coatings have significant potential for the development of advanced surfaces, where high repellency can be combined with mechanical robustness. However, lack of understanding of the wear mechanism in such coatings and lack of recognised test methodologies to enable comparison of various approaches hinders effective progress in advanced surfaces development. Furthermore, there is no comprehensive classification system that allows categorization of highly repellent surfaces. New multi-variable analysis methodology for the evaluation of durability in highly repellent coatings was developed in this study. Key coating parameters were identified, including initial wettability, abrasive wear, adhesive wear and ability to retain repellency. Coating characteristics were examined with FTIR, SEM, AFM, DSA, Taber Abrader, roughness profilometer and goniometer. Furthermore, these characteristics were presented in a form of spider diagrams and performance indices and are used to generate plot of performance indices. In this study, six types of TWI coating anti-soiling materials (based on patented TWI’s Vitolane® technology, containing silsesqioxanes and functionalized silica nanoparticles) and two commercial easy clean products were prepared and subjected to new assessment methodology. It has been found that this novel methodology for evaluation of highly repellent surfaces allows comparison and categorizing different families of coatings. The data obtained from plot of performance indices supports the statement that there is an inverse relation between repellency and durability of hydrophobic surfaces. It has been found that coatings with low Ra value (no more than 10nm) and symmetric distribution of peaks and valleys are the most durable, yet their WCA value doesn’t exceed more than 105°. It has been also found that some nanostructured coatings behave beyond this inverse relationship. Addition of novel inorganic building blocks with controlled size (Ra in a range of 200nm and symmetric distribution in roughness profile) and functionalities (3-(trimethoxysilyl)propyl methacrylate and 1H 1H 2H 2H-perfluorooctyltriethoxysilane) improves overall coating performance by linking mechanical robustness with desired wetting characteristics (WCA reaches 112°C). The progress in testing and classification criteria of repellent coatings enables further development of next generation of materials. This novel knowledge-based approach for highly repellent coatings validation has the potential to accelerate uptake. The findings open a promising new direction in materials development, where advanced coatings and surface treatments can be developed by design, reducing the number of development iterations, ultimately leading to reduced cost and development time.

The main task of this work was to compare the being used process fluids in mechanical engineering with a focus on polymer and additive fluids. Selected polymer and nanoadditive fluids were compared based on a thread forming test, during which different concentrations of individual fluids were tested. All data from experiments were evaluated, assessed in terms of their properties, and recommended for further practice.