Self-sealing of thermal fatigue and mechanical damage in fiber-reinforced composite materials

Fiber reinforced composite tanks provide a promising method of storage for liquid oxygen and hydrogen for aerospace applications. The inherent thermal fatigue of these vessels leads to the formation of microcracks, which allow gas phase leakage across the tank walls. In this dissertation, self-healing functionality is imparted to a structural composite to effectively seal microcracks induced by both mechanical and thermal loading cycles. Two different microencapsulated healing chemistries are investigated in woven glass fiber/epoxy and uni-weave carbon fiber/epoxy composites. Self-healing of mechanically induced damage was first studied in a room temperature cured plain weave E-glass/epoxy composite with encapsulated dicyclopentadiene (DCPD) monomer and wax protected Grubbs' catalyst healing components. A controlled amount of microcracking was introduced through cyclic indentation of opposing surfaces of the composite. The resulting damage zone was proportional to the indentation load. Healing was assessed through the use of a pressure cell apparatus to detect nitrogen flow through the thickness direction of the damaged composite. Successful healing resulted in a perfect seal, with no measurable gas flow. The effect of DCPD microcapsule size (51 um and 18 um) and concentration (0 - 12.2 wt%) on the self-sealing ability was investigated. Composite specimens with 6.5 wt% 51 um capsules sealed 67% of the time, compared to 13% for the control panels without healing components. A thermally stable, dual microcapsule healing chemistry comprised of silanol terminated poly(dimethyl siloxane) plus a crosslinking agent and a tin catalyst was employed to allow higher composite processing temperatures. The microcapsules were incorporated into a satin weave E-glass fiber/epoxy composite processed at 120C to yield a glass transition temperature of 127C. Self-sealing ability after mechanical damage was assessed for different microcapsule sizes (25 um and 42 um) and concentrations (0 - 11 vol%). Incorporating 9 vol% 42 um capsules or 11 vol% 25 um capsules into the composite matrix leads to 100% of the samples sealing. The effect of microcapsule concentration on the short beam strength, storage modulus, and glass transition temperature of the composite specimens was also investigated. The thermally stable tin catalyzed poly(dimethyl siloxane) healing chemistry was then integrated into a [0/90]s uniweave carbon fiber/epoxy composite. Thermal cycling (-196C to 35C) of these specimens lead to the formation of microcracks, over time, formed a percolating crack network from one side of the composite to the other, resulting in a gas permeable specimen. Crack damage accumulation and sample permeability was monitored with number of cycles for both self-healing and traditional non-healing composites. Crack accumulation occurred at a similar rate for all sample types tested. A 63% increase in lifetime extension was achieved for the self-healing specimens over traditional non-healing composites.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.