Creep and shrinkage behavior of ultra high-performance concrete under compressive loading with varying curing regimes

This Ultra High Performance Concrete research involves observing early-age creep and shrinkage under a compressive load throughout multiple thermal curing regimes. The goal was to mimic the conditions that would be expected of a precast/prestressing plant in the United States, where UHPC beams would be produced quickly to maximize a manufacturing plant’s output. The practice of steam curing green concrete to accelerate compressive strengths for early release of the prestressing tendons was utilized (140°F [60°C], 95% RH, 14 hrs), in addition to the full thermal treatment (195°F [90°C], 95% RH, 48 hrs) while the specimens were under compressive loading. Past experimental studies on creep and shrinkage characteristics of UHPC have only looked at applying a creep load after the thermal treatment had been administered to the specimens, or on ambient cured specimens. However, this research looked at mimicking current U.S. precast/prestressed plant procedures, and thus characterized the creep and shrinkage characteristics of UHPC as it is thermally treated under a compressive load. Michigan Tech has three moveable creep frames to accommodate two loading criteria per frame of 0.2f’ci and 0.6f’ci. Specimens were loaded in the creep frames and moved into a custom built curing chamber at different times, mimicking a precast plant producing several beams throughout the week and applying a thermal cure to all of the beams over the weekend. This thesis presents the effects of creep strain due to the varying curing regimes. An ambient cure regime was used as a baseline for the comparison against the varying thermal curing regimes. In all cases of thermally cured specimens, the compressive creep and shrinkage strains are accelerated to a maximum strain value, and remain consistent after the administration of the thermal cure. An average creep coefficient for specimens subjected to a thermal cure was found to be 1.12 and 0.78 for the high and low load levels, respectively. Precast/pressed plants can expect that simultaneously thermally curing UHPC elements that are produced throughout the week does not impact the post-cure creep coefficient.

Water transport in complex, non-wetting porous layers with applications to water management in low temperature fuel cells

An experimental setup was designed to visualize water percolation inside the porous transport layer, PTL, of proton exchange membrane, PEM, fuel cells and identify the relevant characterization parameters. In parallel with the observation of the water movement, the injection pressure (pressure required to transport water through the PTL) was measured. A new scaling for the drainage in porous media has been proposed based on the ratio between the input and the dissipated energies during percolation. A proportional dependency was obtained between the energy ratio and a non-dimensional time and this relationship is not dependent on the flow regime; stable displacement or capillary fingering. Experimental results show that for different PTL samples (from different manufacturers) the proportionality is different. The identification of this proportionality allows a unique characterization of PTLs with respect to water transport. This scaling has relevance in porous media flows ranging far beyond fuel cells. In parallel with the experimental analysis, a two-dimensional numerical model was developed in order to simulate the phenomena observed in the experiments. The stochastic nature of the pore size distribution, the role of the PTL wettability and morphology properties on the water transport were analyzed. The effect of a second porous layer placed between the porous transport layer and the catalyst layer called microporous layer, MPL, was also studied. It was found that the presence of the MPL significantly reduced the water content on the PTL by enhancing fingering formation. Moreover, the presence of small defects (cracks) within the MPL was shown to enhance water management. Finally, a corroboration of the numerical simulation was carried out. A threedimensional version of the network model was developed mimicking the experimental conditions. The morphology and wettability of the PTL are tuned to the experiment data by using the new energy scaling of drainage in porous media. Once the fit between numerical and experimental data is obtained, the computational PTL structure can be used in different types of simulations where the conditions are representative of the fuel cell operating conditions.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.