The successful manufacture of thick-sectioned composites is challenging, since the highly exothermic nature of thermoset resins and limited temperature control make it difficult to avoid detrimental thermal and cure gradients within the composite. In order to make quality parts, it has been found experimentally that cure temperatures must be lowered as much as 50% from those suggested for thin parts. Differential Scanning Calorimetry (DSC) experiments of a vinyl-ester resin system at these lower temperatures revealed a significant dependence on temperature for the maximum extent of cure. If the resin is cured isothermally at 55°C, the final conversion of the resin was found to only reach 70%. When the maximum extent of cure parameter was incorporated into an empirical autocatalytic kinetic model, it was found to significantly improve the description of the cure kinetics. Inhibitors, added to the resin to improve shelf-life, disappear rapidly at higher cure temperatures but can double the time required to cure a thick composite processed at 55°C. A zeroth order kinetic relationship was developed to estimate the amount of inhibitor in the system during the resin's cure. The inhibitor relationship and the improved kinetic model were used in a finite difference cure simulation to successfully predict the thermal gradients during cure of a 2.54 cm thick composite manufactured by resin transfer molding (RTM).
In the present work, we validate experimentally the cure simulation of a thick-sectioned composite processed by resin transfer molding (RTM). The simulation was based on an improved version of the model equations presented previously [Michaud, D.J., Beris, A.N. and Dhurjati, P.S. (1998). Curing behavior of thick-sectioned RTM composites. J. Compos. Mat., 32(14): 1273–1296.]. The presence of fiber reinforcement was found to significantly impact the curing behavior of the resin, leading to significant changes from the neat resin kinetic parameters. Thus, experimental processing data from seven 2.54 cm thick laminates were used to characterize the composite’s heat transfer and kinetic model parameters not readily computable from pure component values. The apparent initial concentration of inhibitor additives within the resin system decreased almost 80%, possibly due to absorption on the fiber surfaces. The presence of fibers was also found to reduce the extent of polymerization within the system. Key heat transfer model parameters, for both uncured and cured states, were also identified from experimental data. The importance of considering batch to batch variations and the temperature dependence of the resulting model parameters is discussed. In addition to the development and validation of the RTM cure simulation, heat flux sensors (HFSs)were evaluated as a nonintrusive sensor to replace internal thermocouples as a means of measuring internal cure behavior within a thick-sectioned composite.
This paper proposes a single stage topology suitable for small to medium power systems with high inertia loads such as home appliances. This approach features a single controlled power stage which implements both conventional brushless DC motor speed control and a novel power factor correction strategy. This approach eliminates the Boost Unity Power Factor (UPF) stage and bulk electrolytic capacitor typically used for single phase applications. With an appropriate current modulation strategy, the input current can be shaped and high input power factor can be obtained. Design equations are derived, a comparison with the conventional two-stage approach is performed and simulation and experimental results are presented. I.
The explosive growth in genomic (and soon, expression and proteomic) data, exemplified by the Human Genome Project, is a fertile domain for the application of multi-agent information gathering technologies. Furthermore, hundreds of smaller-profile, yet still economically important organisms are being studied that require the efficient and inexpensive automated analysis tools that multi-agent approaches can provide. In this paper, we discuss the use of DECAF, a multi-agent system toolkit based on RETSINA and TAEMS, to build reusable information gathering systems for bioinformatics. We will cover why bioinformatics is a classic application for information gathering, how DECAF supports it, and several extensions that support new analysis paths for genomic information.
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