Polymer Process Partitioning: Extruding Plastic Pipe

When plastic pipe is solidified, it proceeds through a long cooling chamber. Inside this chamber, inside the hollow extrudate, the plastic is molten, and this inner surface solidifies last. Sag, the flow due to the self-weight of the molten plastic, then happens in this cooling chamber, and sometimes, thickened regions (called knuckles) arise in the lower quadrants, especially of large diameter thickwalled pipes. To compensate for sag, engineers normally shift the die centerpiece downward. This thesis focuses on the consequences of this decentering. Specifically, when the molten polymer is viscoelastic, as is normally the case, a downward lateral force is exerted on the mandrel. Die eccentricity also affects the downstream axial force on the mandrel. These forces govern how rigidly the mandrel must be attached (normally, on a spider die). We attack this flow problem in eccentric cylindrical coordinates, using the Oldroyd 8-constant constitutive model framework. Specifically, we revise the method of Jones (1964), called polymer process partitioning. We estimate both axial and lateral forces. We develop a corresponding map to help plastics engineers predict the extrudate shape, including extrudate knuckles. From the mass balance over the postdie region, we then predict the shape of the extrudate entering the cooling chamber. We further include expressions for the stresses in the extruded polymer melt. We include detailed dimensional worked examples to show process engineers how to use our results to design pipe dies, and especially to suppress extrudate knuckling.

A cloud-based system for measuring radiation treatment plan similarity

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.