Design, Optimization and Validation of Conformal Cooling Technique for Additively Manufactured Mold Insert

Injection molding is a cyclic process comprising of cooling phase as the largest part of this cycle. Providing efficient cooling in lesser cycle times is of significant importance in the molding industry. Conformal cooling is a proven technique for reduction in cycle times for injection molding. In this study, we have replaced a conventional cooling circuit with an optimized conformal cooling circuit in an injection molding tool (mold). The required heat transfer rate, coolant flow rate and diameter of channel was analytically calculated. Hybrid Laser powder bed fusion technique was used to manufacture this mold tool with conformal channels. The material used for manufacturing mold was maraging steel (M300). Thermal efficiency of the conformal channels was experimentally calculated using thermal imaging. Autodesk MoldFlow software was used to simulate and predict the cooling time required using conformal cooling channels. The results showed a decrease in cooling time and increase in cooling efficiency with the help of conformal cooling in additively manufactured mold insert.

Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit

In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.