Abstract
In the manufacturing of metal matrix composites (MMC), liquid-metal reactive infusion with a solid mesh or particles composed of ceramic or metal may be used. The objective of this study is to determine the uncertainty quantification of the modeling of liquid hafnium infusion to expedite the processing and improve properties of MMCs ultimately. Uncertainty quantification (UQ) characterized the uncertainty scientifically especially for high-performance computing with observed physics and/or chemistry of the phenomena and predicted from estimated parameters. In this work, molten hafnium infusing through a boron carbide packed bed is modeled to optimize the manufacturing of components used for a hypersonic vehicle. The creation of molten matrix composites by the infiltration of molten metal represents a formidable challenge to be accurately modeled. First, the structural randomness associated with porous mediums complicates the prediction of the flow passing through it. Secondly, the properties of the molten metal could vary inside our control volume, since the temperature inside the control volume is not constant. Also, there are several chemical reactions and solidification rates occurring in during the impregnation. Given the recent advances in high-performance computing, an in-house pore network simulator are implemented along with Dakota, an open-source, exascale software, to determine the optimal parameters (e.g., porosity and temperature) and uncertainty quantification for the modeling.
A multi-core high performance computing framework for probabilistic solutions of distribution systems
