Enhancing Turbine Deposition Prediction Capability With Conjugate Mesh Morphing

A framework for performing mesh morphing in a conjugate simulation in the commercial computational fluid dynamics (CFD) software ansys fluent is presented and validated. A procedure for morphing both the fluid and solid domains to simulate the protrusion of deposit into the fluid while concurrently altering and adding to the solid regions is detailed. The ability to delineate between the original metal sections of the solid and the morphed regions which represent deposit characteristics is demonstrated. The validity and predictive capability of the process are tested through simulation of a canonical impingement jet. A single oversized impingement jet (6.35 mm) at 894 K and an average flow velocity of 56.5 m/s is used to heat a nickel-alloy target plate. One gram of 0- to 5-µm Arizona road dust (ARD) is delivered to the target and a Particle Shadow Velocimetry (PSV) technique is used to capture the transient growth of the deposit structure on the target. Thermal infrared images are taken on the backside of the target and synchronized with the PSV images. The experiment is modeled computationally using the Fluent Discrete Phase Model (DPM) and the Ohio State University (OSU) Deposition Model for sticking prediction. The target is morphed according to the particulate volume prediction. The deposit regions are assigned an effective conductivity (k_eff) representative of a porous deposit, and the fluid and thermal computations are reconverged; 10 mesh morphing iterations are performed accounting for the first half of the experiment. The morphed deposit volume and height are compared to those of the experiment and show reasonable agreement. The backside target temperatures are also compared, and the simulations show the ability to predict the reduction in temperature that occurs as the growing deposit insulates the metal surface. It is demonstrated that the assignment of unique thermal conductivities to the deposit and metal cells within the solid is critical. With a more robust and accurate implementation of the deposit k_eff, this conjugate mesh morphing framework shows potential as a tool for predicting the thermal impact of deposition.