In recent years, the study of flow-induced erosion phenomena has gained interest as erosion has a direct influence on the life, reliability and safety of equipment. Particularly significant erosion can occur inside the drilling tool components caused by the low particle loading (<10%) in the drilling fluid. Due to the difficulty and cost of conducting experiments, significant efforts have been invested in numerical predictive tools to understand and mitigate erosion within drilling tools. Computational fluid dynamics (CFD) is becoming a powerful tool to predict complex flow-erosion and a cost-effective method to re-design drilling equipment for mitigating erosion. Existing CFD-based erosion models predict erosion regions fairly accurately, but these models have poor reliability when it comes to quantitative predictions. In many cases, the error can be greater than an order of magnitude. The present study focuses on development of an improved CFD-erosion model for predicting the qualitative as well as the quantitative aspects of erosion.
A finite-volume based CFD-erosion model was developed using a commercially available CFD code. The CFD model involves fluid flow and turbulence modeling, particle tracking, and application of existing empirical erosion models. All parameters like surface velocity, particle concentration, particle volume fraction, etc., used in empirical erosion equations are obtained through CFD analysis. CFD modeling parameters like numerical schemes, turbulence models, near-wall treatments, grid strategy and discrete particle model parameters were investigated in detail to develop guidelines for erosion prediction. As part of this effort, the effect of computed results showed good qualitative and quantitative agreement for the benchmark case of flow through an elbow at different flow rates and particle sizes.
This paper proposes a new/modified erosion model. The combination of an improved CFD methodology and a new erosion model provides a novel computational approach that accurately predicts the location and magnitude of erosion. Reliable predictive methodology can help improve designs of downhole equipment to mitigate erosion risk as well as provide guidance on repair and maintenance intervals. This will eventually lead to improvement in the reliability and safety of downhole tool operation.
A Computational Fluid Dynamics (CFD) model to simulate the inactivation of Geobacillus stearothermophilus spores in different moist heat sterilization environments