Cardiovascular flows are subject to high shear rates that result in large velocity dynamic range for particle image velocimetry measurements. Such measurements are subject to high errors that ultimately contaminate subsequent shear stress estimation. In order to overcome such limitations they propose to develop and demonstrate a PIV processing methodology that is robust to shear rate and offers an extended measurement dynamic range. Furthermore in contrast to the traditional paradigm that relies on velocity field derivative estimation for calculating the wall shear stress they will develop a novel approach that directly calculates the complete deformation of a flow particle pattern. As a result trhey will be able to directly estimate vorticity and shear stress without relying on any post processing of the velocity field. Finally, they will explore the effect of the wall position and wall interaction with the flow on the velocity and shear stress estimation. These developments correspond to state of the art innovative advancements that promise to dramatically improve the accuracy with which wall shear stresses are estimated in cardiovascular devices.
This work aimed on the development of a high accuracy and high resolution measurement data set on a flow geometry representative of implantable cardiovascular devices. Such devices are subject to inducing blood damage and pose great health risk. This effort produced novel data that can used for the validation of computational model that asses those risks ultimately allowing for the development of safer implants. We presented benchmark experimental data obtained using advanced Particle Image Velocimetry (PIV) processing and post-processing techniques for validation of computational fluid dynamics (CFD) analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Time-resolved PIV analysis was performed in five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2,000, 5,000, and 8,000. Images included a two-fold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions (RBF) to improve accuracy. In-field spatially resolved pressure distributions, Reynolds stresses and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90 - 99% of the total uncertainty. This study provides advancements over the previous work through increased PIV image resolution, improvements in velocity measurement accuracy, calculations of wall shear stress, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress data, with appropriate uncertainty estimates, will be useful for performing comprehensive validation of current and future CFD simulations of the nozzle model.
