An Uncertainty Visualization Framework for Large-Scale Cardiovascular Flow Simulations: A Case Study on Aortic Stenosis

TL;DR

This paper introduces a comprehensive uncertainty quantification and visualization framework for large-scale cardiovascular flow simulations, enhancing interpretability and clinical decision support in patient-specific aortic stenosis models.

Contribution

It combines advanced UQ techniques with visualization tools for high-fidelity vascular flow simulations on supercomputers, addressing spatial uncertainty mapping and model variability analysis.

Findings

Uncertainty in downstream flow increases with inlet velocity variation.

Wall shear stress uncertainty is localized in high-shear regions due to Smagorinsky constant.

The framework improves interpretability by linking UQ metrics directly to vascular anatomy.

Abstract

We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated on a patient-specific stenosed aorta. The framework combines EasyVVUQ for parameter sampling with large-eddy simulation turbulence modeling in HemeLB, and executes ensembles on the Frontier exascale supercomputer. Spatially resolved metrics, including entropy and isosurface-crossing probability, are used to map uncertainty in pressure and wall shear stress fields directly onto vascular geometries. Two sources of model variability are examined: inlet peak velocity and the Smagorinsky constant. Inlet velocity variation produces high uncertainty downstream of the stenosis where turbulence develops, while upstream regions remain stable. Smagorinsky constant variation has little effect on the large-scale pressure field but increases WSS uncertainty in localized high-shear regions. In both cases, the stenotic throat manifests low entropy, indicative of robust identification of elevated WSS. By linking quantitative UQ measures to three-dimensional anatomy, the framework improves interpretability over conventional 1D UQ plots and supports clinically relevant decision-making, with broad applicability to vascular flow problems requiring both accuracy and spatial insight.