Time-Dependent Strategy for Improving Aortic Blood Flow Simulations with Boundary Control and Data Assimilation

Abstract

Understanding time-dependent blood flow dynamics in arteries is crucial for diagnosing and treating cardiovascular diseases. However, accurately predicting time-varying flow patterns requires integrating observational data with computational models in a dynamic setting. This study explores the application of data assimilation and boundary optimization techniques to enhance the accuracy of time-dependent blood flow simulations. We propose an integrated approach that combines data assimilation methods with boundary optimization strategies, minimizing the disparity between model predictions and observed data over time. Using synthetic time-series observational data with added noise, we validate our method by comparing its predictions with an exact reference solution, computing the L2-norm to assess accuracy improvements. Results demonstrate that the optimization process consistently refines velocity magnitudes, reducing discrepancies and aligning them more closely with the exact solution. Pressure analysis reveals a remarkable correspondence between optimized and exact values, even without prior knowledge of pressure conditions. Additionally, wall shear stress (WSS) analysis confirms the method’s effectiveness in noise reduction and improving predictions of clinically relevant hemodynamic indicators. These findings highlight the potential of our approach for significantly improving the accuracy of time-dependent blood flow simulations, paving the way for better diagnostic and therapeutic strategies in cardiovascular medicine.

Short bio

Muhammad Adnan Anwar is a researcher specializing in computational fluid mechanics, fluid-structure interaction (FSI), and blood flow modeling. His work focuses on integrating advanced numerical methods, optimal control strategies, and data assimilation techniques to improve the accuracy of cardiovascular simulations. With expertise in fluid dynamics, boundary control, and patient-specific modeling, Adnan aims to develop innovative computational frameworks that contribute to personalized medicine and cardiovascular diagnostics.