Time-dependent strategy for improving aortic blood flow simulations with boundary control and data assimilation

May 2025

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 environment. This study investigates the application of data assimilation and boundary optimization techniques to improve the accuracy of time-dependent blood flow simulations. We propose an integrated approach that combines data assimilation methods with boundary optimization strategies tailored for time-dependent cases. Our method aims to minimize the disparity between model predictions and observed data over time, thereby enhancing the fidelity of time-dependent blood flow simulations. Using synthetic time-series observational data with added noise, we validate our approach by comparing its predictions with the known exact solution, computing the -norm to demonstrate improved accuracy in time-dependent blood flow simulations. Our results indicate that the optimization process consistently aligns the optimized data with the exact data. In particular, velocity magnitudes showed reduced discrepancies compared to the noisy data, aligning more closely with the exact solutions. The analysis of pressure data revealed a remarkable correspondence between the optimized and exact pressure values, highlighting the potential of this methodology for accurate pressure estimation without any previous knowledge on this quantity. Furthermore, wall shear stress (WSS) analysis demonstrated the effectiveness of our optimization scheme in reducing noise and improving prediction of a relevant indicator determined at the postprocessing level. These findings suggest that our approach can significantly enhance the accuracy of blood flow simulations, ultimately contributing to better diagnostic and therapeutic strategies.

Autores da comunidade :

• 

  J

  Jorge Filipe Duarte Tiago

  ist90590

  Afiliações e fontes

  IST > CEMAT

  Centro de Matemática Computacional e Estocástica

  IST > DM

  Departamento de Matemática

  De: scopus

  ID: 2-s2.0-85214103069

  De: cienciavitae

  ID: 4265995

  De: orcid

  ID: 175797687

VoR - Versão publicada

Elsevier BV

https://www.sciencedirect.com/science/article/pii/S0096300324007276?via%3Dihub

Applied Mathematics and Computation

129266

493

0096-3003

10.1016/j.amc.2024.129266

mathematics - Matemática

Palavras-chave

• Adjoint problem
• Data assimilation
• Computational fluid dynamics
• Navier-Stokes equations
• Boundary control

eng - Inglês

Entidade financiadora da bolsa/projeto

Fundação para a Ciência e a Tecnologia

Identificador da Entidade Financiadora: PTDC/MAT-APL/7076/2020