Hybrid CFD PINN FSI Simulation in Coronary Artery Trees
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis manuscript proposes a novel Hybrid CFD approach for simulating fluid flow in stenotic coronary artery trees, there are still some issues that to be addressed.
1. Figure 1 shows that four different loss functions are used, how is the final loss function value calculated?
2. The PINN residual history comparison in Figure 10 becomes rather unclear when the iteration exceeds 1000.
Author Response
See attached file.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe present work is concerned with the simulation of fluid flow in stenotic coronary artery tress by means of a Hybrid Computational Fluid Dynamics approach integrating Physics-Informed Neural Networks. The goal is to utilize a one dimensional such model to predict outlet flow conditions. Numerical outcomes reveal authentically that the proposed method effectively generates realistic outflow boundary conditions for diagnosing stenosis. Hence, it is concluded that the present approach is able to distinguish the methodology in cardiology from conventional data-driven models relying heavily on extensive datasets during the traditional Finite Element Analysis.
The work seems to be publishable in MDPI: Fluids if a careful major attention is given to the following coments;
A)- Abstract is good overall. Significance can be accentuated more in terms of physical orientation.
B)- Language is fine overall, take care of local typos deficiencies. Also, punctuation, commas, dots, etc.
C)- The literature survey is fully linked to the topic, indicating the significance of the topic. However, give reference to the statement “In contrast, the hybrid CFD PINN FSI technique leverages the geometric characteristics of the arterial tree, conservation laws in CFD, and patient-specific parameters to derive outlet conditions”.
D)- I am unsure whether the statement “The current stage of this model involves conceptual proof-of-concept validation 94 against experimental benchmarks and outputs from other simulations, with ethical con-95 siderations and institutional review board (IRB) approval for patient data collection al-96 ready in place” fits in the place given there. It may be emphasized in the footprint.
E) – In simplified model (1), amplify the unknowns versus knowns. Are there enough equations in terms of unknowns?
F)- Is the simulation under laminar fluid flow conditions? Is the Newtonian assumption suitable for non-Newtonian blood flow?
G) – Although well-known, NS Equations (3-5) should be referenced.
H) – Some less used info can be moved to an Appendix.
K) – Figures 7-9 indicate a close correlation between the area and pressure. Is there a correlation?
L) – Further exploration into optimizing these methods 748 could lead to even more robust applications in clinical practice. Give perspectives on how to optimize.
M) – Although it is concluded that the 1D PINN code effectively replicates fluid flow patterns in coronary artery networks and identifies potential stenotic regions with exceptional accuracy and efficiency, outperforming Finite Element Method by approximately tenfold, it is now found in the discussion part!
N) - Present discussions on how the ideas introduced here can be used in other areas, like in epidemiology, referring to “Solutions to SIR/SEIR Epidemic Models with Exponential Series: Numerical and non Numerical Approaches. Computers in Biology and Medicine 2024; 183: 109294”. Can the epidemic peak time be estimated through the innovative Hybrid CFD PINN FSI method given here?
Author Response
See attached file please
Author Response File: Author Response.pdf
