Fluid–Structure Interaction and Non-Fourier Effects in Coupled Electro-Thermo-Mechanical Models for Cardiac Ablation
Bibliographic record
Abstract
In this study, a fully coupled electro-thermo-mechanical model of radiofrequency (RF)-assisted cardiac ablation has been developed, incorporating fluid–structure interaction, thermal relaxation time effects and porous media approach. A non-Fourier based bio-heat transfer model has been used for predicting the temperature distribution and ablation zone during the cardiac ablation. The blood has been modeled as a Newtonian fluid and the velocity fields are obtained utilizing the Navier–Stokes equations. The thermal stresses induced due to the heating of the cardiac tissue have also been accounted. Parametric studies have been conducted to investigate the effect of cardiac tissue porosity, thermal relaxation time effects, electrode insertion depths and orientations on the treatment outcomes of the cardiac ablation. The results are presented in terms of predicted temperature distributions and ablation volumes for different cases of interest utilizing a finite element based COMSOL Multiphysics software. It has been found that electrode insertion depth and orientation has a significant effect on the treatment outcomes of cardiac ablation. Further, porosity of cardiac tissue also plays an important role in the prediction of temperature distribution and ablation volume during RF-assisted cardiac ablation. Moreover, thermal relaxation times only affect the treatment outcomes for shorter treatment times of less than 30 s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".