Dilute Viscoelastic Fluids for Enhanced Heat Transfer in Immersion Cooling Concepts
Bibliographic record
Abstract
Viscoelastic fluids are promising candidates for the thermal management of high heat-flux components of electrified powertrains, such as the battery pack via immersion cooling concepts.This study investigates the ability of viscoelasticity-inducing additives to manipulate flow patterns and enhance heat transfer in a benchmark bluff body geometry, under inertial laminar flows using dilute polymer solutions.Simulations were conducted in OpenFOAM to model viscoelastic fluid flow using the Phan-Thien-Tanner (PTT) constitutive equation and were validated against particle image velocimetry (PIV) experimental data.Two benchmark-flow configurations were studied: (i) a 180-degree channel bend and (ii) flow around a heated bluff body.Results show that viscoelastic fluids enhance vorticity in both geometries to varying extents compared to Newtonian fluids, as a function of the PTT-model slip () parameter.Heat transfer studies with a heated bluff body showed a trend of increasing heat flux for the viscoelastic fluid, with a measurable 3% enhancement at a Reynolds number of 800 also captured by the numerical results.The study highlights the potential of leveraging the influence of second normal stress difference in viscoelastic fluids for thermal management and discusses avenues for further optimisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".