Computation and Modelling of Convection Heat Transfer of Supercritical Fluids
Bibliographic record
Abstract
Current literature suggests that large spatial gradients of thermophysical properties, which occur in the vicinity of the pseudo-critical thermodynamic state, may result in significant variations in forced-convection heat transfer rates.Specifically, these property gradients induce inertia-and buoyancy-driven flow phenomena that may enhance or deteriorate the turbulence-dominated heat convection process.Understanding of these inertia/buoyancy-driven mechanisms has not been sufficiently established to date.Consequently, the full set of dynamic similarity parameters remains to be identified.Through direct numerical simulations of turbulent boundary layers and channel flows, the present study investigates the characteristics of the flow structures of turbulence in heated flows of supercritical water under buoyant and non-buoyant conditions.In the absence of buoyancy forces, notable reductions in the density and viscosity in close proximity of the heated wall are observed to promote an increase in the wall shear stress, with resultant loss of coherence of the new near-wall flow structures.This leads to the dominance of larger-scale structures in the wall-normal thermal mixing process that comes at the expense of the smaller-scale thermal mixing, and yields a net reduction in the overall thermal mixing.Under the influence of wall-normal gravitational acceleration, the wall-normal density gradients are noted to enhance ejection motions due to baroclinic vorticity generation on the lower wall of the channel, thus providing additional wall-normal thermal mixing.Professor Yaras' uncompromising diligence has helped me reach a technical potential that I did not envision I could attain as pertains to undertaking fundamental and applied research in the field of science.I gratefully acknowledge the support, advice, and constructive feedback that he has provided to me throughout the course of the present endeavour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".