Analytical Thermal Boundary Condition for Quasi-Transient Simulation of Internal Flow Experiments
Bibliographic record
Abstract
Transient thermochromic liquid crystal (TLC) experiments can provide high-fidelity spatially resolved heat transfer data for complex geometries, particularly where infrared techniques cannot be applied. One challenge when applying transient methods to internal geometries is the local definition of the driving gas temperature. The transient nature of the streamwise driving gas temperature profile has led to comparisons with steady-state computational fluid dynamics being questioned. This paper explores simulating the temporal behavior of transient TLC experiments directly. A novel technique is developed to account for differences in the gas and solid time scales, where surface temperature is calculated at each spatial location analytically from the surface heat flux, using an impulse response method assuming one dimensional, semi-infinite conduction. Postprocessing of the simulated surface temperature history is performed using the same method as experimentally, allowing for direct comparison. This analytical thermal boundary condition (ATBC) is applied to simulate a transient TLC experiment of a stationary superscaled rib turbulated internal cooling passage in a gas turbine engine. Traditional steady-state simulations were also performed with constant temporal and spatial temperature boundary conditions. Results show that calculations using the new ATBC and traditional steady-state method give very similar Nusselt number distributions and mean values in relation to the experimental data, suggesting the larger discrepancy between simulations and the experiments is not the definition of the driving gas temperature. Analysis of transient variation of Nusselt number indicated brief highly localized maximum variations up to 40%, although this was not found to significantly affect the mean values, and passage-averaged values converged to within 0.5% of the final value within 0.2 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.005 | 0.001 |
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".