A Multi-layered Integral Approach to the de-icing process
Bibliographic record
Abstract
The modification of the shallow water icing model to handle de-icing phenomenon is the main focus of this study. As stated in the original model [1], the runback water is modeled utilizing a lubrication assumption for the water film velocity profile. A constant film temperature Tf(t,x) is then calculated under the thin-film hypothesis. Unlike the simplified icing model, the temperature field within the ice layer Tice(t,x,z) is no longer assumed to be constant. Instead, a temperature profile is utilized, enabling the generation of a static film on the wall when a heat conduction source term from a resistance is present [2]. A Temperature profile Ts(t,x,z) is also used in the static film layer if the model predicts the occurrence of this state. In the energy equation for both the solid ice and liquid portion of the static water film, transverse transfers are not considered, a 1D heat equation is then resolved. An integral approach and proper boundary conditions are used to close the problem. The validity of the integral method deteriorates as the thickness over which vertical integration is performed increases. To avoid this problem, a multi-layer approach is proposed. The thickness of the ice block is then divided into three layers of identical thickness. The purpose of this study is to offer a straightforward and robust method suitable for conducting industrial test cases. The model will first be introduced, followed by a description of the numerical approach. Subsequently, validation test cases will be conducted. Realistic de-icing scenarios will then be designed to evaluate the model [3]. Additionally, non uniform roughness effects will be examined.
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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".