CFD Model for Aircraft Ground Deicing: Verification and Validation of an Extended Enthalpy-Porosity Technique in Particulate Two Phase Flows
Bibliographic record
Abstract
Researchers have focused in the last five years on modelling the aircraft ground deicing process using CFD (computational fluid dynamics) in order to reduce its costs and pollution. As preliminary efforts, those studies did not model the ice melting nor the diffusion between deicing fluids and water resulting from the melting process. This paper proposes a CFD method to simulate this process filling these gaps. A particulate two-phase flow approach is used to model the spray impact on ice near the contaminated surface. Ice melting is modelled using an extended version of the enthalpy-porosity technique. The water resulting from the melting process is diffused into the deicing fluid forming a single-phase film. This paper presents a new model of the process. The model is verified and validated through three steps. (i) verification of the species transport. (ii) validation of the transient temperature field of a mixture. (iii) validation of the convective heat transfer of an impinging spray. The permeability coefficient of the enthalpy-porosity technique is then calibrated. The proposed model proved to be a suitable candidate for a parametric study of the aircraft ground deicing process. On the validation test cases, the precision of heat transfer prediction exceeds 88%. The model has the ability of predicting the deicing time and the deicing fluid quantities needed to decontaminate a surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".