Heat Transfer Coefficient Prediction of a Porous Material by Implementing a Machine Learning Model on a CFD Data Set
Bibliographic record
Abstract
During many years, the search for new and improved materials has been an arduous task.It has mainly focused on experimentation and in recent years on computer aided techniques (i.e.numerical simulation).These two approaches defined the way material science works.Yet, both techniques have shown cost-efficiency disadvantages.Optimization algorithms, like the ones used in machine learning, have proven to be an alternative tool when dealing with lots of data and finding a particular solution.Even though the use of machine learning is a well stablished technique in other fields, its application in material science is relatively new.Material Informatics provides a new approach to analyse materials such as porous metals by employing previous data sets.This paper studies a new technique to predict the heat transfer coefficient of an open-cell porous structure while running water passes through the material.A CFD data set was employed by a Machine Learning technique in order to establish a relationship between the input parameters (porosity, pore size, pore distribution and flow rate) and the heat transfer coefficient of the sample.The results obtained from the analyses were compared with previous findings, concluding that by utilising a Machine Leaning technique is possible to obtain a more accurate and much better fit model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".