Evolutionary strategy algorithm for a complete characterization of porous materials using a standing wave tube
Bibliographic record
Abstract
A widely used model for describing the attenuation of an acoustical wave propagating in a rigid open-cell porous material is the Johnson–Champoux–Allard (JCA) model. This model is based on five macroscopic parameters describing the porous medium: flow resistivity, porosity, tortuosity, viscous and thermal characteristic lengths. Simultaneously with the development of direct methods for measuring these five parameters, defining and solving inverse problems based on an artificial intelligence approach appears to overcome some of the limitations of the direct methods. In this work, an application of the evolutionary strategy (ES) algorithm for the estimation of the five parameters of a porous material from simple acoustical measurements is presented. First, a number of numerical tests are performed and the results are used like a priori knowledge of how to set up an evolutionary algorithm to solve such a difficult problem in the shortest time. In the second step, the final setup of the evolutionary algorithm is applied for evaluation of the five parameters from experimental measurements. The method seems practical and promising since it is based on simple acoustical measurements and avoids using complicated and unreliable measurement setups.
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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 | 0.001 | 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".