Using Radius of Gyration in Order to Determine Surrogate Mechanical Properties of a Porous Structure
Bibliographic record
Abstract
Abstract Advances in additive manufacturing have now made printing very fine design features possible, giving rise to the possibility of incorporating a porous structure within a component to act as a thermal barrier. The model of such a component may be simplified during finite element analysis by replacing the topographically complex porous structure with that of a homogeneous material having surrogate material properties equivalent to those of the porous structure. Determining changes in length within a porous structure for the purpose of calculating strain presents a challenge. Due to the nature of the porous structure, non-uniform displacements are possible, introducing difficulty in assessing aggregate displacements. The possibility of using the radius of gyration as a measure of displacements within a structure is explored in the present study. The positions of finite element meshing nodes were used to calculate mass distribution within unit cells of a gyroid structure, and from this, a radius of gyration was derived. Unit strain, and consequently Young’s modulus and Poisson’s ratio were obtained for the porous structure from the changes in radii of gyration, which can be used to describe an equivalent bulk material. Excellent agreement was obtained when directly comparing the elastic response of a gyroid cluster and equivalent homogeneous material under identical loading conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".