On deriving test cases for benchmarking methods for simulating transportcharacteristics of triply periodic minimal surfaces
Bibliographic record
Abstract
Abstract: Triply periodic minimal surfaces (TPMS) have found a wide spectrum of applications ranging from synthesis of artificial bone to aerospace engineering. These structures are adopted in these fields due to their favorable mechanical and thermal properties. The necessity of precise predictive characterization of their transport properties is evident. However, there exists a considerable gap in the estimated transport properties of TPMS structures between numerical simulations and experiments in the literature. Conflicting results are present in the literature even for the three most common TPMS types (gyroid, diamond, and primitive). For example, Santos et al. found through experiment that at 50% porosity, the gyroid lattice was the most permeable of the three. However, Jung et al., using finite volume method (FVM), showed that the primitive lattice had the highest permeability. Difficulties in both accurately representing their complex geometry and prescribing precise boundary conditions are the major sources of uncertainty in numerical characterization of TPMS structures. In this paper, we propose a few benchmark test cases for numerical characterization of TPMS structures. The test cases include both mesoscopic and macroscopic simulation strategies. Lattice Boltzmann Method (LBM) is employed for deriving the test cases at mesoscale level and FVM is considered for macroscopic level analysis. Canonical flows through periodic arrays of spheres are studied using both LBM and FVM. The effect of grid resolution on the estimated permeability is studied to quantify the error and to suggest optimum simulation parameters for both methods. Further, flows through three different TPMS structures are studied using the two methods and results are compared.
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 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".