On the lattice Boltzmann method and its application to turbulent, multiphase flows of various fluids including cryogens: A review
Bibliographic record
Abstract
Cryogenic fluids are used in a myriad of different applications not limited to green fuels, medical devices, spacecraft, and cryoelectronics. In this review, we elaborate on these applications and synthesize recent lattice Boltzmann methods (LBMs) including collision operators, boundary conditions, grid-refinement techniques, and multiphase models that have enabled the simulation of turbulence, thermodynamic phase change, and non-isothermal effects in a wide array of fluids, including cryogens. The LBM has reached a mature state over the last three decades and become a strong alternative to the conventional Navier–Stokes equations for simulating complex, rarefied, thermal, multiphase fluid systems. Moreover, the method's scalability boosts the efficiency of large-scale fluid flow computations on parallel clusters, including heterogeneous clusters with graphics card-based accelerators. Despite this maturity, the LBM has only recently experienced limited use in the study of cryogenic fluid systems. Therefore, it is fitting to emphasize the usefulness of the LBM for simulating computationally prohibitive, complex cryogenic flows. We expect that the method will be employed more extensively in the future owing to its simple representation of molecular interaction and consequently thermodynamic changes of state, surface tension effects, non-ideal effects, and boundary treatments, among others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".