Developing Cross Drag Expressions for Nanotube Bundles Using Molecular Dynamics
Bibliographic record
Abstract
The nonequilibrium molecular dynamics (NEMD) simulations are performed to calculation the cross drag over a nanotube located in a uniform liquid argon flow. As is known, the behavior of fluid flows in nano-scale sizes is very different from that in microscopic and macroscopic sizes. In this work, our concern is on the flow of argon molecules over a nanotube which occurs in nanoscale sizes. We calculate the cross drag enforced the nanotube at Re≤1.0. In this regard, we use the molecular dynamics and simulate the flow of argon molecules over (6,0), (8,0) and (10,0) nanotubes. The simulations are performed at different velocities and the cross drag coefficient is computed at different Reynolds numbers. To improve the efficiency of simulations, we use USHER algorithm and examin the insertion of molecules at the end of the simulation box, the argon molecules are located out of box. Using the power trend line, we derived a formula, which approximates the cross drag of chosen nanotube. In all simulations, only the first two and the last two rings of the nanotube are frozen. All non-bonded interactions are calculated based on the Lennard-Jones potential. The results if molecular dynamics are compared with two empirical expressions provided by experiments performed on the flow over a macro-scale cylinder. The results show that the cross drag force on a single-walled nanotube calculated from MD simulations is larger than that provided by the empirical expressions in slow flows (Re≪ 1.0). As is expected the results of continuum flow calculations cannot be trusted to predict the drag of a nanotubes if Re≪1.0. The difference increases as the flow velocity decreases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".