Invalidity of Molecular Dynamics in Heat Transfer
Bibliographic record
Abstract
Molecular Dynamics (MD) simulations based on classical statistical mechanics always allow the atom to have thermal heat capacity. Quantum mechanics (QM) differs in that the heat capacity of atoms in submicron nanostructures vanishes. Nevertheless, MD simulations of heat transfer in discrete nanostructures are routlinely performed and abound in the literature. Not only are discrete MD sumultions invalid by QM, but give unphysical results, e.g., thermal conducitvity in nanofluids is found to exceed standard mixing rules while in solid metal films depends on thickness. QM negates the heat capacity of atoms in discrete nanostructures, thereby precluding the usual conservation of absorbed electromagnetic (EM) energy by an increase in temperature. Instead, conservation proceeds by QED inducing the absorbed EM energy to create non-thermal EM radiation inside the nanostructure that by the photoelectric effect chargea the nanostructure, or is emitted to the surroundings. QED stands for quantum electrodynamics. Unphysical results occur because QED induced radiation is not included in the nanoscale heat balance, but if included physical results for discrete nanostructures are found. Examples of unphysical MD simulatons are presented. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/35192
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 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".