Ice crystal structure melting: insights from molecular dynamics simulations
Bibliographic record
Abstract
Climate change poses many new engineering challenges, such as the increasing number of ice falling incidents on cables of cable-supported bridges. This presents a significant risk to bridge users and society. A thorough understanding of the ice melting process and the impact of weather conditions on its progression is pivotal to elucidate the mechanisms of ice detachment from bridge stay cables and predict its occurrence to prevent any potential ice falling events. The current paper presents a numerical investigation of the transition process from ice to water based on molecular dynamics (MD) simulations using three water models of SPC/E, TIP3P and TIP4P. The water phase change in a piece of pure ice crystal consisting of 3072 atoms is tracked via the tetrahedral order parameter. The performance of these three water models is evaluated based on the predicted ice melting process, melting temperature, numerical stability and computational cost. The impact of thermal conditions, cut-off distance, and ice crystal structure size on the simulated ice melting process are assessed. Ice melting characteristics are revealed by examining the ice cube's internal structure at the molecular level. In addition, the computational efficiency of various CPUs and GPUs in performing MD simulations are compared. The findings from this study not only enhance the understanding of ice melting process at the molecular level, but also provide valuable guidance for optimizing practices in simulations prior to conducting more complex simulations of ice detachment from stay cables. • Developed a molecular-dynamics-based numerical simulation technique to study ice melting process. • The developed techniques will help to improve accuracy in future predictions of ice melting and detachment from structures. • The obtained knowledge will deepen the current understanding of ice melting at the molecular level.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".