Navigating the Dynamics: Modeling of Wave Propagation at Taiping Bay Port for Enhanced Design and Management
Bibliographic record
Abstract
This study presents a systematic analysis of wave propagation dynamics at Taiping Bay Port in Dalian, characterized by its deep navigation channel, narrow port entrance, and complex bathymetric features. This study addresses the gap in numerical simulations for extra-large port areas using an integrated modeling approach. Specifically, this research advances our understanding of wave behaviors in harsh maritime environments through an innovative coupling of the parabolic mild slope (PMS) wave model with the phase-resolving Boussinesq wave (BW) model. The PMS model, validated against measured data, effectively computes the incident boundary conditions for the BW model, which in turn has been refined to enhance wave prediction accuracy and model stability through optimized boundary settings. Our findings elucidate the intricate wave patterns and transformations within the harbor, highlighting the significant impact of the deep navigation channel on wave attenuation. This work not only contributes to the theoretical modeling of wave dynamics but also offers practical insights for the design and management of similar complex port structures, potentially guiding future developments in coastal engineering.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".