A multi-objective optimization framework for reducing the impact of ship noise on marine mammals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The underwater radiated noise (URN) emanating from ships presents a significant threat to marine mammals, given their heavy reliance on hearing. The intensity of URN from ships is correlated to their speed, making speed reduction a crucial operational mitigation strategy. This paper presents a new multi-objective optimization framework to optimize the ship speed for effective URN mitigation without compromising fuel consumption. This framework addresses a fixed-path voyage scheduling problem, incorporating two objective functions namely, noise intensity levels and fuel consumption. The optimization is performed using the state-of-the-art non-dominated sorting genetic algorithm under voyage constraints. A 2D ocean acoustic environment, comprising randomly scattered marine mammals of diverse audiogram groups and realistic conditions, including sound speed profiles and bathymetry, is simulated. To estimate the objective functions, we consider empirical relations for fuel consumption and near-field noise modeling together with a ray-tracing approach for far-field noise propagation. The optimization problem is solved to determine the Pareto solutions and the trade-off solution. The effectiveness of the framework is demonstrated via practical case studies involving a large container ship. A comparative analysis illustrates the adaptability of the framework across different oceanic environments, affirming its potential as a robust tool for reducing the URN from shipping. • Novel multi-objective optimization framework for ship voyage optimization. • Fuel consumption and underwater radiated noise as two objective functions. • Underwater noise modeling with near-field source and far field propagation. • New objective function to characterize noise impact on marine mammals. • Demonstration of framework to reduce noise impact without increasing fuel cost.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it