Energy finite energy analysis for shipboard noise
Bibliographic record
Abstract
Machinery-induced habitability noise is difficult to model efficiently and accurately. The potential of energy finite-element analysis (EFEA) is compared to other prediction tools such as statistical energy analysis (SEA). This paper will explore the benefits and costs of EFEA with respect to SEA for acoustic modeling. The focus will be on issues relating to structural modeling for EFEA purposes. EFEA techniques will be evaluated to see if they possess the capabilities of verified SEA approaches for predicting habitability and radiated noise, where it is necessary to account for the impact of diverse marine constructions and sources such as the lack of machinery source information with respect to force or moment inputs or the finite impedance of machinery foundations. The effort proposed herein will provide the necessary engineering to research and identify salient features of EFEA that are potentially applicable for the detailed analysis of the acoustic environment and response of surface ships to various excitation sources. The paper will also address the pros and cons of SEA versus energy-finite element analysis (EFEA) methods used to predict the habitability noise of surface ship platforms. [This work is supported by an Office of Navy Research contract.]
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".