Improving accuracy and efficiency of CFD predictions of propeller open water performance
Bibliographic record
Abstract
This research proposes mesh and domain optimization strategies for a popular Computational Fluid Dynamics (CFD) technique to estimate the open water propulsive characteristics of fixed pitch propellers accurately and time-efficiently based on examining the effect of various mesh and computation domain parameters. It used a Reynolds-Averaged Navier-Stokes (RANS) solver to predict the propulsive performance of a fixed pitch propeller with varied meshing, simulation domain and setup parameters. The optimized mesh and domain size parameters were selected using Design of Experiments (DoE) methods enabling simulations in a limited memory and in a timely manner without compromising the accuracy of results. The predicted thrust and torque for the propeller were compared to the corresponding measurements for determining the prediction accuracy. The authors found that the optimized meshing and setup arrangements reduced the propeller opens simulation time by at least a factor of six as compared to the generally popular CFD parameter setup. In addition, the accuracy of propulsive characteristics was improved by up to 50% as compared to published simulation results. The methodologies presented in this paper can be similarly applied to other simulations such as calm water ship resistance, ship propulsion etc. to systematically derive the optimized meshing arrangement for simulations with minimal simulation time and maximum accuracy. This investigation was carried out using a commercial CFD package; however, the findings can be applied to any RANS solver.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".