A study of the effect of recesses on the motion resistance of submarines by methods of computational fluid dynamics
Bibliographic record
Abstract
A new method of reducing the resistance of submarines is presented, which consists in installing special circular recesses on its surface in the stern. It is found that during the movement, in the recesses there is a macro-vortex flow, in which pressure decreases significantly. This phenomenon affects the characteristics of the boundary layer and in general the pressure distribution on the surface of the hull, i. e. the resistance of the submarine. Using the methods of computational fluid dynamics, the influence of the number and size of the recesses at their fixed location on the resistance of two types of “Lira” and “Gepard” submarines is investigated. The results show that the decrease in resistance increases with increasing Reynolds number and reaches 6 % for “Lira” with 4 recesses with a diameter of d=0.01 D at Re=1.55·108 and 2 % at Re=1.35·108 for “Gepard” with 7 recesses with a diameter of d=0.01 D. The effect of the number of cells of the computational grid on the results of calculations in the Flow Simulation (USA, France, Canada) and Flow Vision (Russian Federation) software packages was also studied. The effect of resistance reduction obtained in both software packages is approximately the same, but the absolute values differ due to the small number of cells in Flow Vision, which is due to the limited capabilities of the used 2nd version of this complex. There was also a slight effect of resistance reduction on the model of the “Persia-110” (Iran) submarine with recesses during towing tests in the research basin at significantly lower Reynolds numbers. Unlike most resistance reduction means, the use of this method does not require significant changes in the design of the housing. This makes it possible to use it both on new facilities and on facilities that have already been commissioned
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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".