Simulation of the Internal Flow Field of Jet Pumps Using the RANS Method
Bibliographic record
Abstract
This paper focuses on CFD simulations of the internal flow field of jet pumps, which are passive pumping devices.A numerical method based on the RANS equations is proposed, and its abilities are analysed by comparing the obtained results with the available experimental data.The turbulent flow is simulated using Boussinesq hypothesis which relates Reynolds stresses to the mean velocity gradients via turbulent viscosity.The k-ε model (along with the Standard wall function) and the k-ω SST model are used to calculate the turbulent viscosity for solving the flow in near-wall regions.Before checking the validity of the numerical results, Grid Convergence Index (GCI) is estimated to evaluate the mesh.The analysis showed that the maximum GCI is at the cell near the wall with the value of 0.08%.Comparison of the numerical results and experimental data shows that the model captures the jet pump efficiency range with average relative errors of 7.3% and 8.47% for the k-ε and k-ω SST turbulence models, respectively.The numerical results also confirmed that increasing the flow ratio causes the mixing location between the primary and secondary flows to move toward the outlet of the jet pump, which was observed in the previous experimental investigations.The static pressure coefficient, which indicates the pumping effect of the jet pump, is calculated via both of the turbulence models and the comparison with the experimental data showed that the average relative error is 10.68% and 3.21% for the k-ε model and the k-ω SST model, respectively.The study shows that accurate modelling of jet pump parameters is feasible using RANS approach.
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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 | 0.000 | 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 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".