Numerical simulation of desalination brine discharges: Effects of inlet boundary conditions
Bibliographic record
Abstract
This study examines the influence of inlet boundary conditions for a high-resolution numerical method of Large Eddy Simulation (LES) of desalination discharges and compares the results with high-resolution Particle Image Velocimetry (PIV). This study presents a robust nondimensional numerical model for high-resolution simulation of desalination jets, investigating the effects of various inlet boundary conditions, including Uniform Velocity (UV), Mapped Inlet (MI), Divergence-Free Synthetic Eddy Method (DFSEM), Turbulent Velocity (TV), and Fully Developed Constant Velocity (FDCV). Two approaches are employed for nozzle representation: an explicit method incorporating a 20 d 0 nozzle length, and an implicit method in which the nozzle geometry is omitted (the nozzle representations for Uniform Velocity (UV) and Turbulent Velocity (TV) boundary conditions are denoted by -p for explicit and -np for implicit, respectively). The results demonstrate that the choice of inlet boundary condition significantly impacts the velocity fields obtained from LES compared to the PIV, consequently influencing the mixing processes of a desalination discharge. The MI and DFSEM methods provide the closest agreement with experiments, capturing the intricate details of the jet velocity profile. In contrast, the UV and FDCV methods exhibited substantial discrepancies, particularly in the near-field region, where they failed to capture the expected turbulent development. Analyzing the turbulent flow characteristics revealed that the DFSEM, MI, TV-p and TV-np conditions more accurately reproduced the turbulence intensity and spectral scales observed in the PIV experiments. The application of Proper Orthogonal Decomposition (POD) to the concentration field revealed the presence of dominant flow structures, with the MI, DFSEM, and TV-np inlet conditions exhibiting spatial patterns consistent with the formation of helical structures near the nozzle. In contrast, the remaining boundary conditions did not exhibit these features, suggesting a less accurate representation of the flow physics. Although all tested boundary conditions could be applied for LES, the findings demonstrate that the DFSEM and MI approaches yield the most accurate and physically consistent results, showing strong agreement with experimental PIV data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".