Multifidelity numerical modeling and Bayesian hierarchical prediction of the desalination discharges
Bibliographic record
Abstract
Accurate prediction of desalination jet behavior in coastal environments is essential for optimizing discharge design and minimizing environmental impacts. This study presents a Multifidelity Gaussian Process (MFGP) framework for predicting the behavior of desalination discharges under different configurations. The framework integrates computationally efficient low-fidelity (LF) Reynolds averaged Navier–Stokes (RANS) simulations with high-fidelity (HF) Large Eddy Simulation (LES) data, using experimental true values (TV) measurements to correct residual bias and ensure consistency across fidelity levels. Two representative scenarios were investigated, an inclined dense desalination jet in shallow ambient conditions (RANS-LES validated with PIV) and a vertical thermal desalination jet (RANS-LES combined with LIF data). The formulation systematically links LF, HF, and TV datasets through hierarchical inference, enabling bias correction and uncertainty quantification. Results show that the MFGP accurately predicts desalination jet behavior including dilution and geometrical characteristics while reducing prediction error compared with single fidelity models. The framework achieves high accuracy using only a fraction of the computational and experimental effort. This study demonstrates that multifidelity modeling provides an efficient and reliable approach for the design, operational assessment, and optimization of desalination discharges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".