Multifidelity numerical modeling and Bayesian hierarchical prediction of the desalination discharges

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. • Bayesian multifidelity GP framework for desalination jet prediction. • Three-level integration of RANS, LES, and LIF experimental data. • Achieves high accuracy with quantified predictive uncertainty. • Demonstrates robust scalability across complex jet configurations.