Computational Fluid Dynamics Modeling of Pressure-Retarded Osmosis: Towards a Virtual Lab for Osmotic-Driven Process Simulations

Pressure-Retarded Osmosis (PRO) is an osmotically driven membrane-based process that has recently garnered significant attention from researchers due to its potential for clean energy harvesting from salinity gradients. The complex interactions between mixed-mode channel flows and osmotic fluxes in real PRO membrane modules necessitate high-fidelity modeling approaches. In this work, an efficient CFD framework is developed for the 3D simulation of osmotically driven membrane processes. This approach is based on a two-way coupling between a CFD solver, which captures external concentration polarization (ECP) effects, and an analytical representation of internal concentration polarization (ICP). Consequently, the osmotic water flux and reverse salt flux (RSF) can be accurately determined, accounting for all CP effects without any limitations on the geometrical complexity of the membrane chamber or its flow mode/regime. The proposed model is validated against experimental data, showing good agreement across various PRO case studies. Additionally, the model's flexibility to simulate other types of osmotically driven processes such as forward osmosis (FO) is examined. Thus, the contributions of ECP and ICP effects in local osmotic pressure drop along the membrane chamber are comprehensively compared for FO and PRO modes. Finally, the capability of the CFD model to simulate a lab-scale PRO module is demonstrated across a range of Reynolds numbers from low-speed laminar up to turbulent flow regimes.

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Publication Details

Journal
Membranes
Published
2024-11-11
DOI
https://doi.org/10.3390/membranes14110236
Citations
4
Primary Topic
Membrane Separation Technologies
Type
article
Field-Weighted Citation Impact
0.81
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article

Computational Fluid Dynamics Modeling of Pressure-Retarded Osmosis: Towards a Virtual Lab for Osmotic-Driven Process Simulations

Ulrich Krühne, Mazyar Amin
4 citations
Membranes
Membrane Separation Technologies
0.81
article

Computational Fluid Dynamics Modeling of Pressure-Retarded Osmosis: Towards a Virtual Lab for Osmotic-Driven Process Simulations

Ulrich Krühne, Mazyar Amin
article en
4 citations

Abstract

Pressure-Retarded Osmosis (PRO) is an osmotically driven membrane-based process that has recently garnered significant attention from researchers due to its potential for clean energy harvesting from salinity gradients. The complex interactions between mixed-mode channel flows and osmotic fluxes in real PRO membrane modules necessitate high-fidelity modeling approaches. In this work, an efficient CFD framework is developed for the 3D simulation of osmotically driven membrane processes. This approach is based on a two-way coupling between a CFD solver, which captures external concentration polarization (ECP) effects, and an analytical representation of internal concentration polarization (ICP). Consequently, the osmotic water flux and reverse salt flux (RSF) can be accurately determined, accounting for all CP effects without any limitations on the geometrical complexity of the membrane chamber or its flow mode/regime. The proposed model is validated against experimental data, showing good agreement across various PRO case studies. Additionally, the model's flexibility to simulate other types of osmotically driven processes such as forward osmosis (FO) is examined. Thus, the contributions of ECP and ICP effects in local osmotic pressure drop along the membrane chamber are comprehensively compared for FO and PRO modes. Finally, the capability of the CFD model to simulate a lab-scale PRO module is demonstrated across a range of Reynolds numbers from low-speed laminar up to turbulent flow regimes.

MembranesVol. 14(11)
Technical University of Denmark (DK)
Industry, innovation and infrastructure
Openalex Percentile: Top 28%
Membrane Separation Technologies
0.81
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Computational Fluid Dynamics Modeling of Pressure-Retarded Osmosis: Towards a Virtual Lab for Osmotic-Driven Process Simulations — Ulrich Krühne, Mazyar Amin · Membranes (2024) | TGRS Research Map | TGRS