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Reverse Osmosis Simulation Dataset

In curation License 

This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to "Sitaraman et al., 2022, Impact of large-scale effects on mass transfer and concentration polarization in Reverse Osmosis membrane systems", for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.

Citation Formats

National Renewable Energy Lab - NREL. (2024). Reverse Osmosis Simulation Dataset [data set]. Retrieved from https://data.openei.org/submissions/6211.
Export Citation to RIS
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. Reverse Osmosis Simulation Dataset. United States: N.p., 22 Apr, 2024. Web. https://data.openei.org/submissions/6211.
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, & Egan, Hilary. Reverse Osmosis Simulation Dataset. United States. https://data.openei.org/submissions/6211
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. 2024. "Reverse Osmosis Simulation Dataset". United States. https://data.openei.org/submissions/6211.
@div{oedi_6211, title = {Reverse Osmosis Simulation Dataset}, author = {Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary.}, abstractNote = {This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to "Sitaraman et al., 2022, Impact of large-scale effects on mass transfer and concentration polarization in Reverse Osmosis membrane systems", for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.}, doi = {}, url = {https://data.openei.org/submissions/6211}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {04}}

Details

Data from Apr 22, 2024

Last updated Oct 15, 2024

Submitted Oct 10, 2024

Organization

National Renewable Energy Lab - NREL

Contact

Saumya Sinha

303.384.6764

Authors

Sreejith Nadakkal Appukuttan

National Renewable Energy Lab - NREL

Hariswaran Sitaraman

National Renewable Energy Laboratory NREL

Hilary Egan

National Renewable Energy Laboratory NREL

DOE Project Details

Project Number 711409

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