Reverse Osmosis Simulation Data
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 the "Related Work" resource below 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
TY - DATA
AB - 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 the "Related Work" resource below 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.
AU - Nadakkal Appukuttan, Sreejith
A2 - Sitaraman, Hariswaran
A3 - Egan, Hilary
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.7481/2478402
KW - energy
KW - water
KW - reverse osmosis
KW - clean water
KW - AI surrogate
KW - data-driven
KW - data
KW - CFD
KW - dataset
KW - resource extraction
KW - RO
KW - brine
KW - membrane surface
LA - English
DA - 2024/04/22
PY - 2024
PB - National Renewable Energy Lab - NREL
T1 - Reverse Osmosis Simulation Data
UR - https://doi.org/10.7481/2478402
ER -
Nadakkal Appukuttan, Sreejith, et al. Reverse Osmosis Simulation Data. National Renewable Energy Lab - NREL, 22 April, 2024, NAWI. https://doi.org/10.7481/2478402.
Nadakkal Appukuttan, S., Sitaraman, H., & Egan, H. (2024). Reverse Osmosis Simulation Data. [Data set]. NAWI. National Renewable Energy Lab - NREL. https://doi.org/10.7481/2478402
Nadakkal Appukuttan, Sreejith, Hariswaran Sitaraman, and Hilary Egan. Reverse Osmosis Simulation Data. National Renewable Energy Lab - NREL, April, 22, 2024. Distributed by NAWI. https://doi.org/10.7481/2478402
@misc{OEDI_Dataset_6475,
title = {Reverse Osmosis Simulation Data},
author = {Nadakkal Appukuttan, Sreejith and 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 the "Related Work" resource below 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.},
url = {https://waterdams.nawihub.org/submissions/18},
year = {2024},
howpublished = {NAWI, National Renewable Energy Lab - NREL, https://doi.org/10.7481/2478402},
note = {Accessed: 2025-04-25},
doi = {10.7481/2478402}
}
https://dx.doi.org/10.7481/2478402
Details
Data from Apr 22, 2024
Last updated Jan 2, 2025
Submitted Oct 10, 2024
Organization
National Renewable Energy Lab - NREL
Contact
Saumya Sinha
303.384.6764
Authors
Original Source
https://waterdams.nawihub.org/submissions/18Keywords
energy, water, reverse osmosis, clean water, AI surrogate, data-driven, data, CFD, dataset, resource extraction, RO, brine, membrane surfaceDOE Project Details
Project Name NAWI Integrated Data and Analysis
Project Lead Melissa Klembara
Project Number 36496