Reverse Osmosis Simulation Dataset
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
National Renewable Energy Lab - NREL. (2024). Reverse Osmosis Simulation Dataset [data set]. Retrieved from https://dx.doi.org/10.7481/2478402.
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. Reverse Osmosis Simulation Dataset. United States: N.p., 22 Apr, 2024. Web. doi: 10.7481/2478402.
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, & Egan, Hilary. Reverse Osmosis Simulation Dataset. United States. https://dx.doi.org/10.7481/2478402
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. 2024. "Reverse Osmosis Simulation Dataset". United States. https://dx.doi.org/10.7481/2478402. https://waterdams.nawihub.org/submissions/18.
@div{oedi_6226, 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 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.}, doi = {10.7481/2478402}, url = {https://waterdams.nawihub.org/submissions/18}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {04}}
https://dx.doi.org/10.7481/2478402
Details
Data from Apr 22, 2024
Last updated Nov 25, 2024
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