U.S. Department of EnergyOffice of Scientific and Technical Information
Reverse Osmosis Simulation Data
Abstract
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 usedmore »
- Authors:
-
- National Renewable Energy Lab - NREL
- Publication Date:
- Other Number(s):
- 18
- Research Org.:
- NAWI Water DAMS (Data Analysis and Management System); National Renewable Energy Lab - NREL
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office (EE-5A)
- Collaborations:
- National Renewable Energy Lab - NREL
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; AI surrogate; CFD; RO; brine; clean water; data; data-driven; dataset; energy; membrane surface; resource extraction; reverse osmosis; water
- OSTI Identifier:
- 2478402
- DOI:
- https://doi.org/10.7481/2478402
Citation Formats
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. Reverse Osmosis Simulation Data. United States: N. p., 2024.
Web. doi:10.7481/2478402.
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, & Egan, Hilary. Reverse Osmosis Simulation Data. United States. doi:https://doi.org/10.7481/2478402
Nadakkal Appukuttan, Sreejith, Sitaraman, Hariswaran, and Egan, Hilary. 2024.
"Reverse Osmosis Simulation Data". United States. doi:https://doi.org/10.7481/2478402. https://www.osti.gov/servlets/purl/2478402. Pub date:Mon Apr 22 04:00:00 UTC 2024
@article{osti_2478402,
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.},
doi = {10.7481/2478402},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Apr 22 04:00:00 UTC 2024},
month = {Mon Apr 22 04:00:00 UTC 2024}
}