Session
Session 10 2022
Start Date
10-27-2022 12:00 AM
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Crookston B.M., Rasmussen, M., Larsen, E., Kartchner, H., and Dutta, S. (2022). "Predicting Density-Driven Exchange Flows Through the West Crack Breach of the Great Salt Lake Causeway with CFD and ANN" in "9th IAHR International Symposium on Hydraulic Structures (9th ISHS)". Proceedings of the 9th IAHR International Symposium on Hydraulic Structures – 9th ISHS, 24-27 October 2022, IIT Roorkee, Roorkee, India. Palermo, Ahmad, Crookston, and Erpicum Editors. Utah State University, Logan, Utah, USA, 9 pages (DOI: 10.26077/4026-ecaa) (ISBN 978-1-958416-07-5).
Abstract
The Great Salt Lake is of environmental and economic value yet is threatened by various factors including drought and water diversion for irrigation purposes. Management efforts are required to preserve this saline lake; such efforts include accurately estimating the exchange flow through an opening in a railroad causeway that divides the lake. This study investigated two modeling approaches for predicting these discharges, a physics-based computational fluid dynamics model and a data-driven artificial neural network model. Good agreement was found between both models, and the advantages each provides to water management efforts are noted. Results indicate that, regardless of the modeling tool, accurate field data is invaluable when studying a hydraulic structure.
Predicting Density-Driven Exchange Flows Through the West Crack Breach of the Great Salt Lake Causeway with CFD and ANN
The Great Salt Lake is of environmental and economic value yet is threatened by various factors including drought and water diversion for irrigation purposes. Management efforts are required to preserve this saline lake; such efforts include accurately estimating the exchange flow through an opening in a railroad causeway that divides the lake. This study investigated two modeling approaches for predicting these discharges, a physics-based computational fluid dynamics model and a data-driven artificial neural network model. Good agreement was found between both models, and the advantages each provides to water management efforts are noted. Results indicate that, regardless of the modeling tool, accurate field data is invaluable when studying a hydraulic structure.