Digital twinning of a flood control structure using an open-source software (OpenFOAM)
Bibliographic record
Abstract
Understanding the dynamics of water flow around hydraulic structures is vital for safety, operation and optimization. The use of Computational Fluid Dynamics (CFD) in design and assessment of hydraulic structures has become popular. However, for efficient use of CFD models, it’s crucial to focus on key aspects of the model setup such as mesh, turbulence models, numerical methods, and boundary conditions. This study investigates the capabilities of an open-source CFD Toolbox, OpenFOAM, in simulating the hydrodynamics of water flow through the flood diversion structure of the Springbank Off-stream Reservoir. The Springbank Off-stream Reservoir was designed for the management of extreme flood risk. The design was previously evaluated through a series physical experiment at National Research Council Canada and the performance of the structure was examined in various condition. By creating a digital twin of the physical model with OpenFOAM, we examined the model’s performance in replicating measured water levels and velocity. The validated digital twin showed an average error of less than 5% between the model predictions and experimental data for water levels. The results provide insights into the best practices for employing OpenFOAM to assess the hydraulic behavior of flood diversion structures with characteristics similar to the Springbank Off-stream Reservoir. The findings also demonstrate that OpenFOAM can be a reliable and efficient tool for evaluating and optimizing new designs. Moreover, the tool can be utilized for the digital twinning of existing hydraulic structures, thereby informing operational needs, facilitating structural safety assessments, and enabling climate risk assessments and adaptation planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".