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CFD and data-driven modeling for safe spillway discharge

Time: Fri 2023-06-09 10.00

Location: D3, Lindstedtsvägen 5, Stockholm

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Language: English

Subject area: Civil and Architectural Engineering, Concrete Structures

Doctoral student: Shicheng Li , Betongbyggnad, KTH Royal Institute of Technology

Opponent: Professor Robert Boes, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich

Supervisor: Professor Anders Ansell, Betongbyggnad; Dr James Yang, Betongbyggnad

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QC 20230516


A spillway is a crucial dam structure that regulates water levels and prevents the dam from overtopping. The majority of the dams in the world were constructed before the 1980s. Compared to the current design flood standards, many spillways are undersized, e.g., in Sweden. The higher floods also change the air-water flow properties, giving rise to risks in spillway operations. High-velocity flow is a major concern in spillway design. If the flow rate exceeds 20 m/s, there is a risk of cavitation. Another issue connected with the increased floods is energy dissipation. Many existing energy-dissipating arrangements are insufficient or constructed only for a design flood standard at the time of dam construction. To this end, it is essential to assess the discharge behaviors of spillways, including discharge capacity, two-phase flow features, energy dissipation, cavitation, etc. Engineering solutions for improved hydraulic performance are also critical to ensure safe operation.

This thesis focuses on CFD and data-driven modeling for safe spillway discharge, presenting new insights into spillway hydraulics, improved methods for accurate flow predictions, and structural modifications for better hydraulic performance. The research consists of five parts. (1) For discharge estimation, ensemble learning models are established to forecast the river flows, aiming to provide accurate inflow information, even multiple days ahead. To directly assess spillway discharge capacity, three data-driven models are constructed, which yield accurate flow predictions and outperform empirical correlations. (2) For modified designs, several unconventional step layouts are conceived, with their hydraulic behaviors investigated. The modified configurations include steps with chamfers and cavity blockages and inclined steps. The goal is to devise engineering solutions to enhance energy dissipation and reduce cavitation risks. (3) For improved two-phase flow modeling, a population balance model is first established to account for the evolutionary process of air bubbles. Then machine learning techniques are integrated for error correction. The resulting model generates accurate results, with 99.6% and 89.6% of the velocity and air concentration predictions within a 20% relative error range. (4) For cavitation reduction, an aerator is often used to artificially entrain air into the flow. This part develops an embedded multi-gene genetic programming model for accurate predictions of aerator air demand. Compared with the best empirical formula, the proposed approach improves the fitness indexes by 23% and reduces the errors by 48%. (5) This part reviews the theoretical basis and deals with several aspects of physical scale modeling, supported exclusively by field measurements. For proper upscaling, model-prototype comparisons indicate that the approach flow velocity at the aerator should exceed the 7.00–7.50 m/s threshold, or the Reynolds number should be above 1.58 ×106. If this condition was not met, attempts to scale up the air flow would lead to errors.