Integrated numerical modeling and data-driven techniques for thermal effluent simulation in coastal waters
File(s)
Author(s)
Alsulaiman, Nada
Type
Thesis
Abstract
This thesis investigates the integration of numerical modelling and data-driven techniques to improve the simulation of thermal effluent discharged from coastal power plants into tidal waters. Uncertainties in key effluent characteristics, particularly the discharge flow rate and excess temperature relative to ambient water, pose significant challenges to accurate model representation, often resulting in errors in seawater temperature predictions. These challenges are further complicated by the dynamic nature of tidal systems, where strong advective and dispersive processes govern the transport and spread of discharged thermal plumes.
Three methodologies were integrated into the modelling framework to estimate discharge parameters and improve the accuracy of seawater temperature simulations. These methodologies include: (1) data assimilation (DA) using the Ensemble Kalman Filter (EnKF) for state estimation of seawater temperature; (2) Bayesian optimisation to calibrate hydrodynamic model parameters using temperature observations from in-situ profiles, thermal unmanned aerial vehicle (UAV) imagery, and satellite data; and (3) Large-Scale Particle Image Velocimetry (LSPIV) to integrate discharge rate estimates from optical UAV footage into the hydrodynamic model. Each approach was systematically evaluated for its effectiveness in reducing parameter uncertainties and improving model accuracy.
The data assimilation findings demonstrate that optimising data collection locations and their assimilation frequencies significantly reduces errors in simulated seawater temperature by maximising the spread and retention of DA adjustments across the modelled domain. Bayesian optimisation highlighted the critical role of spatial coverage in temperature observations on parameter estimation outcomes, demonstrating the importance of strategic data collection. LSPIV-derived discharge offers a viable, cost-effective method for estimating outflows from coastal power plants in tidal environments, with its integration into hydrodynamic models enhancing simulations and complementing calibration efforts.
Three methodologies were integrated into the modelling framework to estimate discharge parameters and improve the accuracy of seawater temperature simulations. These methodologies include: (1) data assimilation (DA) using the Ensemble Kalman Filter (EnKF) for state estimation of seawater temperature; (2) Bayesian optimisation to calibrate hydrodynamic model parameters using temperature observations from in-situ profiles, thermal unmanned aerial vehicle (UAV) imagery, and satellite data; and (3) Large-Scale Particle Image Velocimetry (LSPIV) to integrate discharge rate estimates from optical UAV footage into the hydrodynamic model. Each approach was systematically evaluated for its effectiveness in reducing parameter uncertainties and improving model accuracy.
The data assimilation findings demonstrate that optimising data collection locations and their assimilation frequencies significantly reduces errors in simulated seawater temperature by maximising the spread and retention of DA adjustments across the modelled domain. Bayesian optimisation highlighted the critical role of spatial coverage in temperature observations on parameter estimation outcomes, demonstrating the importance of strategic data collection. LSPIV-derived discharge offers a viable, cost-effective method for estimating outflows from coastal power plants in tidal environments, with its integration into hydrodynamic models enhancing simulations and complementing calibration efforts.
Version
Open Access
Date Issued
2025-01-31
Date Awarded
01/07/2025
License URL
Advisor
Piggott, Matthew
van Reeuwijk, Maarten
Sponsor
Kuwait Institute for Scientific Research
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)