Experimentally supported reduced order modeling for hydrokinetic energy system design

by Griffin, Austin Lee
Abstract:
This research advances the field of hydrokinetic energy by introducing a novel control design approach, termed duct contraction control strategy (DCCS), that dynamically optimizes the power output of ducted horizontal axis hydrokinetic turbines (HAHkT). Focusing on the adjustment of duct contraction ratios (CR) and blade pitch in response to varying flow conditions, this strategy is explored through augmented XFoil and Blade Element Momentum Theory (BEMT) simulations using QBlade software and experimentation in an open-channel water flume. The study uses a dynamic surrogate model (SM) to predict turbine performance in a wide range of flow regimes, particularly examining how adjustments in CR and blade pitch can maximize energy extraction efficiency. Optimization tests, carried out with a variety of velocity profiles, aim to identify turbine configurations that improve power generation and significantly reduce the levelized cost of energy (LCOE), making hydrokinetic energy a more economically viable option. The findings highlight the potential of precise CR and blade pitch control strategies to improve energy yield and reduce costs, providing a robust framework for the design and operational optimization of hydrokinetic turbine systems. This approach not only deepens the understanding of turbine dynamics, but also contributes to the development of efficient and cost-effective renewable energy solutions.
Reference:
Austin Lee Griffin, "Experimentally supported reduced order modeling for hydrokinetic energy system design", Master’s thesis, The University of Memphis, Memphis, TN, USA, 2024.
Bibtex Entry:
@mastersthesis{Griffin2024MS,
    author = "Griffin, Austin Lee",
    title = "Experimentally supported reduced order modeling for hydrokinetic energy system design",
    school = "The University of Memphis",
    address = "Memphis, TN, USA",
    year = "2024",
    month = may,
%    pdf = "",
    url = "https://www.proquest.com/pqdtglobal1/dissertations-theses/experimentally-supported-reduced-order-modeling/docview/3020558436/sem-2?accountid=14582",
    abstract = "This research advances the field of hydrokinetic energy by introducing a novel control design approach, termed duct contraction control strategy (DCCS), that dynamically optimizes the power output of ducted horizontal axis hydrokinetic turbines (HAHkT). Focusing on the adjustment of duct contraction ratios (CR) and blade pitch in response to varying flow conditions, this strategy is explored through augmented XFoil and Blade Element Momentum Theory (BEMT) simulations using QBlade software and experimentation in an open-channel water flume. The study uses a dynamic surrogate model (SM) to predict turbine performance in a wide range of flow regimes, particularly examining how adjustments in CR and blade pitch can maximize energy extraction efficiency. Optimization tests, carried out with a variety of velocity profiles, aim to identify turbine configurations that improve power generation and significantly reduce the levelized cost of energy (LCOE), making hydrokinetic energy a more economically viable option. The findings highlight the potential of precise CR and blade pitch control strategies to improve energy yield and reduce costs, providing a robust framework for the design and operational optimization of hydrokinetic turbine systems. This approach not only deepens the understanding of turbine dynamics, but also contributes to the development of efficient and cost-effective renewable energy solutions.",
%    comment = "",
}