Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session ZC37: Energy Applications and Optimization |
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Chair: Michael Howland, Massachusetts Institute of Technology Room: 355 C |
Tuesday, November 26, 2024 12:50PM - 1:03PM |
ZC37.00001: Power Output and Aerodynamic Loads of Scaled Wind Turbines Under Yaw Misalignment Emmanuvel Joseph Aju, Yujie Zhang, Mario A Rotea, Yaqing Jin Wind tunnel experiments were conducted to evaluate the performance of two scaled wind turbines (G06 Turbine with 0.6 m rotor diameter). Three turbine layouts were examined with the turbines positioned at stream-wise spacing of 5 times the rotor diameter (5D). The first layout (aligned layout) was tested with no lateral offset of the turbine towers. In contrast, the second and third layouts, known as the staggered-left and staggered-right layouts, were tested with turbines staggered laterally by 0.5D to the left and right, respectively. Results indicate that adjusting the yaw angles of upstream turbines reduces wake velocity deficits at the downstream turbine's location, with this effect being more pronounced in the staggered layouts. For the aligned layout, the wind farm power output can increase by up to 2% when the first-row turbine (hereafter T1) is yawed to -20°, whereas in the staggered layouts, wake steering leads to an increase in power output up to 13% with T1 yawed to -20° for the staggered-left layout and 9% with T1 yawed to 10° for the staggered-right layout. Measurements of the aerodynamic loads revealed that the resultant average tower base bending moments under intentional yaw misalignment decrease, while the resultant main bearing average bending moments have modest increase at the optimal yaw angles. |
Tuesday, November 26, 2024 1:03PM - 1:16PM |
ZC37.00002: Enhancing Solar Panel Efficiency Through Wind Turbine Wake Cooling: A Large Eddy Simulation Study Oluwatuyi Nelson Johnson, Abishek Sriram, Luciano Castillo, Jhon J Quinones Elevated surface temperatures have an adverse effect on photovoltaic (PV) solar panel efficiency by reducing their energy conversion efficiency. By putting photovoltaic (PV) panels in the wakes produced by horizontal axis wind turbines (HAWT), this study explores a potential solution to this problem. In particular, the possibility of the wake cooling effects from a 5MW NREL HAWT to lower PV panel temperatures is examined through LES. Evaluating and modeling the cooling effect while testing different panel height and distance combinations behind the wind turbine are the goals. By using these simulations, the research hopes to determine where PV panels should be placed in order to maximum efficiency benefits. The results of the study will leverage natural resources to help construct more sustainable and effective wind-solar hybrid energy systems. |
Tuesday, November 26, 2024 1:16PM - 1:29PM |
ZC37.00003: Control co-design of wind farms using joint yaw-induction control Ilan M. L. Upfal, Michael F Howland Collective wind farm flow control and layout optimization have both demonstrated potential to increase farm energy production. Flow control and layout optimizations are interdependent. Therefore, optimizing control and layout simultaneously, known as control co-design, is necessary to achieve a globally optimal result. Here, we study the impact of control co-design on optimal turbine spacing, farm efficiency, cost of energy and lifetime farm profit. Co-design is investigated with three different control strategies: induction (thrust) control, yaw control and joint yaw-induction control. The high dimensional optimization problem is efficiently handled using gradient-based optimization with automatic differentiation of an analytical wind farm model. A generalized momentum model is used to accurately predict the induction and initial wake velocities of the turbine rotor depending on the yaw misalignment and thrust coefficient. Co-design optimizations are performed using an idealized actuator disk representation to capture the full theoretical range of possible operating setpoints as well as a blade element model representation to capture the impact of realistic rotor aerodynamics. We show that the mitigation of wake losses via collective control lowers the optimal turbine spacing of control co-designed wind farms resulting in reduced cost of energy, increased lifetime farm profit and reduced land use. |
Tuesday, November 26, 2024 1:29PM - 1:42PM |
ZC37.00004: Enhancing Wind Farm Efficiency through Multi-Fidelity Bayesian Optimisation Andrew Mole, Sylvain Laizet Enhancing wind farm power output is crucial for accelerating the transition to renewable energy. Turbines operating in the wake of others experience reduced wind speeds and greater turbulence, lowering power output and increasing fatigue. Wake steering, where the turbine yaw angles are adjusted to redirect wakes, can mitigate these effects and boost total farm power. |
Tuesday, November 26, 2024 1:42PM - 1:55PM |
ZC37.00005: ABSTRACT WITHDRAWN
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Tuesday, November 26, 2024 1:55PM - 2:08PM |
ZC37.00006: Geothermally Heated Replacement Water in Pressurized Water Reactors Michael Commins, Bryan Lewis In pressurized water reactors, about 5% of the water used in the energy production process is lost in the evaporative cooling process. Cold water is then used to replace this lost water and must be preheated back to saturation before entering the boiler of the plant to be converted to steam. This leads to a decrease in efficiency, as energy is needed to reheat this water, creating a loss in power. We hypothesized that geothermally heated replacement water would increase the efficiency of a nuclear power plant. We simulated a rankine cycle inside a nuclear power plant using python, with boundary pressures of 4000 and 16e^6 pascals, and an isentropic efficiency of 80% in the modeled turbine. We found that geothermally heated replacement water linearly increased the efficiency of the plant as a whole. Modeling temperatures from 15 to 80 degrees celsius increased the total efficiency of the cycle by 0.87%, which is an estimated 260 megawatt increase in output. We believe that this method could be useful in the future when deciding on cites for nuclear power plants as building near geothermal sources could increase efficiency of the plant overall. |
Tuesday, November 26, 2024 2:08PM - 2:21PM |
ZC37.00007: Geothermal Energy Feasibility Study in Southern Texas Christina Hunter, Cody Bingham, Richard Bishop, Roy Cook, Josue Melgar, Bryan Lewis Geothermal energy is largely underused in many regions due to low geothermal activity or high ground surface temperatures[LB1] . A geothermal power plant's efficiency depends on the difference in temperature between the heat reservoir and surface temperature, so a lesser difference would raise costs to produce the same amount of energy. This study evaluates the feasibility of a geothermal power plant in Texas, where both limitations apply. However, because drilling equipment is easily accessible due to the active oil industry, it is worthwhile to determine whether the endeavor would be financially rewarding. |
Tuesday, November 26, 2024 2:21PM - 2:34PM |
ZC37.00008: Critical conditions for enhancing close-contact melting on superhydrophobic surfaces Nan Hu, Liwu Fan, Xiang Gao, Howard A Stone Close-contact melting (CCM) is a technique that is employed widely in a variety of industrial contexts, including glacier drilling, food processing, and thermal energy storage. CCM is initiated by the application of force to compress an unmelted solid against a heated surfaces, resulting in the formation of a thin film flow between them. The use of superhydrophobic surfaces is often regarded as a means of reducing drag force and enhancing liquid transport. However, the presence of trapped air within the structures of these surfaces also introduces additional thermal resistance. It is currently unclear whether superhydrophobic surfaces can accelerate the melting rate for CCM. |
Tuesday, November 26, 2024 2:34PM - 2:47PM |
ZC37.00009: Exploring the impact of Hybrid Pitching on Foil-Based Energy Harvester Performance Zihan Zhang, Alex Sorensen, Qiang Zhong Oscillating hydrofoil-based energy harvesters are renowned for their high performance in converting hydrokinetic energy into usable power. By utilizing a combination of prescribed pitching and passive heaving motions, these systems can effectively capture the kinetic energy from water currents, maximizing energy output. However, the prescribed pitching motion can sometimes result in negative pitching power, which detracts from the overall efficiency of the system. Negative pitching power occurs when the energy required to maintain the prescribed motion exceeds the energy being harvested, leading to a net loss. Additionally, the positive pitching power cannot be harvested in the prescribed pitching motion. We propose hybrid pitching as a solution to these limitations. Ideally, hybrid pitching motion can effectively reduce the power consumed and let the energy be harvested when it is in the passive mode. However, the effectiveness of hybrid and the corresponding optimal switching strategies remain unexplored. In this study, we use a cyber-physical traverse to map the performance of various prescribed pitching profiles. We then explore a series of hybrid switching strategies based on diverse switching thresholds, such as pitching power sign, kinetic energy, or composite metrics, aiming to improve energy harvesting performance. Corresponding wake dynamics and passive heaving behaviors are also analyzed. |
Tuesday, November 26, 2024 2:47PM - 3:00PM |
ZC37.00010: Predicting Lean Blowout in Combustion Systems Using Multi-Fidelity Modeling Philip O John, Naga V Guntapalli, Shyam K Menon, Ope Owoyele The accurate prediction of lean blowout (LBO) in combustion systems is essential for designing safe and efficient energy solutions, especially in aircraft. However, the complexity and cost associated with high-fidelity experimental data acquisition often limits the quantity of data available for model training. To address this challenge, we present a multi-fidelity modeling approach that leverages both high-fidelity data from limited experiments and low-fidelity data from cost-effective reactor network simulations. The high-fidelity dataset consists of LBO measurements obtained under specific experimental conditions, providing a trustworthy but sparse foundation for model development. In contrast, the low-fidelity dataset is derived from a reactor network designed to use liquid fuel, enabling the generation of additional data points under a wider range of conditions not covered by the experiments. Using these two sources of data, we train a machine learning model that effectively predicts LBO across various conditions. Our multi-fidelity model not only enhances prediction accuracy by capturing the underlying physical processes more effectively but also demonstrates the potential of multi-fidelity modeling in overcoming the limitations posed by data scarcity from experimental measurements. The resulting model showcases significant promise for predicting LBO in combustor systems, providing a powerful tool for optimizing combustion processes and enhancing operational safety |
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