Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Volume 67, Number 19
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session Z15: Energy: Water Power and Other Topics |
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Chair: Amneet Bhalla, San Diego State University Room: 142 |
Tuesday, November 22, 2022 12:50PM - 1:03PM |
Z15.00001: Hydrodynamic Optimization of Ducted Hydrokinetic Turbine Jeongbin Park, Bradford G Knight, Yingqian Liao, Kevin J Maki, Joaquim Martins, Yulin Pan It has long been hypothesized that a duct can accelerate and condition the fluid flow passing the hydrokinetic turbine and improve overall energy extraction efficiency. To investigate this problem, we explore the optimal design of a ducted hydrokinetic turbine to maximize hydrodynamic efficiency in this work. Our method relies on combining both gradient-free and gradient-based optimizations. We first conduct gradient-free Bayesian optimization in conjunction with a low-fidelity steady RANS varying a few important design parameters. The optimal turbine geometry obtained is then used as the baseline design for subsequent gradient-based optimization using the adjoint method with substantially more design variables (representing both blade and duct geometries). The optimized geometry is finally evaluated through unsteady RANS simulations. |
Tuesday, November 22, 2022 1:03PM - 1:16PM |
Z15.00002: Optimizing wave energy converter performance using model-free reinforcement learning algorithms Kaustubh M Khedkar, Amneet Pal S Bhalla Several critical challenges in ocean wave energy harvesting can be addressed by designing a robust controller to optimize the wave energy converter (WEC) performance under changing sea states and WEC dynamics in its lifespan. In contrast to the renowned model-based controllers, this can be done using model-free reinforcement learning (RL) techniques. We present the optimization of a cylindrical point absorber WEC device using deep Q-network (DQN) and double DQN (DDQN) controls. These RL algorithms use deep neural networks (DNN) as function approximators to decide the optimal control action that maximizes the power absorption. The RL agent trains the DNN using experiences generated by interacting with the WEC environment. The environment is simulated using a linear dynamical model of the device, derived using the linear potential theory (LPT). Multiple independent environments are simulated in parallel using the message passing interface (MPI) to generate experiences and train the agent faster. Once trained, the agent drives the device to optimal performance. Next, the RL agent is employed in computational fluid dynamics (CFD) based WEC simulations that fully resolve the nonlinear wave structure interaction (WSI) phenomenon and produces device dynamics closer to reality. |
Tuesday, November 22, 2022 1:16PM - 1:29PM |
Z15.00003: Oxygenation using a draft tube deflector : ultrasonic deaeration optimized to obtain initial dissolved oxygen level Pouria Rahmati, Vadoud Naderi, Suk Yi Lo, Susan J Gaskin
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Tuesday, November 22, 2022 1:29PM - 1:42PM |
Z15.00004: On the effects of cavitation and free surface for hydrokinetic H-Darrieus cross-flow turbine Olivier Lévesque, Antoine Rochette, Guy Dumas Choosing the appropriate design of hydrokinetic turbine for a given resource and projected deployment is crucial for energy extraction. Cavitation plays a major role in such design choices. Previous experimental studies have suggested that cavitation might explain the low performance of H-Darrieus cross-flow turbines in high-speed inflows. To better understand the effects of cavitation and free surface on these turbines, 2D URANS numerical simulations at high Reynolds number are conducted at different inflow velocities, tip-speed ratios and immersion depths. The results show that cavitation is greatly susceptible to appear for high velocities and low immersion depths. This cavitation is detrimental to the performances since it deteriorates the effective shape of the blades as well as promoting vortex shedding. The impact is even more pronounced at low tip-speed ratios because, for higher tip-speed ratios, the effective angles of attack are smaller, thus reducing the suction peak on the blades. The present results provide insight to help better choose the turbine solidity depending on deployment site characteristics. |
Tuesday, November 22, 2022 1:42PM - 1:55PM |
Z15.00005: Evolution of Coherent Structures in the Near Wake of a Tidal Stream Turbine under Non-Homogeneous Inflow Cong Han, Arindam Banerjee For successful deployment of a Tidal Stream Turbine (TST) array at tidal energy sites, it is critical to fully understand the wake structure of an isolated TST. While the wake of a TST has been extensively studied in the last decade, those studies are mainly restricted to low turbulent, homogeneous inflow conditions and do not take into consideration the inhomogeneous and elevated levels of free-stream turbulence at those sites. To address some of those deficiencies, we have developed a Tidal Turbulence Test Facility (T3F) at Lehigh University which can mimic several turbulence parameters at high-energy tidal sites using a Makita-style active grid. We will report results from a measurement campaign using a 1:20 model scale 3-bladed TST operated under a high turbulent and non-homogenous inflow (shear) condition. The near wake velocity field was measured by a stereoscopic Particle Image Velocimetry (sPIV) system and the evolution of the near wake coherent structure is investigated via proper orthogonal decomposition (POD) and Dynamic Mode Decomposition (DMD). By tracking the coherent structures in the wake under the non-homogenous inflow, their effect on mean wake flow can be identified, which will be beneficial to the development of models of tidal turbine arrays. |
Tuesday, November 22, 2022 1:55PM - 2:08PM |
Z15.00006: Deep Learning-based Forecasting of Renewable Energy Sources for Locations with Scarce Data Jhon J Quinones, Luis R Pineda, Antonio Esquivel-Puentes, Jason K Ostanek, Luciano Castillo Deep learning-based models are one of the most popular data-driven approaches for forecasting renewable energy sources (RES) with reasonable accuracy since they can handle sequential data, such as the time series from weather variables. However, these models demand a large amount of historical weather data for training, which in most cases is limited in locations selected for installing distributed energy resources (DERs). This work uses an Encoder-Decoder Sequence to Sequence (Seq2Seq) model along a transfer learning methodology to predict wind speed and solar radiation on a medium-long term horizon. The transfer learning strategy is incorporated in the Seq2Seq model to improve prediction accuracy for a target location (e.g., A DERs with insufficient data) by using data generated from similar source locations (e.g., weather stations with extensive data). The results show that the transfer learning approach enhances the forecasting performance of the Seq2Seq model against the case of only using data from source locations. The proposed methodology offers an opportunity for the widespread adoption of sustainable technologies in urban and rural communities even when there are insufficient on-site meteorological measurements. |
Tuesday, November 22, 2022 2:08PM - 2:21PM |
Z15.00007: On estimating the Carbon Footprint of Computational Fluid Dynamics Jeremy Horwitz Since their invention, the capability of computational machines to simulate detailed physical phenomenon has grown by many orders of magnitude. These innovations have meant that one generation's main frame computer simulations are next generation's workstation simulations and are next generation's smartphone simulations. Historically unachievable simulations, flow at high Reynolds number has started to become feasible. Yet, there is a cost to the growing reliance on large-scale simulations, not just in time and money, but carbon equivalent produced from the electricity consumption. This cost is connected to the number of core-hours of each simulation and is especially onerous for direct numerical simulation. In this talk, we develop estimates of the carbon footprint associated with this paradigm by examining simulations of canonical turbulent flows (isotropic, channel, boundary layer, etc.). We propose that the developed scalings should be used in conjunction with other factors used in tackling a new problem. While high resolution simulations can be carbon heavy, they offer the promise of accurate reduced order model development which are themselves lower sources of carbon. Finally, the adopted scalings may serve as motivation to invest in funding for renewable energy sources which reduce the impact of studying physics. |
Tuesday, November 22, 2022 2:21PM - 2:34PM |
Z15.00008: Numerical investigation of the wind loading exerted on an East-West (EW) ground mount solar system in low-rise scenarios. Andre Aquino, Victor Rego, Tracie Barber, Rhett Evans The wind loading is a cost driver of photovoltaic (PV) systems as it has a direct impact on structural requirements. A reduction in the wind loads experienced by PV systems is expected to reduce initial capital and operational costs, improving its technical and economical feasibility. In this study, Reynolds-averaged Navier-Stokes simulations have been performed to investigate the mean wind load exerted on an East-West (EW) fixed tilt ground mount structure. Due to the industry interest to model low-rise scenarios, ground clearances of 250, 350, and 550 mm at Reynolds number of 1.478×107 were tested for wind directions of 0°, 40°, and 90°. The system investigated here is composed of 50 PV modules (2x1m), arranged in 5 E-W bays at 10° pitch. For the same ground clearance, the results indicated that, at 40° wind direction, the system experienced the highest uplift, whereas, at 90°, the system produced downforce. For the 40° and 90° wind directions, the individual east and west panels produced wind loads in opposite directions, where the west-facing panels tended to generate downforce and the east-facing panels tended to create uplift. It has also been demonstrated that the total uplift increased as the structure was raised from the ground, whilst leading edge modules experienced a relative reduction in uplit. This trend is due to the higher airflow velocities in the atmospheric boundary at higher ground heights and the reduction of the ground effect, which led to an airflow deceleration underneath the system and a change in the effective incidence angle of the flow. |
Tuesday, November 22, 2022 2:34PM - 2:47PM |
Z15.00009: CFD Study of Vertical U-Loop Thermo-Active Foundations for Cold Climates Prem Agarwala, Shayan Davani, Jordan Gruenes, Amirhossein Darbandi, Alison Hoxie, Aggrey Mwesigye Ground source heat pump (GSHP) systems have received considerable interest for their efficient and relatively stable performance for building heating and cooling. To reduce the high initial cost of this technology, coupling the ground heat exchanger of the GSHP system with the building foundation aka “thermo-active foundation” is a promising approach. However, the performance of such systems in cold climates has not been explored sufficiently. This work computationally investigates the performance of a U-loop helical pile heat exchanger. The conjugate heat transfer between the heat exchanger and the ground domain is solved using a transient computational fluid dynamics (CFD) model validated with an existing experimental study. The realizable k-ϵ turbulence model was used for turbulence closure. Moreover, to accurately predict heat transfer and fluid flow in the near-wall regions, the enhanced wall treatment model was used with y+ values of less than 1.The CFD model is used to explore the transient performance of the system in a cold climate where the high heating dominant loads lead to a significant thermal imbalance of the ground. Furthermore, the performance at various inlet temperatures and fluid velocities is investigated. |
Tuesday, November 22, 2022 2:47PM - 3:00PM Author not Attending |
Z15.00010: Science of cutting multi-layer systems: an inspiration from our kitchens Udita Ringania, Sunny Kumar, Saad Bhamla The majority of us are familiar with the phrases cutting, slicing, and dicing from our culinary experiences. In this talk, we will explore the topic of cutting, specifically in kitchen, with an aim to leverage the physics to optimize the force exerted in the process. However, the majority of kitchen materials are complex, made up of several layers with various elastic moduli, fracture toughnesses, and relative thicknesses, such as the skin, pulp, and seeds of fruits and vegetables. So, using a multilayer system, we empirically record the force profiles when slicing and chopping a variety of fruits and vegetables in this study. |
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