Multiscale Modeling of Streamers: High-Fidelity Versus Computationally Efficient Methods

2022 ◽  
Author(s):  
Lee R. Strobel ◽  
Cuong Nguyen ◽  
Carmen Guerra-Garcia
Author(s):  
Andrea G. Sanvito ◽  
Giacomo Persico ◽  
M. Sergio Campobasso

Abstract This study provides a novel contribution toward the establishment of a new high-fidelity simulation-based design methodology for stall-regulated horizontal axis wind turbines. The aerodynamic design of these machines is complex, due to the difficulty of reliably predicting stall onset and poststall characteristics. Low-fidelity design methods, widely used in industry, are computationally efficient, but are often affected by significant uncertainty. Conversely, Navier–Stokes computational fluid dynamics (CFD) can reduce such uncertainty, resulting in lower development costs by reducing the need of field testing of designs not fit for purpose. Here, the compressible CFD research code COSA is used to assess the performance of two alternative designs of a 13-m stall-regulated rotor over a wide range of operating conditions. Validation of the numerical methodology is based on thorough comparisons of novel simulations and measured data of the National Renewable Energy Laboratory (NREL) phase VI turbine rotor, and one of the two industrial rotor designs. An excellent agreement is found in all cases. All simulations of the two industrial rotors are time-dependent, to capture the unsteadiness associated with stall which occurs at most wind speeds. The two designs are cross-compared, with emphasis on the different stall patterns resulting from particular design choices. The key novelty of this work is the CFD-based assessment of the correlation among turbine power, blade aerodynamics, and blade design variables (airfoil geometry, blade planform, and twist) over most operational wind speeds.


2021 ◽  
Vol 54 (27) ◽  
pp. 274002
Author(s):  
Yong He ◽  
Shupei Lin ◽  
Hadrien Marc Louis Robert ◽  
Hong Li ◽  
Pu Zhang ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4495
Author(s):  
Rocco Adduci ◽  
Martijn Vermaut ◽  
Frank Naets ◽  
Jan Croes ◽  
Wim Desmet

Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.


Author(s):  
Kevin J. Kircher ◽  
Walter Schaefer ◽  
K. Max Zhang

Abstract Advanced building climate control systems have the potential to significantly reduce greenhouse gas emissions and energy costs, but more research is needed to bring these systems to market. A key component of building control research is testing algorithms through simulation. Many high-fidelity simulation testbeds exist, but they tend to be complex and opaque to users. Simpler, more transparent testbeds also exist, but they tend to neglect important nonlinearities and disturbances encountered in practice. In this paper, we develop a simulation testbed that is computationally efficient, transparent and high fidelity. We validate the testbed empirically, then demonstrate its use through the examples of system identification, online state and parameter estimation, and model predictive control (MPC). The testbed is intended to enable rapid, reliable analysis of building control algorithms, thereby accelerating progress toward reducing greenhouse gas emissions at scale. We call the resulting testbed and supporting functions the bldg toolbox, which is free, open source, and available online.


Author(s):  
A. G. Sanvito ◽  
G. Persico ◽  
M. S. Campobasso

Abstract This study provides a novel contribution towards the establishment of a new high–fidelity simulation–based design methodology for stall–regulated horizontal axis wind turbines. The aerodynamic design of these machines is complex, due to the difficulty of reliably predicting stall onset and post–stall characteristics. Low–fidelity design methods, widely used in industry, are computationally efficient, but are often affected by significant uncertainty. Conversely, Navier–Stokes CFD can reduce such uncertainty, resulting in lower development costs by reducing the need of field testing of designs not fit for purpose. Here, the compressible CFD research code COSA is used to assess the performance of two alternative designs of a 13–meter stall–regulated rotor over a wide range of operating conditions. Validation of the numerical methodology is based on thorough comparisons of novel simulations and measured data of the NREL Phase VI turbine rotor, and one of the two industrial rotor designs. An excellent agreement is found in all cases. All simulations of the two industrial rotors are time–dependent, to capture the unsteadiness associated with stall which occurs at most wind speeds. The two designs are cross-compared, with emphasis on the different stall patterns resulting from particular design choices. The key novelty of this work is the CFD–based assessment of the correlation among turbine power, blade aerodynamics, and blade design variables (airfoil geometry, blade planform and twist) over most operational wind speeds.


2016 ◽  
Vol 156 ◽  
pp. 2-9 ◽  
Author(s):  
Trenton M. Ricks ◽  
Thomas E. Lacy ◽  
Evan J. Pineda ◽  
Brett A. Bednarcyk ◽  
Steven M. Arnold

2020 ◽  
Author(s):  
Qing Li ◽  
Luke Van Roekel

Abstract. A multiscale modeling approach for studying the ocean surface turbulent mixing is explored by coupling an ocean general circulation model (GCM) MPAS-Ocean with the PArallel Large eddy simulation Model (PALM). The coupling approach is similar to the superparameterization approach that has been used mostly to represent the effects of deep convection in atmospheric GCMs. However, since the emphasis here is on the small-scale turbulent mixing processes and their interactions with the larger-scale processes, a high-fidelity, three-dimensional large eddy simulation (LES) model is used, in contrary to a simplified process-resolving model with reduced physics or reduced dimension commonly used in the superparameterization literature. To reduce the computational cost, a customized version of PALM is ported on the general-purpose graphics processing unit (GPU) with OpenACC, achieving 10–16 times overall speedup as compared to running on a single CPU. Even with the GPU-acceleration technique, superparameterization of the ocean surface turbulent mixing using high-fidelity and three-dimensional LES over the global ocean in GCMs is still computationally intensive and infeasible for long simulations. However, running PALM regionally on selected MPAS-Ocean grid cells is shown to be a promising approach moving forward. The flexible coupling between MPAS-Ocean and PALM outlined here allows further exploration of the interactions between ocean surface turbulent mixing and larger-scale processes, and development of better ocean surface turbulent mixing parameterizations in GCMs.


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