Mandar Tabib

Numerical Analysis of NREL 5MW Wind Turbine: A Study Towards a Better Understanding of Wake Characteristic and Torque Generation Mechanism

With the increased feasibility of harvesting offshore wind energy, scale of wind turbines is growing rapidly and there is a trend towards clustering together higher number of turbines in order to harvest maximum yield and to leave a smaller footprint on the environment. This causes complex flow configurations inside the farms, the study of which is essential to making offshore wind energy a success. The present study focuses on NREL 5MW wind turbine with the following objectives (a)To compare Sliding Mesh Interface and Multiple Reference Frame modeling approaches and their predictive capabilities in reproducing the characteristics of flow around the full scale wind turbine. (b)To get a better insight into wake dynamics behind the turbine in near and far wake regions operating under different tip-speed-ratio and incoming turbulence intensities.

LES and RANS simulation of onshore Bessaker wind farm: analysing terrain and wake effects on wind farm performance

This work compares the predictive performance of RANS and LES solver in capturing the effect of terrain and wakes on the performance of the Bessaker wind farm. This 25 turbine wind farm is located in a highly complex terrain and is exposed to predominantly high westerly and south easterly winds. A one-equation sub-grid scale LES turbulence model has been used to help capture the wake dynamics: particularly the effects of wake meandering and wake-turbine interactions. A comparison between RANS and LES models highlights the influence of turbulence model on wake decay and its subsequent effect on prediction of power production. The LES model predicts delayed decay of the wake and more pronounced wake interference leading to a lower power production in wind farm than the RANS case. The RANS model overpredicts turbulence, which cause faster turbulent momentum diffusivity and faster wake recovery. This study has given some insights regarding the power production at Bessaker wind farm for neutral conditions and westerly flow.

Analyzing complex wake-terrain interactions and its implications on wind-farm performance.

Rotating wind turbine blades generate complex wakes involving vortices (helical tip-vortex, root-vortex etc.).These wakes are regions of high velocity deficits and high turbulence intensities and they tend to degrade the performance of down-stream turbines. Hence, a conservative inter-turbine distance of up-to 10 times turbine diameter (10D) is sometimes used in wind-farm layout (particularly in cases of flat terrain). This ensures that wake-effects will not reduce the overall wind-farm performance, but this leads to larger land footprint for establishing a wind-farm. In-case of complex-terrain, within a short distance (say 10D) itself, the nearby terrain can rise in altitude and be high enough to influence the wake dynamics. This wake-terrain interaction can happen either (a) indirectly, through an interaction of wake (both near tip vortex and far wake large-scale vortex) with terrain induced turbulence (especially, smaller eddies generated by small ridges within the terrain) or (b) directly, by obstructing the wake-region partially or fully in its flow-path. Hence, enhanced understanding of wake- development due to wake-terrain interaction will help in wind farm design. To this end the current study involves: (1) understanding the numerics for successful simulation of vortices, (2) understanding fundamental vortex-terrain interaction mechanism through studies devoted to interaction of a single vortex with different terrains, (3) relating influence of vortex-terrain interactions to performance of a wind-farm by studying a multi-turbine wind-farm layout under different terrains. The results on interaction of terrain and vortex has shown a much faster decay of vortex for complex terrain compared to a flatter-terrain. The potential reasons identified explaining the observation are (a) formation of secondary vortices in flow and its interaction with the primary vortex and (b) enhanced vorticity diffusion due to increased terrain-induced turbulence. The implications of this vortex-terrain interactions on wind-farm performance is observed by comparing two LES simulations of a multi-turbine wind-farm layout (in real actual complex terrain and a made-up flat terrain scenario) with the observed annual power data at the actual wind-farm. The comparison reveals drop in power production due to terrain and wake effects for flatter terrain case. The insights from this study can serve as a step towards enhancing wake-dissipation through either artificial obstruction or artificial terrain modifications.

Near wake region of an industrial scale wind turbine: comparing LES-ALM with LES-SMI simulations using data mining (POD)

Accurate prediction of power generation capability needs proper assessment of blade loading and wake behavior. In this regard, the Sliding Mesh Interface (SMI) approach and the Actuator Line Model (ALM) are two diverse computational fluid dynamics (CFD) based approaches of simulating the turbine behavior, each having its own merits and demerits. The SMI technique simulates the unsteady flow by explicitly modeling the blades and their rotation using a dynamic mesh, while in Actuator Line Model, the blades are not modeled explicitly but each blade is resolved as a rotating line (made of N actuator segments), over which the forces are computed. The current work focuses on simulating an industrial scale reference turbine and in differentiating the near wake dynamics predicted by these two approaches using Large Eddy Simulation (LES) and Proper Orthogonal Decomposition (POD) technique (a data mining tool). Initially, the ALM is compared with FAST model for the prediction of variation of power coefficient with the Tip Speed Ratio (TSR). The ALM is able to capture the varying trend and it predicts a similar optimum tip speed ratio as the FAST model. At this optimum TSR condition, the ALM is compared with the SMI method for a study limited to the near wake region. Comparisons between SMI and ALM shows that : (a) The SMI is predicting more complex 3D nature of the flow, and (b) the POD shows that ALM captures the shear regions of wake but it does not capture the vast compendium of length and time scales of eddies as SMI does. However, despite these limitations, the ALM has been able to capture the qualitative trend in wake deficit and the power coefficient variation with tip speed.

Numerical Modeling Framework for Wind Turbine Analysis & Atmospheric Boundary Layer Interaction

Prevailing atmospheric conditions can have a significant impact on the performance of large mega-watt wind turbines. A purely experimental evaluation of this impact is currently not possible and hence numerical techniques have been employed in this work. With the focus on aerodynamic performance of wind turbine, an attempt is made to realize the following objectives: (a) To evaluate the predictive capabilities of fully resolved Sliding Mesh Interface (SMI) transient simulations around the wind turbine against the steady state Multiple Reference Frame (MRF) simulations (b) To investigate the performance of the wind turbine subjected to uniform inlet profiles against atmospheric boundary layer profiles. (c) To study the effect of atmospheric stability on wind turbine performance. The methods are validated first and then implemented on a national renewable energy laboratory 5 MW reference wind turbine model for the aerodynamic study. Highly uneven and irregular wake profiles are seen with variation in input conditions(TKE). Uneven distribution of flow velocity in the lateral direction enhances the momentum transfer with in the shear layers and contributes positively towards the wake recovery. It is also found that in unstable stratified conditions, the positive buoyancy flux at the surface creates thermal instabilities which enhances the turbulent kinetic energy and the turbulent mixing, and helps the wake to recover faster.

Discovering Thermoelectric Materials Using Machine Learning: Insights and Challenges

This work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chemical compounds involves expensive and time consuming experiments. In the current work, the density functional theory (DFT) simulations are used to compute the descriptors (features) and thermoelectric characteristics (labels) of a set of compounds. The DFT simulations are computationally very expensive and hence the database is not very exhaustive. With an anticipation that the important features can be learned by machine learning (ML) from the limited database and the knowledge could be used to predict the behavior of any new compound, the current work adds knowledge related to (a) understanding the impact of selection of influence of training/test data, (b) influence of complexity of ML algorithms, and (c) computational efficiency of combined DFT-ML methodology.

Influence of Tip Speed Ratio on Wake Flow Characteristics Utilizing Fully Resolved CFD Methodology

Dominant flow structures in the wake region behind the turbine employed in the Blind Test campaign [1], [2] is investigated numerically. The effect on the wake configuration at variable operating conditions are studied. The importance of the introduction of turbine tower inside the numerical framework is highlighted. High-fidelity simulations are performed with Multiple Reference Frame (MRF) numerical methodology. A thorough comparison among the cases is presented, and the wake evolution is analyzed at variable stations downstream of the turbine. Streamlines of flow field traveled towards ground adjacent to turbine tower and strongly dependent on the operating tip speed ratio. Wake is composed of tower shadow superimposed by rotor wake. Shadow of the tower varies from x/R=2 until x/R=4 and breaks down into small vortices with the interaction of rotor wake. This study also shows that the wake distribution consists of two zones; inner zone composed of disturbances generated by blade root, nacelle and the tower, and an outer zone consisting of tip vortices.

On interactions between wind turbines and the marine boundary layer

Most mesoscale models are developed with grid resolution in the range of kilometers. Therefore, they may require spatial averaging to analyze flow behavior over the domain of interest. In doing so, certain important features of sub-grid scales are lost. Moreover, spatial averaging on the governing equations results in additional terms known as dispersive fluxes. These fluxes are ignored in the analysis. The aim of this paper is to identify the significance of these fluxes for accurate assessment of flow fields related to wind farm applications. The research objectives are hence twofold: 1) to quantify the impact of wind turbines on MBL characteristics. 2) to account for the magnitude of dispersive fluxes arising from spatial averaging and make a comparison against the turbulent flux values. To conduct the numerical study the NREL 5MW reference wind turbine model is employed with a RANS approach using k-ε turbulence model. The results are presented concerning spatially averaged velocity, wake deficit behind the turbine, dispersive and turbulent fluxes. ∗Address all correspondence to this author. . NOMENCLATURE ρ Density(kg/m3) zo Surface roughness Ω Angular rotation rate(rad/sec) ui Spatially filtered velocity in tensor form(m/s) u ′ Fluctuation in velocity with time(m/s) CFD Computational Fluid Dynamics RANS Reynolds Averaged Navier Stokes BEM Blade Element Momentum LES Large Eddy Simulation MBL Marine Boundary Layer NREL National Renewable Energy Laboratories INTRODUCTION With the size of operational offshore wind turbines increasing rapidly and already in the range of 100–150m, modeling of 1 Copyright

Wake modeling in complex terrain using a hybrid Eulerian-Lagrangian Split Solver

Wake vortices (WVs) generated by aircraft are a source of risk to the following aircraft. The probability of WV related accidents increases in the vicinity of airport runways due to the shorter time of recovery after a WV encounter. Hence, solutions that can reduce the risk of WV encounters are needed to ensure increased flight safety. In this work we propose an interesting approach to model such wake vortices in real time using a hybrid Eulerian- Lagrangian approach. We derive an appropriate mathematical model, and show a comparison of the different types of solvers. We will conclude with a real life application of the methodology by simulating how wake vortices left behind by an aircraft at the Vffirnes airport in Norway get transported and decay under the influence of a background wind and turbulence field. Although the work demonstrates the application in an aviation context the same approach can be used in a wind energy context.