Jan Bartl
Norwegian University of Science and Technology
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Publication
Featured researches published by Jan Bartl.
Journal of Physics: Conference Series | 2016
Jan Bartl; Lars Sætran
In state-of-the-art wind farms each turbine is controlled individually aiming for optimum turbine power not considering wake effects on downstream turbines. Wind farm control concepts aim for optimizing the overall power output of the farm taking wake interactions between the individual turbines into account. This experimental wind tunnel study investigates axial induction based control concepts. It is examined how the total array efficiency of two in-line model turbines is affected when the upstream turbines tip speed ratio (λcontrol) or blade pitch angle (β-control) is modified. The focus is particularly directed on how the wake flow behind the upstream rotor is affected when its axial induction is reduced in order to leave more kinetic energy in the wake to be recovered by a downstream turbine. It is shown that the radial distribution of kinetic energy in the wake area can be controlled by modifying the upstream turbines tip speed ratio. By pitching out the upstream turbines blades, however, the available kinetic energy in the wake is increased at an equal rate over the entire blade span. Furthermore, the total array efficiency of the two turbine setup is mapped depending on the upstream turbines tip speed ratio and pitch angle. For a small turbine separation distance of x/D=3 the downstream turbine is able to recover the major part of the power lost on the upstream turbine. However, no significant increase in the two-turbine array efficiency is achieved by altering the upstream turbines operation point away from its optimum.
Journal of Physics: Conference Series | 2016
Kf Sagmo; Jan Bartl; Lars Sætran
2D and 3D steady state simulations were done using the commercial CFD package Star-CCM+ with three different RANS turbulence models. Lift and drag coefficients were simulated at different angles of attack for the NREL S826 airfoil at a Reynolds number of 100 000, and compared to experimental data obtained at NTNU and at DTU. The Spalart-Allmaras and the Realizable k-epsilon turbulence models reproduced experimental results for lift well in the 2D simulations. The 3D simulations with the Realizable two-layer k-epsilon model predicted essentially the same lift coefficients as the 2D Spalart-Allmaras simulations. A comparison between 2D and 3D simulations with the Realizable k-epsilon model showed a significantly lower prediction in drag by the 2D simulations. From the conducted 3D simulations surface pressure predictions along the wing span were presented, along with volumetric renderings of vorticity. Both showed a high degree of span wise flow variation when going into the stall region, and predicted a flow field resembling that of stall cells for angles of attack above peak lift.
Journal of Physics: Conference Series | 2017
Jannik Schottler; Franz Mühle; Jan Bartl; Joachim Peinke; Muyiwa S. Adaramola; Lars Sætran; Michael Hölling
In this wind tunnel campaign, detailed wake measurements behind two different model wind turbines in yawed conditions were performed. The wake deflections were quantified by estimating the rotor-averaged available power within the wake. By using two different model wind turbines, the influence of the rotor design and turbine geometry on the wake deflection caused by a yaw misalignment of 30° could be judged. It was found that the wake deflections three rotor diameters downstream were equal while at six rotor diameters downstream insignificant differences were observed. The results compare well with previous experimental and numerical studies.
INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016) | 2017
Erik R. Prytz; Øyvind Huuse; Bernhard Müller; Jan Bartl; Lars Sætran
Turbulent flow at Reynolds numbers 5 · 104 to 106 around the NREL S826 airfoil used for wind turbine blades is simulated using delayed detached eddy simulation (DDES). The 3D domain is built as a replica of the low speed wind tunnel at the Norwegian University of Science and Technology (NTNU) with the wind tunnel walls considered as slip walls. The subgrid turbulent kinetic energy is used to model the sub-grid scale in the large eddy simulation (LES) part of DDES. Different Reynoldsaveraged Navier-Stokes (RANS) models are tested in ANSYS Fluent. The realizable k - ∈ model as the RANS model in DDES is found to yield the best agreement of simulated pressure distributions with the experimental data both from NTNU and the Technical University of Denmark (DTU), the latter for a shorter spanwise domain. The present DDES results are in excellent agreement with LES results from DTU. Since DDES requires much fewer cells in the RANS region near the wing surface than LES, DDES is computationally much more efficient than LES. Whereas DDES is able to predict lift and drag in close agreement with experiment up to stall, pure 2D RANS simulations fail near stall. After testing different numerical settings, time step sizes and grids for DDES, a Reynolds number study is conducted. Near stall, separated flow structures, so-called stall cells, are observed in the DDES results.Turbulent flow at Reynolds numbers 5 · 104 to 106 around the NREL S826 airfoil used for wind turbine blades is simulated using delayed detached eddy simulation (DDES). The 3D domain is built as a replica of the low speed wind tunnel at the Norwegian University of Science and Technology (NTNU) with the wind tunnel walls considered as slip walls. The subgrid turbulent kinetic energy is used to model the sub-grid scale in the large eddy simulation (LES) part of DDES. Different Reynoldsaveraged Navier-Stokes (RANS) models are tested in ANSYS Fluent. The realizable k - ∈ model as the RANS model in DDES is found to yield the best agreement of simulated pressure distributions with the experimental data both from NTNU and the Technical University of Denmark (DTU), the latter for a shorter spanwise domain. The present DDES results are in excellent agreement with LES results from DTU. Since DDES requires much fewer cells in the RANS region near the wing surface than LES, DDES is computationally much more efficient ...
Energy Procedia | 2012
Jan Bartl
Wind Energy Science Discussions | 2016
Jan Bartl; Lars Sætran
Wind Energy Science Discussions | 2018
Jannik Schottler; Jan Bartl; Franz Mühle; Lars Sætran; Joachim Peinke; Michael Hölling
Wind Energy Science Discussions | 2018
Jan Bartl; Franz Mühle; Jannik Schottler; Lars Sætran; Joachim Peinke; Muyiwa S. Adaramola; Michael Hölling
Wind Energy Science Discussions | 2017
Julie Krøgenes; Lovisa Brandrud; Richard Hann; Jan Bartl; Tania Bracchi; Lars Sætran
Energy Procedia | 2016
Clio Ceccotti; Andrea Spiga; Jan Bartl; Lars Sætran