TY - GEN A1 - Klein, Marten A1 - Medina Méndez, Juan Alí A1 - Schmidt, Heiko T1 - Simulating Volatile Wind Energy: Stochastic Forward Modeling and Machine Learning N2 - The transformation of the energy sector is based on the integration of various renewable sources, such as wind and solar energy. One of the key challenges for the integration of these sources into the existing power grid is their erratic and sometimes discontinuous availability (volatility). Wind energy is one of the most relevant sources of CO2 neutral electric energy, but volatile due to fluctuating wind fields on multiple scales. This has already been realized so that senors provide real-time information on the scale of individual wind turbines. However, fore- casting remains an unresolved problem since numerical weather prediction models cannot provide the necessary level of detail. New modeling strategies are required that integrate turbine-scale and meso-scale information for accurate site-specific short-term prediction. Present and forthcoming research aims to incorporate fluctuations on multiple levels of fidelity, depending on the abstraction layer KW - AI transfer KW - AI teaching KW - stochastic modeling KW - machine learning KW - wind energy KW - volatility modeling and prediction Y1 - 2022 UR - https://ai-science-atlas.innohub13.de/files/pdf/NSGSimulatingVolatileWindEnergyBTUpdf.pdf UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Klein_poster_KI-Atlas22.pdf PB - Innovation Hub 13, TH Wildau CY - Wildau ER - TY - GEN A1 - Medina Méndez, Juan Alí A1 - Klein, Marten A1 - Schmidt, Heiko T1 - Fractal roughness representation in a stochastic one-dimensional turbulence modeling approach T2 - Proceedings of the 13th International Symposium on Turbulence and Shear Flow Phenomena (TSFP13), Montréal, Canada, June 25-28, 2024 Y1 - 2024 UR - http://www.tsfp-conference.org/proceedings/2023/171.pdf SP - 1 EP - 6 ER - TY - CHAP A1 - Klein, Marten A1 - Glawe, Christoph A1 - Ehlert, Mark Simon A1 - Medina Méndez, Juan Alí A1 - Schmidt, Heiko ED - Dillmann, Andreas ED - Heller, Gerd ED - Krämer, Ewald ED - Breitsamer, Christian ED - Wagner, Claus ED - Krenkel, Lars T1 - Stochastic modeling of intermittent inflow turbulence in the atmospheric boundary layer T2 - New Results in Numerical and Experimental Fluid Mechanics XV : contributions to the 24th STAB/DGLR Symposium, Regensburg, Germany, 2024 N2 - Wind turbine predesign is challenged by the representation of site-specific wind conditions. A good deal of that challenge lies in the modeling of the inflow turbulence in the atmospheric boundary layer (ABL). A stochastic one-dimensional turbulence (ODT) model is applied to an idealized neutrally stratified ABL and evolves the instantaneous velocity profile with full-scale resolution. The model is able to reproduce the law of the wall consistently after an initial calibration with the surface drag law. Investigating turbulent time series of the horizontal velocity components it is demonstrated that the model generates physically justified intermittency features with increasing turbulence intensity. KW - Atmospheric boundary layer KW - Intermittency KW - Stochastic modeling KW - Turbulent inflow Y1 - 2026 SN - 978-3-032-11115-9 U6 - https://doi.org/10.1007/978-3-032-11115-9_63 SN - 1612-2909 SP - 684 EP - 693 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Naik Burye, Nishidh Shailesh A1 - Medina Méndez, Juan Alí A1 - Klein, Marten A1 - Schmidt, Heiko ED - Dillmann, Andreas ED - Heller, Gerd ED - Krämer, Ewald ED - Breitsamer, Christian ED - Wagner, Claus ED - Krenkel, Lars T1 - Revisiting near-wall modeling of fully developed turbulent flow in concentric annuli T2 - New Results in Numerical and Experimental Fluid Mechanics XV : contributions to the 24th STAB/DGLR Symposium, Regensburg, Germany, 2024 N2 - We report on a systematic study for Reynolds–Averaged Navier-Stokes (RANS) modeling and simulations of turbulent annular pipe flow. Several simulations were performed using the most readily-available RANS models in the open-source library OpenFOAM. A customized 1-D RANS solver was also developed for ease of analysis. The focus of the study is on the reproduction of the mean velocity profile, its maximum, and maximum radial location, as well as modeled low-order fluctuation statistics. The flow in the annular gap is characterized by a radius ratio of 0.1, and a friction Reynolds number equal to 600 that is based on a mean friction velocity. Deviations from the mean velocity profile are observed for all RANS models investigated when compared with Direct Numerical Simulation (DNS) reference data. The representation of the near-wall outer cylinder flow is better than that of the near-wall inner cylinder flow. KW - Annular pipe flow KW - HRN and LRN wall model formulations KW - Wall function KW - Reynolds-averaged Navier-Stokes (RANS) modeling Y1 - 2026 SN - 978-3-032-11115-9 U6 - https://doi.org/10.1007/978-3-032-11115-9_68 SN - 1612-2909 SP - 737 EP - 747 PB - Springer Nature Switzerland CY - Cham ER -