@misc{GlaweKleinSchmidt, author = {Glawe, Christoph and Klein, Marten and Schmidt, Heiko}, title = {ODT augmented RaNS}, series = {Book of Abstracts of the 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics}, journal = {Book of Abstracts of the 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics}, publisher = {GAMM e.V.}, address = {Dresden}, pages = {368}, language = {en} } @misc{GlaweKleinSchmidt, author = {Glawe, Christoph and Klein, Marten and Schmidt, Heiko}, title = {Stochastic deconvolution of wall statistics in Reynolds-averaged Navier-Stokes simulations based on one-dimensional turbulence}, series = {Proceedings in applied mathematics and mechanics : PAMM}, volume = {23}, journal = {Proceedings in applied mathematics and mechanics : PAMM}, number = {3}, issn = {1617-7061}, doi = {10.1002/pamm.202300055}, pages = {9}, abstract = {Reynolds-averaged Navier-Stokes simulation (RaNS) is state-of-the-art for numerical analysis of complex flows at high Reynolds number. Standalone RaNS may yield a reasonable estimate of the wall-shear stress and turbulent drag if a proper wall-function is prescribed, but detailed turbulence statistics cannot be obtained, especially at the wall. This lack in modeling is addressed here by a stochastic deconvolution strategy based on a stochastic one-dimensional turbulence (ODT) model. Here, a one-way coupling strategy is proposed in which a forcing term is computed from the balanced RaNS solution that is in turn utilized in the ODT model. The temporally developing ODT solution exhibits turbulent perturbations but relaxes toward the local RaNS solution due to resolved molecular-diffusive processes. It is demonstrated that the approach is able to recover the distribution of positive wall-shear stress fluctuations in turbulent channel flow. When formulated as post-processing tool, it is suggested that RaNS can be enhanced by ODT providing economical means for local high-fidelity numerical modeling based on a low-fidelity flow solution.}, language = {en} } @misc{KleinMedinaMendezSchoepsetal., author = {Klein, Marten and Medina M{\´e}ndez, Juan Al{\´i} and Sch{\"o}ps, Mark Simon and Schmidt, Heiko and Glawe, Christoph}, title = {Towards physics-based nowcasting of the instantaneous wind velocity profile using a stochastic modeling approach}, series = {STAB Jahresbericht 2024 zum 24. DGLR-Fachsymposium der STAB, 13. - 14. November 2024, Regensburg}, journal = {STAB Jahresbericht 2024 zum 24. DGLR-Fachsymposium der STAB, 13. - 14. November 2024, Regensburg}, publisher = {Deutsche Str{\"o}mungsmechanische Arbeitsgemeinschaft, STAB}, address = {Regensburg [et al.]}, pages = {162 -- 163}, abstract = {The primary objective of this contribution is to provide an overview of the regime-spanning forward modeling capabilities offered by the stochastic one-dimensional turbulence model. The focus is on the applicability of the model and its validation for neutral and stable atmospheric boundary layer flows as a prerequisite for future applications to challenging atmospheric conditions.}, language = {en} }