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Item type:Publication, Access status: with files , Datenbasierte Modellierung der aerodynamischen Lasten auf Überwasserschiffe(2023); The aerodynamic loads on the superstrucure of ships are influenced by different environmental and operational parameters. In order to determine the loads depending on the system parameters on board in near-real time, data-based surrogate models are developed. High-resolution pressure fields based on hybrid RANS/LES simulations are used as data basis. For the data basis a dimension reduction is performed. To create a fully parametrized model, the functional relationship between the system parameters and the model coefficients is represented by a regression. The surrogate modelling allows an efficient and accurate prediction of the pressure fields and resulting loads for arbitrary values of the system parameters.Publicationtype: Doctoral Thesis116 121 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Data-driven surrogate modeling of aerodynamic forces on the superstructure of container vesselsThe operation of fluid engineering systems is usually governed by a wide range of different parameters. Investigations of the entire parameter spectrum using classical, first-principle based CFD methods are costly with regards to CPU and wall-clock time. Therefore, a near real-time assessment of complex flows using CFD to support the operation is deemed unfeasible. The paper is concerned with methods for data-based surrogate models to predict the forces exerted by the aerodynamic pressure field on the superstructure of a full-scale container ship for different container loading conditions and wind directions. The strategy aims to assist a fuel-efficient operation and is based on a two-step approach. During an initial step, a reduced representation of pressure fields obtained from 3D Navier–Stokes simulations is compiled. To this extent, a classical proper orthogonal decomposition is compared with convolutional neural network autoencoders. A subsequent parameterization employs a feedforward neural network to link the reduced model with the operational parameters, i.e. the angle of attack and container loading condition, and to enable a rapid on board assessment. Both methods provide a similar agreement for the pressure fields, as well as the resulting forces, with the CNN-based surrogate model being significantly more compact.Publicationtype: Journal ArticleScopus© Citations 7 181 389 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Hybrid RANS/LES Simulations of Aerodynamic Flows Around Superstructures of ShipsThe paper reports on a combined computational and experimental study on the aerodynamics of the superstructure of a container vessel. The study aims to assess the potential of add-on components to reduce the aerodynamic drag and validate the capabilities of hybrid RANS/LES approaches against traditional URANS methods. Results indicate the superior predictive performance of hybrid approaches and highlight the potential of aerodynamic add-on devices in the deckhouse region.Publicationtype: Book Part194 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Numerical Simulation of Propeller-Rudder Interaction for Non-Cavitating and Cavitating Flows Using Different ApproachesThe present article deals with the numerical investigation of propeller-rudder interaction. The involved simulation method is based on coupling a RANS solver and a potential-theory based panel method, where the panel method primarily handles the propeller flow and the RANS solver is used for the simulation of the complex rudder flow. Compared to full RANS simulations, this separation allows numerical investigations in a time-saving manner. In a numerical study, a rudder-propeller combination is evaluated by means of the developed simulation method for different propeller advance coefficients and rudder angles. Rudder cavitation is also adressed. The results are compared to results obtained by a full RANS simulation.Publicationtype: Conference Paper96