Browsing by Author "Antoniadis, Antonios"

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    Aerodynamic analysis of large wind farms using two-scale coupled modelling approaches.
    (2021-08) Ma, Lun; Tsoutsanis, Panagiotis; Antoniadis, Antonios
    The effects of turbine aerodynamics and response characteristics of the atmospheric boundary layer on the overall wind farm efficiency are investigated in this research. Various wind farm modelling strategies, which include a theoretical and several CFD models, are presented. This study consists of three main parts: (i) improve and validate an existing theoretical wind farm model, (ii) infinitely large wind farm modelling with actuator-disc and fully-resolved turbine models, and (iii) finite-size wind farm modelling with a numerical weather prediction model. In the first part, an extended theoretical model based on a two-scale coupled momentum balance method is proposed to estimate aerodynamic effects of wind turbine towers on the performance of large wind farms. The modified theoretical model predicts that the optimal turbine spacing should increase with the value of normalised support-structure drag, as well as additional parameters describing the response characteristics of the atmospheric boundary layer to the total farm drag. The Detached-Eddy simulations of a periodic array of fully staggered actuator discs (AD) show a reasonably good agreement (within 10% in the prediction of power) with the modified theoretical model. In the second part, a fully resolved (FR) NREL 5MW turbine model is employed in two URANS simulations (with and without the turbine tower) of a fully developed wind farm boundary layer. The FR-URANS results show stronger tower effects than both AD-RANS and theoretical model predictions, which is a strong indication of the necessity of considering turbine support structure within large wind farm models. The possibility of performing DDES is also investigated with the same FR turbine model and periodic domain setup. The results show complex turbulent flow characteristics within a large wind farm, where typical hairpin and hub vortices have been clearly captured. In addition, the computational cost of DDES has been found to be similar to URANS (for a given number of rotations), which is a positive sign for conducting DDES in future studies. In the third part, a numerical weather prediction model is used as a realistic farm-scale flow model to investigate how the streamwise pressure gradient, Coriolis force and acceleration/deceleration terms in the farm-scale momentum balance equation tend to change in time. The results suggest that the streamwise pressure gradient may be enhanced substantially by the resistance caused by the wind farm, whereas its influence on the other two terms appears to be relatively minor. These results suggest the importance of modelling the farm-induced pressure gradient accurately for various weather conditions in future studies of large wind farms
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    Data for Paper "Numerical Investigation of Orifice Nearfield Flow Development in Oleo-Pneumatic Shock Absorbers"
    (Cranfield University, 2022-02-18 09:57) Sheikh Al Shabab, Ahmed; Skote, Martin; Tsoutsanis, Panagiotis; Antoniadis, Antonios; Vitlaris, Dimitrios; Grenko, Bojan
    Data for the journal paper titled: Numerical Investigation of Orifice Nearfield Flow Development in Oleo-Pneumatic Shock Absorbers
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    Data for Paper "Unsteady Multiphase Simulation of Oleo-Pneumatic Shock Absorber Flow"
    (Cranfield University, 2024-02-21 18:05) Sheikh Al Shabab, Ahmed; Grenko, Bojan; Silva, Paulo; Antoniadis, Antonios; Tsoutsanis, Panagiotis; Skote, Martin
    Dataset for the paper "Unsteady Multiphase Simulation of Oleo-Pneumatic Shock Absorber Flow"
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    Data for: Numerical Investigation of Oleo-Pneumatic Shock Absorber: A Multi-Fidelity Approach
    (Cranfield University, 2023-08-25) Sheikh Al Shabab, Ahmed; Grenko, Bojan; Vitlaris, Dimitrios; Tsoutsanis, Panagiotis; Antoniadis, Antonios; Skote, Martin
    Raw data of simulations used in the ECCOMAS 2022 paper titled: Numerical Investigation of Oleo-Pneumatic Shock Absorber: A Multi-Fidelity Approach
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    Inverse design of transonic/supersonic aerofoils based on deep neural networks
    (Cranfield University, 2019-12) Feria Alanis, Aaron; Antoniadis, Antonios
    Transonic and supersonic aerofoil inverse design for different flight conditions is carried out using Deep Neural Networks (DNN). DNN are combined with a comprehensive and complete database of aerodynamic data and aerofoil geometry parameters to form the pillars of a surrogate inverse aerodynamic design tool. The framework of this research starts with the aerofoil parameterisation. The Class/Shape Transformation functions (CST) was selected for the parameterisation process due to its high accuracy and flexibility when describing complex shapes. An automated mesh technique is created and implemented to discretise the flow domain. The aerodynamic computations are performed for 395 aerofoils. Spatial discretisation is accomplished with the Jameson-Schmidt-Turkel (JST) scheme and convergence is reached by the backward Euler implicit numerical scheme. Data are collected and managed with the CST parameters for all aerofoils and their respective aerodynamic characteristics from the CFD solver. The Deep Neural Network is then trained, validated using cross-validation and evaluated against CFD data. An extensive investigation of the effect from different DNN configurations takes place in this research. Within this thesis, different case studies are presented for different numbers of design objectives. For the inverse design process the NACA 66-206 aerofoil was selected as the baseline aerofoil, to reduce the aerodynamic drag coefficient while maintaining or improving the lift coefficient, to obtain a superior lift/drag ratio compared with the baseline aerofoil. The framework of this thesis have proved to output aerofoil designs with an improved lift/drag ratio in comparison with the baseline aerofoil.