Comparison of multiple surrogates for 3D CFD model in tidal farm optimisation
- Moore, William, Mala-Jetmarova, Helena, Gebreslassie, Mulualem, Tabor, Gavin, Belmont, Michael, Savic, Dragan
- Authors: Moore, William , Mala-Jetmarova, Helena , Gebreslassie, Mulualem , Tabor, Gavin , Belmont, Michael , Savic, Dragan
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 12th International Conference on Hydroinformatics - Smart Water for the Future, HIC 2016; Songdo Convensialncheon, South Korea; 21st-26th August 2016; published in Procedia Engineering Vol. 154, p. 1132-1139
- Full Text:
- Reviewed:
- Description: Marine currents have been identified as a considerable renewable energy source. Therefore, in recent years, research on optimising tidal stream farm layouts in order to maximise power output has emerged. Traditionally, computational fluid dynamics (CFD) models are used to model power output, but their computational cost is prohibitive within an optimisation algorithm. This paper uses surrogate models in place of CFD simulations to optimise the layout of tidal stream farm layouts. Surrogates are functions which are designed to emulate the behaviour of other models with radically reduced computational expense. Two surrogate models are applied and compared: artificial neural network (ANN) and k-nearest neighbours regression (k-NN). We measure their suitability by four criteria: accuracy, efficiency, robustness and performance within an optimisation algorithm. The results reveal that the ANN surrogate is superior in every criteria to the k-NN surrogate. However, the k-NN surrogate is also able to perform adequate optimisation. Finally, we demonstrate that optimisation relying solely on surrogate models is a viable approach, with dramatically reduced computational expense of optimisation. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
- Description: Procedia Engineering
- Authors: Moore, William , Mala-Jetmarova, Helena , Gebreslassie, Mulualem , Tabor, Gavin , Belmont, Michael , Savic, Dragan
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 12th International Conference on Hydroinformatics - Smart Water for the Future, HIC 2016; Songdo Convensialncheon, South Korea; 21st-26th August 2016; published in Procedia Engineering Vol. 154, p. 1132-1139
- Full Text:
- Reviewed:
- Description: Marine currents have been identified as a considerable renewable energy source. Therefore, in recent years, research on optimising tidal stream farm layouts in order to maximise power output has emerged. Traditionally, computational fluid dynamics (CFD) models are used to model power output, but their computational cost is prohibitive within an optimisation algorithm. This paper uses surrogate models in place of CFD simulations to optimise the layout of tidal stream farm layouts. Surrogates are functions which are designed to emulate the behaviour of other models with radically reduced computational expense. Two surrogate models are applied and compared: artificial neural network (ANN) and k-nearest neighbours regression (k-NN). We measure their suitability by four criteria: accuracy, efficiency, robustness and performance within an optimisation algorithm. The results reveal that the ANN surrogate is superior in every criteria to the k-NN surrogate. However, the k-NN surrogate is also able to perform adequate optimisation. Finally, we demonstrate that optimisation relying solely on surrogate models is a viable approach, with dramatically reduced computational expense of optimisation. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
- Description: Procedia Engineering
Modeling the effects of particle shape on damping ratio of dry sand by simple shear testing and artificial intelligence
- Baghbani, Abolfazl, Costa, Susanga, Faradonbeh, Roohoollah, Soltani, Amin, Baghbani, Hasan
- Authors: Baghbani, Abolfazl , Costa, Susanga , Faradonbeh, Roohoollah , Soltani, Amin , Baghbani, Hasan
- Date: 2023
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 13, no. 7 (2023), p.
- Full Text:
- Reviewed:
- Description: This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter. © 2023 by the authors.
- Authors: Baghbani, Abolfazl , Costa, Susanga , Faradonbeh, Roohoollah , Soltani, Amin , Baghbani, Hasan
- Date: 2023
- Type: Text , Journal article
- Relation: Applied Sciences (Switzerland) Vol. 13, no. 7 (2023), p.
- Full Text:
- Reviewed:
- Description: This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter. © 2023 by the authors.
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