This study investigates the performance of dual curved-leg pontoon floating breakwaters in finite water depth under the assumption of linear wave theory. The analysis is carried out for four different models of curved...This study investigates the performance of dual curved-leg pontoon floating breakwaters in finite water depth under the assumption of linear wave theory. The analysis is carried out for four different models of curvedleg geometries, which are combinations of convex and concave shapes. The models are classified as follows. Model-1: Seaside and leeside face concave, Model-2: Seaside and leeside face convex, Model-3: Seaside face convex and leeside face concave, and Model-4: Seaside face concave and leeside face convex. The Boundary Element Method is utilized in order to find a solution to the associated boundary value problem. The numerical results are validated against existing analytical and experimental data. Further, the study examines the wave reflection, wave transmission, and the hydrodynamic forces acting on the structure for different values of waves and structural parameters. Overall, the different dual curved-leg pontoon breakwaters are more effective, reducing wave transmission by over 15% and increasing wave reflection by more than 5% compared to traditional models. The study shows that the wave reflected by Model 1 significantly increased and attenuated the wave transmission relative to other models. The study found that the height of the curved-leg of Model 1 plays a critical role in blocking waves and redirecting the flow. More precisely, the present analysis concludes that the hydrodynamic performance of Model-1 presents an optimized breakwater design that outperforms the proposed models.展开更多
Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remaine...Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability,it is essential that practitioners pay attention to the issues concerned.Here aUQstudy is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters,which affect key quantities of interest,including material properties and binding free energy predictions in drug discovery and personalized medicine.Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes,the sensitivity of the input parameters is ranked.Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed.This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.展开更多
基金supported by Vellore Institute of Technology,Vellore,under a SEED grant(Sanction Order No.SG20230081)。
文摘This study investigates the performance of dual curved-leg pontoon floating breakwaters in finite water depth under the assumption of linear wave theory. The analysis is carried out for four different models of curvedleg geometries, which are combinations of convex and concave shapes. The models are classified as follows. Model-1: Seaside and leeside face concave, Model-2: Seaside and leeside face convex, Model-3: Seaside face convex and leeside face concave, and Model-4: Seaside face concave and leeside face convex. The Boundary Element Method is utilized in order to find a solution to the associated boundary value problem. The numerical results are validated against existing analytical and experimental data. Further, the study examines the wave reflection, wave transmission, and the hydrodynamic forces acting on the structure for different values of waves and structural parameters. Overall, the different dual curved-leg pontoon breakwaters are more effective, reducing wave transmission by over 15% and increasing wave reflection by more than 5% compared to traditional models. The study shows that the wave reflected by Model 1 significantly increased and attenuated the wave transmission relative to other models. The study found that the height of the curved-leg of Model 1 plays a critical role in blocking waves and redirecting the flow. More precisely, the present analysis concludes that the hydrodynamic performance of Model-1 presents an optimized breakwater design that outperforms the proposed models.
基金funding support from(i)the UK EPSRC for the UK High-End Computing Consortium(EP/R029598/1)the Software Environment for Actionable&VVUQ-evaluated Exascale Applications(SEAVEA)grant(EP/W007762/1)+5 种基金the UK Consortium on Mesoscale Engineering Sciences(UKCOMES grant no.EP/L00030X/1)the Computational Biomedicine at the Exascale(CompBioMedX)grant(EP/X019276/1)(ii)the UK MRC Medical Bioinformatics project(grant no.MR/L016311/1)(iii)the European Commission for EU H2020 CompBioMed2 Center of Excellence(grant no.823712)EU H2020 EXDCI-2 project(grant no.800957)We made use of SuperMUC-NG at Leibniz Supercomputing Center under project COVID-19-SNG1,and the ARCHER2 UK National Supercomputing Service under the SEAVEA grant(EP/W007762/1).
文摘Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability,it is essential that practitioners pay attention to the issues concerned.Here aUQstudy is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters,which affect key quantities of interest,including material properties and binding free energy predictions in drug discovery and personalized medicine.Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes,the sensitivity of the input parameters is ranked.Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed.This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.
