Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trai...Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence,the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approach–The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics.Then the modified vehicle modeling function(VMF)parameterization method and surface discretization method are adopted for the parametric design of the nose.For the 12 key design parameters extracted,combined with computational fluid dynamics(CFD),support vector machine(SVR)model and multi-objective particle swarm optimization(MPSO)algorithm,the multi-objective aerodynamic optimization design of highspeed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint.The engineering improvement and wind tunnel test verification of the optimized shape are done.Findings–Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train.The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/value–Compared with the original shape,the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%,and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%,respectively,after adopting the optimized shape modified according to engineering design requirements.展开更多
The hydrodynamic shape of the heaving buoy is an important factor of the motion response in waves and thus concerns the energy conversion efficiency for the point absorbers(PAs).The current experience-based designs ar...The hydrodynamic shape of the heaving buoy is an important factor of the motion response in waves and thus concerns the energy conversion efficiency for the point absorbers(PAs).The current experience-based designs are time consuming and not very efficient,hence,faster and smarter methods are desirable.An automated optimization method based on a fully parametric modeling method and computational fluid dynamics(CFD),is proposed in this paper.Using this method,a benchmark buoy is screen designed and then optimized by maximizing the heave motion response.The geometry is described parametrically and deformed by means of the free-form deformation(FFD)method.During the optimization process,the expansion factor of control points is the basis for the variations.A combination of the Sobol and the non-dominated sorting genetic algorithm II(NSGA-II)is used to search for the solutions.After several iterations,the heaving buoy shape with optimal heave motion response is obtained.The analyses show that the heave motion response has increased 55.3%after optimization.The developed methodology is valid and seems to be a promising way to design a novel buoy that can significantly improve the wave energy conversion efficiency of the PAs in future.展开更多
This paper is concerned with the optimal design of an obstacle located in the viscous and incompressible fluid which is driven by the steady-state Oseen equations with thermal effects. The structure of shape gradient ...This paper is concerned with the optimal design of an obstacle located in the viscous and incompressible fluid which is driven by the steady-state Oseen equations with thermal effects. The structure of shape gradient of the cost functional is derived by applying the differentiability of a minimax formulation involving a Lagrange functional with a space parametrization technique. A gradient type algorithm is employed to the shape optimization problem. Numerical examples indicate that our theory is useful for practical purpose and the proposed algorithm is feasible.展开更多
文摘Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence,the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approach–The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics.Then the modified vehicle modeling function(VMF)parameterization method and surface discretization method are adopted for the parametric design of the nose.For the 12 key design parameters extracted,combined with computational fluid dynamics(CFD),support vector machine(SVR)model and multi-objective particle swarm optimization(MPSO)algorithm,the multi-objective aerodynamic optimization design of highspeed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint.The engineering improvement and wind tunnel test verification of the optimized shape are done.Findings–Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train.The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/value–Compared with the original shape,the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%,and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%,respectively,after adopting the optimized shape modified according to engineering design requirements.
基金supported by the Key Area Research and Development Program of Guangdong Province(Grant Nos.2021B0101200002,2021B0202070002)the Natural Science Foundation of Guangdong Province(Grant Nos.2022A1515011285,2021A1515011771)Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.SML2022008).
文摘The hydrodynamic shape of the heaving buoy is an important factor of the motion response in waves and thus concerns the energy conversion efficiency for the point absorbers(PAs).The current experience-based designs are time consuming and not very efficient,hence,faster and smarter methods are desirable.An automated optimization method based on a fully parametric modeling method and computational fluid dynamics(CFD),is proposed in this paper.Using this method,a benchmark buoy is screen designed and then optimized by maximizing the heave motion response.The geometry is described parametrically and deformed by means of the free-form deformation(FFD)method.During the optimization process,the expansion factor of control points is the basis for the variations.A combination of the Sobol and the non-dominated sorting genetic algorithm II(NSGA-II)is used to search for the solutions.After several iterations,the heaving buoy shape with optimal heave motion response is obtained.The analyses show that the heave motion response has increased 55.3%after optimization.The developed methodology is valid and seems to be a promising way to design a novel buoy that can significantly improve the wave energy conversion efficiency of the PAs in future.
文摘This paper is concerned with the optimal design of an obstacle located in the viscous and incompressible fluid which is driven by the steady-state Oseen equations with thermal effects. The structure of shape gradient of the cost functional is derived by applying the differentiability of a minimax formulation involving a Lagrange functional with a space parametrization technique. A gradient type algorithm is employed to the shape optimization problem. Numerical examples indicate that our theory is useful for practical purpose and the proposed algorithm is feasible.
