The steam ejector is a crucial component in the waste heat recovery system.Its performance determines the amount of recovered heat and system efficiency.However,poor ejector performance has always been the main bottle...The steam ejector is a crucial component in the waste heat recovery system.Its performance determines the amount of recovered heat and system efficiency.However,poor ejector performance has always been the main bottleneck for system applications.Therefore,this study proposes an optimization methodology to improve the steam ejector's performance by utilizing computational fluid dynamics(CFD) techniques,response surface methodology(RSM),and genetic algorithm(GA).Firstly,a homogeneous equilibrium model(HEM) was established to simulate the two-phase flow in the steam ejector.Then,the orthogonal test was presented to the screening of the key decision variables that have a significant impact on the entrainment ratio(ER).Next,the RSM was used to fit a response surface regression model(RSRM).Meanwhile,the effect of the interaction of geometric parameters on the performance of the steam ejector was revealed.Finally,GA was employed to solve the RSRM's global optimal ER value.The results show that the RSRM exhibits a good fit for ER(R^(2)=0.997).After RSM and GA optimization,the maximum ejector efficiency is 27.94%,which is 48.38% higher than the initial ejector of 18.83%.Furthermore,the optimized ejector efficiency is increased by 46.4% on average under off-design conditions.Overall,the results reveal that the combination of CFD,RSM,and GA presents excellent reliability and feasibility in the optimization design of a two-phase steam ejector.展开更多
In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed us...In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed using body-fitted mesh and structured grids. The parameters of the hull modification function are the design variables. A three-dimensional modeling method is used to alter the geometry. The Non-Linear Programming(NLP) method is utilized to optimize a David Taylor Model Basin(DTMB) model 5415 ship under the constraints, including the displacement constraint. The optimization results show an effective reduction of the resistance. The two hull form design methods developed in this study can provide technical support and theoretical basis for designing green ships.展开更多
This paper shows how to improve the hydrodynamics performance of a ship by solving a shape optimization design problem at different speeds using the simulation-based design(SBD) technique. The SBD technique is impleme...This paper shows how to improve the hydrodynamics performance of a ship by solving a shape optimization design problem at different speeds using the simulation-based design(SBD) technique. The SBD technique is implemented by integrating the advanced CFD codes, the global optimization algorithms and the geometry modification methods, which offers a new way for the hullform optimization design and the configuration innovation. The multiple speed integrated optimization for the hullform design is a challenge. In this paper, an example of the technique application for a fishing ship hullform optimization at different speeds is demonstrated. In this optimization process, the free-form deformation method is applied to automatically modify the geometry of the ship, and the multi-objective particle swarm optimization(MOPSO) algorithm is adopted for exploring the design space. Two objective functions, the total resistances at two different speeds(12 kn and 14 kn) are assessed by the RANS solvers. The optimization results show that the decrease of the total resistance is significant after the optimization at the two speeds, with a reduction of 5.0% and 11.2%, respectively. Finally, dedicated experimental validations for the design model and the optimized model are carried out for the computation and the optimization processes. At the two speeds, the reduction of the total resistance in the model scale is about 6.0% and 11.8% after the optimization. It is a valuable result in view of the small modifications allowed and the good initial performances of the original model. The given practical example demonstrates the feasibility and the superiority of the proposed SBD technique for the multiple speed integrated optimization.展开更多
文摘The steam ejector is a crucial component in the waste heat recovery system.Its performance determines the amount of recovered heat and system efficiency.However,poor ejector performance has always been the main bottleneck for system applications.Therefore,this study proposes an optimization methodology to improve the steam ejector's performance by utilizing computational fluid dynamics(CFD) techniques,response surface methodology(RSM),and genetic algorithm(GA).Firstly,a homogeneous equilibrium model(HEM) was established to simulate the two-phase flow in the steam ejector.Then,the orthogonal test was presented to the screening of the key decision variables that have a significant impact on the entrainment ratio(ER).Next,the RSM was used to fit a response surface regression model(RSRM).Meanwhile,the effect of the interaction of geometric parameters on the performance of the steam ejector was revealed.Finally,GA was employed to solve the RSRM's global optimal ER value.The results show that the RSRM exhibits a good fit for ER(R^(2)=0.997).After RSM and GA optimization,the maximum ejector efficiency is 27.94%,which is 48.38% higher than the initial ejector of 18.83%.Furthermore,the optimized ejector efficiency is increased by 46.4% on average under off-design conditions.Overall,the results reveal that the combination of CFD,RSM,and GA presents excellent reliability and feasibility in the optimization design of a two-phase steam ejector.
基金financially supported by the National P&D Program of China(Grant No.2016YFB0300700)the National Natural Science Foundation of China(Grant Nos.51779135 and 51009087)the Natural Science Foundation of Shanghai(Grant No.14ZR1419500)
文摘In order to reduce the resistance and improve the hydrodynamic performance of a ship, two hull form design methods are proposed based on the potential flow theory and viscous flow theory. The flow fields are meshed using body-fitted mesh and structured grids. The parameters of the hull modification function are the design variables. A three-dimensional modeling method is used to alter the geometry. The Non-Linear Programming(NLP) method is utilized to optimize a David Taylor Model Basin(DTMB) model 5415 ship under the constraints, including the displacement constraint. The optimization results show an effective reduction of the resistance. The two hull form design methods developed in this study can provide technical support and theoretical basis for designing green ships.
基金Project supported by the National Natural Science Foundation of China(Grant No.51479181)the Ministry of Industry and Information Technology [2012] No.534
文摘This paper shows how to improve the hydrodynamics performance of a ship by solving a shape optimization design problem at different speeds using the simulation-based design(SBD) technique. The SBD technique is implemented by integrating the advanced CFD codes, the global optimization algorithms and the geometry modification methods, which offers a new way for the hullform optimization design and the configuration innovation. The multiple speed integrated optimization for the hullform design is a challenge. In this paper, an example of the technique application for a fishing ship hullform optimization at different speeds is demonstrated. In this optimization process, the free-form deformation method is applied to automatically modify the geometry of the ship, and the multi-objective particle swarm optimization(MOPSO) algorithm is adopted for exploring the design space. Two objective functions, the total resistances at two different speeds(12 kn and 14 kn) are assessed by the RANS solvers. The optimization results show that the decrease of the total resistance is significant after the optimization at the two speeds, with a reduction of 5.0% and 11.2%, respectively. Finally, dedicated experimental validations for the design model and the optimized model are carried out for the computation and the optimization processes. At the two speeds, the reduction of the total resistance in the model scale is about 6.0% and 11.8% after the optimization. It is a valuable result in view of the small modifications allowed and the good initial performances of the original model. The given practical example demonstrates the feasibility and the superiority of the proposed SBD technique for the multiple speed integrated optimization.