Combining modem Computational Fluid Dynamics （CFD） evaluator with optimization method, a new approach of hullform design for low carbon shipping is presented. Using the approach, the designers may find the minimum o...Combining modem Computational Fluid Dynamics （CFD） evaluator with optimization method, a new approach of hullform design for low carbon shipping is presented. Using the approach, the designers may find the minimum of some user-defined objective functions under constrains. An example of the approach application for a surface combatant hull optimization is demonstrated. In the procedure, the Particle Swarm Optimization （PSO） algorithm is adopted for exploring the design space, and the Bezier patch method is chosen to automatically modify the geometry of bulb. The total resistance is assessed by RANS solvers. It＇s shown that the total resistance coefficient of the optimized design is reduced by about 6.6% comparing with the original design. The given combatant design optimization example demonstrates the practicability and superiority of the proposed approach for low carbon shipping.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
In this work,we constructed a neural network proxy model(NNPM)to estimate the hydrodynamic resistance in the ship hull structure design process,which is based on the hydrodynamic load data obtained from both the poten...In this work,we constructed a neural network proxy model(NNPM)to estimate the hydrodynamic resistance in the ship hull structure design process,which is based on the hydrodynamic load data obtained from both the potential flow method(PFM)and the viscous flow method(VFM).Here the PFM dataset is applied for the tuning,pre-training,and the VFM dataset is applied for the fine-training.By adopting the PFM and VFM datasets simultaneously,we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow,while ensuring a moderate data-acquiring workload.The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures.The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design(AIAD)technology for various marine structures.展开更多
Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine th...Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.展开更多
Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship.To find the optimum hull shape unde...Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship.To find the optimum hull shape under the design requirements,the state-of-art of ship hull design combines computational fluid dynamics computation with geometric modeling.However,this process is very computationally intensive,which is only suitable at the final stage of the design process.To narrow down the design parameter space,in this work,we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process.In this work,we have demonstrated how to use the developed DNN model to carry out the initial ship hull design.The validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained,where the average error of all samples in the testing dataset is lower than 4%.Simultaneously,the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes.The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.展开更多
文摘Combining modem Computational Fluid Dynamics （CFD） evaluator with optimization method, a new approach of hullform design for low carbon shipping is presented. Using the approach, the designers may find the minimum of some user-defined objective functions under constrains. An example of the approach application for a surface combatant hull optimization is demonstrated. In the procedure, the Particle Swarm Optimization （PSO） algorithm is adopted for exploring the design space, and the Bezier patch method is chosen to automatically modify the geometry of bulb. The total resistance is assessed by RANS solvers. It＇s shown that the total resistance coefficient of the optimized design is reduced by about 6.6% comparing with the original design. The given combatant design optimization example demonstrates the practicability and superiority of the proposed approach for low carbon shipping.
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supported by a fellowship from China Scholar Council(No.201806680134).
文摘In this work,we constructed a neural network proxy model(NNPM)to estimate the hydrodynamic resistance in the ship hull structure design process,which is based on the hydrodynamic load data obtained from both the potential flow method(PFM)and the viscous flow method(VFM).Here the PFM dataset is applied for the tuning,pre-training,and the VFM dataset is applied for the fine-training.By adopting the PFM and VFM datasets simultaneously,we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow,while ensuring a moderate data-acquiring workload.The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures.The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design(AIAD)technology for various marine structures.
基金the sponsors of this project: American Bureau of Shipping
文摘Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.
基金supported by a fellowship from China Scholar Council(No.201806680134)this support is greatly appreciated.
文摘Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship.To find the optimum hull shape under the design requirements,the state-of-art of ship hull design combines computational fluid dynamics computation with geometric modeling.However,this process is very computationally intensive,which is only suitable at the final stage of the design process.To narrow down the design parameter space,in this work,we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process.In this work,we have demonstrated how to use the developed DNN model to carry out the initial ship hull design.The validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained,where the average error of all samples in the testing dataset is lower than 4%.Simultaneously,the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes.The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.
