With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric a...With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric approach called local shape function(LSF) was adopted based on the free form surface deformation(FFD) method and a new efficient optimization method based on the response surface method(RSM) of GA-GRNN.The optimization results show that the parametric method can control the large deformation with a few design parameters,and can ensure the deformation zones smoothness and smooth transition of different deformation regions.With the same sample points for training,GA-GRNN performs better than GRNN to get the global optimal solution.As an example,the aerodynamic drag for a simplified shape with head + one carriage + tail train is reduced by 8.7%.The proposed optimization method is efficient for the engineering design of high-speed train.展开更多
Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production de...Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.展开更多
基金supported by the National Basic Research Program of China ("973" Project) (Grant No. 2011CB711100)the National Hi-Tech Research and Development Program of China ("863" Project) (Grant No.2009BAQG12A03)Computing Facility for Computational Mechanics,Institute of Mechanics,Chinese Academy of Sciences
文摘With the speed upgrade of the high-speed train,the aerodynamic drag becomes one of the key factors to restrain the train speed and energy saving.In order to reduce the aerodynamic drag of train head,a new parametric approach called local shape function(LSF) was adopted based on the free form surface deformation(FFD) method and a new efficient optimization method based on the response surface method(RSM) of GA-GRNN.The optimization results show that the parametric method can control the large deformation with a few design parameters,and can ensure the deformation zones smoothness and smooth transition of different deformation regions.With the same sample points for training,GA-GRNN performs better than GRNN to get the global optimal solution.As an example,the aerodynamic drag for a simplified shape with head + one carriage + tail train is reduced by 8.7%.The proposed optimization method is efficient for the engineering design of high-speed train.
基金supported by the National Natural Science Foundation of China (Grant No. 61004095F030202)the China Important National Sci-ence & Technology Specific Projects (Grant No. 2008ZX05030-05-002)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No. 09CX05007A)the National Basic Research Program of China (Grant No. 2011CB201000)
文摘Closed-loop production management combines the process of history matching and production optimization together to peri-odically updates the reservoir model and determine the optimal control strategy for production development to realize the goal of decreasing the knowledge of model uncertainty as well as maximize the economic benefits for the expected reservoir life. The adjoint-gradient-based methods seem to be the most efficient algorithms for closed-loop management. Due to complicated calculation and limited availability of adjoint-gradient in commercial reservoir simulators, the application of this method is still prohibited for real fields. In this paper, a simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed for reservoir closed-loop production management with the combination of a parameterization way for history matching and a co-variance matrix to smooth well controls for production optimization. By using a set of unconditional realizations, the proposed parameterization method can transform the minimization of the objective function in history matching from a higher dimension to a lower dimension, which is quite useful for large scale history matching problem. Then the SPSA algorithm minimizes the objective function iteratively to get an optimal estimate reservoir model. Based on a prior covariance matrix for production op-timization, the SPSA algorithm generates a smooth stochastic search direction which is always uphill and has a certain time correlation for well controls. The example application shows that the SPSA algorithm for closed-loop production management can decrease the geological uncertainty and provide a reasonable estimate reservoir model without the calculation of the ad-joint-gradient. Meanwhile, the well controls optimized by the alternative SPSA algorithm are fairly smooth and significantly improve the effect of waterflooding with a higher NPV and a better sweep efficiency than the reactive control strategy.
