The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940...The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was computed.The results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation temperature.The activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-Ti.Considering the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was established.The error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,respectively.The processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability criterion.According to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic recrystallization.The optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).展开更多
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.展开更多
The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear o...The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.展开更多
Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP ...Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.展开更多
基金the National Natural Science Foundation of China(Grant No.51464035).
文摘The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was computed.The results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation temperature.The activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-Ti.Considering the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was established.The error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,respectively.The processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability criterion.According to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic recrystallization.The optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).
基金supported by the Aviation Science Funds of China(2010ZC13012)the Fund of Jiangsu Innovation Program for Graduate Education (CXLX11 0203)
文摘There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
基金supported bythe National Natural Science Foundation of China(Grant Nos40975063 and 40830955)
文摘The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation （CNOP） method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on–off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm （GA） to the CNOP method, named GA-CNOP, to solve the predictability problems involving on–off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on–off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.
文摘Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.
