Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase ...Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem （an individual for genetic algorithm）. An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.展开更多
Particle swarm algorithm(PSO) and genetic algorithm(GA) were used to optimize the back propagation(BP) artificial neural network for predicting the dynamic responses of the through silicon via(TSV) based three-dimensi...Particle swarm algorithm(PSO) and genetic algorithm(GA) were used to optimize the back propagation(BP) artificial neural network for predicting the dynamic responses of the through silicon via(TSV) based three-dimensional packaging structures.A finite element model of the TSV packaging structure with a strain-rate dependent constitutive model for solder joints was created to simulate the drop impact due to a free fall of 0.8 m to the rigid ground to investigate the structural dynamic responses during the whole impact process.The spatial coordinates of the solder joints were used as the input parameters of the hybrid neural network model for the drop impact responses,while the maximum Von Mises stress and PEEQ(plastic strain) values are identified the output parameters.The correlation coefficient(R),the mean absolute percentage error(MAPE) and the training time were used as the measures to validate and compare the proposed PSO-BP and GA-BP neural networks.The results show that both the PSO-BP model and the GA-BP model can achieve high accuracy predictions with strong generalization capability.Apparently,both optimized algorithms outperform the original BP model,but the PSO-BP model is slightly more superior than the GA-BP model.It is also demonstrated that the proposed optimized algorithms efficiently predict the drop impact responses of TSV packaging structures by greatly saving the computational and experimental cost of drop impact tests.展开更多
文摘Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem （an individual for genetic algorithm）. An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.
基金supported by the National Natural Science Foundation of China (No. 52175148)the Natural Science Foundation of Shaanxi Province (No. 2021KW-25)the Astronautics Supporting Technology Foundation of China (No. 2019-HT-XG)。
文摘Particle swarm algorithm(PSO) and genetic algorithm(GA) were used to optimize the back propagation(BP) artificial neural network for predicting the dynamic responses of the through silicon via(TSV) based three-dimensional packaging structures.A finite element model of the TSV packaging structure with a strain-rate dependent constitutive model for solder joints was created to simulate the drop impact due to a free fall of 0.8 m to the rigid ground to investigate the structural dynamic responses during the whole impact process.The spatial coordinates of the solder joints were used as the input parameters of the hybrid neural network model for the drop impact responses,while the maximum Von Mises stress and PEEQ(plastic strain) values are identified the output parameters.The correlation coefficient(R),the mean absolute percentage error(MAPE) and the training time were used as the measures to validate and compare the proposed PSO-BP and GA-BP neural networks.The results show that both the PSO-BP model and the GA-BP model can achieve high accuracy predictions with strong generalization capability.Apparently,both optimized algorithms outperform the original BP model,but the PSO-BP model is slightly more superior than the GA-BP model.It is also demonstrated that the proposed optimized algorithms efficiently predict the drop impact responses of TSV packaging structures by greatly saving the computational and experimental cost of drop impact tests.
