Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome th...Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.展开更多
Kinetic Monte Carlo(KMC)algorithm has been widely applied for simulation of radiation damage,grain growth and chemical reactions.To simulate at a large temporal and spatial scale,domain decomposition is commonly used ...Kinetic Monte Carlo(KMC)algorithm has been widely applied for simulation of radiation damage,grain growth and chemical reactions.To simulate at a large temporal and spatial scale,domain decomposition is commonly used to parallelize the KMC algorithm.However,through experimental analysis,we find that the communication overhead is the main bottleneck which affects the overall performance and limits the scalability of parallel KMC algorithm on large-scale clusters.To alleviate the above problems,we present a communication aggregation approach to reduce the total number of messages and eliminate the communication redundancy,and further utilize neighborhood collective operations to optimize the communication scheduling.Experimental results show that the optimized KMC algorithm exhibits better performance and scalability compared with the well-known open-source library—SPPARKS.On 32-node Xeon E5-2680 cluster(total 640 cores),the optimized algorithm reduces the total execution time by 16%,reduces the communication time by 50%on average,and achieves 24 times speedup over the single node(20 cores)execution.展开更多
SYLVIA Nyathia is a school principal of a home-based Montessori school in a township southwest of Johannesburg. She has 22 students and a teacher under her care.
基金supported by Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory (No. 2012afd1010)
文摘Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.
文摘Kinetic Monte Carlo(KMC)algorithm has been widely applied for simulation of radiation damage,grain growth and chemical reactions.To simulate at a large temporal and spatial scale,domain decomposition is commonly used to parallelize the KMC algorithm.However,through experimental analysis,we find that the communication overhead is the main bottleneck which affects the overall performance and limits the scalability of parallel KMC algorithm on large-scale clusters.To alleviate the above problems,we present a communication aggregation approach to reduce the total number of messages and eliminate the communication redundancy,and further utilize neighborhood collective operations to optimize the communication scheduling.Experimental results show that the optimized KMC algorithm exhibits better performance and scalability compared with the well-known open-source library—SPPARKS.On 32-node Xeon E5-2680 cluster(total 640 cores),the optimized algorithm reduces the total execution time by 16%,reduces the communication time by 50%on average,and achieves 24 times speedup over the single node(20 cores)execution.
文摘SYLVIA Nyathia is a school principal of a home-based Montessori school in a township southwest of Johannesburg. She has 22 students and a teacher under her care.
