Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational s...Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational speed in numerical simulation of casting thermal stress based on FDM, one for handling of nonconstant material properties and the other for dealing with the various coefficients in discretization equations. The use of the two techniques has been discussed and an application in wave-guide casting is given. The results show that the computational speed is almost tripled and the computer storage needed is reduced nearly half compared with those of the original method without the new technologies. The stress results for the casting domain obtained by both methods that set the temperature steps to 0.1 ℃ and 10 ℃, respectively are nearly the same and in good agreement with actual casting situation. It can be concluded that both handling the material properties as an assumption of stepwise profile and eliminating the repeated calculation are reliable and effective to improve computational speed, and applicable in heat transfer and fluid flow simulation.展开更多
We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new conden...We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.展开更多
Steam piping networks are essential for optimizing performance in industrial processes and district heating systems.However,dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain li...Steam piping networks are essential for optimizing performance in industrial processes and district heating systems.However,dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited.In response,this paper presents a new discretized steam pipe model based on the plug flow approach,capturing key thermo-hydraulic behaviors while simplifying steam phase change processes.Implemented in Modelica,the model accurately calculates temperature and pressure distributions along steam pipelines.To improve computational efficiency for district-scale simulations,five model simplifications are introduced:lumped thermo-hydraulic functions,empirical correlations,fluid state approximations,steady-state dynamics and inclusion of flow derivatives.These simplified models achieve 85%-98%accuracy in predicting pressure drop and condensation losses,including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models.The models support diverse network configurations,scaling effectively to systems with multiple distribution pipes and connected building loads.Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N^(2.42)).In contrast,lumped models simulate 10–28 times faster than discrete,offering quadratic scaling of simulation time O(N^(1.73)).However,they still require 6 times more computation time than a lossless network,highlighting the inherent computational challenges of modeling compressible fluid flow.The steady-state lumped variant,with its near-linear scalability in computational time O(N^(1.01)),emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.展开更多
Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bott...Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bottleneck due to the slowdown of Moore's law.Optical neural networks(ONNs),in which NNs are realized via optical components with information carried by photons at the speed of light,are drawing more attention nowadays.Despite the advantages of higher processing speed and lower system power consumption,one major challenge is to realize reliable and reusable algorithms in physical approaches,particularly nonlinear functions,for higher accuracy.In this paper,a versatile parametric-process-based ONN is demonstrated with its adaptable nonlinear computation realized using the highly nonlinear fiber(HNLF).With the specially designed modelocked laser(MLL)and dispersive Fourier transform(DFT)algorithm,the overall computation frame rate can reach up to 40 MHz.Compared to ONNs using only linear computations,this system is able to improve the classification accuracies from 81.8%to 88.8%for the MNIST-digit dataset,and from 80.3%to 97.6%for the Vowel spoken audio dataset,without any hardware modifications.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 50827102 and 50931004)National Basic Research Program of China (Grant No. 2010CB631202 and No. 2006CB605202)High Technology Research and Development Program of China (Grant No. 2007AA03Z552)
文摘Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational speed in numerical simulation of casting thermal stress based on FDM, one for handling of nonconstant material properties and the other for dealing with the various coefficients in discretization equations. The use of the two techniques has been discussed and an application in wave-guide casting is given. The results show that the computational speed is almost tripled and the computer storage needed is reduced nearly half compared with those of the original method without the new technologies. The stress results for the casting domain obtained by both methods that set the temperature steps to 0.1 ℃ and 10 ℃, respectively are nearly the same and in good agreement with actual casting situation. It can be concluded that both handling the material properties as an assumption of stepwise profile and eliminating the repeated calculation are reliable and effective to improve computational speed, and applicable in heat transfer and fluid flow simulation.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0403301, 2017YFB0503301, and2018YFB0504302)the National Natural Science Foundation of China (Grant Nos. 11991073, 61975229, and Y8JC011L51)+2 种基金the Key Program of CAS (Grant No. XDB17030500)the Civil Space Project (Grant No. D040301)the Science Challenge Project (Grant No. TZ2018005)。
文摘We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.
基金supported by the U.S.Department of Energy’s Office of Energy Efficiency and Renewable Energy(EERE)under the Industrial Efficiency&Decarbonization Office,Award Number DE-EE0009139the National Renewable Energy Laboratory,operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308。
文摘Steam piping networks are essential for optimizing performance in industrial processes and district heating systems.However,dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited.In response,this paper presents a new discretized steam pipe model based on the plug flow approach,capturing key thermo-hydraulic behaviors while simplifying steam phase change processes.Implemented in Modelica,the model accurately calculates temperature and pressure distributions along steam pipelines.To improve computational efficiency for district-scale simulations,five model simplifications are introduced:lumped thermo-hydraulic functions,empirical correlations,fluid state approximations,steady-state dynamics and inclusion of flow derivatives.These simplified models achieve 85%-98%accuracy in predicting pressure drop and condensation losses,including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models.The models support diverse network configurations,scaling effectively to systems with multiple distribution pipes and connected building loads.Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N^(2.42)).In contrast,lumped models simulate 10–28 times faster than discrete,offering quadratic scaling of simulation time O(N^(1.73)).However,they still require 6 times more computation time than a lossless network,highlighting the inherent computational challenges of modeling compressible fluid flow.The steady-state lumped variant,with its near-linear scalability in computational time O(N^(1.01)),emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.
基金Research Grants Council of the Hong Kong Special Administrative Region of China(HKU 17212824,HKU 17210522,HKU C7074-21G,HKU R7003-21,HKU 17205321)Innovation and Technology Commission—Hong Kong(MHP/073/20,MHP/057/21,Health@Inno HK programc)。
文摘Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bottleneck due to the slowdown of Moore's law.Optical neural networks(ONNs),in which NNs are realized via optical components with information carried by photons at the speed of light,are drawing more attention nowadays.Despite the advantages of higher processing speed and lower system power consumption,one major challenge is to realize reliable and reusable algorithms in physical approaches,particularly nonlinear functions,for higher accuracy.In this paper,a versatile parametric-process-based ONN is demonstrated with its adaptable nonlinear computation realized using the highly nonlinear fiber(HNLF).With the specially designed modelocked laser(MLL)and dispersive Fourier transform(DFT)algorithm,the overall computation frame rate can reach up to 40 MHz.Compared to ONNs using only linear computations,this system is able to improve the classification accuracies from 81.8%to 88.8%for the MNIST-digit dataset,and from 80.3%to 97.6%for the Vowel spoken audio dataset,without any hardware modifications.
