In order to numerically evaluate the acoustic characteristics of liquid rocket engine thrust chambers by means of a computational fluid dynamics method, a mathematical model of an artificial constant-volume bomb is pr...In order to numerically evaluate the acoustic characteristics of liquid rocket engine thrust chambers by means of a computational fluid dynamics method, a mathematical model of an artificial constant-volume bomb is proposed in this paper. A localized pressure pulse with a very high amplitude can be imposed on specified regions in a combustion chamber, the numerical procedure of which is described. Pressure oscillations actuated by the released constant-volume bomb can then be analyzed via Fast Fourier Transformation（FFT）, and their modes can be identified according to the theoretical acoustic eigenfrequencies of the thrust chamber. The damping performances of the corresponding acoustic modes are evaluated by the half-power bandwidth method. The predicted acoustic characteristics and their damping for a special engine combustor agree well with the experimental data, validating the mathematical model and its numerical procedures. A small-thrust liquid rocket engine chamber is then analyzed by the present model. The First Longitudinal（1L） acoustic mode can be excited easily and is hard to be damped. The axial position of the central constantvolume bomb has little influence on the amplitude and damping capacity of the First Radial（1R） and 1L acoustic modes. Tangential acoustic modes can only be triggered by an off-centered constant-volume bomb, among which the First Tangential（1T） mode is the strongest and regarded as the most harmful one. The amplitude of the 1L acoustic mode is smaller, but its damping factor is larger, as a constant-volume bomb is imposed approaching the injector face. These results are contributed to evaluate the acoustic characteristics and their damping of the combustion chamber.展开更多
A photonic crystal nanobeam cavity(M-PCNC)with a structure incorporating a mixture of diamond-shaped and circular air holes is pro-posed.The performance of the cavity is simulated and studied theoretically.Using thefin...A photonic crystal nanobeam cavity(M-PCNC)with a structure incorporating a mixture of diamond-shaped and circular air holes is pro-posed.The performance of the cavity is simulated and studied theoretically.Using thefinite-difference time-domain method,the parameters of the M-PCNC,including cavity thickness and width,lattice constant,and radii and numbers of holes,are optimized,with the quality factor Q and mode volume Vm as performance indicators.Mutual modulation of the lattice constant and hole radius enable the proposed M-PCNC to realize outstanding performance.The optimized cavity possesses a high quality factor Q 1.45105 and an ultra-small mode=×volume Vm 0.01(λ/n)[Zeng et al.,Opt Lett 2023:48;3981–3984]in the telecommunications wavelength range.Light can be progres-=sively squeezed in both the propagation direction and the perpendicular in-plane direction by a series of interlocked anti-slots and slots in the diamond-shaped hole structure.Thereby,the energy can be confined within a small mode volume to achieve an ultra-high Q/Vm ratio.展开更多
With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning ...With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.展开更多
Simultaneous localization of light to extreme spatial and spectral scales is of high importance for testing fundamental physics and various applications.However,there is a longstanding trade-off between localizing a l...Simultaneous localization of light to extreme spatial and spectral scales is of high importance for testing fundamental physics and various applications.However,there is a longstanding trade-off between localizing a light field in space and in frequency.Here we discover a new class of twisted lattice nanocavities based on mode locking in momentum space.The twisted lattice nanocavity hosts a strongly localized light field in a 0.048𝜆3 mode volume with a quality factor exceeding 2.9×1011(∼250𝜇s photon lifetime),which presents a record high figure of merit of light localization among all reported optical cavities.Based on the discovery,we have demonstrated silicon-based twisted lattice nanocavities with quality factor over 1 million.Our result provides a powerful platform to study light-matter interaction in extreme conditions for tests of fundamental physics and applications in nanolasing,ultrasensing,nonlinear optics,optomechanics and quantum-optical devices.展开更多
基金Financial support from the National Natural Science Foundation of China (Nos.51676111 and 11628206)
文摘In order to numerically evaluate the acoustic characteristics of liquid rocket engine thrust chambers by means of a computational fluid dynamics method, a mathematical model of an artificial constant-volume bomb is proposed in this paper. A localized pressure pulse with a very high amplitude can be imposed on specified regions in a combustion chamber, the numerical procedure of which is described. Pressure oscillations actuated by the released constant-volume bomb can then be analyzed via Fast Fourier Transformation（FFT）, and their modes can be identified according to the theoretical acoustic eigenfrequencies of the thrust chamber. The damping performances of the corresponding acoustic modes are evaluated by the half-power bandwidth method. The predicted acoustic characteristics and their damping for a special engine combustor agree well with the experimental data, validating the mathematical model and its numerical procedures. A small-thrust liquid rocket engine chamber is then analyzed by the present model. The First Longitudinal（1L） acoustic mode can be excited easily and is hard to be damped. The axial position of the central constantvolume bomb has little influence on the amplitude and damping capacity of the First Radial（1R） and 1L acoustic modes. Tangential acoustic modes can only be triggered by an off-centered constant-volume bomb, among which the First Tangential（1T） mode is the strongest and regarded as the most harmful one. The amplitude of the 1L acoustic mode is smaller, but its damping factor is larger, as a constant-volume bomb is imposed approaching the injector face. These results are contributed to evaluate the acoustic characteristics and their damping of the combustion chamber.
基金supported by the Open Fund of the State Key Laboratory of Advanced Optical Communication Systems and Networks (SJTU)(Grant No. 2023GZKF018)the Open Fund of IPOC (BUPT)(Grant No. IPOC2021B03)+4 种基金the National Natural Science Foundation of China (NSFC)(Grant No. 11974188)the China Postdoctoral Science Foundation (Grant Nos. 2021T140339 and 2018M632345)the Jiangsu Province Postdoctoral Science Foundation (Grant No. 2021K617C)the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No.KYCX22_0945)the Qing Lan Project of Jiangsu Province
文摘A photonic crystal nanobeam cavity(M-PCNC)with a structure incorporating a mixture of diamond-shaped and circular air holes is pro-posed.The performance of the cavity is simulated and studied theoretically.Using thefinite-difference time-domain method,the parameters of the M-PCNC,including cavity thickness and width,lattice constant,and radii and numbers of holes,are optimized,with the quality factor Q and mode volume Vm as performance indicators.Mutual modulation of the lattice constant and hole radius enable the proposed M-PCNC to realize outstanding performance.The optimized cavity possesses a high quality factor Q 1.45105 and an ultra-small mode=×volume Vm 0.01(λ/n)[Zeng et al.,Opt Lett 2023:48;3981–3984]in the telecommunications wavelength range.Light can be progres-=sively squeezed in both the propagation direction and the perpendicular in-plane direction by a series of interlocked anti-slots and slots in the diamond-shaped hole structure.Thereby,the energy can be confined within a small mode volume to achieve an ultra-high Q/Vm ratio.
文摘With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.
基金This work is supported by the National Key R&D Program of China(2018YFA0704401)the Beijing Natural Science Foundation(Z180011)+1 种基金the National Natural Science Foundation of China(12225402,91950115,11774014,61521004 and 62175003)the Tencent Foundation.
文摘Simultaneous localization of light to extreme spatial and spectral scales is of high importance for testing fundamental physics and various applications.However,there is a longstanding trade-off between localizing a light field in space and in frequency.Here we discover a new class of twisted lattice nanocavities based on mode locking in momentum space.The twisted lattice nanocavity hosts a strongly localized light field in a 0.048𝜆3 mode volume with a quality factor exceeding 2.9×1011(∼250𝜇s photon lifetime),which presents a record high figure of merit of light localization among all reported optical cavities.Based on the discovery,we have demonstrated silicon-based twisted lattice nanocavities with quality factor over 1 million.Our result provides a powerful platform to study light-matter interaction in extreme conditions for tests of fundamental physics and applications in nanolasing,ultrasensing,nonlinear optics,optomechanics and quantum-optical devices.
