Formula for average energy required to produce a secondary electron in an insulator

Based on a simple classical model specifying that the primary electrons interact with the electrons of a lattice through the Coulomb force and a conclusion that the lattice scattering can be ignored, the formula for the average energy required to produce a secondary electron （ε） is obtained. On the basis of the energy band of an insulator and the formula for e, the formula for the average energy required to produce a secondary electron in an insulator （εi） is deduced as a function of the width of the forbidden band （Eg） and electron affinity X. Experimental values and the εi values calculated with the formula are compared, and the results validate the theory that explains the relationships among Eg, X, and ei and suggest that the formula for εi is universal on the condition that the primary electrons at any energy hit the insulator.

Long-and short-term average intensity for multi-Gaussian beam with a common axis in turbulence

With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere has been derived. The intensity in the absence of turbulence and the long-term average intensity in turbulence can both also be expressed in this formula. As special cases, comparisons among short-term average intensity, long-term average intensity, and the intensity in the absence of turbulence for flat topped beam and annular beam are carried out. The effects of the order of MGB, propagation distance and aperture radius on beam spreading are analysed and discussed in detail.

Energy average formula of photon gas rederived by using the generalised Hermann-Feynman theorem

By virtue of the generalised Hermann-Feynmam theorem we re-derive the energy average formula of photon gas. This is another useful application of the theorem.

Comparison of Calculated Fit and Experimental Calculations of Average Dose Deposited in Aluminum by High Energy Electron Beams

This paper presents the formalism for absorbed dose determination to Aluminum in high-energy electron beams using Rhodotron accelerator. Depth dose curve for Aluminum at electron energy of 10 MeV was calculated. The calculated curve in the model as a function of the depth is compared to the experimental. The agreement of the final results remained well within the expected acceptable range. The calculated values of dose-to-Aluminum are completely fit with the measured values in the range of 0.07% for electron energy of 10 MeV.

Constraint dependence of average potential energy of a passive particle in an active bath

We quantify the mean potential energy of a passive colloidal particle harmonically confined in a bacterial solution using optical traps.We find that the average potential energy of the passive particle depends on the trap stiffness,in contrast to the equilibrium case where energy partition is independent of the external constraints.The constraint dependence of the mean potential energy originates from the fact that the persistent collisions between the passive particle and the active bacteria are influenced by the particle relaxation dynamics.Our experimental results are consistent with the Brownian dynamics simulations,and confirm the recent theoretical prediction.

Average Bond Energy and Fermi Level on Free Electronic Band Model

On the basis of free-electronic bands, the Fermi energy is calculated by summing the band eigenvalues over Brillouin-zones ,and the results may lead to understand the physical basis of the average-bond-energy model in the calculation of valence-band offsets.

Case Study of a Short-Term Wave Energy Forecasting Scheme:North Indian Ocean

Short-term forecasts of wave energy play a key role in the daily operation,maintenance planning,and electrical grid operation of power farms.In this study,we propose a short-term wave energy forecast scheme and use the North Indian Ocean(NIO)as a case study.Compared with the traditional forecast scheme,our proposed scheme considers more forecast elements.In addition to the traditional short-term forecast factors related to wave energy(wave power,significant wave height(SWH),wave period),our scheme emphasizes the forecast of a series of key factors that are closely related to the effectiveness of the energy output,capture efficiency,and conversion efficiency.These factors include the available rate,total storage,effective storage,co-occurrence of wave power-wave direction,co-occurrence of the SWH-wave period,and the wave energy at key points.In the regional nesting of nu-merical simulations of wave energy in the NIO,the selection of the southern boundary is found to have a significant impact on the simulation precision,especially during periods of strong swell processes of the South Indian Ocean(SIO)westerly.During tropical cyclone‘VARDAH’in the NIO,as compared with the simulation precision obtained with no expansion of the southern boundary(scheme-1),when the southern boundary is extended to the tropical SIO(scheme-2),the improvement in simulation precision is significant,with an obvious increase in the correlation coefficient and decrease in error.In addition,the improvement is much more significant when the southern boundary extends to the SIO westerly(scheme-3).In the case of strong swell processes generated by the SIO westerly,the improvement obtained by scheme-3 is even more significant.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting efficiency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Modelling and Forecasting of Greenhouse Gas Emissions by the Energy Sector in Kenya Using Autoregressive Integrated Moving Average (ARIMA) Models

The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.

基于功率补偿的波浪能多发电机组平均功率送出方法

针对自治控制模式下液压蓄能式波能装置多液压发电机组在启动或停止时输出功率脉动问题,提出一种基于有功功率补偿的波浪能平均功率送出方法。根据波浪能转换发电系统特点及工作原理,在并网逆变器交流侧挂一台有功功率补偿装置,能同时平抑多台液压发电机组的功率脉动,并以平均功率送出。为验证该方法的有效性,建立多液压发电机组并网功率脉动平抑仿真模型。仿真结果表明,经功率补偿后波能装置并网功率平缓变化,多发液压发电机组的启动或停止时的功率脉动能被完全吸收,平均功率送出方法可提高大功率波能装置在海岛微电网等场合的适应性。

计及状态量平均超限比的综合能源系统动态能量流双层优化

综合能源系统(IES)的最优动态能量流能够最大限度地减少系统运行成本。针对IES能量流优化过程中状态量的越限现象,引入状态量平均超限比,统一刻画状态变量的超限程度,并建立计及状态量平均超限比的电-气-热IES多目标动态时序能量流模型,以解决状态量超限惩罚代价系数选取不当所导致的优化结果偏离可行最优解的问题。为了防止蜜獾算法(HBA)对能量流的优化陷入局部极小值,建立一种基于多目标差分进化(MODE)算法的双层动态能量流优化模型,上层稳态能量流模型以IES运行成本和状态量平均超限比为优化目标,采用MODE算法求解全局空间内的Pareto非支配解集;下层动态能量流模型以IES运行成本和状态量平均超限惩罚成本的加权和为优化目标,基于Pareto解集生成HBA的初始种群决策量,通过HBA加快求解IES全局最优动态能量流的速度。通过算例仿真验证了所提模型和优化方法的有效性。

Multi-objective optimization scheduling for new energy power system considering energy storage participation

For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.

A high order energy preserving scheme for the strongly coupled nonlinear Schr¨odinger system

A high order energy preserving scheme for a strongly coupled nonlinear Schrōdinger system is roposed by using the average vector field method. The high order energy preserving scheme is applied to simulate the soliton evolution of the strongly coupled Schrōdinger system. Numerical results show that the high order energy preserving scheme can well simulate the soliton evolution, moreover, it preserves the discrete energy of the strongly coupled nonlinear Schrōdinger system exactly.

Parallel Reinforcement Learning-Based Energy Efficiency Improvement for a Cyber-Physical System

As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

Dynamics Analysis of Nonlinear Energy Sink for Metal-Rubber Vibration Absorber

Recent advances in the application of the nonlinear energy sink under a sinusoidal excitation make it possible to investigate metal-rubber vibration absorber. To provide such a vibration absorber for the integrated spacecraft platform,we analyze the targeted energy transfer of the simplified model with nonlinear energy sink using the complex-variables averaging method. Theoretical study shows two quasi-periodic responses that are essentially different in this nonlinear system. The steady-state response which is one of two quasi-periodic responses is caused by the linear instability of system,and another one appears as a result of the nonlinear normal modes between the linear and nonlinear oscillators,resulting from the energy transfer of different oscillators,and it can be used to vibration absorber. Secondly,this paper also discusses the performance of the proposed nonlinear absorber by using the phase portraits. All conclusion derived by the analytic model is verified numerically and the results are consistent with numerical simulations.

Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks

In the past few decades,Energy Efficiency(EE)has been a significant challenge in Wireless Sensor Networks(WSNs).WSN requires reduced transmission delay and higher throughput with high quality services,it further pays much attention in increased energy consumption to improve the network lifetime.To collect and transmit data Clustering based routing algorithm is considered as an effective way.Cluster Head(CH)acts as an essential role in network connectivity and perform data transmission and data aggregation,where the energy consumption is superior to non-CH nodes.Conventional clustering approaches attempts to cluster nodes of same size.Moreover,owing to randomly distributed node distribution,a cluster with equal nodes is not an obvious possibility to reduce the energy consumption.To resolve this issue,this paper provides a novel,Balanced-Imbalanced Cluster Algorithm(B-IBCA)with a Stabilized Boltzmann Approach(SBA)that attempts to balance the energy dissipation across uneven clusters in WSNs.B-IBCA utilizes stabilizing logic to maintain the consistency of energy consumption among sensor nodes’.So as to handle the changing topological characteristics of sensor nodes,this stability based Boltzmann estimation algorithm allocates proper radius amongst the sensor nodes.The simulation shows that the proposed B-IBCA outperforms effectually over other approaches in terms of energy efficiency,lifetime,network stability,average residual energy and so on.

Structure and Propagation Characteristics of Climatological Mean Kinetic Energy of Disturbance of Intraseasonal Oscillation in Asian Summer Monsoon Zone

[Objective] The research aimed to study the structure and propagation characteristics of climatological mean kinetic energy of disturbance of intraseasonal oscillation in Asian summer monsoon zone. [Method] When South China Sea monsoon started to break out, the kinetic energy of intraseasonal oscillation disturbance in the monsoon zone was analyzed, especially the researches about the variation of South China Sea monsoon, the development of Indian monsoon and the advancement of East Asian monsoon. [Result] The developed process of Asian summer monsoon had the close relationship with the kinetic energy activity of 30-60 d low-frequency oscillation disturbance. The kinetic energy of disturbance explained the eruption, occurrence, development and termination of monsoon from the energy angle. It was found that the kinetic energy of disturbance in Arabian Sea zone, Bay of Bengal and South China Sea area was the strongest, especially in Arabian Sea zone. It illustrated that Arabian Sea zone (Somali jet) was the biggest energy source of Asian monsoon. The starting mark of monsoon eruption in the whole Asia was the abrupt eruption of South China Sea monsoon. The eruption of South China Sea monsoon in the middle dekad of May was the westward transmission result of kinetic energy of disturbance on the east sea surface of Philippines. The kinetic energy of disturbance in East Asian monsoon zone had the seasonal northward advancement in summer. The high kinetic energy center of disturbance in Indian monsoon zone changed from one to two. They were respectively in Arabian Sea and Bay of Bengal. [Conclusion] The research provided the theory basis for analyzing the atmospheric intraseasonal oscillation.

Restrictions on negative energy density for the Dirac field in flat spacetime

This paper investigates the quantum Dirac field in n ＋ 1-dimensional flat spacetime and derives a lower bound in the form of quantum inequality on the energy density averaged against spacetime sampling functions. The stateindependent quantum inequality derived in the present paper is similar to the temporal quantum energy inequality and it is stronger for massive field than for massless one. It also presents the concrete results of the quantum inequality in 2 and 4-dimensional spacetimes.