Time Delay Analysis Research Articles

Experimental and Modeling Study on the Ignition Kinetics of Nitromethane behind Reflected Shock Waves.

Nitromethane (NM) is the simplest nitroalkane fuel and has demonstrated potential usage as propellant and fuel additive. Thus, understanding the combustion characteristics and chemistry of NM is critical to the development of hierarchical detailed kinetic models of nitro-containing energetic materials. Herein, to further investigate the ignition kinetics of NM and supplement the experimental database for kinetic mechanism development, an experimental and kinetic modeling analysis of the ignition delay times (IDTs) of NM behind reflected shock waves at high fuel concentrations is reported against previous studies. Specifically, the IDTs of NM are measured via a high-pressure shock tube within the temperature from 900 to 1150 K at pressures of 5 and 10 bar and equivalence ratios of 0.5, 1.0, and 2.0. Brute-force sensitivity analysis and chemical explosive mode analysis in combination with reaction path analysis are employed to reveal the fundamental ignition kinetics of NM. Finally, a skeletal mechanism for NM is derived via the combination of directed relation graph-based methods, which demonstrates good prediction accuracy of NM ignition and flame speeds. The present work should be valuable for understanding the combustion chemistry of NM and the development of the fundamental reaction mechanism of nitroalkane fuels.

Spheroidal-wave analysis of time delay in molecular reconstruction of attosecond beating by interference of two-photon transitions around a Cooper-like minimum

Spheroidal-wave analysis of time delay in molecular reconstruction of attosecond beating by interference of two-photon transitions around a Cooper-like minimum

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.

Theoretical analysis of time delay echoes in multimode semiconductor diode laser with optical feedback

Theoretical analysis of time delay echoes in multimode semiconductor diode laser with optical feedback

Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods.

Attosecond Time Delay Trends across the Isoelectronic Noble Gas Sequence

The analysis and measurement of Wigner time delays can provide detailed information about the electronic environment within and around atomic and molecular systems, with one the key differences being the lack of a long-range potential after a halogen ion undergoes photoionization. In this work, we use relativistic random-phase approximation to calculate the average Wigner delay from the highest occupied subshells of the atomic pairings (2p, 2s in Fluorine, Neon), (3p, 3s in Chlorine, Argon), (4p, 4s, 3d, in Bromine, Krypton), and (5p, 5s, 4d in Iodine, Xenon). The qualitative behaviors of the Wigner delays between the isoelectronic pairings were found to be similar in nature, with the only large differences occurring at photoelectron energies less than 20 eV and around Cooper minima. Interestingly, the relative shift in Wigner time delays between negatively charged halogens and noble gases decreases as atomic mass increases. All atomic pairings show large differences at low energies, with noble gas atoms showing large positive Wigner delays, while negatively charged halogen ions show negative delays. The implications for photoionization studies in halide-containing molecules is also discussed.

DELAMINATION DETECTION IN THIN-WALLED COMPOSITE STRUCTURES USING ACOUSTIC PITCH-CATCH TECHNIQUE WITH FIBER BRAGG GRATING SENSORS

Structural health monitoring (SHM) of a composite structure is essential in maintaining the integrity of the structure. Over the years, various studies have reported on the use of conventional electrical sensors in analysing acoustic wave propagation for delamination detection. However, electrical sensors are associated with drawbacks such as high signal attenuation, are prone to electromagnetic interference (EMI) and are not suitable for harsh environments. Therefore, this paper reported on the use of fiber Bragg grating (FBG) sensors for delamination detection. Two composite structures with delamination sizes of 10 cm × 2 cm and 10 cm × 6 cm were fabricated. Two FBGs were bonded before and after the delamination. In addition, three trials of impacts were induced at the centre of the structure. Multiple signal parameters were obtained and analysed, which were the time delay, amplitude difference and velocity difference. The experimental results revealed that the time delay, amplitude and velocity analysis varied for both the delamination sizes with an average percentage of 42.36%, 97.09% and 42.39%, respectively. Therefore, it was confirmed that the increase in delamination size resulted in a longer time delay, higher signal amplitude attenuation and slower wave propagation.

Inversion-Mode In0.53Ga0.47 As MOSFET with f T = 275 GHz and high V eff

In this work, we present an inversion-mode In0.53Ga0.47As planar MOSFETs with current gain cutoff frequency (f T) = 275 GHz and maximum oscillation frequency (f max) = 75 GHz. To the best of our knowledge, this is the highest f T value among all the reported inversion-mode InGaAs MOSFETs. Meanwhile, peak transconductance (g m) shows 1035 (μS/μm). These extraordinary properties are attributed to the N2 remote plasma treatment which results in excellent high-k/III-V interface quality. With the assistance of delay-time analysis, effective electron velocity (V eff) of 2.88 × 107(cm s–1) is extracted for a possible explanation of the observed record f T performance.

Coherent-control phase lag across doubly excited atomic strontium resonances in an ω−2ω interference scheme

Accurate calculations of phase lag associated with coherent control where an excited system decays into more than one product channel have recently been reported for atomic barium in Wang and Greene [Y. Wang and C. H. Greene, Phys. Rev. A 105, 013113 (2022)]. The present study extends the calculations to make predictions of that observable for another alkaline-earth-metal atom, strontium, with a discussion of its spectrum and phase lag for energies between the ${\mathrm{Sr}}^{+}\phantom{\rule{4pt}{0ex}}4{d}_{3/2}$ and $4{d}_{5/2}$ thresholds. We explore the physics influenced by the electron correlations of strontium and the long-range Coulomb potential. The behavior of the phase lag cannot be simply addressed by a time-delay analysis, although the latter is often used to address the prominent channel of resonance decay of doubly excited states.

Modelling and real time performance evaluation of a 5 MW grid-connected solar photovoltaic plant using different artificial neural networks

Renewable energy sources have gained greater importance in the past decade owing to the continuous growth in energy demand of developing nations. The modular nature of solar energy has made it possible to utilize it in a variety of applications without any adverse economic impact. In this research, a 5 MW grid-connected solar PV plant was modelled using various ANN networks namely Cascade forward backdrop, ELMAN back prop, Feed forward back prop, Feed forward Distribute Time delay, Layer current and Regression Analysis to identify the impact of various environmental parameters on the PV power generation. Comparatively the Lavenberg- Marquardt model is obtained as a reliable model for smart grid system especially for solar PV power prediction with high R value of 0.9966. Also, the plants real time annual energy production, performance ratio (PR), and capacity utilization factors (CUFs) were analysed under various geometrical condition. The shading losses of the entire PV plant was evaluated using PV SYST software at different tilting conditions (seasonal and fixed tilt). Further, the annual PV generation and energy consumption of the plant is calculated under various load conditions. Out of the total energy consumption of the plant, only 11.2 % of the load will be handled by the 5 MW solar PV plant. Compared with fixed tilt, the proposed optimized tilt increased PR and CUF values to 48.5 % and 9.3 % for the months of November and December, respectively (excluding Sunday maintenance). During Sunday, energy loss was nearly 5248 kWh/day due to improper shutdown of power control units. With proper supply management schemes, the average power saving was nearly 48,000 kWh/year and the total cost saving was nearly INR 38 lakh per year. Owing to proper cleaning (twice in a month), the average power gain was increased to 5.4 %.

Combined frequency and voltage regulation in multi-area system using an equilibrium optimiser based non-integer controller with penetration of electric vehicles

The frequency and terminal voltages in power systems are the two critical operating points that balance active and reactive powers, which must be maintained within predefined limits through automatic generation control (AGC) and automatic voltage regulator (AVR) control loops. To address this issue, in this manuscript, the problem of a combined AGC and AVR is solved using a non-integer order PID controller. Its parameters are adjusted using the recently proposed equilibrium optimiser (EO) technique. Along with conventional sources, renewable energy sources such as geothermal power plants and electric vehicles are considered. The EO-tuned non-integer order PID controller outperformed the traditional integer order PI and PID controllers in view of peak overshoots, undershoots and settling times. The performance of EO is evaluated and found superior against widely used particle swarm optimisation and firefly algorithms in the presence of non-integer order PID controller. Different case studies such as random load disturbance, the effect of nonlinearities, the impact of communication time delay and sensitivity analysis have been performed, which also test the heftiness of the non-integer order PID controller. The obtained results are verified using the real-time digital simulator OPAL-RT (OP4510) for nominal conditions.

The Analysis of SIIR Mathematical Model Time Delay as The Solution of Tuberculosis Transmission in South Sulawesi

This study discusses the numerical solution of the Susceptible-Infected-Recovered (SIIR) transmission model for Tuberculosis (TB) transmission in South Sulawesi. The data used is secondary data from the number of Tuberculosis (TB) sufferers in South Sulawesi from the South Sulawesi Provincial Health Office. The discussion begins with a study of the SIIR model, then builds a time delay SIIR model on the spread of tuberculosis. Next, simulate the delay time SIIR model to find out the number of tuberculosis cases in South Sulawesi. Determining the parameters, simulation and analysis of the results in this study obtained a graph of the movement of the SIIR model of time delay of the spread of tuberculosis with real data. After analyzing the numerical simulation, it is seen that there is a tendency for the spread of Tuberculosis (TB) in South Sulawesi. The SIIR model is a four-dimensional non-linear differential equation. The results of the modeling are simulated using MatLab software to predict the number of TB cases so that early prevention measures are the government’s attention to prevent the spread of Tuberculosis (TB) in South Sulawesi.

秦巴山地植被绿度特征及其对地表水热的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 秦巴山地植被绿度特征及其对地表水热的响应 DOI: 10.5846/stxb202107272040 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 科技基础资源调查专项-中国南北过渡带综合科学考察（2017FY100902） Vegetation greenness characteristics in Qinling-Daba Mountains and its response to surface hydrothermal Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:地表水热通过影响土壤和大气间的水分交换以及植被生长用水对植被格局起关键作用。使用2000-2020年MODIS叶面积指数（Leaf area index，LAI）作为秦巴山地植被绿度的评价指标，基于遥感技术获取同时段地表温度（Land surface temperature，LST）和温度植被干旱指数（Temperature vegetation drought index，TVDI），用于指示地表水热状况。结合坡向和海拔等地形因素，使用均值和趋势法分析了秦巴山地植被绿度的时空特征；采用时滞相关性分析、主导因素分析探究了植被绿度与地表水热的相关关系，以及影响植被绿度变化的主导因素；借助广义可加模型，结合植被类型探讨了植被绿度与地表水热的非线性关系。结果表明：（1）秦巴山地LAI集中在1-5之间，LAI均值随海拔先增后减，不同坡向LAI差异较大，表现为大巴山北坡 > 大巴山南坡 > 秦岭南坡 > 秦岭北坡 > 西秦岭。秦巴山地LAI高值区与地表水热较好的地区在空间分布上具有一致性，LAI低值区与LST低值区或TVDI高值区较为一致。（2）秦巴山地LAI以由增变减趋势为主，变化趋势的海拔分异大于坡向分异。在LAI由增变减区，地表水热以由增变减为主，其次是持续增加。（3）秦巴山地LAI与LST主要为显著正相关，与TVDI主要为显著负相关。LAI对地表水热的时滞响应不明显，LAI受TVDI的影响较受LST的影响面积广。（4）除LST增加导致落叶阔叶林和混交林LAI单调递增外，地表水热对各植被类型LAI均为非线性影响，即存在一定阈值，阈值两侧LAI的响应规律不同，不同植被类型对地表水热响应的阈值差异较大。 Abstract:Surface hydrothermal plays a key role in vegetation pattern by affecting soil-atmosphere water exchange and vegetation growth water use. MODIS Leaf area index (LAI) from 2000 to 2020 was used as the evaluation index of vegetation greenness in Qinling-Daba Mountains. Land surface temperature (LST) and temperature vegetation drought index (TVDI) in the same period were obtained based on remote sensing technology to indicate surface hydrothermal conditions. Combined with terrain factors such as aspect and altitude, the temporal and spatial characteristics of vegetation greenness in Qinling-Daba Mountains were analyzed by mean and trend methods. The correlation between vegetation greenness and surface hydrothermal, as well as the leading factors affecting the change of vegetation greenness were explored by time-delay correlation analysis and dominant factor analysis. The nonlinear relationship between vegetation greenness and surface hydrothermal was discussed with the help of the generalized additive model and vegetation types. The results showed that:(1) the LAI of Qinling-Daba Mountains was concentrated in the range of 1-5. The average value of LAI increased first and then decreased with altitude, while the LAI varied greatly in different aspects, which was shown as the north slope of Daba Mountains > the south slope of Daba Mountains > the south slope of Qinling Mountains > the north slope of Qinling Mountains > the west Qinling Mountains. The LAI high value area in the Qinling-Daba Mountains was consistent with the area with good surface hydrothermal in terms of spatial distribution. The LAI low value area was consistent with LST low value area or TVDI high value area. (2) The LAI of the Qinling-Daba Mountains mainly changed from increase to decrease, and the change trend of altitude differentiation was larger than aspect differentiation. In the area of LAI from increase to decrease, surface hydrothermal was mainly from increase to decrease, followed by continuous increase. (3) The LAI of Qinling-Daba mountains was significantly positive correlation with LST, but significantly negative correlation with TVDI. The time lag effect of surface hydrothermal on LAI was not obvious, and LAI was more affected by TVDI than LST. (4) Except for the increase of LST which led to monotonous increase in LAI of deciduous broad-leaved forest and mixed forest, the surface hydrothermal had nonlinear effects on the LAI of various vegetation types. There was a certain threshold, and the response law of LAI on both sides of the threshold was different, and the thresholds of different vegetation's response to surface hydrothermal were quite different. 参考文献 相似文献 引证文献

Advanced delay-time analysis applied to carbon black powder production

Advanced delay-time analysis applied to carbon black powder production

Real-Time Sensing and Fault Diagnosis for Transmission Lines

Article Real-Time Sensing and Fault Diagnosis for Transmission Lines Fatemeh Mohammadi Shakiba 1, Milad Shojaee 1, S. Mohsen Azizi 1,2, and Mengchu Zhou 1,* 1 Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark 07102, NJ, USA. 2 The school of Applied Engineering and Technology, New Jersey Institute of Technology, Newark 07102, NJ, USA. * Correspondence: mengchu.zhou@njit.edu Received: 12 October 2022 Accepted: 8 November 2022 Published: 22 December 2022 Abstract: Protection of high voltage transmission lines is one of the crucial problems in the power system engineering. Accurate and timely detection and identification of transmission line short circuit faults can considerably improve and simplify their recovery process and hence save the costs associated with the downtime of a power system. Hence, it is essential that a robust and reliable fault diagnosis system completes its operation within an acceptable time window after fault occurrence in the presence of uncertainties and disturbances in the system. The significant costs of mistakenly detected or undetected faults based on the conventional techniques motivate us to present a robust detection and identification system by using the convolutional neural networks. The robustness of this technique is analyzed for the variations of the phase difference between two connected buses, fault resistance, source inductance fluctuations, fault inception angle, local bus voltage fluctuations, and measurement noises. The time delay analysis is also conducted to indicate that the presented technique is able to detect, identify, and estimate the location of faults before tripping relays and circuit breakers disconnect a faulty region.

Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics

An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development.

Time-delay Stability Analysis of the Distributed Economic Dispatch System of an Active Distribution Network

Distributed economic dispatch system has gained a lot of attention because it has better robustness, faster computation and less communication burden, but the system stability in the presence of communication delay is still researched at the fundamental level. By introducing the concept of virtual distributed generation, this paper develops a fully version of the distributed economic dispatching strategy for an active distribution network, and establishes the stability analysis model of the distribution network considering symmetric and asymmetric communication time-delay. The Lyapunov stability theorem and the free weight matrix method are comprehensively applied to derive the time-delay stability criterion of the system and solve the delay stability margin with a lower conservativeness. The distributed economic dispatch strategy in this paper under various operating conditions, the delay stability analysis method and delay stability margin solution are verified by the simulation results on an active distribution network modified based on the IEEE-14 system.

Delay Time (δt) and Polarization Direction (φ) Analysis Based on Shear Wave Splitting (SWS) Method

The information of dominant polarization direction and the mapping of fracture intensity are among the most important informations during the monitoring of geothermal field reservoir evaluation, as an effort to develop geothermal energy production. The appearance of geothermal reservoir fractures caused by fluid injection and the production activity resulting in the decreased pore pressure and appearance of open weak zone. The micro-earthquake activity around the area can represent these fractures that appear in the geothermal reservoir. Shear Wave Splitting (SWS) analysis can be done based on the polarization of S wave through the anisotropy medium recorded by seismograph. There are two parameters related to Shear Wave Splitting: the polarization direction (φ) related to the micro fracture direction with its delay time (δt), showing the fractures density and its permeability area. The result of Shear Wave Splitting Analysis of the field X geothermal shows that two dominant polarization directions are NW-SE and NE-SW. It is caused by the fractures around the X field geothermal with similar fractures direction, and it is compatible with the distribution micro-earthquake hypocenter of the previous study. Based on the map of fractures intensity, the value range shows a relatively dense intensity value around 6.6 – 8.0 ms/km. The high value of intensity fractures indicates a high value of anisotropy around the area, and it is also confirming the presumption of the high permeability potential of the X geothermal field.

Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is significant for spacecraft safety. In this study, using GOES satellite data during 2011–2020, we propose two neural network models with two hidden layers to predict geosynchronous relativistic electron fluxes at two energy channels (>0.8 MeV and > 2 MeV). The number of input neurons of the two channels (>0.8 MeV and > 2 MeV) are determined to be 36 and 44, respectively. The > 0.8 MeV model has 22 and 9 neurons in the hidden layers, while the > 2 MeV model has 25 and 15 neurons in the hidden layers. The input parameters include the north–south component of the interplanetary magnetic field, solar wind speed, solar wind dynamic pressure and solar wind proton density. Through the analysis of different time delays, we determine that the optimal time delays of two energy channels (>0.8 MeV and > 2 MeV) are 8 days and 10 days, respectively. The training set and validation set (Jan 2011-Dec 2018) are divided by the 10-fold cross-validation method, and the remaining data (Jan 2019-Feb 2020) is used to analyze the model performance as a test set. The prediction results of both energy channels show good agreement with satellite observations indicated by low RMSE (∼0.3 cm-2sr-1s−1), high PE (∼0.8) and CC (∼0.9). These results suggest that only using solar wind parameters is capable of obtaining reasonable predictions of geosynchronous relativistic electron fluxes.

Modelling Approach to Develop a Multimodal Transportation Network

Multimodal transportation (MMT) refers to a system that utilizes various means of transportation to transfer people & goods from one place to another. Transit operations are measured as the maximum number of passengers that can be carried past a fixed route, in a given period. Investing in and using multiple modes of transportation does not only bring efficiencies but also goes a long way to reduce time & delay, as well as the overall cost of transportation. This paper presents the efficient mode of transportation system at selected routes in the Bengaluru city, the route stretch selected is 12.2 km in length and it has 8 major intersections. The type of pavement surface consists of both bituminous and Reinforced cement concrete (R.C.C). The complete study stretch is a 2-way 4-lane road. This study provides walkability-index, traffic volume studies in terms of passenger car unit (PCU), time and delay study, and Cost & time analysis. Four modes of transportation are used in this study to find the effective multimodal-transportation system namely, 2-wheeler, 4-wheeler, para-transit [15], and buses/metro. The outcome of this paper is to arrive at the shortest distance for reducing delay & time, and cost-effective travel using multi-mode transport. While focusing on the present condition of the para-transit transport facilities. The time, delay & cost during MMT and to provide a safe, cost-effective & convenient way to travel or transport. The result shows the reduction in time, cost & delay of transportation by using the MMT model. This MMT system could be adopted as an efficient mode of transportation on this route. Similar rural-urban junction routes around the world could use this approach for effective transit by making a modal study.