Assessment of Axial Power Peaking Factors in GHARR-1 LEU Core: A Decadal Simulation Analysis

This study aims to thoroughly investigate the axial power peaking factors (PPF) within the low-enriched uranium (LEU) core of the Ghana Research Reactor-1 (GHARR-1). This study uses advanced simulation tools, like the MCNPX code for analysing neutron behavior and the PARET/ANL code for understanding power variations, to get a clearer picture of the reactor’s performance. The analysis covers the initial six years of GHARR-1’s operation and includes projections for its whole 60-year lifespan. We closely observed the patterns of both the highest and average PPFs at 21 axial nodes, with measurements taken every ten years. The findings of this study reveal important patterns in power distribution within the core, which are essential for improving the safety regulations and fuel management techniques of the reactor. We provide a meticulous approach, extensive data, and an analysis of the findings, highlighting the significance of continuous monitoring and analysis for proactive management of nuclear reactors. The findings of this study not only enhance our comprehension of nuclear reactor safety but also carry significant ramifications for sustainable energy progress in Ghana and the wider global context. Nuclear engineering is essential in tackling global concerns, such as the demand for clean and dependable energy sources. Research on optimising nuclear reactors, particularly in terms of safety and efficiency, is crucial for the ongoing advancement and acceptance of nuclear energy.

Research on the Spatiotemporal Evolution,and Optimized Paths of Carbon Pressure in Northwestern Sichuan under the Carbon Peaking and Carbon Neutrality Goals

Consolidating carbon sink capacity and reducing carbon pressure are important channels to achieve the carbon peaking and carbon neutrality goals actively yet prudently.In order to study the current situation of carbon pressure in the Northwestern Sichuan,we took the carbon pressure of the Aba Tibetan-Qiang autonomous prefecture(Aba prefecture)as an example and used the Intergovernmental Panel on Climate Change(IPCC)approach to measure the carbon emissions,carbon uptake,and the carbon balance index(CBI)of each county-level city in Aba prefecture from 2012 to 2020.The study found that:(a)There was a continuous trend of declining carbon emissions,increased carbon uptake,and decreased CBI in Aba prefecture during the sample period,but there is a large variability among county-level cities;(b)Aba prefecture differs in the spatiotemporal distribution of carbon emissions,carbon uptake,and CBI.Based on the research results,we propose several optimized paths for alleviating the current carbon pressure situation in the Northwestern Sichuan.

Combined Optimal Dispatch of Thermal Power Generators and Energy Storage Considering Thermal Power Deep Peak Clipping and Wind Energy Emission Grading Punishment

Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.

Peak Shaving Strategy of Concentrating Solar Power Generation Based on Multi-Time-Scale and Considering Demand Response

According to the multi-time-scale characteristics of power generation and demand-side response(DR)resources,as well as the improvement of prediction accuracy along with the approaching operating point,a rolling peak shaving optimization model consisting of three different time scales has been proposed.The proposed peak shaving optimization model considers not only the generation resources of two different response speeds but also the two different DR resources and determines each unit combination,generation power,and demand response strategy on different time scales so as to participate in the peaking of the power system by taking full advantage of the fast response characteristics of the concentrating solar power(CSP).At the same time,in order to improve the accuracy of the scheduling results,the combination of the day-ahead peak shaving phase with scenario-based stochastic programming can further reduce the influence of wind power prediction errors on scheduling results.The testing results have shown that by optimizing the allocation of scheduling resources in each phase,it can effectively reduce the number of starts and stops of thermal power units and improve the economic efficiency of system operation.The spinning reserve capacity is reduced,and the effectiveness of the peak shaving strategy is verified.

Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

Spatiotemporal variations of ecosystem services and driving factors in the Tianchi Bogda Peak Natural Reserve of Xinjiang,China

Nature reserves play a significant role in providing ecosystem services and are key sites for biodiversity conservation.The Tianchi Bogda Peak Natural Reserve(TBPNR),located in Xinjiang Uygur Autonomous Region,China,is an important ecological barrier area in the temperate arid zone.The evaluation of its important ecosystem services is of great significance to improve the management level and ecological protection efficiency of the reserve.In the present study,we assessed the spatiotemporal variations of four ecosystem services(including net primary productivity(NPP),water yield,soil conservation,and habitat quality)in the TBPNR from 2000 to 2020 based on the environmental and social data using the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.In addition,the coldspot and hotspot areas of ecosystem services were identified by hotspot analysis,and the trade-off and synergistic relationships between ecosystem services were analyzed using factor analysis in a geographic detector.During the study period,NPP and soil conservation values in the reserve increased by 48.20%and 25.56%,respectively;conversely,water yield decreased by 16.56%,and there was no significant change in habitat quality.Spatially,both NPP and habitat quality values were higher in the northern part and lower in the southern part,whereas water yield showed an opposite trend.Correlation analysis revealed that NPP showed a synergistic relationship with habitat quality and soil conservation,and exhibited a trade-off relationship with water yield.Water yield and habitat quality also had a trade-off relationship.NPP and habitat quality were affected by annual average temperature and Normalized Difference Vegetation Index(NDVI),respectively,while water yield and soil conservation were more affected by digital elevation model(DEM).Therefore,attention should be paid to the spatial distribution and dynamics of trade-off and synergistic relationships between ecosystem services in future ecological management.The findings of the present study provide a reference that could facilitate the sustainable utilization of ecosystem services in the typical fragile areas of Northwest China.

Chopping-peaking开关形成高功率超宽带双极脉冲的实验研究

设计研制了一种chopping peaking组合开关型高功率双极脉冲形成装置,通过调节两个开关的导通时刻来得到不同形状的双极脉冲。理想情况下,形成的双极脉冲电压峰 峰值等于入射脉冲峰值电压的2倍,实验中入射脉冲峰值电压为205kV的单极脉冲,获得了最大峰 峰电压为360kV的双极脉冲,是入射脉冲峰值电压的1.76倍。双极脉冲的持续时间可以改变,最大值为单极脉冲的脉宽。入射脉冲上升时间越小,得到的双极脉冲峰 峰电压就越大。

基于Chopping-Peaking开关组合的亚纳秒双极化器的PSPICE模拟

给出了单极脉冲经过Chopping-Peaking组合开关形成双极脉冲的原理,用PSPICE程序模拟计算了不同因素(入射单极脉冲上升时间及Chopping开关与Peaking开关特性等)对输出双极脉冲的影响,实验结果与模拟结果符合较好。

Policy and Management of Carbon Peaking and Carbon Neutrality:A Literature Review

The vision of reaching a carbon peak and achieving carbon neutrality is guiding the low-carbon transition of China’s socioeconomic system.Currently,a research gap remains in the existing literature in terms of studies that systematically identify opportunities to achieve carbon neutrality.To address this gap,this study comprehensively collates and investigates 1105 published research studies regarding carbon peaking and carbon neutrality.In doing so,the principles of development in this area are quantitively analyzed from a space–time perspective.At the same time,this study traces shifts and alterations in research hotspots.This systematic review summarizes the priorities and standpoints of key industries on carbon peaking and carbon neutrality.Furthermore,with an emphasis on five key management science topics,the scientific concerns and strategic demands for these two carbon emission-reduction goals are clarified.The paper ends with theoretical insights on and practical countermeasures for actions,priority tasks,and policy measures that will enable China to achieve a carbon-neutral future.This study provides a complete picture of the research status on carbon peaking and carbon neutrality,as well as the research directions worth investigating in this field,which are crucial to the formulation of carbon peak and carbon neutrality policies.

Multi-model comparison of CO2 emissions peaking in China:Lessons from CEMF01 study

The paper summarizes results of the China Energy Modeling Forum's(CEMF)first study.Carbon emissions peaking scenarios,consistent with China's Paris commitment,have been simulated with seven national and industry-level energy models and compared.The CO2 emission trends in the considered scenarios peak from 2015 to 2030 at the level of 9e11 Gt.Sector-level analysis suggests that total emissions pathways before 2030 will be determined mainly by dynamics of emissions in the electric power industry and transportation sector.Both sectors will experience significant increase in demand,but have low-carbon alternative options for development.Based on a side-by-side comparison of modeling input and results,conclusions have been drawn regarding the sources of emissions projections differences,which include data,views on economic perspectives,or models'structure and theoretical framework.Some suggestions have been made regarding energy models'development priorities for further research.

The economic impact of emission peaking control policies and China's sustainable development

To achieve the goals of national sustainable development,the peaking control of CO2emissions is pivotal,as well as other pollutants.In this paper,we build a Chinese inter-regional CGE model and simulate 13 policies and their combinations.By analyzing the energy consumptions,coal consumptions,relating emissions and their impacts on GDP,we found that with the structure adjustment policy,the proportion of coal in primary fossil fuels in 2030 will decrease from 53%to 48%and CO2emissions will decrease by 11.3%e22.8%compared to the baseline scenario.With the energy intensity reduction policy,CO2emissions will decrease by 33.3%in 2030 and 47.8%in 2050 than baseline scenario.Other pollutants will also be controlled as synergetic effects.In this study we also find that although the earlier the peaking time the better for emission amounts control,the economic costs can not be ignored.The GDP will decrease by 2.96%e8.23%under different scenarios.Therefore,integrated policy solutions are needed for realizing the peaks package and more targeted measures are required to achieve the peaks of other pollutants earlier.

An analysis of China's CO_2 emission peaking target and pathways

China has set the goal for its CO2emissions to peak around 2030,which is not only a strategic decision coordinating domestic sustainable development and global climate change mitigation but also an overarching target and a key point of action for China's resource conservation,environmental protection,shift in economic development patterns,and CO2emission reduction to avoid climate change.The development stage where China maps out the CO2emission peak target is earlier than that of the developed countries.It is a necessity that the non-fossil energy supplies be able to meet all the increased energy demand for achieving CO2emission peaking.Given that China's potential GDP annual increasing rate will be more than 4%,and China's total energy demand will continue to increase by approximately 1.0%e1.5%annually around2030,new and renewable energies will need to increase by 6%e8%annually to meet the desired CO2emission peak.The share of new and renewable energies in China's total primary energy supply will be approximately 20%by 2030.At that time,the energy consumption elasticity will decrease to around 0.3,and the annual decrease in the rate of CO2intensity will also be higher than 4%to ensure the sustained growth of GDP.To achieve the CO2emission peaking target and substantially promote the low-carbon development transformation,China needs to actively promote an energy production and consumption revolution,the innovation of advanced energy technologies,the reform of the energy regulatory system and pricing mechanism,and especially the construction of a national carbon emission cap and trade system.

Impact of E×B flow shear stabilization on particle confinement and density peaking at JET

The impact of the E×B flow shear stabilization on particle transport and density peaking at JET is analyzed in the framework of integrated modelling with the CRONOS code.For that purpose,plasmas from a power scan which show a significant increasing of density peaking with the injected neutral beam injection power have been used as a modeling basis.By means of simulations with the quasilinear model GLF23 for the heat and particle transport,a strong link between the particle confinement and E×B flow shear stabilization is found.This is particularly important close to the pedestal region where the particle pinch direction becomes strongly inward for high E×B flow shear values.Such impact introduces some non-negligible deviation from the well-known collisonality dependence of the density peaking,whose general trend has been also obtained in the framework of this modelling by performing pedestal density scans.

Loss Factors and their Effect on Resonance Peaks in Mechanical Systems

The loss factors and their effects on the magnitude and frequency of resonance peaks in various mechanical sys-tems are reviewed for acoustic,vibration,and vibration fatigue applications.The main trends and relationships were obtained for linear mechanical models with hysteresis damping.The well-known features(complex module of elasticity,total loss factor,etc.)are clarified for practical engineers and students,and new results are presented(in particular,for 2-DOF in-series models with hysteresis friction).The results are of both educational and prac-tical interest and may be applied for NVH analysis and testing,mechanical and aeromechanical design,and noise and vibration control in buildings.

Numerical Calculation of the Peaking Factor of a Water-Cooled W/Cu Monoblock for a Divertor

In order to accurately predict the incident critical heat flux（ICHF,the heat flux at the heated surface when CHF occurs） of a water-cooled W/Cu monoblock for a divertor,the exact knowledge of its peaking factors（f_p） under one-sided heating conditions with different design parameters is a key issue.In this paper,the heat conduction in the solid domain of a water-cooled W/Cu monoblock is calculated numerically by assuming the local heat transfer coefficients（HTC）of the cooling wall to be functions of the local wall temperature,so as to obtain f_p.The reliability of the calculation method is validated by an experimental example result,with the maximum error of 2.1% only.The effects of geometric and flow parameters on the f_p of a water-cooled W/Cu monoblock are investigated.Within the scope of this study,it is shown that the f_p increases with increasing dimensionless W/Cu monoblock width and armour thickness（the shortest distance between the heated surface and Cu layer）,and the maximum increases are 43.8% and 22.4% respectively.The dimensionless W/Cu monoblock height and Cu thickness have little effect on f_p.The increase of Reynolds number and Jakob number causes the increase of f_p,and the maximum increases are 6.8% and 9.6% respectively.Based on the calculated results,an empirical correlation on peaking factor is obtained via regression.These results provide a valuable reference for the thermal-hydraulic design of water-cooled divertors.

Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

Risks,Challenges and Countermeasures for China’s Power Industry to Cope with Carbon Peaking

The situation of China’s power industry to achieve carbon peaking and risks and challenges for China’s power industry to cope with carbon peaking were analyzed, and then macro countermeasures for the power industry to cope with carbon peaking were proposed.

Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.

A method to predict the peak shear strength of rock joints based on machine learning

In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.