DYNAMIC MODELS OF MOISTURE CONTENT OF FOREST FUELS

The fuel moisture content is an integral part of any workable fire danger-rating system. This paper presented dynamic models for estimating 1-h, 10-h. 100-h and 1000-h timelag fuels, that were developed by multiple regression and stepwise variable selection of statistics. The variables include both meteorological factors and moisture contents observed prior to the day correspondingly. The analysis revealed that the fuel moisture content are correlated positively with the precipitation of 24 hours prior to the observation time, and negatiyely with air temperature at observing height of 1.5 meter in forest.

Diurnal variation models for fine fuel moisture content in boreal forests in China

Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.

Modelling the dead fuel moisture content in a grassland of Ergun City,China

The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

DYNAMIC MODELS OF AQUIFEROUS CONTENT OF LIVING FUELS

Forest fire occurrence is closely relative with fuel water content. There are a lot of research about dead fuels. but forest fuels consist of both dead fuels and living fuels. Each large fire occurrence has a good relationship with living fuels. Especially living fuels can influence the production and development of big forest fire, so, we selected Tahe, in Daxingan Mountains, as observation site. According to actual data,we can establish a set of models of different living fuel water content variation with linear -regression method.

Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain,Northeast China

Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.

Modeling surface fuels moisture content in Pinus brutia stands

Fuel moisture content is an important variable for forest fires because it affects fuel ignition and fire behavior. In order to accurately predict fuel ignition potential, fuel moisture content must be assessed by evaluating fire spread, fireline intensity and fuel consumption.Our objective here is to model moisture content of surface fuels in normally stocked Calabrian pine(Pinus brutia Ten.) stands in relation to weather conditions, namely temperature, relative humidity, and wind speed in the Mugla province of Turkey. All surface fuels were categorized according to diameter classes and fuel types. Six fuel categories were defined: these were 0–0.3, 0.3–0.6, and0.6–1 cm diameter classes, and cone, surface litter, and duff. Plastic containers 15 9 20 cm in size with 1 9 1 mm mesh size were used. Samples were taken from 09:00 to19:00 h and weighed every 2 h with 0.01 g precision for10 days in August. At the end of the study, samples were taken to the laboratory, oven-dried at 105 °C for 24 h and weighed to obtain fuel-moisture contents. Weather measurements were taken from a fully automated weather station set up at the study site prior to the study. Correlation and regression analyses were carried out and models were developed to predict fuel moisture contents for desorption and adsorption phase for each fuel type categories. Practical fuel moisture prediction models were developed for dry period. Models were developed that performed well with reasonable accuracy, explaining up to 92 and 95.6%of the variability in fuel-moisture contents for desorption and adsorption phases, respectively. Validation of the models were conducted using an independent data set and known fuel moisture prediction models. The predictive power of the models was satisfactory with mean absolute error values being 1.48 and 1.02 for desorption and adsorption as compared to the 2.05 and 1.60 values for the Van Wagner's hourly litter moisture content prediction model. Results obtained in this study will be invaluable for fire management planning and modeling.

DYNAMIC PREDICION OF FOREST FUEL LOADS BY GREY VERHULST MODEL

The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling region was discussed. The dynamic models were developed by gray theory for estimating the fuels loads of arbor- shrub, herbs’ grass, litter, and semi-decomposed litter, inflamma ble fuel and the total fuels in each forest type. After a fire, the inflammabIe fuel loads in phododendron-- Larix gmelinii forest and in the herb- - Betula platyphlla fores was estimated at 10.958 t/hm2and 10.473 t/hm2 respectively’ by 13 years later. and that was 12.297 t/hm 2 in herb--Larix gmeliniiforest by 7 years later.. It was predicated that a big fire may occur after 10 years based on inflammable fuel biomass accumulated.

Dynamic Thermal Model and Temperature Control of Proton Exchange Membrane Fuel Cell Stack

A dynamic thermal transfer model of a proton exchange membrane fuel cell (PEMFC) stack is developed based on energy conservation in order to reach better temperature control of PEMFC stack. Considering its uncertain parameters and disturbance, we propose a robust adaptive controller based on backstepping algorithm of Lyaponov function. Numerical simulations indicate the validity of the proposed controller.

One-dimensional Dynamic Modeling and Simulation of a Planar Direct Internal Reforming Solid Oxide Fuel Cell

This article aims to investigate the transient behavior of a planar direct internal reforming solid oxide fuel cell （DIR-SOFC） comprehensively. A one-dimensional dynamic model of a planar D1R-SOFC is first developed based on mass and energy balances, and electrochemical principles. Further, a solution strategy is presented to solve the model, and the International Energy Agency （IEA） benchmark test is used to validate the model. Then, through model-based simulations, the steady-state performance of a co-flow planar DIR-SOFC under specified initial operating conditions and its dynamic response to introduced operating parameter disturbances are studied. The dynamic responses of important SOFC variables, such as cell temperature, current density, and cell voltage are all investigated when the SOFC is subjected to the step-changes in various operating parameters including both the load current and the inlet fuel and air flow rates. The results indicate that the rapid dynamics of the current density and the cell voltage are mainly influenced by the gas composition, particularly the H2 molar fraction in anode gas channels, while their slow dynamics are both dominated by the SOLID （including the PEN and interconnects） temperature. As the load current increases, the SOLID temperature and the maximum SOLID temperature gradient both increase, and thereby, the cell breakdown is apt to occur because of excessive thermal stresses. Changing the inlet fuel flow rate might lead to the change in the anode gas composition and the consequent change in the current density distribution and cell voltage. The inlet air flow rate has a great impact on the cell temperature distribution along the cell, and thus, is a suitable manipulated variable to control the cell temperature.

Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network

The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.

基于动态主题网络的新兴技术主题识别——以氢燃料电池领域为例

[研究目的]新兴技术代表着未来的技术发展方向,是抢占科技前沿制高点必须把握的关键技术,准确识别新兴技术对国家发展具有重要战略意义。[研究方法]综合专利文本信息和分类信息,构建SBERT-LDA-IPC模型,识别各时间段的技术主题;根据主题相似度矩阵绘制动态主题网络,识别具有创新性和连续性的主题为候选主题,评估候选主题的新颖性和影响力,确定新兴技术主题;以氢燃料电池领域为例进行实证检验。[研究结论]研究表明,SBERT-LDA-IPC模型提高了主题聚类的连贯性和准确性,结合国家在氢燃料电池产业发布的系列政策作为验证依据,识别出的三个新兴技术主题,与国家政策制定和产业发展方向一致。

大功率质子交换膜燃料电池建模及仿真

针对70 kW大功率燃料电池系统进行建模,基于Simulink平台搭建质子交换膜燃料电池电堆系统模型。基于该模型在稳态下研究温度、膜含水量、压力对电池输出性能的影响,分析水气跨膜运输情况。仿真结果表明电流密度在0.4~1.0 A/cm范围内时,电堆输出电压保持稳定。然后在新欧洲驾驶循环(NEDC)动态工况下,分析电池系统输出性能和水气跨膜传输情况,结果表明所建模型可用于大功率车用PEMFC研究。

氢燃料电池局部动态特征三维模型

车用燃料电池运行过程中,动态负载可能引起电池局部物理量剧烈变化,进而导致电池性能和使用寿命急剧下降。为了防止上述问题的发生,需要深入分析动态工况下电池局部动态特征。本研究考虑膜电极7层结构特征、气体组分扩散以及气液两相宏观对流带来的能量输运,同时考虑水在气-液-膜三相之间相变引起的局部能量变化,建立了燃料电池两相、非等温、三维动态物理模型,揭示动态载荷下燃料电池内部特征,特别是在流道方向和极板沟脊下催化层区域的热质输运以及电化学反应动态响应特征,并阐明了动态行为形成机理。研究结果显示,在电流负载阶跃过程中膜电极内部存在显著的热质时空响应不均现象,导致变载过程中电池输出电压下冲约20 mV,引发额外功率损失和产热,从而造成局部温度进一步上升。

北京西山林场侧柏林可燃物含水率及林火预测

以北京西山林场侧柏林为研究对象,对选择林分的特征进行调查,经内业试验对侧柏林地表细小可燃物含水率进行测定,选择通过Pearson相关性分析呈显著的气象因子为拟合变量建立细小可燃物含水率模型,根据引燃试验建立地表火发生概率模型,同时结合Cruz树冠火发生模型,利用实际气象条件对地表火与树冠火发生概率的动态变化进行分析。结果表明:侧柏林细小可燃物含水率模型预测效果较好;防火期内,侧柏林发生地表火的概率较大,而不同样地发生树冠火的概率差别较大,在风速较大时发生树冠火的概率较大。

基于模糊自抗扰的PEMFC输出电压控制研究

针对质子交换膜燃料电池(PEMFC)自启动后输出电压不稳定、发电效率低下等问题,提出一种基于模糊-自抗扰控制器的控制策略,以提高PEMFC的输出电压稳定性。在建立PEMFC电化学动态模型的基础上,采用优化的自抗扰控制器实时估计并补偿系统受到的总扰动,跟踪输出电压;同时,加入模糊控制器实现双参数在有限范围内的自整定。通过仿真实验验证控制技术的有效性,并与比例-积分-微分(PID)、常规自抗扰控制方式进行对比分析。结果表明:所提控制策略可有效降低PEMFC电压振荡幅度,使PEMFC电压达到稳定状态;并且在相同扰动下的闭环系统超调量最小,调节时间最短,系统拥有更优的鲁棒性和抗干扰性。

基于阴极再循环的燃料电池系统建模与动态性能分析

阴极再循环通过调节电堆进气中的氧分压和水蒸气分压特别适合解决车用燃料电池系统在低怠速工况下的缺水和高电势问题。建立了一个带阴极再循环功能的燃料电池非线性系统控制模型,使用阴极再循环泵将阴极出口的气体引回到空压机出口实现了进气加湿和氧分压调节。该模型可捕捉由阴极再循环带来的一系列动态响应。结果表明,阴极再循环可以极大地改善小电流密度下的进气湿度,随着阴极再循环比的增加,加湿动态响应加快;氧分压调节的动态响应较慢,但通过合理的阴极再循环策略仍能将小电流密度下的高电势限制在0.8V以下。通过抑制小电流密度下的电压,实现了50%的怠速功率降幅。

大娄山典型林分草本可燃物载量及其影响因素分析

以大娄山6种典型林分的草本可燃物为研究对象,通过对相同林分和不同林分下草本可燃物载量进行差异性及相关性分析,结合林分特征因子进行逐步回归分析,得到草本可燃物载量的预测模型。结果表明:阔叶混交林内草本可燃物载量最大值为0.759 t/hm^(2),杉木林最小,仅为0.058 t/hm^(2);马尾松林的草本可燃物载量主要与平均厚度相关,而阔叶混交林的草本可燃物载量主要受平均盖度和林分密度影响;相同林分时,马尾松林下模型的拟合优度为0.885,拟合效果较好,平均厚度、郁闭度和平均地径显著负向影响草本可燃物的载量;阔叶混交林下模型拟合优度为0.973,拟合效果好,平均盖度显著正向影响草本可燃物的载量。本研究对大娄山森林可燃物调控和科学林火管理具有重要意义。

等效电路模型法预测动态工况下微型直接甲醇燃料电池剩余使用寿命

微型直接甲醇燃料电池(μDMFC)具有能量密度高、可便携使用、快速补能以及环境友好等优点.然而,由于膜电极在电化学反应中会退化,μDMFC的有效使用寿命有限,所以需要对其健康状态与剩余使用寿命进行评估,为燃料电池改性和控制策略提供决策支持.在结合数据驱动和机理模型预测方法的基础上,针对动态运行工况,提出一种基于等效电路模型(ECM)的μDMFC剩余使用寿命预测方法.在燃料电池的性能退化指标中,电池输出电压可以被实时监测从而获得电池的退化趋势,但这一指标无法单独提供精确的预测结果.通过测量电化学阻抗谱并结合ECM可以得到电池内部阻抗等深层信息,但这些深层信息不易被实时监测,通常只能低频离线测量.此外,燃料电池在实际应用中多处于变工况状态,其退化趋势和使用寿命受工作环境影响,传统基于电压退化趋势回归的预测方法无法应对工况的动态变化.因此,可通过定期离线获取内部退化参量建立预测模型.实验结果表明:与传统数据驱动的方法相比,基于内部退化参量的预测方法能更好地适应变工况环境,在燃料电池剩余使用寿命预测中具有更好的性能.

地表细小可燃物含水率实测值与自动测量仪器值的比较和校正

为检验可燃物含水率自动测量仪器的准确性,利用仪器所得含水率数据和人工实测含水率数据,结合环境因子和仪器的工作原理分析造成误差的原因,找寻校正方法,提高仪器的准确性。该研究以哈尔滨市3种典型林分地表死可燃物作为研究对象,布设新一代含水率自动测量仪器获取含水率数据和气象数据,结合野外人工实测含水率数据进行对比分析。通过建立线性与非线性回归模型的方法对仪器数据进行校正,最后检验模型的外推性,确定校正方法。结果表明,(1)仪器数据与实测数据之间的差异主要来自系统误差,造成的原因是网兜上所附着的水分等杂质。这主要与林内复杂的环境和仪器的工作原理有关,需要通过模型进行校正;(2)气象因子对仪器测量含水率数据与人工实测数据之间的显著差异也产生一定影响;(3)2种校正模型中,只有线性回归模型具有良好的校正效果和外推性,可以对仪器测量含水率数据进行校正,校正后的数据满足精度要求。可燃物含水率自动测量仪器存在一定的误差,导致其与人工实测数据存在显著差异。可以通过线性回归模型进行校正,校正后的仪器数据满足测量的精度要求,提高了仪器测量含水率数据的准确性。该研究可以为今后进行快速和准确的火险预测预报提供重要的技术支撑。

基于PROSAIL模型的广州市过渡带森林植被冠层可燃物含水率估算

基于PROSAIL模型,结合野外实测叶片等水分厚度、干物质重量、叶面积指数数据,构建一种基于归一化红外指数和归一化干物质指数的植被冠层可燃物含水率估算方法。首先,在PROSAIL模型输入实测参数模拟植被冠层光谱曲线,计算归一化红外指数、归一化干物质指数用于叶片等水分厚度、干物质重量的反演。结果表明:归一化红外指数与叶片等水分厚度、归一化干物质指数与干物质重量存在明显的线性关系,基于该关系建立叶片等水分厚度、干物质重量的经验估算模型,经验证估算结果精度较高;将该经验模型推广至利用Landsat 8数据拟合植被冠层可燃物含水率,并与实测数据进行验证,结果显示R^(2)达到0.743,RMSE达到34.2%,具有较高的精度。文章提出的植被冠层可燃物含水率估算模型,可实现广州市过渡带森林大面积、较高精度植被冠层可燃物含水率监测,为预防森林火灾提供参考。