高性能大型金属构件激光增材制造技术研究现状与发展趋势

高性能大型金属构件激光增材制造技术,将“高性能金属激光熔化/快速凝固材料制备”与“大型构件近净成形制造”结合,为航空、航天、船舶、电力、石化、海洋工程等高端装备中大型难加工金属构件的制造提供了新途径。综述了北京航空航天大学大型金属构件增材制造国家工程实验室在钛合金等高性能大型关键承力构件激光熔化沉积增材制造技术方向的主要进展:突破以“凝固晶粒”、“内部缺陷”及“显微组织”为核心的钛合金大型关键主承力构件激光增材制造“质量性能”控制瓶颈难题;提出系列激光增材制造工艺新方法,揭示激光增材制造过程内应力形成机理与演化规律,初步建立“变形开裂”预防方法;研制出具有原创核心关键技术的系列化大型激光增材制造工程化成套装备;自主制定了整套应用技术标准体系。北航团队研究成果在国家大型运输机、舰载机、大型运载火箭等重大装备研制生产中的工程应用,为解决装备研制生产制造瓶颈难题、提升装备结构设计制造水平、促进装备快速研制等发挥了重要作用,同时使我国在此领域处于国际领先地位。

锂离子动力电池滥用条件下热失控特性研究

锂离子动力电池作为新能源汽车的核心部件,其热失控问题受到广泛关注。对三元材料、磷酸铁锂及钛酸锂三种锂离子动力电池在机械、电及热等极端滥用条件下的热稳定性进行了研究,对不同种类的锂离子动力电池在各种滥用条件下的温度和电压变化特点和规律进行了分析归纳。

锂离子电池热失控机理研究现状

锂离子电池是纯电动汽车的"动力源泉",其安全性问题一直被广泛关注,其中热失控是锂离子电池安全问题的主要诱因之一。锂离子电池热失控时内部组分发生剧烈复杂的化学反应,产生大量热发生燃烧甚至爆炸,威胁人类的生命和财产安全。简述了锂离子电池热失控机理国内外研究进展,对锂离子电池内部不同组分的热失控反应机制和热失控诱因进行了归纳。最后对锂离子电池热失控的研究进行了展望。

一种三维多孔碳材料的制备及电容特性

目前超级电容器研究主要集中在高性能电极材料的制备上,而三维多孔结构有助于提高电极的电化学性能。实验中采用聚氨酯海绵作为模板,形成三维中空大孔结构的Fe_3O_4/C,Fe/C复合物。通过刻蚀液将铁或铁的氧化物刻蚀掉,形成在管壁上有介孔结构的三维多孔碳超级电容器材料。通过BET吸附测试、扫描电镜测试及恒流充放电测试等对合成的材料的结构、电化学性能进行表征,结果表明,这种三维多孔材料具有丰富的孔隙结构,有利于电解质的导入与导出,而且管壁上介孔孔径密布在3.6nm左右,比表面积较高达到339.5m^2/g,作为超级电容器电极材料具有良好电容特性。

The Long-Term Field Experiment Observatory and Preliminary Analysis of Land-Atmosphere Interaction over Hilly Zone in the Subtropical Monsoon Region of Southern China

To improve current understanding of the water cycle,energy partitioning and CO2 exchange over hilly zone vegetative land surfaces in the subtropical monsoon environment of southern China,a long-term field experiment observatory was set up at Ningxiang,eastern Hunan Province.This paper presents a preliminary analysis of the field observations at the observatory collected from August to November 2012.Results show that significant diurnal variations in soil temperature occur only in shallow soil layers(0.05,0.10,and 0.20 m),and that heavy rainfall affects soil moisture in the deep layers(≥ 0.40 m).During the experimental period,significant diurnal variations in albedo,radiation components,energy components,and CO2 flux were observed,but little seasonal variation.Strong photosynthesis in the vegetation canopy enhanced the CO2 absorption and the latent heat released in daylight hours;Latent heat of evaporation was the main consumer of available energy in late summer.Because the field experiment data are demonstrably reliable,the observatory will provide reliable long-term measurements for future investigations of the land-atmosphere interaction over hilly land surfaces in the subtropical monsoon region of southern China.

Real-data assimilation experiment with a joint data assimilation system: assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.

An Economical Approach to Flow-Adaptive Moderation of Spurious Ensemble Correlations and Its Application in the Proper Orthogonal Decomposition-Based Ensemble Four Dimensional Variational Assimilation Method

The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.

Model-Simulated Atmospheric Carbon Dioxide: Comparisons with Satellite Retrievals and Ground-Based Observations

Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.

Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia

The study on how the variations in CO2 sources and sinks can affect the CO2 concentration over East Asia would be useful to provide information for policymaker concerning carbon emission reduction.In this study,a nested-grid version of global chemical transport model(GEOS-Chem)is employed to assess the impacts of variations in meteorological parameters,terrestrial fluxes,fossil fuel emissions,and biomass burning on inter-annual variations of CO2 concentrations over East Asia in 2004—2012.Simulated CO2 concentrations are compared with observations at 14 surface stations from the World Data Centre for Greenhouse Gases(WDCGG)and satellite-derived C 02 column density(XCO,)from the Gases Observing SATellite(GOSAT).The comparison shows that the simulated CO2 column density is generally higher than that of GOSAT by 1.33×10^6(annual mean point by point biases averaged over East Asia).The model reasonably captures the temporal variations of CO2 concentrations observed at the ground-based stations,but it is likely to underestimate the peaks-to-troughs amplitude of the seasonal cycle by 50%or more.The simulated surface CO2 concentration in East Asia exhibits the largest inter-annual variation in December-January—February(DJF).The regional mean absolute deviation(MAD)values over East Asia are within(4.4—5.0)×10^-6 for all seasons.Model sensitivity simulations indicate that the inter-annual variations of surface CO2 concentrations are mainly driven by variations of meteorological parameters,and partly modulated by the inter-annual variations of terrestrial fluxes and fossil fuel emissions in local regions.The variations of the terrestrial fluxes and fossil fuel emissions may account for〜28%of the inter-annual variation of surface CO2 concentration in southern China.The inter-annual variations of the peaks-to-troughs amplitude are dependent on variations of meteorological parameters,terrestrial fluxes and fossil fuel emissions in local regions.The influence of biomass burning emissions is relatively weak.

A Local Implementation of the POD-Based Ensemble 4DVar with R-Localization

The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.