超高强度钢300M含碳量,等温组织与力学行为的关系

研究了四种含碳量、三种等温温度与力学行为的关系，并与淬火回火３００Ｍ钢进行比较得出，３００Ｍ钢在Ｍｓ点以下温度等温获得Ｍ＋Ｂ＿下＋Ａ＿Ｒ混合组织、超高强度和优良的韧性。断裂韧性对含碳量不敏感，冲击韧性随含碳量升高而降低。较高温度等温时，钢的冲击韧性和断裂韧性显著提高且对含碳量不敏感。     

计算机模拟高温下温度波动对简化环氧分子结构参数的影响

对４００Ｋ下简化环氧分子结构的键长、键角、四原子扭转角及非键合原子间距离等结构参数在微小温度波动时进行动态模拟，分析了这些结构参数在该条件下的变形刚性。     

模具约束条件对H96黄铜双脊矩形管E弯截面变形的影响

 双脊矩形管的绕弯成形受内外侧模具的共同约束,不同模具约束下管坯的受力不同,使得其截面变形情况也不相同,而截面变形严重地影响弯管件的成形质量和使用性能。因此,基于ABAQUS有限元平台建立了双脊矩形管E弯成形三维有限元模型,并通过实验验证了模型的可靠性。采用所建模型,研究了内外侧模具约束条件对双脊矩形管E弯截面变形的影响规律,发现当只有内腹板脊槽受约束时,内腹板脊槽的内缩变形可得到较好的控制,而其他部位的变形则有增大的趋势;当只有外腹板脊槽受约束时,内腹板脊槽宽度变形基本不发生变化,而其他部位的变形则有减小的趋势;当内外腹板脊槽均受约束时,可较好地控制双脊矩形管E弯过程中的截面变形。芯头个数对整管截面高度、宽度、外腹板脊槽宽度与两脊槽底部的间距的变形影响较大,但对内腹板脊槽宽度的变形影响不显著。     

航空发动机燃油计量装置特性仿真与试验研究

燃油计量装置作为航空发动机燃油控制系统的执行机构对航空发动机的性能有直接影响,全面掌握燃油计量装置特性是非常重要的。基于AMESim软件建立了航空发动机燃油计量装置的热液压模型,分析了燃油计量装置特性及受温度影响的规律,并通过试验验证了仿真结果。结果表明:建立的模型具有较高的精度,在控制器指令不变的情况下,随着油温的升高,燃油质量流量会有一定程度减小。     

RTS-20A便携式低温恒温槽

介绍了一种便携式低温恒温槽的工作原理及组成结构，并给出了试验数据。     

大型齿圈加工变形与防治措施的研究

在生产中大直径薄壁结构齿圈加工变形是经常遇到的问题。本文以某产品齿圈制齿后出现严重锥度变形问题为例，分析引起变形的原因，通过反复实验，找到了避免变形的有效措施。     

微特电机全温度自动补偿原理及参数的确定

设计了全温度自动补偿电路，提高微特电机主要性能指标的温度稳定性．它的工作原理简单；电路参数的确定采用计算机辅助计算方法——具有连续延伸和自动改变初值的加速寻优的单纯形方法，不仅快捷、准确，而且稍加修改也可适用于任何多元非线性方程组的求解     

超高强铝合金LC9的超塑性研究

工业牌号超高强铝合金LC9经过两种不同方式的预处理后,在一定的温度和应变速率范围内呈现出良好的超塑性。材料经过形变热处理(TMT)后,在最佳超塑性条件下拉伸(T_(TMT)=515℃,ε_(TMT)=1.66×10~(-3)s~(-1)),获得很高的延伸率δ_(TMT)=1300％,低的流变应力σ_(TMT)=1.7MPa和高的应变速率敏感性指数m_(TMT)=0.66。经过简单锻造预处理后,在最佳超塑条件下(T_f=405℃、ε_f=1.66×10~(-3)s~(-1)),材料仍能获得δ_(f)=380％、σ_(f)=16MPa、m_(f)=0.3。TMT预处理中,η相粒子分布状态对获取微细组织起着决定性作用,η相的回溶降低了材料的空洞敏感性,抑制试样早期断裂。经过两种方式预处理的试样超塑性断裂形式分别为空洞型失稳断裂和颈缩型稳定断裂。     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.