整流支板和火焰稳定器的一体化设计及性能分析

针对航空发动机高推质比、高隐身的需求,提出一种加力燃烧室整流支板和火焰稳定器一体化设计的方法,并开展了相关模型加力燃烧室的计算和试验研究.研究结果表明:采用一体化设计加力燃烧室,能够缩短加力燃烧室长度,大幅降低非加力状态的冷态流阻,明显改善发动机加力燃烧室的性能,提高发动机的推质比.可为解决高推质比、高隐身发动机技术提供了一种新的思路和研究方向.      

二维过渡区喷流干扰流场的DSMC/EPS仿真

应用DSMC／EPS混合算法，数值模拟过渡区有RCS横向喷流干扰的二维有限平板高超声速流场，分析了流场结构、分离形态及物面气动系数分布等特性。结果表明，不同：Knudsen数下，喷流干扰流场存在差异，过渡区中的稀薄气体效应得以体现，证明了DSMC／EPS混合算法是研究稀薄，连续混合区域高超声速流动的有效方法。     

钛合金热成形和超塑成形金属平台材料选型和制备

介绍了钛合金热成形和超塑成形用金属平台的材料选型和制备工艺研究情况,研究认为,选择高温合金替代目前国外采用的耐热钢材料优势非常明显;钢铁研究总院自主开发的热成形金属平台制备工艺比国外相关技术更加先进,制备的K403高温合金热成形平台服役表现良好,具备了生产钦合金热成形和超塑成形金属平台的能力.     

金属薄板超声无损检测

介绍了金属薄板的缺陷类型，建立了超声喷水穿透C扫描系统和检测方法。检测试验结果表明：采用建立的系统和方法能够检测出厚度≤6mm金属薄板中的分层性缺陷。     

飞机复杂系统故障诊断的灰色粗集推理方法

传统的复杂系统故障诊断规则不易获取且方法单一,不能满足系统维护要求.文中全面考虑复杂系统诊断的数据来源,用灰色关联理论降低系统复杂性并通过粗糙集约简数据的思想实现灰色粗集推理.基于灰色粗集推理方法实现了故障诊断规则的获取,并通过实例验证方法的可行性,结果明显优于神经网络算法,可有效提高诊断效率.     

New Cutting Force Modeling Approach for Flat End Mill

A new mechanistic cutting force model for flat end milling using the instantaneous cutting force coefficients is proposed. An in-depth analysis shows that the total cutting forces can be separated into two terms: a nominal component independent of the runout and a perturbation component induced by the runout. The instantaneous value of the nominal component is used to calibrate the cutting force coefficients. With the help of the perturbation component and the cutting force coefficients obtained above, the cutter runout is identified. Based on simulation and experimental results, the validity of the identification approach is demonstrated. The advantage of the proposed method lies in that the calibration performed with data of one cutting test under a specific regime can be applied for a great range of cutting conditions.     

污水处理装置在冶金行业中的应用

对污水处理装置的技术参数、技术要求及设计方案的选择、工作原理做了进一步的阐述。对工业循环冷却水的处理有实际指导意义。     

低温泵常见故障分析及其解决措施

文章对低温泵运行过程中所发生的冷头及氦压缩机典型故障进行了原因分析,并提出了解决措施。最后提出了使低温泵可靠稳定运行的建议。     

LEVEL DECISION-MAKING FOR AERO-ENGINE MODULES BASED ON INCREMENTAL KNOWLEDGE LEARNING

The maintenance of an aero-engine usually includes three levels, and the maintenance cost and period greatly differ depending on the different maintenance levels. To plan a reasonable maintenance budget program, airlines would like to predict the maintenance level of aero-engine before repairing in terms of performance parame- ters, which can provide more economic benefits. The maintenance level decision rules are mined using the histori- cal maintenance data of a civil aero-engine based on the rough set theory, and a variety of possible models of upda- ting rules produced by newly increased maintenance cases added to the historical maintenance case database are in- vestigated by the means of incremental machine learning. The continuously updated rules can provide reasonable guidance suggestions for engineers and decision support for planning a maintenance budget program before repai- ring. The results of an example show that the decision rules become more typical and robust, and they are more accurate to predict the maintenance level of an aero-engine module as the maintenance data increase, which illus- trates the feasibility of the represented method.