真空断路器弹簧操动机构合闸凸轮优化设计

针对在设计真空断路器弹簧操动机构凸轮轮廓线的过程中,依靠经验或者反复试验的方法设计出的凸轮轮廓线存在无法准确匹配负载特性的缺陷,采用一种负反馈优化设计方法。首先,根据负载特性推导出动力特性;其次,通过动力特性倒推出凸轮转角,进而计算出凸轮轮廓线;然后,根据凸轮呈现的合闸动力特性再次验证并修正负载特性,保证分合闸速度;最终,通过设计验证证明了使用该方法设计的凸轮能够使得动力特性准确匹配负载特性,并保证断路器可靠合闸。

一种反馈式组合机构的运动分析及其仿真

首先分析了螺旋凸轮反馈式组合机构的工作原理,并推导得出该组合机构的位移函数、速度函数及加速度函数.探讨了求解这些函数的数值方法,分析指出,该反馈式组合机构的输出特性与螺旋机构的旋向、导程及凸轮廓线的形状等因素有关.同时,基于前述数学模型,开发了该机构运动学仿真应用程序.最后,通过算例对前述方法予以验证.

一种SMA驱动的新型仿生手

采用独立化设计方法,将仿生手分为控制模块、手指模块、散热模块并进行单独设计,制作了集成化的仿生手,可以快捷地实现模块化手指的分离与组装。采用形状记忆合金丝作为驱动器,设计了可以实现驱动位移放大的凸轮结构,使S M A在驱动过程中的有效输出位移得到有效放大。在Matlab中对仿生手进行了运动空间仿真,并通过3 D打印制作了总质量为410 g的仿生手样机。采用温度反馈方式,提高了仿生手在不同温度环境中的响应能力以及对SMA的温度保护,对日常生活物品进行了抓取实验验证。结果表明,新型仿生手具有较大的抓取尺寸和抓取重量,具有拆装方便、控制简单、抓取稳定的优点,实现了控制与驱动的高度集成。

力反馈凸轮廓线的理论设计方法

力反馈凸轮机构一般与操纵杆连接,为操作员提供操纵感觉力.力反馈凸轮机构要求凸轮轴在给定的旋转角度下输出给定的反作用力矩,凸轮轴既作为输入机构也作为输出机构.力反馈凸轮机构从动件的运动规律不容易确定,因此难以用传统方法设计凸轮廓线.为此,利用弹簧力做功和反馈力矩做功相等的原则,计算出凸轮任意旋转角度时从动件啮合点的模和幅角,给出了力反馈凸轮廓线的理论设计方法,并推导出了从动摆杆小角度摆动条件下的简易设计公式.

高精度高速线性液压机械式转速传感器设计与分析

提出一种现代航空发动机上高精度高速线性液压机械离心式转速传感器反设计的方法。通过坐标平面投影方法设计反馈凸轮型面,将离心力引起的非线性弹簧压缩位移转换为线性位移。对双摆活门式液压放大器进行建模,分析其动态特性。并通过实际算例验证该设计方法的正确性。

曲线加工中误差问题的研究

通过对数控机床在加工凸轮曲线的过程中存在较大轮廓误差问题的研究,总结出影响凸轮轮廓加工时产生误差的因素,利用数控机床闭环伺服系统可对机床本身的刚性、间隙、螺距误差等造成加工误差的因素予以适当的补偿,有效地减小了加工误差值.

数控锻铜浮雕加工关键技术的研究

针对手工锻铜存在的缺陷和不足,提出了一种数控锻铜浮雕加工方法。采用Art CAM建立浮雕模型并保存模型的三维数据。同时根据二维图像,生成数控铣刀的走刀路线,在铜板上錾出图像的几何轮廓。结合浮雕模型数据,利用设计的机械锻打设备,按照由外轮廓到铜板中心的锻打方向锻敲铜板。锻打过程中,通过高速高精度激光测距传感器实现闭环反馈回路控制机械锻打设备的锻打深度。实验结果表明:该方法有效降低了人工劳动强度,很大程度上缩短了铜浮雕的加工时间。

Cloud Radiative Feedbacks during the ENSO Cycle Simulated by CAMS-CSM

This study evaluated the simulated cloud radiative feedbacks(CRF)during the El Ni?o–Southern Oscillation(ENSO)cycle in the latest version of the Chinese Academy of Meteorological Sciences climate system model(CAMS-CSM).We conducted two experimental model simulations:the Atmospheric Model Intercomparison Project(AMIP),forced by the observed sea surface temperature(SST);and the preindustrial control(PIcontrol),a coupled run without flux correction.We found that both the experiments generally reproduced the observed features of the shortwave and longwave cloud radiative forcing(SWCRF and LWCRF)feedbacks.The AMIP run exhibited better simulation performance in the magnitude and spatial distribution than the PIcontrol run.Furthermore,the simulation biases in SWCRF and LWCRF feedbacks were linked to the biases in the representation of the corresponding total cloud cover and precipitation feedbacks.It is interesting to further find that the simulation bias originating in the atmospheric component was amplified in the PIcontrol run,indicating that the coupling aggravated the simulation bias.Since the PIcontrol run exhibited an apparent mean SST cold bias over the cold tongue,the precipitation response to the SST anomaly(SSTA)changes during the ENSO cycle occurred towards the relatively warmer western equatorial Pacific.Thus,the corresponding cloud cover and CRF shifted westward and showed a weaker magnitude in the PIcontrol run versus observational data.In contrast,the AMIP run was forced by the observational SST,hence representing a more realistic CRF.Our results demonstrate the challenges of simulating CRF in coupled models.This study also underscores the necessity of realistically representing the climatological mean state when simulating CRF during the ENSO cycle.

Climate Sensitivity and Feedbacks of a New Coupled Model CAMS-CSM to Idealized CO_2 Forcing: A Comparison with CMIP5 Models

Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.