车载空调器设计平台研究与开发

针对车载空调器设计手工计算繁琐、设计周期长等诸多不便利特点,研制开发了基于VB和SOLIDEDGE软件的车载空调器设计平台。该平台不仅具有车载空调器整机开发、机组设备单独设计或选型以及换热器参数化设计的功能,还兼容了家用空调器设计开发的能力。平台运行平稳,结果直观可靠。     

某小型空调器的设计思考

介绍了某小型空调器的设计思路,主要从结构的紧凑性、制冷系统的匹配、高空工作的适应性、加热器的确定及可靠性等方面给予了阐述,同时与国外同类型空调器进行了对比,通过实际应用说明了其设计的可行性。     

空调器换热器设计影响因素分析

换热器是空调器的主要部件,换热器的设计好坏直接反映空调器的性能,也决定了空调器的设计成本。文中对影响换热器换热效率的各种因素进行分析,选择最佳的换热器设计方案。     

变频压缩机空调系统的理论分析及实验研究

变频空调器是由变频压缩机驱动的空调系统,压缩机通过变频调节其转速使压缩机单位时间内的排气量变化,从而达到调节制冷量的目的。本文提出了变频空调器制冷系统的原理和设计方法,具体给出了制冷量和压缩机频率的关系方程,并从理论和实验两方面对变频空调器制冷系统进行了分析,得到了频率曲线图,为变频空调器制冷系统的设计提供了依据。     

空调制冷/制热时室内气流及温度分布数值研究

对于挂壁式空调器在制冷/制热工况运行时的室内气流及温度分布进行了数值模拟分析,采用k-ε紊流模型和非稳态求解方法,分别获得了在不同时间以及在不同的导风板旋转角度和出口风速情况下的室内气流及温度分布,并对原因进行了合理的分析,为合理控制温度和空调器的优化工作设计提供了理论基础。     

可燃制冷剂空调器性能测试实验室的研制

依据国家标准GB/T7725-2004《房间空气调节器》、GB/50016-2006《建筑设计防火规范》和上海出入境检验检疫局技术协议要求,设计并建造一套可燃制冷剂空调器性能测试实验室。目前,国内主要采用空气焓值法对空调器进行其性能的测试。为了保护臭氧层及降低温室效应,现代制冷工业以逐渐开始用环境友好型碳氢化合物制冷剂取代传统制冷剂,并已在家用空调器中逐渐开始使用。     

基于PID控制的空调器性能测试装置研制

介绍了PID控制技术在空调器性能测试室内温湿度控制中的应用,设计了一个空调器性能测试系统,并基于VB编程技术,在Win2000/WinXP操作系统上,开发了系统数据采集,显示数据处理功能的测控系统。试验证明,该系统稳定、可靠,能满足国家标准的规定。     

带冷凝热回收的家用空调器的研究

随着生活水平的提高,家用空调器的使用逐年增加.空调器在夏季向室内供冷的同时,不仅给室外环境造成了热污染,而且还造成了能源的浪费.因此,很多学者都提出了空调冷凝热回收理论并进行了实验研究.文中分析了在家用空调器回收冷凝热的可行性及研究现状;介绍了家用空调器采用的冷凝热回收系统的形式及其分类;提出了一种带冷凝热回收装置的多...     

变频空调器制冷剂最佳充灌量试验研究

建立了变频空调器制冷剂最佳充灌量的数学模型,并通过实验验证了模型的可靠型。分析了在不同频率下,制冷剂充灌量对变频空调器制冷量、功率、EER、过热度、压缩机排气温度、吸气温度的影响及原因。提出了变频空调器制冷系统最佳充灌量的确定原则,以高频时蒸发器出口刚好达到饱和状态,此时蒸发器的过热度为零。     

小型家用空调器除霜方式的探讨

提高低温制热量是小型家用空调器技术的一个难点,除霜带来的热量损失是低温制热量损失的一个重要方面。对小型家用空调器的除霜技术的发展进行了分析和总结,对小型家用空调器提出了一些新的除霜方式,对提高空调器的低温制热量、使用性能有重要意义。     

基于离散伴随方法的透平叶栅气动优化设计

本文研究并给出了基于离散伴随理论和自动微分技术构建离散伴随系统的方法、伴随系统的求解策略以及基于离散伴随方法的透平叶栅气动优化设计流程,建立了相应的优化设计系统。利用该优化系统在无黏环境下,以叶栅通道进出口的熵增率为目标函数、以叶栅通道内的质量流量为约束,对某二维跨音速透平叶栅进行了气动优化设计。与优化前相比,优化后透平叶栅进出口熵增率减少8.82%,质量流量变化幅度小于0.003%。优化结果表明,本文提出的优化系统能够有效改善透平叶栅的气动优化性能,验证了本文提出的基于离散伴随方法的透平叶栅气动优化设计方法的正确性与有效性。     

基于蚁群算法的二元光学优化设计

为了提高二元光学优化的速度,进一步提高衍射效率,提出一种采用蚁群算法直接设计二元衍射光学元件(BOE) 的方法, 构造出了用蚁群算法对具体的二元光学元件——矩形孔径多阶相位调制型光栅优化设计的有向图, 用Matlab对其结果进行仿真,缩短了运行时间,并使元件的衍射效率得到进一步提高。     

蜂窝型C/SiC椭圆反射镜镜坯的优化设计

进行了蜂窝型C/SiC椭圆反射镜镜坯的最小质量优化.首先确定了优化参量及相应的变化范围.其次进行了双设计参量的零阶近似优化和一阶优化.结果表明,零阶近似优化优于一阶优化且更适合于反射镜最小质量的优化.最后进行了不同初始设计参量值下的零阶近似优化,结果表明,零阶近似优化得到的解接近最优设计,虽然初始设计参量值对最优结果的影响较小,但小的初始M值和适中的初始TP值更倾向于得到较优结果.优化的机理可能是较大的设计参量M的单位变形变化造成的质量变化率.     

A numerical optimization on the vibroacoustics of a centrifugal fan volute

A numerical optimization is presented to reduce the vibration and noise of a centrifugal fan volute. Minimal vibration was considered as the aim of the optimization, and the calculation of sound field induced by the vibration of the volute was only based on the final results of the optimization. After the three-dimensional unsteady flow simulation of a centrifugal fan, the parametric finite element model of the volute was created using the pressure fluctuations at blade passing frequency on the volute as external excitation forces. To validate the finite element model of the volute, natural frequencies and amplitudes of the normal velocities of the volute at blade passing frequency were measured. A good agreement was found between the numerical and the experimental results. Then, random method and first-order optimization method were applied in the optimization process. The numerical optimization of the volute was carried out using the local thickness of the volute as design variables and the quadratic sum of the nodal velocities as an objective function. Numerical optimization results show that the volute vibration is reduced by the optimization method. Finally, vibroacoustics of the volute before and after the optimization were calculated by direct boundary element method. The results show that the radiated power of the vibroacoustics of the volute is reduced significantly as well as the vibration of the volute after the optimization.This numerical optimization process provides a useful reference for vibroacoustic reduction of centrifugal compressors and centrifugal fans whose fluids should be kept strictly in a system without leakage.     

Ant colony optimization: Introduction and recent trends

Ant colony optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we outline ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowadays best-performing ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how ant colony optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent research direction: The hybridization with more classical techniques from artificial intelligence and operations research.     

Determination of calcium and magnesium in the Solanaceae plant by near infrared spectroscopy combined with interval combination optimization algorithm

Determination of the content of inorganic nutrients was done as part of a project to improve crop yield by use of fertilizers. Near-infrared spectroscopy coupled with interval combination optimization was used to determine the inorganic nutrients of calcium and magnesium in the Solanaceae plant. Interval combination optimization was employed to choose effective intervals with the merit of assigning smaller weights to less significant variables. Compared with full spectrum partial least squares regression, interval combination optimization improved the predictive accuracy and precision for both calcium and magnesium. The result was a high predictive ability for calcium, with a root mean squared error prediction of 0.159%, and for magnesium, with a root mean squared error prediction of 0.064%. In this study, the superiority of interval combination optimization over full spectrum partial least squares was shown in the aspects of effective variables and accurate results. Near-infrared spectroscopy combined with interval combination optimization was efficient for the determination of inorganic nutrients, calcium and magnesium, in the Solanaceae plant.     

基于感知模糊自适应蚁群算法的非线性PID控制

随着系统复杂度的提高和对象不确定性因素的增加,为克服线性PID动态性能和稳态性能差的缺陷,分析了非线性PID控制器各控制参数对误差的理想变化过程,构造非线性PID控制器。由于增益参数大量增加,传统参数优化方法不再适用,在分析蚁群算法的基础上,提出了基于感知自适应蚁群算法,并加入模糊自适应信息素更新机制,用于优化非线性PID控制器的设计方法。通过仿真实验将该控制器与基于蚁群算法的非线性PID控制器和基于蚁群算法、Z-N法的PID控制器进行对比,并对控制性能和收敛性能进行了分析,结果表明该算法有效克服了传统蚁群算法收敛速度较慢、容易陷入局部最优而停滞的缺陷,该控制器具有更好的动态性能和稳态性能。     

Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimization

A computational fluid dynamics (CFD) code was connected with a systematic optimization algorithm to find the optimum settings for nitric oxide (NO) emission minimization in the bubbling fluidized bed boiler. This approach is superior to the typical approach in which CFD is used to calculate only a few intuitively selected cases, and the best case found is treated as the ‘optimum’ solution. Ammonia injection (selective non-catalytic reduction) is applied to control NO emission in the modelled fluidized bed boiler. Design variables consisted of ammonia mass flow from distinct injections. Genetic algorithm (GA) was used for the optimization. The predicted NO emission was reduced approximately 15% with respect to the current operating point by the application of GA and CFD. The use of CFD connected with systematic optimization algorithm seems to be a promising design tool for combustion process optimization.     

基于Kriging代理模型的铅铋反应堆智能优化方法北大核心CSCD

铅铋反应堆广泛应用的需求要求研究人员在现有堆芯方案的基础上开展大量优化设计工作。针对铅铋反应堆多物理、多变量、多约束耦合影响的多维非线性约束优化设计问题,基于Kriging代理模型、正交拉丁超立方抽样和SEUMRE空间搜索技术构建铅铋反应堆智能优化方法,耦合物理蒙卡计算/热工分析程序,开发包含抽样、耦合程序前后处理、反应堆优化分析功能的优化平台,并以铅铋反应堆SPALLER-4,URANUS为原型分别开展最小燃料装载量的方案寻优与参数优化验证。验证结果表明,该智能优化方法用于铅铋反应堆设计方案寻优和堆芯参数优化可行、有效,相比传统蒙卡程序计算寻优,在保证预测精度前提下极大地降低了计算成本,与URANUS初始模型比较,燃料装载量、堆芯总质量、活性区体积、堆芯总体积分别优化10.8%,11.5%,18.1%,17.1%,为基于代理模型的智能优化方法应用于铅铋反应堆的优化设计提供参考。     

The study of reactive silencers by shape and parametric optimization techniques

The aim of this work is to show the application of shape and parametric optimization techniques in the study of reactive silencers with extended inlet and outlet ducts. Parametric optimization is employed to evaluate the appropriate size of the inlet and outlet ducts. Shape optimization is employed to establish the proper profile of these ducts in order to improve the acoustic features of these mufflers in a specific frequency range. The objective function used in the optimization processes is defined through the average transmission loss (TL) for the desired frequency range. This type of objective function is strongly non-linear and the genetic algorithm, GA, was chosen as a mathematical method for determining the maximum of this function. The Finite Element Method with an axisymmetric formulation along with the modified four-parameter method are used to calculate the TL(ω). The Hermite polynomials were used in the shape optimization in order to obtain local boundary approximations with C¹ continuity. The results showed the optimization efficiency of the inlet ducts profile for acting in specific frequency ranges with gains up to 20 dB with respect to silencers without shape optimization. The numerical analyses agree well with experimental results.