基于改进蚁群算法的混凝土坝热学参数反演

鉴于混凝土坝温度场仿真演算中通过试验得到的温度参数与实际参数相差较大,且传统温度场反分析算法计算量很大,计算效率低下,引进人工蚁群算法(ACA)并将其优化,利用优化后的算法对混凝土坝热学参数进行反分析,基于反演参数,用有限元方法对其温度场进行正演算,对比所得结果与监测数据真实值的差异,发现两者拟合程度较高,说明改进的ACA算法在大坝混凝土热力学参数反分析中具有较好的适应性。     

基于小波神经网络的混凝土热力学参数反演

在混凝土温度场计算中,热力学参数一般由室内试验或经验公式获得,往往与现场实际参数出入较大。为此通过测量埋设于混凝土中不同温度测点的温度,采用粒子群算法优化初始权值和阈值的小波神经网络对混凝土的热力学参数进行反演,以控温防裂。通过对某水闸工程底板混凝土的热学参数的反演分析,验证了小波神经网络反演计算的可行性。     

基于改进鲸鱼优化算法的大体积混凝土热学参数反演

为提高混凝土热学参数反演分析时的计算效率和精度,引入Logistic映射和惯性权重对鲸鱼优化算法进行改进,并结合有限元仿真计算,提出一种基于改进鲸鱼优化算法的混凝土热学参数反演分析方法。结合上海ZJH泵闸工程开展现场非绝热温升试验,采用该方法对大体积混凝土试块的导温系数和表面散热系数进行了反演分析,并与粒子群算法反演结果进行比较。结果表明,改进鲸鱼优化算法在计算精度及迭代效率方面均较粒子群算法有明显提高,该算法在求解混凝土热学参数反演问题方面具有可行性和有效性,能够满足工程精度要求。     

改进人工蜂群算法及其在反演分析中的应用

将人工蜂群算法用于混凝土坝参数反演,为提高算法的搜索效率、减少搜索过程中的停滞现象,引入了文化算法中的信仰空间和模拟退火的可控性概率突跳特性,提出一种具有知识引导功能的文化退火人工蜂群算法,采用上层知识进化空间和下层群体进化空间两个进化空间,在知识空间的引导下重新设计搜索算子并按一定的概率进行模拟退火搜索.算例分析表明,改进后算法的收敛性能优于原算法,为解决类似系统的优化和参数识别提供了一种新方法.     

考虑水管冷却及表面保温的闸墩施工期温度应力场仿真分析

鉴于施工期的混凝土内外温差及变形约束是闸墩产生温度裂缝的主要原因,基于温度应力场和水管冷却算法等基本理论,通过与施工现场的温度监测数据的对比分析,反演混凝土热学计算参数,利用三维有限元计算软件进行闸墩施工期考虑水管冷却的温度场及应力场仿真计算。结果表明,水管冷却温控效果良好,但混凝土表面因降温幅度较大而产生早期较大的拉应力,应进一步做好表面保温措施,降低混凝土内外温差。     

基于烟花算法的重力坝坝基模量在线反演分析

针对混凝土坝材料力学参数反演过程中存在寻优性能不高、反演效率低的问题,提出一种基于烟花算法的重力坝坝基模量反演分析的方法,并利用Python语言编制基于烟花算法的在线反演分析系统,将测点的有限元计算的位移值与实测值的残差加权平方和作为目标函数,利用爆炸火花、高斯变异火花和精英选择策略等建立烟花算法模型,在可行域内搜索最优反演参数,再应用Python轻量级框架Flask进行坝基模量反演分析模块开发,最后应用开源可视化库Echarts在Web端展示有限元分析结果信息。实际应用结果表明,烟花算法寻优能力很强、算法收敛速度快,测点计算位移值与监测值吻合较好,说明该算法在混凝土重力坝坝基模量中具有一定的实际应用价值。     

基于遗传算法的泵站混凝土热学参数反演分析

根据施工现场实测资料，在温度场三维有限元仿真计算的基础上，运用遗传算法对泵站混凝土的热学参数进行反演分析。反演结果表明，计算值与实测值较接近，误差较小；反演结果可靠，且反演收敛速度快．效率高。     

通水冷却对碾压混凝土消力池温度应力的影响

针对碾压混凝土施工普遍存在的温度裂缝问题,以某水利枢纽工程碾压混凝土消力池为例,采用三维有限元浮动网格法对其全过程温度场和应力场进行仿真研究,计算过程中考虑了冷却水管间距、通水水温、通水时间、混凝土水化热温升及弹性模量等对消力池温度场和应力场的影响,对比分析了不同方案的温度及应力变化规律。结果表明,施工期对高温季节浇筑混凝土埋设冷却水管进行通水冷却,可将最高温度降低4～6℃,最大温度应力降低0.38～0.47MPa,通水冷却效果明显;在不改变通水时间和通水水温的条件下,冷却水管水平间距减小0.5m,可将基础混凝土最高温度降低0.6℃左右,最大温度应力降低0.11～0.13 MPa;在不改变通水时间和冷却水管间距的条件下,混凝土大层浇筑完毕通3d10℃的制冷水、7d14℃的制冷水和20d的河水相比单一的通30d河水,可将基础弱约束区混凝土最高温度降低1.5℃左右,最大温度应力降低0.3MPa左右。     

混凝土坝参数的随机反分析方法探讨

针对混凝土坝施工与运行过程中存在诸多的不确定性因素,基于随机理论和结构计算成果的分析,提出混凝土坝材料参数的随机反分析方法,研制开发了相应的优化计算分析程序。实例表明,随机反分析方法能较好地应用于混凝土坝材料参数的反演和识别中。     

基于仿真分析的混凝土坝热学参数反演方法

根据景洪电站右冲坝段施工现场温度实测资料,以温度场仿真计算为基础,建立了温度场反演计算模型,采用复合形法反演实际浇筑的碾压混凝土材料热学参数,提出了选择实测温度监测点的参与反演分析的依据,并分析了反演结果的合理性。研究结果验证了该方法可行有效,并可为大坝后期施工提供参考。     

Numerical Procedure for Heat Transfer Coefficient Identification in Solidification of Binary Alloys and Its Experimental Verification

In this article the inverse problem consisting of determination of the heat transfer coefficient in the process of binary alloy solidification is considered. Additional information on the inverse problem is delivered by measurements of temperature at selected points of the cast. In the model discussed, the distribution of temperature is described by means of the heat conduction equation with the substitute thermal capacity, with the liquidus and solidus temperatures varying in dependence on the concentration of the alloy component. For describing the concentration, the Scheil model is used. For solving the direct problem, the finite-difference method supplemented by the generalized alternating phase truncation method is applied. For minimization of the properly constructed functional, the artificial bee colony algorithm is employed. Additionally, the article contains the results of a numerical experiment as well as the results of an experimental verification illustrating the usefulness of the proposed procedure.     

Metaheuristic-based energy management strategies for fuel cell emergency power unit in electrical aircraft

This paper aims at presenting a comparative analysis of different metaheuristic algorithms in the application of energy management for fuel cell-based hybrid emergency power unit within electrical aircraft. Two energy management conventional strategies are employed while optimizing the operating temperature. Both the external energy maximization and the equivalent consumption minimization strategies are dealt with. The most efficient up-to-date metaheuristic techniques such as the artificial bee colony, the grey wolf optimization, the cuckoo search, the mine blast algorithm, the whale optimization algorithm, the moth swarm algorithm, the harmony search, the modified flower pollination algorithm and the electromagnetic field optimization are considered. The overall index of optimization performance is considered as a function of hydrogen consumption, overall system efficiency, variations of states of charge and stresses in different energy sources. The numerical simulations, through Matlab™/Simulink, highlights the capability of the different metaheuristic optimization techniques towards reducing the amount of consumed hydrogen in fuel cell-based emergency power unit in electrical aircrafts. The electromagnetic field optimization method results in significant hydrogen consumption reduction in comparison with the other proposed techniques.     

自适应人工蜂群算法在梯级水库优化调度中的应用

针对人工蜂群算法全局搜索能力强、局部搜索能力弱的缺点,提出了自适应人工蜂群算法,即先在搜索策略中引入自适应全局最优学习,以增强算法局部搜索能力;其次,个体使用改进策略进行全维搜索产生进化体,通过自适应交叉概率因子,将进化体与原个体进行交叉构建候选个体,以平衡算法搜索能力。在经典基准测试函数的仿真试验表明,与一些最新的改进人工蜂群算法相比,所提算法具有较大优势;在清江梯级水库优化调度应用中的测试,也证明了所提算法具有更好的适用性。     

共轭梯度法求解竖直环隙间多孔介质的传热反问题

提出一种反演多孔介质热物性参数的一般数值模式。采用有限体积法对竖直环隙间多孔介质模型进行离散化,通过SIMPLE算法获得竖直环隙间多孔介质传热系统的温度场和流体的速度场。通过构建误差信息最小化函数,建立反演模型。利用共轭梯度法对单宗量和多宗量的多孔介质热物性参数进行了反演,并讨论了待反演参数的初始猜测值、温度测点数、温度测量误差等对反演结果的影响。     

Stresses in radiative annular fin under thermal loading and its inverse modeling using sine cosine algorithm (SCA)

This work presents a novel mathematical model for the analysis of thermal stresses in a radiative annular fin with temperature-dependent thermal conductivity and radiative parameter. An approximate analytical solution for thermal stresses is derived using a homotopy perturbation method (HPM)-based closed-form solution of steady-state nonlinear heat transfer equation, coupled with classical elasticity theory. The effect of thermal parameters on the temperature field and the thermal stress fields are discussed. The various thermal parameters, such as a parameter describing the temperature-dependent thermal conductivity, coefficient of thermal expansion, coefficient of radiative parameter, and the variable radiative parameter, are inversely estimated for a given stress field. For inverse modeling, a population-based sine cosine algorithm (SCA) was employed to estimate the thermal parameters. The inverse modeling is verified by using the estimated thermal parameters in the closed-form solution of stress field. The reconstructed stress fields obtained from the inversely estimated parameters are then compared with the reference stress field. Results show a very good agreement between the reference stress field and the inversely estimated stress fields.