基于改进布谷鸟算法识别瞬态热传导问题的导热系数

基于改进布谷鸟算法反演瞬态热传导问题随温度变化的导热系数.采用Kirchhoff变换将非线性热传导问题转换为线性热传导问题，使用边界元法求解瞬态热传导正问题.将导热系数的反演转化为函数表达式中未知参数的反演，使用改进布谷鸟算法求解未知参数.与共轭梯度法相比，改进布谷鸟算法对迭代初值不敏感；与布谷鸟算法相比，改进布谷鸟算法迭代次数大大减少.数值算例表明对改进布谷鸟算法，增加测点数量迭代次数增加；增加鸟巢数量迭代次数减少；减小测量误差计算结果更精确，同时迭代次数更少.数值算例验证了改进布谷鸟算法反演导热系数的准确性和有效性.

Identification of Thermal Conductivity for Transient Heat Conduction Problems by Improved Cuckoo Search Algorithm

An improved cuckoo search (ICS) algorithm is developed to identify temperature dependent thermal conductivity for transient heat conduction problems. Kirchhoff transformation is adopted to transform nonlinear transient heat conduction problems into linear problems. The direct problems are solved by boundary element method. Inversion of thermal conductivity is transformed to estimate unknown coefficients, which is solved by ICS algorithm. ICS algorithm is less sensitive to iterative initialization than conjugate gradient method and ICS algorithm has high efficient convergence compared with cuckoo search (CS) algorithm. For ICS, numerical examples indicate that increase of measured point number causes increase of iteration numbers, whereas increase of nest number decreases iteration numbers. The less the measured noise is, the higher the precision of results is. Iteration numbers decrease at the same time. It shows that ICS algorithm is an accurate and efficient method for identification of thermal conductivity.