在温排水等涉及热交换的环境水力学研究中，湍流普朗特（Prandtl, 简称 Pr）数是控制温度的主要参数。对于一个特定的问题，传统湍流 Pr数的确定方法主要采用经验法或试错法，因而具有一定盲目性和低效性。为了提高湍流Pr数确定的可靠性，采用马尔科夫链蒙特卡罗（Markov Chain Monte Carlo, 简称 MCMC）随机抽样的方法（Metropolis-Hastings 算法）来对湍流Pr数进行识别，其中湍流场计算采用了稳态标准 k-ε模型，温度场计算采用非稳态热传导方程。算例计算结果表明，MCMC方法对湍流Pr数的识别具有良好的适用性和较高的识别精度。

Turbulent Prandtl number (Pr) is a key parameter for controlling the temperature distribution in the research of thermal discharge water and other environmental hydraulics involving heat transfer. For a given problem, turbulent  Pr  number generally comes from previous experiences or trial-and-error method, which is blindfold and inefficient. To increase the reliability of turbulent  Pr  number for a given problem, Metropolis-Hastings algorithm, a Markov Chain Monte Carlo (MCMC) random sampling method was employed in this paper to identify turbulent Pr number. In the numerical simulation, steady standard k- ε model was used for turbulence flow field computation, while unsteady heat transfer equation was adopted for computing the temperature field. The computation results manifested that MCMC method is suitable and can offer precise results for the identification of turbulent Pr number.