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| 考虑环境参数不确定的闭式冷却塔随机模拟研究 |
| Study on Stochastic modeling of Closed Cooling Towers under Uncertainty of Ambient Parameters |
| 投稿时间：2018-09-10 修订日期：2018-09-10 |
| DOI： |
| 中文关键词: 闭式冷却塔 随机模拟 不确定性 采样方案 CFD模型 |
| 英文关键词: closed cooling towers stochastic simulation uncertainty sampling strategy CFD modeling |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| 中文摘要: |
| 本文利用随机模拟的方法探讨了地理位置和季节性环境参数的变化对闭式冷却塔热效率的影响规律。首先，通过试验设计对不确定性随机变量进行量化和表征以便确定的合理的采样方案。以北京和广州为例，我们统计了当地各季有代表性的环境温度和相对湿度数据，结合核密度估计和皮尔逊积矩相关系数法来计算原始数据的累积概率分布函数以及相关系数矩阵，再利用哈默斯利序列将随机变量离散化以确定样本方案。接下来，将生成的样本方案传递到闭式冷却塔的过程模型中，使用COMSOL软件实现对其热质传递过程的严格模拟，并得到采样空间内的热效率概率分布模型。随机模拟结果显示，使用现有确定性设计方法有较大概率导致闭式冷却塔处于设计不足的状态，不同城市间环境参数不确定性的差异会对闭式冷却塔热效率概率分布有较大影响，这表明需要对闭式冷却塔进行详细地柔性设计。 |
| 英文摘要: |
| This article systematically investigates the implying impacts of plant locations and season-related environmental parameters on the thermal efficiency of a closed cooling tower using stochastic modeling. The first step is experimental design in which the uncertain random variables are characterized and quantified so that we can determine an appropriate sampling strategy. Taking Beijing and Guangzhou as examples, the representative statistics of ambient temperature and relative humidity in each season are collected and processed to determine their cumulative probability functions and correlation coefficient matrix by combining Kernel density estimate with Pearson correlation coefficient methods. Hammersley sequence sampling is used to discretize the random values and sample from these probability distributions that generates a set of scenarios. Thereafter, the sampled scenarios can be passed through the rigorous three-dimensional modeling of the closed cooling tower with COMSOL software so that we can simulate the heat-mass transfer process and obtain the uncertain distributions of thermal efficiency in the input space. The stochastic modeling results show that the existing deterministic design methods for closed cooling tower have a relatively high probability of being under designed. The differences in environmental parameters between cities have a great influence on the probability distribution of thermal efficiency, which highlights the necessities of flexible design for the closed cooling towers. |
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