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基于CALPUFF模型的核电站事故源项大气扩散研究

Atmosphere Dispersion Research of Accident Source Terms in Nuclear Power Plant on CALPUFF

【作者】 张玉珍

【导师】 程旭;

【作者基本信息】 上海交通大学 , 核能科学与工程, 2010, 硕士

【摘要】 在发生不可控的放射性释放事故的情况下,放射性物质经由大气的扩散过程,可在短时间内对环境产生大范围的影响。在进行核事故的实时环境后果评估和应急决策时,对大气扩散过程放射性核素的传输、扩散的估计和预测是必须充分考虑的,是采取一系列应急措施的基础和前提。?CALPUFF是美国EPA推荐的由Sigma Research Corporation开发的空气质量扩散模式,可以进行各种尺度的大气污染研究,是当前国际上用于复杂地形下的常用大气扩散模式,并且在大气扩散领域有着广泛的应用。但是国内CALPUFF模型用于核电站正常工况或事故工况的大气扩散模拟研究并不多见,本文介绍了CALPUFF大气扩散模式工作原理和CALPUFF烟团扩散模型,并与RODOS系统中两大扩散RIMPUFF和ATSTEP做了定量的比较,分析了不同大气扩散模型间的差异,结果表明,三种模型模拟结果吻合较好,CALPUFF模型可以用于核电站的事故源项大气扩散研究。同时以大亚湾核电站为背景,对核电站事故源项进行大气扩散研究,对不同季节的特征污染物(本文选取131I)的扩散过程进行了模拟,分析其在不同季节的扩散规律,结果表明核电站发生事故后,冬季会对核电站周围产生较大的影响,而夏季影响较弱。通过对一年的气象数据进行分类,在极限条件下进行扩散模拟,分析预测在极限平均条件下的放射性核素的分布情况。?随着城镇化进程的加快,核电站周围会发生大规模的土地覆被变化,改变了这些地区地表环境的能量平衡,造成大气流场特征和湍流?特征的改变,使得区域内污染物的扩散、转化和积累等规律也发生变化。并且随着内陆核电站的建立,对内陆下垫面状况下的大气扩散研究也具有重要意义,对核电站选址和事故的环境评价均有重要理论价值。因此,本文分别在城镇用地、农业用地和水域三种单一下垫面条件下,应用CALPUFF污染扩散模式,模拟预测土地覆被变化对特征污染物分布、扩散过程的影响,结果表明,下垫面的影响受气象因素影响严重,规律复杂。??

【Abstract】 When uncontrollable radioactivity accidents take place, radioactive materials can make a large-scale impact by way of atmospheric dispersion at short notice. So in progress of real time environment consequence estimate and making emergency decisions, the transport、diffusion estimate and forecast of radioactivity nuclides must be considerate during atmospheric dispersion process, all these are foundation and premise of a series of emergency measures. But the abruptness of accident conditions、timeliness of accident emergencies and the complexity of local actual geography and atmosphere conditions present various kinds of especial requests for practical atmospheric dispersion model. CALPUFF is an air quality dispersion model which is recommended by EPA of USA and developed by Sigma Research Corporation. It can be used to research atmospheric dispersion of different scales. It is in common use of atmospheric dispersion model on condition of complex geography, and it is used extensively in atmospheric dispersion sector. But CALPUFF model has not been used in atmospheric dispersion during normal or accidental conditions actually in nuclear power plant interiorly. This paper introduce CALPUFF atmosphere dispersion model, and make quantitative compare with RIMPUFF model and ATSTEP model, which are two different atmospheric dispersion models in RODOS system, then compare the differences among three atmospheric dispersion models. The differences between three different are acceptable. At the same time, on the background of Daya bay nuclear power plant, research atmospheric dispersion of one severe accident Source terms in nuclear power plant, to simulate dispersion process of characteristic pollutant (131I was used) on the condition of different seasons to analysis seasonal dispersion laws. Meanwhile, classifying weather data in of one year to research atmospheric dispersion on the ultimate conditions,?to analysis and forecast the distribution condition of radioactive nuclides on the extreme average conditions.Along with accelerating urbanization progress, change of land use around the nuclear power plant can take place extensively, these changes can change the energy balance of these area, then cause the change of atmosphere flow field and onflow characteristics, then change the law of pollutant diffusion、transform and accumulation within the sector. Following the establishment of inland nuclear power plant, the atmospheric dispersion research on the conditions of inland underlying surface has made important sense, and is important for site selection of nuclear power plant and accident environment evaluation. So this article applied CALPUFF atmospheric dispersion model to simulate and forecast the influence on characteristic pollutant distribution and diffusion process of land cover change. Three different single underlying surfaces conditions are Urban and built-up land、agriculture land and Water body. The results showed that the affect of different land uses depended on the meteorologic conditions, the laws were complicated.

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