• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Environmental Engineering Science >Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator
【2h】

Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator

机译：新型表面曝气器驱动氧化沟中流动的计算流体动力学模拟

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this article, we present a newly designed inverse umbrella surface aerator, and tested its performance in driving flow of an oxidation ditch. Results show that it has a better performance in driving the oxidation ditch than the original one with higher average velocity and more uniform flow field. We also present a computational fluid dynamics model for predicting the flow field in an oxidation ditch driven by a surface aerator. The improved momentum source term approach to simulate the flow field of the oxidation ditch driven by an inverse umbrella surface aerator was developed and validated through experiments. Four kinds of turbulent models were investigated with the approach, including the standard k−ɛ model, RNG k−ɛ model, realizable k−ɛ model, and Reynolds stress model, and the predicted data were compared with those calculated with the multiple rotating reference frame approach (MRF) and sliding mesh approach (SM). Results of the momentum source term approach are in good agreement with the experimental data, and its prediction accuracy is better than MRF, close to SM. It is also found that the momentum source term approach has lower computational expenses, is simpler to preprocess, and is easier to use.
机译：在本文中，我们介绍了一种新设计的反向伞面曝气器，并测试了其在驱动氧化沟中的性能。结果表明，它比原来的具有更高的平均速度和更均匀的流场，具有更好的驱动氧化沟的性能。我们还提出了一种计算流体动力学模型，用于预测由表面曝气器驱动的氧化沟中的流场。研制了改进的动量源项法，通过反伞面曝气器模拟了氧化沟的流场，并通过实验进行了验证。用该方法研究了四种湍流模型，包括标准k-ɛ模型，RNGk-ɛ模型，可实现的k-ɛ模型和雷诺应力模型，并将预测数据与使用多旋转参考计算得到的数据进行了比较。框架方法（MRF）和滑动网格方法（SM）。动量源项法的结果与实验数据吻合良好，其预测精度优于MRF，接近SM。还发现动量源项法具有较低的计算费用，易于处理，并且易于使用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号