کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6467987 1423261 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Magnetic resonance velocity imaging of gas flow in a diesel particulate filter
ترجمه فارسی عنوان
تصویربرداری سرعت مغناطیسی سرعت جریان گاز در یک فیلتر ذرات دیزلی
کلمات کلیدی
فیلتر ذرات دیزلی، ذرات جامد، جریان گاز، تصویربرداری سرعت رزونانس مغناطیسی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


- Magnetic resonance velocity imaging has been used to study gas flow in a DPF.
- Profiles of the average the axial and through-wall velocity are obtained.
- The uniformity of the through-wall flow field has been quantified.
- The implication for particulate matter deposition is discussed.
- Comparisons with flow fields predicted using numerical models are made.

Magnetic resonance (MR) velocity imaging has been used to investigate the gas flow in a diesel particulate filter (DPF), with sulphur hexafluoride (SF6) being used as the MR-active gas. Images of the axial velocity were acquired at ten evenly spaced positions along the length of the filter, for three flow conditions corresponding to Reynolds number of Re = 106, 254 and 428 in the filter channels. From the velocity images, averaged axial and through-wall velocity, as a function of position along the length of the filter, have been obtained. These experimentally obtained velocity profiles are analysed and a qualitative comparison with the results of previously reported numerical simulations is made. The MR measurements were used in subsequent analysis to quantify the uniformity of the through-wall velocity profiles. From this it was observed that for higher Re flows, the through-wall velocity profile became less uniform, and the implications that this has on particulate matter deposition are discussed. The MR technique demonstrated herein provides a useful method to advance our understanding of hydrodynamics and mass transfer within DPFs and also for the validation of numerical simulations used in their design and optimization.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemical Engineering Science - Volume 158, 2 February 2017, Pages 490-499
نویسندگان
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