Optimization of influence factors for water cooling of high temperature plate by accelerated control cooling process
| Autor: | Yong Tae Kang, Hwan Suk Lim |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Fluid Flow and Transfer Processes
Materials science Carbon steel Water flow Mechanical Engineering Nuclear engineering Prandtl number 02 engineering and technology Heat transfer coefficient engineering.material 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences 010305 fluids & plasmas symbols.namesake 0103 physical sciences Ultimate tensile strength Heat transfer Scale analysis (mathematics) Water cooling symbols engineering 0210 nano-technology |
| Zdroj: | International Journal of Heat and Mass Transfer. 128:526-535 |
| ISSN: | 0017-9310 |
| Popis: | The accelerated control cooling is considered to be one of the most practical technologies in the high tensile strength steel manufacturing industry. In order to achieve a high precision required for the cooling process, the system modeling should be done and optimized by considering the heat transfer mechanism as much as possible. For the system optimization, the parametric analysis for the influence factors such as feed water temperature, plate speed, plate width and specific heat should be carried out. In this study, we focus on optimizing the influence factors during water cooling of high temeperature plate by the accelerated control cooling process. The scale analysis with Reynolds and Prandtl numbers is made to optimize the effect of feed water temperature, and then the water flow density is determined during the water cooling process. The cooling efficiency is calculated in order to optimize the plate speed and plate width. Finally, start cooling temperature(SCT) and finish cooling temperature(FCT) are optimized by comparing the specific heat of standard references about low carbon steel such as NIST-JANAF, Eurocode, ISIJ. It is concluded that the accuracy of the predicted finish cooling temperature and the stability of the cooling process are significantly improved after the optimization of influence factors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect