Journal of Shanghai Jiaotong University ›› 2014, Vol. 48 ›› Issue (12): 1809-1814.

• Others • Previous Articles    

Unsteady Inverse heat Conduction Problem of Loose Coal Bulk in First Kind Boundary Condition

CHEN Qinghuaa,b,XU Manmana,PANG Lia,LIU Zegongb,c,GUAN Weijuand   

  1. (a. College of Mechanical Engineering; b. Flow Station of Mining Engineering Postdoctoral Researchers; c. College of Energy and Safety; d. College of Sciences,  Anhui University of Science and Technology, Huainan 232001, Anhui, China)
  • Received:2014-06-06

Abstract:

Abstract: In order to obtain thermophysical properties of loose coal efficiently and accurately, the unsteady multivariable inverse problem of loose coal bulk based on first kind boundary condition was studied in this paper. The super thermostatic water bath was used to generate constant temperature water, and the constant temperature boundary with the copper plate specimen box was built. Based on the unsteady heat transfer model in first kind boundary condition, the thermal conductivity and thermal capacity of loose coal were estimated using the conjugate gradient method. The results of sensitivity analysis show that the thermal conductivity and thermal capacity can be estimated simultaneously, but because the sensitivity of thermal capacity is too low, it has a big inverse estimation error. Therefore, in order to obtained the thermal conductivity and thermal capacity simultaneously precisely, the thermal diffusivity was estimated and the thermal capacity was modified. Based on the built experimental system, the testing experiment of  thermophysical parameters of loose coal was conducted. The results show that the used inverse algorithm has good accuracy and illposedness, the initial speculated value has no obvious effect on estimation results, and even when the standard deviations of temperature reaches 0.6, the relative error of the inversed results is just 7.53%.
Key words:

Key words: loose coal bulk, first kind boundary condition, inverse heat conduction problem, multi-parameters estimation

CLC Number: