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Numerical simulation to study and optimize the significant hidden temperature gradients in adiabatic tests

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Abstract

In adiabatic thermal analysis, correct measurement of temperature is usually ensured by means of a calibration procedure and thermal inertia corrections. However, there is a significant gradient within system, which not only makes the obtained test results inaccurate, but also causes serious errors in subsequent kinetic calculations and thermal risk assessment. Thus, estimations of the temperature gradients, which appear into the sample, are of highest interest to choose the right operational conditions that minimize that gradient. In this work, the CFD software has been applied to study this temperature gradient issue in adiabatic system. In view of the causes of temperature gradients, the influencing factors and the resulting analysis errors are all deeply researched. In addition, three different kinds of adiabaticity control model cases have also been simulated to explore the right operational conditions that minimize gradient and provide more reliable kinetics and thermal hazard parameters (Q∞, k0, Ea, n, TD24, TCL). After a comparative study, the authors define the more reliable operational conditions, which include selecting a suitable temperature response value and the adiabatic temperature control model. Finally, the reliability of the adiabaticity control model that achieves the minimum temperature gradients and provides the most accurate calculation results was verified through the different adiabatic tests.

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Abbreviations

ρ/kg m−3 :

Density

λ/W m−1 K−1 :

Heat conductivity

C P/J g−1 K−1 :

Specific heat capacity

P/Pa:

Static pressure

u/m s−1 :

Velocity

\(\tau\)/N m−2 :

Stress tensor

μ/Pa s:

Coefficient of kinetic viscosity

g/m s−2 :

Gravitational acceleration

F/N:

External body forces

E/kJ mol−1 :

Internal energy

k eff/W m−1 K−1 :

Effective thermal conductivity

\(h\)/kJ kg−1 :

Specific enthalpy

J :

Diffusion flux

Y :

Mass fraction

\(S_{\text{h}}\)/W m−3 :

Volumetric heat sources

\(Q^{\infty }\)/kJ kg−1 :

Heat of decomposition

f(α):

Kinetic functions

\(k_{0}\)/m3 mol−1 s−1 :

Pre-exponential factor

\(E_{\text{a}}\)/kJ mol−1 :

Apparent activation energy

\(T\)/°C:

Temperature

\(z\) :

Autocatalytic factor

\(n\) :

Reaction order

α :

Reaction progress

\(R\) :

Gas constant (8.31415 J K−1 mol−1)

Φ :

Thermal inertia factor

T D24/°C:

Temperature at which TMRad is 24 h

TMRad/day:

Time to maximum rate under adiabatic condition

TCL/day:

Time to conversion limit

R 2 :

Correlation coefficient

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Acknowledgements

This investigation was financed by the National key R&D Program of China (2017YFC0804701-4). The authors thank for this support.

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Authors and Affiliations

  1. Department of Safety Engineering, School of Chemical Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Road, Xuanwu District, Nanjing, 210094, China

    Jun Zhang, Liping Chen & Wanghua Chen

  2. Solvay (China) Co., Ltd., Shanghai, 201108, China

    Jun Zhang, Yingying Ma & Ze Dong

  3. Jinling Institute of Technology, No. 99, Hongjing Avenue, Jiangning District, Nanjing, 210094, China

    Shunyao Wang

Authors
  1. Jun Zhang
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  2. Yingying Ma
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  3. Ze Dong
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  4. Shunyao Wang
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  5. Liping Chen
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  6. Wanghua Chen
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Correspondence to Wanghua Chen.

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Zhang, J., Ma, Y., Dong, Z. et al. Numerical simulation to study and optimize the significant hidden temperature gradients in adiabatic tests. J Therm Anal Calorim 146, 919–935 (2021). https://doi.org/10.1007/s10973-020-09972-6

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  • DOI: https://doi.org/10.1007/s10973-020-09972-6

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