Linearization of nonlinear grad(T) in the diffusion equation

In summary, the conversation discusses the use of numerical simulations to model heat transfer in a static bath of superfluid helium. The heat conduction can be modeled in two regimes, the Landau regime for low heat flux and the Goerter-Mellink regime for high heat flux. The conversation then delves into the equations and methods used to discretize the nonlinear correlation and discusses potential solutions for solving it iteratively. Suggestions are also made for using automatic stiff integration packages for more accurate results.
  • #1
stockzahn
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Dear all,

I would like to perform numerical simulations of the heat transfer/temperature field in a static bath of superfluid helium. The heat conduction in superfluid helium can be modeled in two regimes depending on the heat flux. For low heat fluxes ##\dot{q}##, the temperature gradient depends linearly on the heat flux (Landau regime). With increasing heat flux, a second term becomes more important (Goerter-Mellink regime), in which the temperature gradient depends non-linearly on the transferred heat flux (the exponent ##m## experimentally was determined to be ##3.4##):

$$grad(T) = \underbrace{- f_{L} \dot{q}}_{Landau} + \underbrace{- f_{GM} \dot{q}^m}_{Goerter-Mellink}$$

Assuming the contribution of the Landau-term negligible (high ##\dot{q}##), after re-arranging the correlation can be plugged in into the diffusion equation (##f_{GM}^{-1} = const.##):

$$\rho c \frac{\partial T}{\partial t} = -\sqrt[m]{f_{GM}^{-1}}\; div\;\sqrt[m]{grad(T)}$$
$$\frac{\partial T}{\partial t} = \underbrace{\frac{-\sqrt[m]{f_{GM}^{-1}}}{\rho c}}_{a_{GM}}\; div\;\sqrt[m]{grad(T)}$$

Exemplarily for the x-direction:

$$\frac{\partial T}{\partial t} = a_{GM}\frac{\partial\left(\frac{\partial T}{\partial x}\right)^{\frac{1}{m}}}{\partial x}$$

Now I would like to discretize the above equation and I obtain

$$\frac{T_i^{n+1}-T_i^{n}}{\Delta t} =a_{GM}\frac{\left(\frac{T_{i+1}-T_{i}}{\Delta x}\right)^{\frac{1}{m}} - \left(\frac{T_{i}-T_{i-1}}{\Delta x}\right)^{\frac{1}{m}}}{\Delta x} $$

$$\frac{T_i^{n+1}-T_i^{n}}{\Delta t} =a_{GM}\frac{\sqrt[m]{T_{i+1}-T_{i}} - \sqrt[m]{T_{i}-T_{i-1}}}{\Delta x^{\frac{m+1}{m}}} $$

$$T_i^{n+1}-T_i^{n} =\underbrace{\frac{a_{GM}\Delta t}{\Delta x^{\frac{m+1}{m}}}}_{M}\left[\sqrt[m]{T_{i+1}-T_{i}} - \sqrt[m]{T_{i}-T_{i-1}}\right] $$

Using the Euler Upwind Scheme:

$$T_i^{n+1} -M\;\sqrt[m]{T_{i+1}^{n+1}-T_{i}^{n+1}} +M\; \sqrt[m]{T_{i}^{n+1}-T_{i-1}^{n+1}}=T_i^{n} $$

This last equation cannot be transformed into an LES for the cells of the numerical mesh. I hope that I'm correct by stating that I need to linearize the equation at a certain point and/or maybe solve it iteratively. Unfortunately I'm not sure, if what I try is even possible at all. I'd appreciate, if someone could guide, help or explain to me if and how this nonlinear correlation can be discretized.

Thanks,
stockzahn
 
Last edited:
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  • #2
Why not use an automatic stiff integration package (based on the method of lines) such as the stiff package in the IMSL library or the DASSL package available online (probably the double precision versions).?
 

1. What is the purpose of linearization in the diffusion equation?

The purpose of linearization is to simplify the diffusion equation by approximating the nonlinear term, grad(T), as a linear term. This allows for easier mathematical analysis and solution of the equation.

2. How is linearization of grad(T) achieved?

Linearization of grad(T) is achieved by using a first-order Taylor series expansion. This involves taking the first derivative of the nonlinear term and evaluating it at a specific point, typically the equilibrium point.

3. What are the limitations of linearization in the diffusion equation?

Linearization is only valid for small deviations from the equilibrium point. If the system experiences large deviations, the linearized equation may not accurately represent the behavior of the system.

4. Can linearization be applied to any nonlinear term in the diffusion equation?

No, linearization can only be applied to terms that are differentiable. Non-differentiable terms, such as step functions, cannot be linearized.

5. What are some applications of linearization in the diffusion equation?

Linearization is commonly used in the analysis of heat transfer and mass transfer processes, such as in the design of heat exchangers and chemical reactors. It is also used in the study of diffusion in biological systems, such as in the transport of nutrients and gases in cells.

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