About divergence, gradient and thermodynamics

In summary, the conversation discusses taking the divergence of a quantity in thermodynamics, and the question is how to evaluate it. The answer is found by writing it out in Cartesian component form and using the chain rule to determine the partial derivatives.
  • #1
fluidistic
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At some point, in Physics (more precisely in thermodynamics), I must take the divergence of a quantity like ##\mu \vec F##. Where ##\mu## is a scalar function of possibly many different variables such as temperature (which is also a scalar), position, and even magnetic field (a vector field).

My question is, how to evaluate that divergence? I am tempted to set it equal to ##\nabla \cdot (\mu \vec F)=\nabla \mu \cdot \vec F + \mu \nabla \cdot \vec F##. I know it doesn't matter here, but it turns out that thanks to some physical fact, the divergence of ##\vec F## vanishes, so we can focus solely on the first term if we want.

And that is where my doubt lies. Precisely, the gradient of ##\mu##. Is it like a total derivative? So that if ##\mu## depends on temperature, magnetic field and position, then I should evaluate ##\nabla \mu## as ##\left ( \frac{\partial \mu}{\partial T}\right)_{\vec B,x}\frac{\partial T}{\partial x} + \left ( \frac{\partial \mu}{\partial x}\right)_{\vec B,T}\frac{\partial x}{\partial x} + \left ( \frac{\partial \mu}{\partial B_x}\right)_{B_y, B_z,x,T}\frac{\partial B_x}{\partial x}+... ##? I am a bit confused on the number of terms and whether what I wrote is correct.
 
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  • #2
To answer the first part of the question, just write it out in Cartesian component form.

For the second part, you correctly determined the partial with respect to x at constant y and z.
 
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  • #3
Chestermiller said:
To answer the first part of the question, just write it out in Cartesian component form.
Done, I confirm what I wrote, namely ##\nabla \cdot (\mu \vec F)=\nabla \mu \cdot \vec F + \mu \nabla \cdot \vec F##

However, more precisely I get (for the first term on the right side): ##\left( \frac{\partial \mu}{\partial x} \right) F_x + \left( \frac{\partial \mu}{\partial y} \right) F_y + \left( \frac{\partial \mu}{\partial z} \right) F_z##. As if only spatial derivatives mattered.

So, even if ##\mu## depends on temperature, I do not see how to reach what I wrote, say the term ##\left ( \frac{\partial \mu}{\partial T}\right)_{\vec B,x}\frac{\partial T}{\partial x}##. How could I reach this?
 
  • #4
fluidistic said:
Done, I confirm what I wrote, namely ##\nabla \cdot (\mu \vec F)=\nabla \mu \cdot \vec F + \mu \nabla \cdot \vec F##

However, more precisely I get (for the first term on the right side): ##\left( \frac{\partial \mu}{\partial x} \right) F_x + \left( \frac{\partial \mu}{\partial y} \right) F_y + \left( \frac{\partial \mu}{\partial z} \right) F_z##. As if only spatial derivatives mattered.

So, even if ##\mu## depends on temperature, I do not see how to reach what I wrote, say the term ##\left ( \frac{\partial \mu}{\partial T}\right)_{\vec B,x}\frac{\partial T}{\partial x}##. How could I reach this?

Apply the chain rule: if [itex]\mu(u_1(x,y,z), \dots, u_n(x,y,z))[/itex] then
[tex]
\frac{\partial \mu}{\partial x} = \sum_{i=1}^n \frac{\partial \mu}{\partial u_i} \frac{\partial u_i}{\partial x}[/tex] and thus [tex]
\nabla \mu = \sum_{i=1}^n \frac{\partial \mu}{\partial u_i} \nabla u_i.[/tex]
 
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  • #5
Thank you, I mathematically get it.
 

1. What is divergence and how is it related to gradient?

Divergence is a mathematical operation that measures the rate at which a vector field spreads out from a given point. It is related to gradient because the gradient is the vector field that points in the direction of the steepest increase of a scalar field, and the magnitude of the gradient is equal to the divergence of the vector field.

2. How does divergence relate to the concept of flow?

Divergence is closely related to the concept of flow, as it measures the amount of fluid or energy that is entering or leaving a given point in a vector field. A positive divergence value indicates that the fluid or energy is spreading out, while a negative divergence value indicates that it is being compressed or converging.

3. What is the significance of divergence in thermodynamics?

Divergence is an important concept in thermodynamics because it is directly related to the conservation of mass and energy. In thermodynamics, divergence is used to calculate the rate of change of a thermodynamic property, such as temperature or pressure, in a given system.

4. How does the concept of gradient relate to thermodynamics?

The gradient is an important concept in thermodynamics because it is used to describe the change in a thermodynamic property, such as temperature or pressure, in a given direction. This allows us to understand how a system will change over time and how energy will flow within the system.

5. Can you provide an example of how divergence and gradient are used in thermodynamics?

One example of how divergence and gradient are used in thermodynamics is in the study of heat transfer. The divergence of the heat flux vector field is used to calculate the rate at which heat is entering or leaving a system, while the gradient of temperature is used to determine the direction in which the temperature is changing. This information is crucial in understanding how heat is transferred within a system and how it affects the overall thermodynamic properties of the system.

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