Computational model for solution convection due to density difference

In summary, a corrosion engineer is looking for a model to simulate localised corrosion caused by the flow of dense salt solutions over less dense solutions. While the Rayleigh-Benard convection model is commonly used for this type of phenomenon, the engineer is hesitant to use it due to potential thermal effects. Instead, the Stefan-Maxwell equations, which describe the transport of mass in binary solutions, can be used to model this effect as long as the concentration difference between the two solutions is known.
  • #1
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I am a corrosion engineer and I am trying to model localised corrosion, where saturated (dense) salt solutions flow over less dense solutions.

I am aware of the Rayleigh-Benard convection model, where hot water rises and cool water sinks (e.g. boiling water on a hob). Is there a well known model that deals with this effect using concentration variation rather than temperature?
I'm cautious of using RB and imposing unexpected thermal effects
 
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  • #2
.There is a model known as the Stefan-Maxwell equations which deals with diffusion and convection of multiple components in a binary solution, such as the flow of salt solutions over less dense solutions. The Stefan-Maxwell equations describe the transport of mass for each species in the solution and can be used to model localised corrosion, as long as the driving force between the two solutions (i.e. the difference in concentration) is known.
 

What is a computational model for solution convection due to density difference?

A computational model for solution convection due to density difference is a mathematical representation of the physical phenomenon of convection, where fluid motion is driven by differences in density. It uses numerical methods and algorithms to simulate the behavior of fluids with varying densities, such as solutions with different concentrations of substances.

Why is a computational model necessary for studying convection due to density difference?

A computational model allows scientists to study convection due to density difference in a controlled and repeatable manner. It also enables them to analyze and understand the complex interactions and patterns of fluid flow that would be difficult to observe in a real-world experiment. Additionally, a computational model can provide insights and predictions that can guide further experimentation and research.

What factors are taken into account in a computational model for solution convection?

A computational model for solution convection takes into account various factors such as fluid properties (e.g. density, viscosity), boundary conditions, and external forces (e.g. gravity, temperature gradients). It also considers the geometry of the system, such as the shape and size of the container or the presence of obstacles that may affect the fluid flow.

How accurate are computational models for solution convection due to density difference?

The accuracy of a computational model for solution convection depends on the assumptions, simplifications, and approximations made in the model. It also relies on the quality of the input data and the accuracy of the numerical methods used. While computational models can provide valuable insights, they should be validated and verified through experimental data to ensure their accuracy and reliability.

What are the applications of a computational model for solution convection due to density difference?

A computational model for solution convection has various applications in fields such as fluid dynamics, chemical engineering, and environmental science. It can be used to study and optimize industrial processes involving fluid flow, such as mixing and heat transfer. It can also help understand natural phenomena, such as ocean currents and atmospheric convection. Additionally, it can aid in designing and improving technologies that rely on convective processes, such as heat exchangers and cooling systems.

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