Pressure drop across a filter medium vs. time modelling

In summary, a computer science PhD student is seeking help with their project involving computational fluid dynamic modelling and pressure drop across a filter medium. They are familiar with equations for pressure drop but are unsure how to incorporate time information for experiments using a constant flow rate pump. The suggestion is to use the steady state versions of the equations and account for the buildup of the filter cake.
  • #1
slayomer
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Hello guys, I am a computer science PhD student. But my project somehow requires some knowledge about computational fluid dynamic modelling (both discrete and continuous). nowadays I am abit stucked in an issue. I want to model pressure drop across a filter medium dynamically. i assume i have every parameter like flowrate, cake thickness, viscosity of suspension, particle diameter, solid ratio (concentration) etc. I know kozeny-carman, ergun, or endo equations used for pressure drop modelling. but they are not dynamic. time information is not included in the equations. how could the formulas converted to dynamic version if the experiments are done with CONSTANT flow rate pump. Any help with this greatly appreciated, thanks.
 
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  • #2
slayomer said:
Hello guys, I am a computer science PhD student. But my project somehow requires some knowledge about computational fluid dynamic modelling (both discrete and continuous). nowadays I am abit stucked in an issue. I want to model pressure drop across a filter medium dynamically. i assume i have every parameter like flowrate, cake thickness, viscosity of suspension, particle diameter, solid ratio (concentration) etc. I know kozeny-carman, ergun, or endo equations used for pressure drop modelling. but they are not dynamic. time information is not included in the equations. how could the formulas converted to dynamic version if the experiments are done with CONSTANT flow rate pump. Any help with this greatly appreciated, thanks.
Hi slayomer! Welcome to Physics Forums!
The dynamic part is not going to be important, because the pressure drop-flow rate behavior is going to be dominated by viscous drag. So just use the steady state versions (i.e., assume instantaneous steady state). Of course you have to take into account the buildup of the filter cake.

Chet
 

What is the purpose of pressure drop across a filter medium vs. time modelling?

The purpose of pressure drop across a filter medium vs. time modelling is to predict the changes in pressure drop over time as a filter medium becomes clogged with particles. This information can be used to optimize filter maintenance schedules and improve overall system efficiency.

What factors affect the pressure drop across a filter medium?

The pressure drop across a filter medium can be affected by a number of factors, including the size and shape of the particles being filtered, the porosity and thickness of the filter medium, and the flow rate of the fluid passing through the filter.

How is pressure drop across a filter medium vs. time modelling performed?

Pressure drop across a filter medium vs. time modelling is typically performed using mathematical models and simulations. These models take into account the properties of the filter medium, the characteristics of the particles being filtered, and the flow dynamics of the system to predict the changes in pressure drop over time.

What are the benefits of pressure drop across a filter medium vs. time modelling?

The benefits of pressure drop across a filter medium vs. time modelling include improved efficiency and cost savings. By understanding how pressure drop changes over time, filter maintenance can be scheduled more effectively and unnecessary filter replacements can be avoided.

Are there any limitations to pressure drop across a filter medium vs. time modelling?

Like any modelling technique, pressure drop across a filter medium vs. time modelling has its limitations. It is important to ensure that the model accurately represents the real-world conditions and to validate the results with experimental data. Additionally, unexpected changes in the system, such as a sudden increase in particle concentration, may affect the accuracy of the model.

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