What should we pay attention to in the application of similarity theory with Fluent

Huo zero · March 27, 2018, 00:45

Hi , dear all,
I have a difficulty in the application of similarity theory with Fluent about natural ventilation. The geometric model is reduced by 1:10 and logarithmically distributed at the speed entrance. How can I get the the original model's wind field? That is , if I get the result using reduced model, can I get the one of original model ? And how I get the ratio ? Is it calculated on the basis of the number of criteria? I want to make sure of my idea and kown more detial about it.
Thanks for your view. Waiting for your reply.

LuckyTran · March 27, 2018, 19:38

see Buckingham pi theorem.

You need to match ALL the pi groups, whatever these might be (it's your job to find out).

However, its a good idea to start with the popular dimensionless parameters. Things like Reynolds number, Rayleigh number, Grashof number, Prandtl number, Mach number, etc. The best ones are similarity variables (like Reynolds number).

March 27, 2018, 00:45	What should we pay attention to in the application of similarity theory with Fluent	#1
Huo zero New Member Huo Join Date: Mar 2018 Posts: 8 Rep Power: 8	Hi , dear all, I have a difficulty in the application of similarity theory with Fluent about natural ventilation. The geometric model is reduced by 1:10 and logarithmically distributed at the speed entrance. How can I get the the original model's wind field? That is , if I get the result using reduced model, can I get the one of original model ? And how I get the ratio ? Is it calculated on the basis of the number of criteria? I want to make sure of my idea and kown more detial about it. Thanks for your view. Waiting for your reply.

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March 27, 2018, 19:38		#2
LuckyTran Senior Member Lucky Join Date: Apr 2011 Location: Orlando, FL USA Posts: 5,761 Rep Power: 66	see Buckingham pi theorem. You need to match ALL the pi groups, whatever these might be (it's your job to find out). However, its a good idea to start with the popular dimensionless parameters. Things like Reynolds number, Rayleigh number, Grashof number, Prandtl number, Mach number, etc. The best ones are similarity variables (like Reynolds number).