adjoint-based optimization

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adjoint-based optimization

Рет қаралды 26,911

learnfluidmechanics

Күн бұрын

A description of adjoint-based optimization applied to Fluid Mechanics, using the flow over an airfoil as an example

Пікірлер: 36

@aflofo 10 ай бұрын

This is probably the most comprehensive explanation of how adjoint optimizations work. Everyone else wants to jump right into the math without giving a good intuitive understanding about what is going on first.

@indukantdeo8125 4 жыл бұрын

A really helpful description of adjoint-based optimization

@henryford2785 8 күн бұрын

excellent video! Thank you!

@OnshapeInc 3 жыл бұрын

Very interesting presentation! Thanks for sharing.

@diegoandrade3912 2 жыл бұрын

what a tremendous explanation!

@rafaeltannenberg7403 3 жыл бұрын

Thanks for the very good explanation! Would it be possible to compute the derivatives directly for the lift/drag-ratio rather then doing it independently for the two quantities? I assume that would reduce the required computational effort further (by the cost of one foward function evaluation)?

@learnfluidmechanics4166 3 жыл бұрын

Yes it would. Indeed this would be quicker and cheaper.

@rafaeltannenberg7403 3 жыл бұрын

@@learnfluidmechanics4166 Thank you for the quick response!

@idiosinkrazijske.rutine 3 жыл бұрын

Very nice explanation indeed.

@sechristen 2 жыл бұрын

I'm confused about why it's more efficient to solve for how the lyft/drag changes with respect to each flow variable. Are you solving a PDE for each flow variable? Or does it have something to do with how you evaluate the lift/drag from the flow variables? Thank you! Lovely explanation of concept.

@luofenghuang8015 4 жыл бұрын

really clever! thanks

@thibautdalemans8601 4 жыл бұрын

Hello, I really enjoyed the video and it is very good basic explanation of the adjoint method. Do you have some references where the concepts that you mention are explained? Thank you in advance.

@learnfluidmechanics4166 3 жыл бұрын

Antony Jameson at Stanford wrote a course for the Von Karman Institute in 2003. You may be able to fine them online.

@hconel 5 ай бұрын

@@learnfluidmechanics4166 I believe the lecture you mention is available here: aero-comlab.stanford.edu/Papers/jameson.vki03.pdf

@cvspvr Жыл бұрын

why do we use the reynold's number? it seems like an arbitrary simplification that should be the result of the calculations rather than be used by the calculations

@davidaugustofc2574 Жыл бұрын

Reynold's number is used to know if we can compare 2 simulations/validation tests. The lower the number the more dependent on viscosity the flow is. Since similar numbers have similar characteristics you can use much smaller scale models to check if the simulations are accurate (as long as the numbers are similar)

@ninepuchar1 3 жыл бұрын

May i ask what software are you using for this? And could you provide the file for simulation so that we can check how is it done? or perhaps a simulationvideo demonstrating the adjoint-based optimisation . Thank you

@learnfluidmechanics4166 3 жыл бұрын

We use Fenics (fenicsproject.org). I can't provide the code right now, but Petr Kungurtsev may make it available with his PhD when it is published around March 2021.

@ninepuchar1 3 жыл бұрын

@@learnfluidmechanics4166 thank you

@ibrahim-sy7ff Жыл бұрын

@@learnfluidmechanics4166 hello, I am enquiring if this code was made available. If so, kindly show me

@chrishermans 3 жыл бұрын

So essentially, this boils down to backpropagation, as performed in neural networks...

@elmattnewniper5898 3 жыл бұрын

Yes. Neural Networks use automatic differentiation. You can automatically differentiate some, but not all, CFD codes.

@chrishermans 3 жыл бұрын

@@elmattnewniper5898 Not sure what you mean by that, but then again my background is not in computational fluid dynamics. I did some stuff with it in my master's, ages ago, but that's pointless atm.

@lemurpotatoes7988 Жыл бұрын

No. Backpropagation is a recursive algorithm for updating parameters arranged in a layered hierarchy. Adjoint methods are an alternative to backpropagation. Both use local gradients, but that's the end of their similarity. Adjoints require more mathematical understanding to use but usually have better computational properties.

@muhammadsarmad9345 3 жыл бұрын

Where can I find the literature related to this example

@learnfluidmechanics4166 3 жыл бұрын

This example is from an undergraduate thesis. You can find details of other examples on my website (the tutorial section is probably most useful) www2.eng.cam.ac.uk/~mpj1001/MJ_publications.html#tutorial

@qr-ec8vd Жыл бұрын

this is just analytical partial derivations, right?

@muhammadusmanshahid4195 3 жыл бұрын

Hey, Please answer my query as soon as possible. I really need it on urgent basis. For finding how flow variables are changing with respect to every parameter, wouldn't you need to solve 200 times by changing those 200 parameters one by one??

@learnfluidmechanics4166 3 жыл бұрын

You run the adjoint code once for each output variable (e.g. lift or drag) rather than once for each input parameter.

@muhammadusmanshahid4195 3 жыл бұрын

@@learnfluidmechanics4166 to find the derivative of flow with respect to parameters, we need to change that parameter. How changing the parameter will change flow variables without solving the flow?

@muhammadusmanshahid4195 3 жыл бұрын

@@learnfluidmechanics4166 can you please tell me your email address?

@learnfluidmechanics4166 3 жыл бұрын

@@muhammadusmanshahid4195 With adjoint methods you do not need to change the parameter to find the derivative of the output with respect to that parameter. The first time you see it, it seems like magic. For a review paper on this see www.annualreviews.org/doi/abs/10.1146/annurev-fluid-010313-141253

@muhammadusmanshahid4195 3 жыл бұрын

@@learnfluidmechanics4166 Thanks!