4,215 research outputs found
Investigation of Deep Learning-Based Filtered Density Function for Large Eddy Simulation of Turbulent Scalar Mixing
The present investigation focuses on the application of deep neural network
(DNN) models to predict the filtered density function (FDF) of mixture fraction
in large eddy simulation (LES) of variable density mixing layers with conserved
scalar mixing. A systematic training method is proposed to select the DNN-FDF
model training sample size and architecture via learning curves, thereby
reducing bias and variance. Two DNN-FDF models are developed: one trained on
the FDFs generated from direct numerical simulation (DNS), and another trained
with low-fidelity simulations in a zero-dimensional pairwise mixing stirred
reactor (PMSR). The accuracy and consistency of both DNN-FDF models are
established by comparing their predicted scalar filtered moments with those of
conventional LES, in which the transport equations corresponding to these
moments are directly solved. Further, DNN-FDF approach is shown to perform
better than the widely used -FDF method, particularly for multi-modal
FDF shapes and higher variances. Additionally, DNN-FDF results are also
assessed via comparison with data obtained by DNS and the transported FDF
method. The latter involves LES simulations coupled with the Monte Carlo (MC)
methods which directly account for the mixture fraction FDF. The DNN-FDF
results compare favorably with those of DNS and transported FDF method.
Furthermore, DNN-FDF models exhibit good predictive capabilities compared to
filtered DNS for filtering of highly non-linear functions, highlighting their
potential for applications in turbulent reacting flow simulations. Overall, the
DNN-FDF approach offers a more accurate alternative to the conventional
presumed FDF method for describing turbulent scalar transport in a
cost-effective manner
RANS Turbulence Model Development using CFD-Driven Machine Learning
This paper presents a novel CFD-driven machine learning framework to develop
Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an
extension of the gene expression programming method (Weatheritt and Sandberg,
2016), but crucially the fitness of candidate models is now evaluated by
running RANS calculations in an integrated way, rather than using an algebraic
function. Unlike other data-driven methods that fit the Reynolds stresses of
trained models to high-fidelity data, the cost function for the CFD-driven
training can be defined based on any flow feature from the CFD results. This
extends the applicability of the method especially when the training data is
limited. Furthermore, the resulting model, which is the one providing the most
accurate CFD results at the end of the training, inherently shows good
performance in RANS calculations. To demonstrate the potential of this new
method, the CFD-driven machine learning approach is applied to model
development for wake mixing in turbomachines. A new model is trained based on a
high-pressure turbine case and then tested for three additional cases, all
representative of modern turbine nozzles. Despite the geometric configurations
and operating conditions being different among the cases, the predicted wake
mixing profiles are significantly improved in all of these a posteriori tests.
Moreover, the model equation is explicitly given and available for analysis,
thus it could be deduced that the enhanced wake prediction is predominantly due
to the extra diffusion introduced by the CFD-driven model.Comment: Accepted by Journal of Computational Physic
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