4,068 research outputs found
Elimination of Glass Artifacts and Object Segmentation
Many images nowadays are captured from behind the glasses and may have
certain stains discrepancy because of glass and must be processed to make
differentiation between the glass and objects behind it. This research paper
proposes an algorithm to remove the damaged or corrupted part of the image and
make it consistent with other part of the image and to segment objects behind
the glass. The damaged part is removed using total variation inpainting method
and segmentation is done using kmeans clustering, anisotropic diffusion and
watershed transformation. The final output is obtained by interpolation. This
algorithm can be useful to applications in which some part of the images are
corrupted due to data transmission or needs to segment objects from an image
for further processing
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
Colour image segmentation by the vector-valued Allen-Cahn phase-field model: a multigrid solution
We propose a new method for the numerical solution of a PDE-driven model for
colour image segmentation and give numerical examples of the results. The
method combines the vector-valued Allen-Cahn phase field equation with initial
data fitting terms. This method is known to be closely related to the
Mumford-Shah problem and the level set segmentation by Chan and Vese. Our
numerical solution is performed using a multigrid splitting of a finite element
space, thereby producing an efficient and robust method for the segmentation of
large images.Comment: 17 pages, 9 figure
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