1,720,959 research outputs found
Machine Learning algorithms in Computational Fluid Dynamics: Improving Reynolds-Averaged Navier-Stokes equations by ML closure models
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Using delayed detached Eddy simulation to create datasets for data-driven turbulence modeling: A periodic hills with parameterized geometry case
Full text linkDespite the emerging field of data-driven turbulence models, there is a lack of systematic high-fidelity datasets at flow configurations changing continuously with respect to geometrical/physical parameters. In this work, we investigate the possibility of using Delayed Detached Eddy Simulation (DDES) to generate reliable datasets in a significantly cheaper manner compared to the DNS or LES counterparts. To do that, we perform 25 simulations with geometrically-parameterized periodic hills geometries to deal with different hills steepness. We firstly check the accuracy of our results by comparing one simulation with the benchmark case of Xiao et al.. Then, we use such database to train the turbulent viscosity-Vector Basis Neural Network data-driven turbulence model. The latter outperforms the classic k-omega SST RANS model, proving that our generated dataset can be useful for data-driven turbulence modeling and opening the opportunity to exploit DDES to create systematic datasets for data-driven turbulence modeling
A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor
Full text linkIn the present paper a new data-driven model is proposed to close and
increase accuracy of RANS equations. The divergence of the Reynolds Stress
Tensor (RST) is obtained through a Neural Network (NN) whose architecture and
input choice guarantee both Galilean and coordinates-frame rotation. The former
derives from the input choice of the NN while the latter from the expansion of
the divergence of the RST into a vector basis. This approach has been widely
used for data-driven models for the anisotropic RST or the RST discrepancies
and it is here proposed for the divergence of the RST. Hence, a constitutive
relation of the divergence of the RST from mean quantities is proposed to
obtain such expansion. Moreover, once the proposed data-driven approach is
trained, there is no need to run any classic turbulence model to close the
equations. The well-known tests of flow in a square duct and over periodic
hills are used to show advantages of the present method compared to standard
turbulence models.Comment: 26 pages, 13 figure
The Lowest-Order Neural Approximated Virtual Element Method
No full textWe introduce the lowest-order Neural Approximated Virtual
Element Method, a novel polygonal method that relies on neural networks to eliminate the need for projection and stabilization operators in
the Virtual Element Method. In this paper, we discuss its formulation
and detail the strategy for training the underlying neural network. The
viability of the new method is tested through numerical experiments on
elliptic problems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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