1,720,977 research outputs found
Data-Driven Aeroacoustic Modelling: Trailing-Edge Noise
Full text linkBroadband noise emitted at the trailing edge of an airfoil represents a significant contribu- tion to the noise emission in rotors, wind turbine and fan blades, in low Mach number flows. High-fidelity calculations are out of the scope when fast parametric calculations are needed. In these cases it is necessary to resort to analytical models and the most popular one is the model proposed by Amiet. In the model, the knowledge of the wall pressure spectrum allows to define an equivalent point source located at the trailing edge. The description of the turbulent wall pressure spectrum is of major importance for the correct noise prediction. Proposed empirical laws of wall pressure spectra in presence of adverse pressure gradients are limited to cases which are not too far from the test cases employed for their calibration. Recently, the development of machine learning techniques make it possible to analyze large amounts of experimental data in order to automatically extract modeling knowledge. However measurements of pressure fluctuations near a trailing edge are difficult. An alternative solution is to measure the far-field trailing-edge noise at each condition. The measures are comparatively simpler and contain all the information about the source. In this work a deep learning algorithm, based on a standard feed-forward Artificial Neural Network (ANN) and a Random Forest (RF) algorithm are applied to far-field directivity data sets. The motivation of the present work is to evaluate the prediction ability of the ANN and RF models. The proposed approach allows to build a general model which can potentially be trained on experimental data and so it is not limited by the simplifying assumptions required by analytical models or empirical wall pressure spectrum. The prediction capabilities of ANN and RF are investigated by considering data not included in the training database. The potential of RF regression for the evaluation of the prediction uncertainty is also addressed. The proposed models are based on a splitting in sub models: the ANN or the RF algorithm is used to describe the noise directivity while a polynomial model is introduced for the prediction of the emitted acoustic power. This splitting, which improves significantly the training performance, can be seen as a possible way to introduce a physical constraint to the machine learning model which is forced to satisfy a constraint on the emitted power. The proposed procedure is tested on an artificial database generated by the Amiet model. However, it can be directly applied to experimental data or high-fidelity calculations
Data-driven modeling of broadband trailing-edge noise
Full text linkThe broadband noise emitted at the trailing edge of an airfoil represents a significant contribution to the noise emission of several industrial components, in both energy and aeronautical fields. Several analytical models focus the attention on some features of the boundary layer close to the trailing edge and use this information to predict the emissions. However, the prediction capability of these models is limited since they are based on several simplifying assumptions. Recently, research efforts have been devoted to the development of machine learning techniques which make it possible to analyze large amounts of experimental data in order to automatically extract modeling knowledge. In this work, Artificial Neural Networks (ANNs) are proposed as empirical models to describe the wall pressure spectrum and the noise directivity. First of all, a study on the choice of the ANN architecture is performed. In order to accomplish this task, an artificial database is generated by using existing semi-empirical models for the prediction of the wall pressure spectrum at different angles of attack: this makes it possible to identify the minimum complexity that the ANN should have in order to accurately describe the spectrum. A second ANN is trained on the directivity distribution obtained by the Amiet analytical theory: both shallow and deep architectures are investigated. The motivation of the present work lies in the fact that the existing analytical models used for building the artificial database are fairly good approximations of the physical phenomena: this means that the chosen ANN architecture is sufficiently complex to accurately describe also a measured noise emission which should represent a perturbation with respect to the models. In this way it is possible to improve the prediction ability of the ANN model by enriching the database with experimental data: this would lead to a general model which is not limited by the simplifying assumptions on which the analytical theories are based
Numerical Prediction of Internal Supersonic Flow in a Regulation Valve for Low-Thrust Space Engines
No full textThis work deals with the numerical prediction of the unsteady flow field developing in a regulation valve for space thrusters.
The flow field displays an unsteady behavior characterized by complex flow patterns, because such valves have a very narrow
throat and because of the presence of geometrical slope discontinuities downstream the throat for design constraints. The narrowness
of the throat induces strong flow accelerations and therefore strong temperature and pressure reductions. The geometrical
discontinuities cause the occurrence of local flow separations and shock waves, with an high degree of
unsteadiness.
Experiments have pointed out how the degree of unsteadiness strongly depends upon the nature of the gas feeding the valve. The
strongest unsteadiness has been observed in the case of xenon at low exit pressures.
Numerical simulations, using a compressible Navier-Stokes flow solver, have been performed under different working conditions
and for two different gases, nitrogen and xenon. The results agree with experiments, and provide details of the unsteadiness
mechanism and of its evolution depending upon the operating conditions
Methods and applications of computational fluid-structure interaction for nvh problems - a review
No full textThe extensive application field of the interactions between fluids and structures makes studying these phenomena crucial in the multiphysics domains. The numerical simulations are the primary solution for investigating the fundamental physics involved in complex interactions. This review delves into several methods employed to solve fluid-structure problems. A fundamental aspect discussed is the distinction between monolithic and partitioned approaches. While the monolithic approach involves formulating a unified set of system equations for the problem, the partitioned approach treats the fluid and solid domains separately.
In addition, the two approaches differ regarding discretization of problem domains, solution strategies and computational costs. Through a comprehensive analysis of numerical methods and their applications, this review is supposed to be a starting point for those who want to approach the topic of multiphysics interaction
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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