1,721,007 research outputs found
Hybrid Model for Cavitation Noise Spectra Prediction
No full textIn the latest years, models combining physical knowledge of a phenomenon and statistical inference are becoming of much interest in many real world applications. In this context, ship propeller underwater radiated noise is an interesting field of application for these so-called hybrid models, especially when the propeller cavitates. Nowadays, model scale tests are considered the state-of-the-art technique to predict the cavitation noise spectra. Unfortunately, they are negatively affected by scale effects which could alter the onset of some interesting cavitating phenomena respect to the full scale propeller; as a consequence, for some ship operational conditions it is not trivial to correctly reproduce the cavitation pattern in model scale tests. Moreover, model scale tests are quite expensive and time-consuming; it is not feasible to include them in the early stage of the design. Nevertheless, data collected during these tests can be adopted in order to tune a data-driven model while the physical equation describing the occurring phenomenon can be used to refine the prediction. In this work, the authors propose a hybrid model for the prediction of ships propeller underwater radiated noise, able to exploit both the physical knowledge of the problem and the real data obtained from cavitation tunnel experiments performed on different propellers in different working conditions. Results on real data will support the validity and the effectiveness of the proposa
Short-term Forecast and Long-term Simulation for Accurate Energy Consumption Prediction
No full textAccurate energy consumption forecasting has become pivotal for many companies as a way to tailor the budget dedicated to energy purchase on their actual power demand, thus sustainably minimizing energy waste and expenses. For these companies, both short-term and long-term energy consumption forecasts are a matter of interest since they would like to both program last-minute buy and sell and also plan future investments for power optimization. For this purpose, in this paper, different Deep Neural Networks techniques will be tested to perform both a supervised short-term energy consumption forecasting and an unsupervised long-term simulation via generative learning since very long-term forecasting (i.e., more than 1 year) is usually too inaccurate. The first task will be performed by adopting both a Recurrent Neural Network and a Long Short-Term Memory Network, while the second one will be performed by adopting a Generative Adversarial Network. Result on public data from the Australian Energy Market Operator will support the proposal
Data driven models for propeller cavitation noise in model scale
No full textModel scale experiments are currently considered the stateof-the-art approach for studying cavitation noise and predict the acoustical performance of a cavitating propeller. Unfortunately, this approach requires time-consuming tests in a cavitation tunnel with a model of the propeller and the experiments are usually affected by scale effects. Among these, viscous effects on the development of vortex cavitation may alter significantly the cavitation pattern, preventing in some cases to consistently reproduce the full scale cavitation pattern in model scale. Due to the above, the availability of methods allowing for the modelling and prediction of cavitating propeller radiated noise may represent an attractive opportunity to estimate propeller noise without requiring an actual experiment. In this paper an approach based on hybrid modelling is proposed for predicting model scale cavitating propeller noise. The proposed approach exploits both the physical knowledge of the problem and the real data obtained from extensive cavitation tunnel experimental campaigns performed on different propellers in many operational conditions. Results on real data will support the validity and the effectiveness of the proposal
A new series on diagnostic echographic cases and living brief reviews: a potentially useful tool for clinicians edited by FADOI
No full textModel scale cavitation noise spectra prediction: Combining physical knowledge with data science
No full textShips underwater radiated noise is a subject of great and increasing interest in naval architecture because of its
impacts on the environment and on the on-board comfort. Among the different noise sources, the propeller is
usually the dominant one, especially when it cavitates. For this reason a lot of efforts have been spent in studying
the cavitation noise spectra in order to be able to predict its main characteristics at design stage. Nowadays, in
order to reach this goal, the state-of-the-art solution is to perform model scale experiments. Unfortunately, this
requires time-consuming tests in a cavitation tunnel with a model of the propeller and results are affected by
scale effects that must be correctly interpreted. For these reasons, in this paper authors propose a hybrid approach
which may be adopted to predict the main characteristics of the cavitation noise spectra without requiring
an actual experiment. Moreover, the same approach may be used also in order to overcome typical model
scale problems. For this purpose a hybrid modelling approach able to exploit both the physical knowledge of the
problem and the real data obtained from many cavitation tunnel experiments performed on different propellers
in different working conditions have been developed. Results on real data in both interpolation and extrapolation
tasks will support the validity and the effectiveness of the proposal
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
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