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Institution

Polytechnic University of Turin

EducationTurin, Piemonte, Italy
About: Polytechnic University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 11553 authors who have published 41395 publications receiving 789320 citations. The organization is also known as: POLITO & Politecnico di Torino.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a model for the study of hyporheic exchange during unsteady conditions has been developed, which provides a sound analytical framework for the analysis of the effects of a varying stream discharge on the exchange between a stream and the hypheic zone.

154 citations

Journal ArticleDOI
TL;DR: The integration of GIS, SD model and 3D visualization, called GISSD system here, can better explain the interaction and the variation of the sustainability indicators for residential development and support the Decision Maker to view the sustainable level of urban residential areas more comprehensively.

154 citations

Journal ArticleDOI
TL;DR: Two remote-controlled systems were tested over two different archaeological sites in order to provide Digital Surface Models (DSMs) and large-scale maps (numeric maps and orthophotos) and an accuracy evaluation of the final products is reported.

154 citations

Journal ArticleDOI
TL;DR: Computer simulations show the effectiveness of the proposed online mutual coupling compensation algorithm and prove that the nice statistical properties of classical super-resolution DOA estimation algorithms can be restored despite the presence of mutual coupling.
Abstract: In this paper, a novel online mutual coupling compensation algorithm especially tailored to uniform and linear arrays is presented. It is conceived to simultaneously compensate for mutual coupling and estimate the direction-of-arrivals (DOAs) of signals impinging on the array since the estimated calibration matrix can be embedded within any classical super-resolution direction-finding method. An alternating minimization procedure based on closed-form solutions is performed to estimate the mutual coupling matrix in the field of complex symmetric Toeplitz matrices. Unlike many existing array calibration methods, it requires neither the presence of calibration sources nor previous calibration information as initialization. Computer simulations show the effectiveness of the proposed technique and prove that the nice statistical properties of classical super-resolution DOA estimation algorithms can be restored despite the presence of mutual coupling

154 citations

Journal ArticleDOI
01 Nov 2004
TL;DR: In this article, thermal sensitivity analysis of total enclosed fan cooled (TEFC) induction motors is presented, and the results are useful for identifying the most important thermal parameters and enabling robust designs to be developed that are insensitive to manufacturing tolerances.
Abstract: With the increasing pressures on electric motor manufactures to develop smaller and more efficient electric motors, there is a trend to carry out more thermal analysis in parallel with the traditional electromagnetic design. It has been found that attention to thermal design can be rewarded by major improvements in the overall performance. Thus, there is a requirement for accurate and reliable thermal analysis models that can be easily incorporated into motor design software. In the paper emphasis is given to thermal sensitivity analysis of total enclosed fan cooled (TEFC) induction motors. In particular, thermal parameters are modified and their effects on the temperature rise shown. The results are useful for identifying the most important thermal parameters and enables robust designs to be developed that are insensitive to manufacturing tolerances.

154 citations


Authors

Showing all 11854 results

NameH-indexPapersCitations
Rodney S. Ruoff164666194902
Silvia Bordiga10749841413
Sergio Ferrara10572644507
Enrico Rossi10360641255
Stefano Passerini10277139119
James Barber10264242397
Markus J. Buehler9560933054
Dario Farina9483232786
Gabriel G. Katul9150634088
M. De Laurentis8427554727
Giuseppe Caire8282540344
Christophe Fraser7626429250
Erasmo Carrera7582923981
Andrea Califano7530531348
Massimo Inguscio7442721507
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023210
2022487
20212,789
20202,969
20192,779
20182,509