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Institution

Mines ParisTech

EducationParis, France
About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a new approach to estimate vehicle tire forces and road maximum adherence is presented, which is not an asymptotic observer based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals.

129 citations

Journal ArticleDOI
TL;DR: Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix.
Abstract: Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix.

129 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated oil-water two-phase flows in microchannels of 793 and 667 µm hydraulic diameters made of quartz and glass, respectively, by injecting one fluid at a constant flow rate and the second at variable flow rate.
Abstract: This paper investigates oil–water two-phase flows in microchannels of 793 and 667 µm hydraulic diameters made of quartz and glass, respectively. By injecting one fluid at a constant flow rate and the second at variable flow rate, different flow patterns were identified and mapped and the corresponding two-phase pressure drops were measured. Measurements of the pressure drops were interpreted using the homogeneous and Lockhart–Martinelli models developed for two-phase flows in pipes. The results show similarity to both liquid–liquid flow in pipes and to gas–liquid flow in microchannels. We find a strong dependence of pressure drop on flow rates, microchannel material, and the first fluid injected into the microchannel.

129 citations

Journal ArticleDOI
TL;DR: In this article, an industrial Si-SiC coated C/C material (reference material) was investigated and the temperature dependence of the linear rate of mass loss was interpreted in the temperature range 773 < T < 1973 K.
Abstract: For an industrial Si-SiC coated C/C material (reference material) the temperature dependence of the linear rate of mass loss is interpreted in the temperature range 773 < T < 1973 K. The Arrhenius plot of the thermogravimetrically determined oxidation rate shows four typical regimes. Only in the temperature range 1323 < T < 1823 K is the oxidation rate close to or lower than the limit for long-term application. Pulsed Laser Deposition (PLD) allows the ablation of nonconductive and high melting targets and the preparation of films with complex composition. High energy impact CO 2 laser pulses (j= 3.10 7 W cm -2 ) lead to melting and evaporation of the target material in a single step. Therefore the flux of the metal components is stoichiometric. Deposited green layers did not show IR peaks typical for mullite. After a short oxidation treatment (15 min at 1673K) the formation of mullite in the coating was completed as was confirmed by IR spectroscopy and XRD investigations. Thin PLD-mullite layers (900 nm) did not markedly improve the oxidation resistance of the reference material in the high temperature range 1473 < T < 1973 K. However, a preoxidation treatment of the substrate material and mullite coatings with a thickness of 2.5 μm improved the oxidation behaviour significantly. Because of SiO 2 formation at the mullite-SiC interface all samples exhibited a mass increase on oxidation. The inward diffusion of oxygen across the outer mullite-containing layer controlled the kinetics of the reaction as was deduced from 18 O diffusivity measurements in PLD mullite layers. The calculated oxidation rates resulting from the diffusion parameters in SiO 2 and mullite are close to the thermogravimetric data. For oxidation durations of three days only amorphous SiO 2 is formed at the mullite-SiC interface.

129 citations

Journal ArticleDOI
TL;DR: In this paper, the number of sulphonic groups on the dye was correlated to the effectiveness of the coagulation-flocculation process, and it was found that the molar ratio between the dye molecules and the amine groups respected the stoichiometry between sulphonic functions and protonated amine group only in the case of Acid Violet 5.

129 citations


Authors

Showing all 6591 results

NameH-indexPapersCitations
Francis Bach11048454944
Olivier Delattre10349039258
Richard M. Murray9771169016
Bruno Latour9636494864
George G. Malliaras9438228533
George S. Wilson8871633034
Zhong-Ping Jiang8159724279
F. Liu8042823869
Kazu Suenaga7532926287
Carlo Adamo7544436092
Edith Heard7519623899
Enrico Zio73112723809
John J. Jonas7037921544
Bernard Asselain6940923648
Eric Guibal6929416397
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202315
202264
2021274
2020260
2019250
2018249