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Refinado por: Base de dados/Biblioteca: 2022 ECC(Springer) remover assunto: Physics remover
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Machine learning for naval architecture, ocean and marine engineering
Machine learning for naval architecture, ocean and marine engineering
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Machine learning for naval architecture, ocean and marine engineering

PANDA, J P

Journal of marine science and technology, 2023-03, Vol.28 (1), p.1-26 [Periódico revisado por pares]

Tokyo: Springer Japan

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2
Development of titanium dioxide nanowire incorporated poly(vinylidene fluoride–trifluoroethylene) scaffolds for bone tissue engineering applications
Development of titanium dioxide nanowire incorporated poly(vinylidene fluoride–trifluoroethylene) scaffolds for bone tissue engineering applications
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Development of titanium dioxide nanowire incorporated poly(vinylidene fluoride–trifluoroethylene) scaffolds for bone tissue engineering applications

Augustine, Anitha ; Augustine, Robin ; Hasan, Anwarul ; Raghuveeran, Varun ; Rouxel, Didier ; Kalarikkal, Nandakumar ; Thomas, Sabu

Journal of materials science. Materials in medicine, 2019-08, Vol.30 (8), p.96-13, Article 96 [Periódico revisado por pares]

New York: Springer US

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3
GeoNDT: a fast general-purpose computational tool for geotechnical non-destructive testing applications
GeoNDT: a fast general-purpose computational tool for geotechnical non-destructive testing applications
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GeoNDT: a fast general-purpose computational tool for geotechnical non-destructive testing applications

Liu, Hongwei ; Maghoul, Pooneh ; Mantelet, Guillaume ; Shalaby, Ahmed

Acta geotechnica, 2022-08, Vol.17 (8), p.3515-3534 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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4
Microscopic mechanism of particle detachment in granular materials subjected to suffusion in anisotropic stress states
Microscopic mechanism of particle detachment in granular materials subjected to suffusion in anisotropic stress states
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Microscopic mechanism of particle detachment in granular materials subjected to suffusion in anisotropic stress states

Ma, Qirui ; Wautier, Antoine ; Zhou, Wei

Acta geotechnica, 2021-08, Vol.16 (8), p.2575-2591 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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5
Enlightening force chains: a review of photoelasticimetry in granular matter
Enlightening force chains: a review of photoelasticimetry in granular matter
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Enlightening force chains: a review of photoelasticimetry in granular matter

Abed Zadeh, Aghil ; Barés, Jonathan ; Brzinski, Theodore A. ; Daniels, Karen E. ; Dijksman, Joshua ; Docquier, Nicolas ; Everitt, Henry O. ; Kollmer, Jonathan E. ; Lantsoght, Olivier ; Wang, Dong ; Workamp, Marcel ; Zhao, Yiqiu ; Zheng, Hu

Granular matter, 2019-11, Vol.21 (4), p.1-12, Article 83 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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6
Grain-scale experimental investigation of localised deformation in sand: a discrete particle tracking approach
Grain-scale experimental investigation of localised deformation in sand: a discrete particle tracking approach
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Grain-scale experimental investigation of localised deformation in sand: a discrete particle tracking approach

Andò, Edward ; Hall, Stephen A. ; Viggiani, Gioacchino ; Desrues, Jacques ; Bésuelle, Pierre

Acta geotechnica, 2012-03, Vol.7 (1), p.1-13 [Periódico revisado por pares]

Berlin/Heidelberg: Springer-Verlag

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7
The bias-extension test for the analysis of in-plane shear properties of textile composite reinforcements and prepregs: a review
The bias-extension test for the analysis of in-plane shear properties of textile composite reinforcements and prepregs: a review
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The bias-extension test for the analysis of in-plane shear properties of textile composite reinforcements and prepregs: a review

Boisse, P. ; Hamila, N. ; Guzman-Maldonado, E. ; Madeo, A. ; Hivet, G. ; dell’Isola, F.

International journal of material forming, 2017-08, Vol.10 (4), p.473-492 [Periódico revisado por pares]

Paris: Springer Paris

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8
Physics-informed neural networks (PINNs) for fluid mechanics: a review
Physics-informed neural networks (PINNs) for fluid mechanics: a review
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Physics-informed neural networks (PINNs) for fluid mechanics: a review

Cai, Shengze ; Mao, Zhiping ; Wang, Zhicheng ; Yin, Minglang ; Karniadakis, George Em

Acta mechanica Sinica, 2021-12, Vol.37 (12), p.1727-1738 [Periódico revisado por pares]

Beijing: The Chinese Society of Theoretical and Applied Mechanics; Institute of Mechanics, Chinese Academy of Sciences

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9
The accuracy of the mean value engine model affected little by the accuracy of compressor mass flow rate model for a marine diesel engine
The accuracy of the mean value engine model affected little by the accuracy of compressor mass flow rate model for a marine diesel engine
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The accuracy of the mean value engine model affected little by the accuracy of compressor mass flow rate model for a marine diesel engine

Tang, Yuanyuan ; Xia, Yu ; Zhang, Jundong ; Jia, Baozhu ; Jiang, Ruizheng

Journal of marine science and technology, 2024-03, Vol.29 (1), p.1-19 [Periódico revisado por pares]

Tokyo: Springer Japan

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10
Experimental verification of the vibro-impact capsule model
Experimental verification of the vibro-impact capsule model
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Experimental verification of the vibro-impact capsule model

Liu, Yang ; Pavlovskaia, Ekaterina ; Wiercigroch, Marian

Nonlinear dynamics, 2016, Vol.83 (1-2), p.1029-1041 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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