List of Journal Publications
2022
Aramfard, Mohammad, RAFOLS, Francisco PEREZ, Nicola, L.
A 2D dual-scale method to address contact problems Journal Article
In: TRIBOLOGY INTERNATIONAL, vol. 171, 2022.
Abstract | BibTeX | Tags: contact mechanics, friction, Indentation, Multiscale modeling | Links:
@article{Aramfard2022,
title = {A 2D dual-scale method to address contact problems},
author = {Mohammad Aramfard and Francisco PEREZ RAFOLS and L. Nicola},
doi = {10.1016/j.triboint.2022.107509},
year = {2022},
date = {2022-01-01},
journal = {TRIBOLOGY INTERNATIONAL},
volume = {171},
publisher = {ELSEVIER SCI LTD},
abstract = {A seamless 2D dual-scale computational scheme is developed to study contact problems. The model consists of an atomistic domain close to the contact, coupled with an elastic continuum domain away from the contact. The atomistic formulation provides a description of the contact interaction through interatomic potentials and permits to capture atomic wear and defect formation in the contact region. The fields in the continuum domain are calculated by an efficient FFT-based Green's function method. The novel scheme is validated against full atomistic simulations and applied to study the effect of adhesion on the scratching of a rough copper surface by a rigid smooth spherical tip.},
keywords = {contact mechanics, friction, Indentation, Multiscale modeling},
pubstate = {published},
tppubtype = {article}
}
2019
Ghavamian, F., Simone, A.
Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network Journal Article
In: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 357, 2019.
Abstract | BibTeX | Tags: Deep learning, Machine learning, Multiscale modeling, Recurrent neural network, Strain softening, Viscoplasticity | Links:
@article{Ghavamian2019,
title = {Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network},
author = {F. Ghavamian and A. Simone},
doi = {10.1016/j.cma.2019.112594},
year = {2019},
date = {2019-01-01},
journal = {COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING},
volume = {357},
publisher = {Elsevier B.V.},
abstract = {FE2 multiscale simulations of history-dependent materials are accelerated by means of a recurrent neural network (RNN) surrogate for the history-dependent micro level response. We propose a simple strategy to efficiently collect stress-strain data from the micro model, and we modify the RNN model such that it resembles a nonlinear finite element analysis procedure during training. We then implement the trained RNN model in the FE scheme and employ automatic differentiation to compute the consistent tangent. The exceptional performance of the proposed model is demonstrated through a number of academic examples using strain-softening Perzyna viscoplasticity as the nonlinear material model at the micro level.},
keywords = {Deep learning, Machine learning, Multiscale modeling, Recurrent neural network, Strain softening, Viscoplasticity},
pubstate = {published},
tppubtype = {article}
}
2018
Vakis, A. I., Yastrebov, V. A., Scheibert, J., Nicola, L., Dini, D., Minfray, C., Almqvist, A., Paggi, M., Lee, S., Limbert, G., Molinari, J. F., Anciaux, G., Aghababaei, R., Restrepo, S. Echeverri, Papangelo, A., Cammarata, A., Nicolini, P., Putignano, C., Carbone, G., Stupkiewicz, S., Lengiewicz, J., Costagliola, G., Bosia, F., Guarino, R., Pugno, N. M., Müser, M. H., Ciavarella, M.
Modeling and simulation in tribology across scales: An overview Journal Article
In: TRIBOLOGY INTERNATIONAL, vol. 125, pp. 169–199, 2018.
Abstract | BibTeX | Tags: Adhesion, Contact, friction, Lubrication, Multiphysics modeling, Multiscale modeling, roughness, Tribochemistry, tribology, Wear | Links:
@article{Vakis2018,
title = {Modeling and simulation in tribology across scales: An overview},
author = {A. I. Vakis and V. A. Yastrebov and J. Scheibert and L. Nicola and D. Dini and C. Minfray and A. Almqvist and M. Paggi and S. Lee and G. Limbert and J. F. Molinari and G. Anciaux and R. Aghababaei and S. Echeverri Restrepo and A. Papangelo and A. Cammarata and P. Nicolini and C. Putignano and G. Carbone and S. Stupkiewicz and J. Lengiewicz and G. Costagliola and F. Bosia and R. Guarino and N. M. Pugno and M. H. Müser and M. Ciavarella},
doi = {10.1016/j.triboint.2018.02.005},
year = {2018},
date = {2018-01-01},
journal = {TRIBOLOGY INTERNATIONAL},
volume = {125},
pages = {169–199},
publisher = {Elsevier Ltd},
abstract = {This review summarizes recent advances in the area of tribology based on the outcome of a Lorentz Center workshop surveying various physical, chemical and mechanical phenomena across scales. Among the main themes discussed were those of rough surface representations, the breakdown of continuum theories at the nano- and microscales, as well as multiscale and multiphysics aspects for analytical and computational models relevant to applications spanning a variety of sectors, from automotive to biotribology and nanotechnology. Significant effort is still required to account for complementary nonlinear effects of plasticity, adhesion, friction, wear, lubrication and surface chemistry in tribological models. For each topic, we propose some research directions.},
keywords = {Adhesion, Contact, friction, Lubrication, Multiphysics modeling, Multiscale modeling, roughness, Tribochemistry, tribology, Wear},
pubstate = {published},
tppubtype = {article}
}