368 citations
- University of MichiganUS24 papers
- Aalto UniversityFI23 papers
- Centre National de la Recherche ScientifiqueFR20 papers
- University College LondonGB17 papers
- RIKENJP16 papers
- RIKEN Advanced Science InstituteJP13 papers
- University of EdinburghGB11 papers
- Imperial College LondonGB10 papers
- Institute of Theoretical and Applied ElectrodynamicsRU10 papers
- Landau Institute for Theoretical PhysicsRU10 papers
- University of NottinghamGB10 papers
- Max Planck Institute for Solid State ResearchDE9 papers
40 papers · 1 filter
One-dimensional physics of the frustrated quantum magnet PHCC
Alexander A. Tsirlin, Oleg Janson, Ioannis Rousochatzakis
We report a comprehensive microscopic study of the frustrated quantum magnet PHCC, (CHN)CuCl, using density-functional band-structure calculations combined w…
Interactions between droplets in immiscible liquid suspensions and the influence of surfactants
A. J. Archer, D. N. Sibley, B. D. Goddard
We develop a general method for determining the effective interaction potential between two or more droplets suspended within a fluid phase. Our approach is based on classical dens…
Electrified EHL line contact with dielectric breakdown of lubricant -- a numerical model
Yang Xu, Nick Morris, Yue Wu
With the rapid growth of the electric vehicles with drive systems with higher voltages, power outputs, frequencies, and speeds, mitigating electrically induced bearing damage (EIBD…
Anisotropic scattering rates in strain-tuned SrRuO
Ben Currie, David T. S. Perkins, Evgeny Kozik +2
Motivated by recent angle-resolved photoemission spectroscopy (ARPES) experiments, we analyze the temperature, frequency, and momentum dependence of the single-particle scattering…
Two-phase flow in porous metal foam flow fields of PEM fuel cells
Xingxiao Tao, Kai Sun, Rui Chen +4
Porous metal foam (PMF) flow field is a potential option for proton exchange membrane fuel cells (PEMFCs) due to its excellent capabilities in gas distribution and water drainage.…
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
Marios Impraimakis, Evangelia Nektaria Palkanoglou
The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology.…