Andreas Almqvist

Prof. Andreas Almqvist

Department of Engineering Science and Mathematics, Luleå University of Technology, Luleå, Norrbotten, Sweden
orcid

Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2018 in Tribology International
Verified
Journal Article
Closed access
AVAntonis I. Vakis
Antonis I. Vakis
VYV.A. Yastrebov
V.A. Yastrebov
JSJ. Scheibert
J. Scheibert
LNL. Nicola
L. Nicola
Daniele DiniDaniele Dini
Daniele Dini
michele ciavarellamichele ciavarella
michele ciavarella

1 November 2011 in Journal of the Mechanics and Physics of Solids
Verified
Journal Article
Closed access
Andreas AlmqvistAndreas Almqvist
Andreas Almqvist
CCC. Campañá
C. Campañá
NPN. Prodanov
N. Prodanov
BPB.N.J. Persson
B.N.J. Persson

17 December 2009 in Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
Verified
Journal Article
Open access
FSF Sahlin
F Sahlin
RLRoland Larsson
Roland Larsson
Andreas AlmqvistAndreas Almqvist
Andreas Almqvist
PLPiet Lugt
Piet Lugt
PMP Marklund
P Marklund
A mixed lubrication model that permits real three-dimensional surface topography as input is developed. The theory of computing flow factors within the model is presented, and with a following paper (Part 2) the method of measuring and adapting the surface roughness, and model validation through flow measurements and application to a bearing is shown. A contact mechanics model is used to calculate the elastoplastic displacement of a periodic topography signal. A method based on homogenization is used to calculate flow factors for all lubrication regimes. The flow factors are compared with the Patir and Cheng method. Results indicate that the two methods compare well for longitudinal roughness lay, but differ significantly for a cross-patterned surface roughness due to the more complete flow description of the current model.

1 April 2007 in Tribology International
Verified
Journal Article
Closed access
Andreas AlmqvistAndreas Almqvist
Andreas Almqvist
FSF. Sahlin
F. Sahlin
RLRoland Larsson
Roland Larsson
SGS. Glavatskih
S. Glavatskih

1 September 2011 in Wear
Verified
Journal Article
Closed access
JAJ. Andersson
J. Andersson
Andreas AlmqvistAndreas Almqvist
Andreas Almqvist
RLRoland Larsson
Roland Larsson

19 August 2021 in Lubricants
Verified
Journal Article
Open access
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Andreas AlmqvistAndreas Almqvist
Andreas Almqvist
This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial and boundary value problems described by linear ordinary differential equations. The objective with this technical note is not to develop a numerical solution procedure which is more accurate and efficient than standard finite element- or finite difference-based methods, but to give a fully explicit mathematical description of a PINN and to present an application example in the context of hydrodynamic lubrication. It is, however, worth noticing that the PINN developed herein, contrary to FEM and FDM, is a meshless method and that training does not require big data which is typical in machine learning.