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Computational Modelling Group: Preprint 220
https://como.ceb.cam.ac.uk/preprints/220/Ab initio MD simulations of a cPAH with 15 rings and two pentagons provided small fluctuations of the dipole moment at 1500 K, 10-20%, as well as binding a -
Computational Modelling Group: News item 91
https://como.ceb.cam.ac.uk/news/91/News item 91. Preprint 99 published. 20 December 2010. Preprint 99, "A coupled CFD-population balance approach for nanoparticle synthesis in turbulent reacting flows". . -
Computational Modelling Group: Preprint 129
https://como.ceb.cam.ac.uk/preprints/129/This gives the vertical stress, σ. 20. , on the disc. ... 20. plus (ii) the effect of τ; this sum gives an equation to predict the breakout factor N. -
Computational Modelling Group: News item 123
https://como.ceb.cam.ac.uk/news/123/News item 123. Preprint 218 published. 20 January 2019. Preprint 218, "OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms", has been published! -
Computational Modelling Group: Preprint 274
https://como.ceb.cam.ac.uk/preprints/274/OESI) at low blend strengths (5%) and a weaker linear reduction at higher blend strengths (10% and 20%). -
Computational Modelling Group
https://como.ceb.cam.ac.uk/resources/flpyro/An example image and data are also available (FlPyroImageAndData.zip, 20 MB). -
Computational Modelling Group: Preprint 38
https://como.ceb.cam.ac.uk/preprints/38/The total mass of soot produced in a laminar premixed flame is found to vary by no more than 20% as the model for the geometric effects of chemical reactions on -
Computational Modelling Group: Past Members
https://como.ceb.cam.ac.uk/people/past/57 (4) 425 5304. 49 (0)721 6087622. 0191 208 4954. 44 (0)20 78825183. -
Computational Modelling Group: Preprint 42
https://como.ceb.cam.ac.uk/preprints/42/f. H. 298K. = −598  20 kJ/mol. Finally, equilibrium calculations are performed to try to identify which intermediates are likely to be most prevalent in the high temperature industrial -
Computational Modelling Group: News item 140
https://como.ceb.cam.ac.uk/news/140/If cyber-physical systems are integrated into our energy systems, carbon abatement potential can be expected to increase by 20 per cent, rising to 30 per cent when artificial intelligence (AI)
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