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  2. Computational Modelling Group: Preprint 76

    https://como.ceb.cam.ac.uk/preprints/76/
    The method is not new, but we present a systematic study that considers several important new aspects of the method. ... In particular we demonstrate a new analytic expression for the DQMoM-IEM source terms.
  3. Computational Modelling Group: Preprint 279

    https://como.ceb.cam.ac.uk/preprints/279/
    In this work we develop new ontologies to extend the World Avatar knowledge graph to represent gas grids, gas consumption statistics, and climate data. ... Using a combination of the new and existing ontologies we construct a Universal Digital Twin that
  4. Computational Modelling Group: Preprint 70

    https://como.ceb.cam.ac.uk/preprints/70/
    In this paper, two new stochastic algorithms for calculating parametric derivatives of the solution to the Smoluchowski coagulation equation are presented. ... The new algorithms (called 'Single' and 'Double') work by coupling two Marcus-Lushnikov
  5. Computational Modelling Group: Clive Wells

    https://como.ceb.cam.ac.uk/people/cgw11/
    Clive Wells. Clive Wells. Post Doc. Biography. I obtained my PhD from the general relativity group in the department of applied mathematics and theoretical physics at Cambridge. There I studied the mathematical theory of black holes and time
  6. Computational Modelling Group: Alastair Smith

    https://como.ceb.cam.ac.uk/people/ajs224/
    Alastair Smith. Alastair Smith. Post Doc. Biography. Alastair couples stochastic algorithms for solving general multidimensional population balance equations to CFD. He is also interested in moment problems and DQMoM. Research Themes. Recent
  7. Computational Modelling Group: Preprint 6

    https://como.ceb.cam.ac.uk/preprints/6/
    The algorithm presented was applied to a PSR model. Moreover, numerical performance of this new algorithm was investigated in a more complex system. ... It was found that, depending on the required accuracy, the new stochastic approach clearly
  8. Computational Modelling Group: Mike Goodson

    https://como.ceb.cam.ac.uk/people/mjg43/
    Mike Goodson. Mike Goodson. Research Student. Biography. I joined the Department of Chemical Engineering as an undergraduate in 1997, having completed a year of Natural Sciences at Part IA. The subsequent three years spent in the department gave me
  9. Computational Modelling Group: Preprint 217

    https://como.ceb.cam.ac.uk/preprints/217/
    New regression based methodology (FLiPPID) for performing the inverse Abel transform is reported. ... Abstract. This letter reports a new regression method based on fitting the line-of-sight projection of a predefined intensity distribution (FLiPPID) to
  10. Computational Modelling Group: Preprint 84

    https://como.ceb.cam.ac.uk/preprints/84/
    steps which need to be taken in order to create a new generation of engineering models.
  11. Computational Modelling Group: Preprint 100

    https://como.ceb.cam.ac.uk/preprints/100/
    Building on the theoretical work presented here and previous experimental results a new kinetic model is constructed consisting of a TiCl. ... Unlike the previous phenomenological models, this new Eley-Rideal model is under the theoretical limit at all
  12. Computational Modelling Group: Preprint 86

    https://como.ceb.cam.ac.uk/preprints/86/
    This work proposes a new kinetic model and a novel inception pathway for the flame synthesis of silica nanoparticles from tetraethoxysilane (TEOS). ... New particle inception and surface growth steps have been incorporated into the particle model in
  13. Computational Modelling Group: Alexander Vikhansky

    https://como.ceb.cam.ac.uk/people/av277/
    Alexander Vikhansky. Alexander Vikhansky. Visitor. Biography. My name is Alexander Vikhansky and I graduated at the Faculty of Mechanics and Mathematics at the Perm University in Perm, Russia. I made my PhD at the Ben-Gurion University of the Negev
  14. Computational Modelling Group: Preprint 55

    https://como.ceb.cam.ac.uk/preprints/55/
    3. TiO. 2. Cl. 3. = 2 TiOCl. 3. , and a number of new elementary reactions are added. ... The new kinetic model is used to simulate a rapid compression machine (RCM) and a plug flow reactor (PFR) described in the literature.
  15. Computational Modelling Group: Preprint 83

    https://como.ceb.cam.ac.uk/preprints/83/
    The energetics and the kinetics of the new reactions are studied using density functional theory (DFT) and transition state theory, respectively, and their evaluated rates are presented. ... Due to a discrepancy in the rate for CO removal from soot
  16. Computational Modelling Group: William Menz

    https://como.ceb.cam.ac.uk/people/wjm34/
    William Menz. William Menz. Research Student. Biography. I was awarded a B.Sc. degree in Chemistry and Applied Mathematics and a B.E. degree in Chemical Engineering with first-class Honours from the University of Adelaide, Australia in December 2009.
  17. Computational Modelling Group: Preprint 27

    https://como.ceb.cam.ac.uk/preprints/27/
    Abstract. At the University of Cambridge, UK, we have developed, used, and evaluated a new exercise in Process Dynamics and Control incorporating a web-based experiment physically located at MIT. ... We describe the experimental equipment, the interface
  18. Computational Modelling Group: Preprint 103

    https://como.ceb.cam.ac.uk/preprints/103/
    The aim of this work is to present a new detailed multivariate population balance model to describe the aerosol synthesis of silica nanoparticles from tetraethoxysilane (TEOS). ... The new model includes a chemical representation of the silica particles
  19. Computational Modelling Group: Preprint 149

    https://como.ceb.cam.ac.uk/preprints/149/
    The numerical behaviour of the new stochastic weighted algorithm is compared against the existing direct simulation algorithm. ... Lastly, the performance of the new compartmental model is then investigated by comparing the predicted particle size
  20. Computational Modelling Group: Preprint 38

    https://como.ceb.cam.ac.uk/preprints/38/
    Completely new bivariate models for soot particle structure are introduced based on some simple assumptions and without any free parameters. ... Bivariate particle distributions are calculated for the new models and are found to be insensitive to the
  21. Computational Modelling Group: Preprint 61

    https://como.ceb.cam.ac.uk/preprints/61/
    The new model is based on the Stochastic Reactor Model (SRM) engine code, which uses detailed chemistry and takes into account convective heat transfer and turbulent mixing, and the soot formation ... The new model is applied to simulate an n-heptane

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