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  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  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 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
  10. 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
  11. 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.
  12. 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.
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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.
  19. 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
  20. Computational Modelling Group: Martin Martin

    https://como.ceb.cam.ac.uk/people/mm864/
    dissertation. Furthermore, I have developed interests in Machine Learning Algorithms, such as the Relevance Vector Machine and new techniques for fast and accurate fluid simulation, such as The Level Set Method.
  21. Computational Modelling Group: Particle Processes

    https://como.ceb.cam.ac.uk/research/particles/
    The interests in the group can be split into two broad classes: modelling industrial particle processes; and developing new computational techniques for representing associated phenomena. ... tablets. There is even research into the perfect crumble
  22. 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
  23. Computational Modelling Group: Preprint 297

    https://como.ceb.cam.ac.uk/preprints/297/
    We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. ... This new era of GeoWeb 2.5 systems lowers the risk of
  24. Computational Modelling Group

    https://como.ceb.cam.ac.uk/resources/molhub/
    The main difference between the old and new versions is that the new one is using ontologies and thus facilitates inferencing and reasoning as well as semantic interoperability with other
  25. Computational Modelling Group: Shraddha Shekar

    https://como.ceb.cam.ac.uk/people/ss663/
    I am a member of New Hall (Murray Edwards) college, and am being funded by Cambridge Commonwealth Trust and New Hall BP Centenary Bursary.
  26. Computational Modelling Group: Preprint 81

    https://como.ceb.cam.ac.uk/preprints/81/
    This new potential has been shown to accurately predict interaction energies for a variety of dimer configurations for four different PAH molecules including certain configurations which are poorly predicted with current
  27. Computational Modelling Group: Preprint 101

    https://como.ceb.cam.ac.uk/preprints/101/
    Chem. Theory Comput. 2010, 6, 683-695) by developing a new transferable electrostatic model for PAH molecules.
  28. Computational Modelling Group: Nick Eaves

    https://como.ceb.cam.ac.uk/people/nae28/
    Nick Eaves. Nick Eaves. Post Doc. Biography. Dr. Nick Eaves studied Mechanical Engineering at the University of Waterloo where he obtained his BASc. After completing his BASc, he join the Combustion Research Lab at the University of Toronto (UofT)
  29. Computational Modelling Group: Preprint 292

    https://como.ceb.cam.ac.uk/preprints/292/
    Ontological representation of MOPs and their related concepts. Knowledge-based rational derivation of new MOP designs by a software agent. ... defines MOPs and their key properties; iv) input agents that populate The World Avatar (TWA) knowledge graph;
  30. Computational Modelling Group: Preprint 168

    https://como.ceb.cam.ac.uk/preprints/168/
    Abstract. A new method of moments for solving the population balance equation is developed and presented. ... The new method predicts mean quantities which are almost as accurate as a high-precision stochastic method calculated using the established
  31. Computational Modelling Group: Jacob Martin

    https://como.ceb.cam.ac.uk/people/jwm50/
    He completed a Bachelor of Science with First Class Honours in Chemistry and Physics followed by Masters in Chemistry at the University of Auckland (New Zealand).
  32. Computational Modelling Group: Jonathan Etheridge

    https://como.ceb.cam.ac.uk/people/jee33/
    Spark ignition to homogeneous charge compression ignition mode transition study: a new modelling approach.
  33. Computational Modelling Group: Preprint 250

    https://como.ceb.cam.ac.uk/preprints/250/
    New measurements of CH and temperature are reported as 2D fields. ... Abstract. New experimental 2D measurements are reported to characterise the flame location, shape and temperature of laminar premixed ethylene jet-wall stagnation flames when the
  34. Computational Modelling Group: Preprint 72

    https://como.ceb.cam.ac.uk/preprints/72/
    Furthermore, two methods of establishing 'efficiency' are considered and the new algorithm is found to be significantly more efficient.
  35. Computational Modelling Group: Preprint 51

    https://como.ceb.cam.ac.uk/preprints/51/
    Abstract. A new, advanced soot particle model is developed which describes soot particles by their aromatic structure including functional site descriptions and a detailed surface chemistry mechanism.
  36. Computational Modelling Group: Preprint 53

    https://como.ceb.cam.ac.uk/preprints/53/
    Simulations are presented of a premixed flame using this improved rate and a new advanced soot particle model, which is developed in this paper.
  37. Computational Modelling Group: Peter Man

    https://como.ceb.cam.ac.uk/people/plwm2/
    Peter Man. Peter Man. Research Student. Biography. I graduated from Cambridge doing BA Mathematics in 2006. I then completed the MPhil in Statistical Science in 2007, and am now part of the Computational Modelling Group, starting from October 2007.
  38. Computational Modelling Group: Numerics

    https://como.ceb.cam.ac.uk/research/algorithms/
    Besides using the latest computational techniques, many members of the group take an active interest in the mathematical development, formulation and practical implementation of new algorithms within the various fields of ... A more detailed - and
  39. Computational Modelling Group: Jasdeep Singh

    https://como.ceb.cam.ac.uk/people/js400/
    Jasdeep Singh. Jasdeep Singh. Research Student. Biography. Dr. Jasdeep Singh finished his doctorate degree in the CoMo group (2006) in "Detailed soot modelling in laminar premixed flames". Presently, he is working with Ranbaxy as Business
  40. Computational Modelling Group: Preprint 87

    https://como.ceb.cam.ac.uk/preprints/87/
    In this work we present the new PAH-PP soot model and use a data collaboration approach to determine some of its parameters.
  41. Computational Modelling Group: Abhijeet Raj

    https://como.ceb.cam.ac.uk/people/ar447/
    Abhijeet Raj. Abhijeet Raj. Research Student. Biography. Hi! This is Abhijeet Raj, currently a PhD student in the CoMo Group. I started my PhD in October 2006, for which I was awarded Cambridge Nehru Scholarship and Overseas Research Studentship. I
  42. Computational Modelling Group: Preprint 104

    https://como.ceb.cam.ac.uk/preprints/104/
    We implement an algorithm which estimates parameters of an internal combustion engine model using a Bayesian approach and employs an experimental design technique to iteratively suggest new experiments with the aim
  43. Computational Modelling Group: Preprint 49

    https://como.ceb.cam.ac.uk/preprints/49/
    The particle model incorporating inception, coagulation, growth, and sintering, is coupled to the new gas phase kinetic model using operator splitting, and is used to simulate a heated furnace laboratory reactor
  44. Computational Modelling Group: Preprint 19

    https://como.ceb.cam.ac.uk/preprints/19/
    Associated Themes:. Abstract. In this paper we investigate a new stochastic particle method (SPM) for solving an extension to the sintering-coagulation equation and model two particle systems: the production of ... A new mass-flow stochastic algorithm to
  45. Computational Modelling Group: Publication CaF-203-56-71

    https://como.ceb.cam.ac.uk/publications/CaF-203-56-71/
    The capability of the new model to predict PSDs in a premixed stagnation flame is investigated. ... ability of this new model to describe the coagulation process of aggregate particles.
  46. Computational Modelling Group: Preprint 277

    https://como.ceb.cam.ac.uk/preprints/277/
    Outlined the outlook for the World Avatar project. Abstract. Digitalisation enhances communication and therefore offers new ways to achieve efficiency gains in science, technology and society at large.
  47. Computational Modelling Group: Preprint 58

    https://como.ceb.cam.ac.uk/preprints/58/
    The numerical convergence of the new algorithm is demonstrated, and its efficiency is compared to that of the Strang splitting algorithm.
  48. Computational Modelling Group: Preprint 85

    https://como.ceb.cam.ac.uk/preprints/85/
    Thermochemical data and enthalpies of formation are calculated for 141 new species using density functional theory (DFT) and statistical mechanics.
  49. Computational Modelling Group: Preprint 303

    https://como.ceb.cam.ac.uk/preprints/303/
    Highlights. Revised novel semantic web systems architecture for inferences. Inference Agent capable of deriving new knowledge by applying graph and ontology based inference algorithms. ... infer new statements by an intelligent autonomous agent capable
  50. Computational Modelling Group: Preprint 3

    https://como.ceb.cam.ac.uk/preprints/3/
    Sci Comput. 22(3):8022-821,2000), we model the system as a Markov process and introduce a new majorant kernel that enables us to extend the use of fictitious jumps ... Gillespie, J. Atmos. Sci. 32(10):1977-1989, 1975). We also compare the efficiency of
  51. Computational Modelling Group: Preprint 239

    https://como.ceb.cam.ac.uk/preprints/239/
    the minimum interaction energy) between two colliding particles. To test the performance of this new coagulation efficiency model, we applied it in detailed population balance modelling of soot particle size distributions ... Moreover, the agreement

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