Stochastic & Numerical Algorithm Development
The generation of random numbers is too important to be left to chance.
— Robert R. Coveyou (Oak Ridge National Laboratory)
Computational techniques are the foundation of all the work done within the Computational Modelling Group. Indeed, many of the physical systems investigated by various members of the group were intractable before the advent of modern computer hardware and efficient numerical 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 interest.
As an example, note that the particle dynamics of many processes of interest (for example, in the Nanoparticles and Particle Processes themes), are governed by a population balance. The simplest form of such a population balance is the Smoluchowski coagulation equation. A more detailed - and physically useful - model is formed by adding additional terms to this equation to account for new particles entering the system through formation in the gas phase, surface growth through contact with gaseous species and coalescence through sintering. The governing equation thus modified is a bivariate population balance with volume and surface area as the two internal coordinates. This can be solved to give particle size distributions at various locations in, for example, a flame.
Bivariate population balances are computationally intensive to solve using standard numerical techniques. To overcome this obstacle, the group has developed a Stochastic Particle Algorithm for solving such equations. This represents a breakthrough in numerical efficiency for solving population balances. The efficiency of the algorithm has enabled the simulation of a greater number of internal coordinates: in the extreme case it is now possible to simulate the full spatial structure of the agglomerates. This allows the visualisation of the simulated particles and subsequent direct comparison with TEM micrographs.
Recent Associated Preprints
289: Modelling a detailed kinetic mechanism for electrocatalytic reduction of CO2
Simon D. Rihm,
Markus Kraft, Technical Report 289, c4e-Preprint Series, Cambridge, 2022.
278: Detailed modelling of aerosol growth dynamics
Markus Kraft, Technical Report 278, c4e-Preprint Series, Cambridge, 2021.
268: Predicting power conversion efficiency of organic photovoltaics: models and data analysis
Markus Kraft, Technical Report 268, c4e-Preprint Series, Cambridge, 2021.
211: A hybrid particle-number and particle model for efficient solution of population balance equations
Markus Kraft, Technical Report 211, c4e-Preprint Series, Cambridge, 2018.
Recent Associated Publications
Stochastic population balance methods for detailed modelling of flame-made aerosol particles
Markus Kraft, Journal of Aerosol Science 159, 105895, (2022).
Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis
Markus Kraft, ACS Omega 6(37), 23764-23775, (2021).
Deep-Learning Architecture in QSPR Modeling for the Prediction of Energy Conversion Efficiency of Solar Cells
Markus Kraft, Industrial & Engineering Chemistry Research 59(42), 18991-19000, (2020).
Sphere Encapsulated Monte Carlo: Obtaining Minimum Energy Configurations of Large Aromatic Systems
Kimberly L. Bowal,
Jacob W. Martin,
Markus Kraft, Journal of Physical Chemistry A 123(33), 7303-7313, (2019).
Funding has generously been provided by