Welcome from the Computational Modelling Group
Welcome to the website of the CoMo Group. We develop and apply modern numerical methods to problems arising in Chemical Engineering. The overall aim is to shorten the development period from research bench to the industrial production stage by providing insight into the underlying physics and supporting the scale-up of processes to industrial level.
The group currently consists of 20 members from various backgrounds. We are keen to collaborate with people from both within industry and academia, so please get in touch if you think you have common interests.
The group's research divides naturally into two inter-related branches. The first of these is research into mathematical methods, which consists of the development of stochastic particle methods, computational fluid dynamics and quantum chemistry. The other branch consists of research into applications, using the methods we have developed in addition to well established techniques. The main application areas are reactive flow, combustion, engine modelling, extraction, nano particle synthesis and dynamics. This research is sponsored on various levels by the UK, EU, and industry.
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PhD studentship available: Modelling the formation of carbon nanotubes
A PhD studentship to start in October 2022 is available in the Computational Modelling Group via the EPSRC Centre for Doctoral Training in Aerosol Science.
The project will develop a reactive molecular dynamics model to investigate the processes controlling the formation of carbon nanotubes. The model will provide important understanding to guide the production of replacements for high-carbon materials such as steel and cement. The project would suit students with a passion for programming and modelling, and with a knowledge of chemistry.
Prospective candidates should simultaneously apply to and win a partial scholarship from the University of Cambridge (selecting Professor Markus Kraft as their supervisor), and apply to the Aerosol Science CDT.
Preprint 284 published
Preprint 284, ''From Platform to Knowledge Graph: Evolution of Laboratory Automation'', has been published!
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching towards the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically-accessible data representations and standardised communication protocols are indispensable. In this perspective, we recategorise the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesise that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems will be the driving force to bring data to knowledge, evolving our way of automating laboratory.
New paper - Soot inception: Carbonaceous nanoparticle formation in flames
Our new review on soot inception was just published in Progress in Energy and Combustion Science
The route by which gas-phase molecules in hydrocarbon flames form condensed-phase carbonaceous nanoparticles (incipient soot) is reviewed. These products of incomplete combustion are introduced as particulates and materials revealing both their useful applications and unwanted impacts as pollutants. Significant advances in experimental techniques in the last decade have allowed the gas phase precursors and the transformation from molecules to nanoparticles to be directly observed. These measurements combined with computational techniques allow for various mechanisms known to date to be compared and explored. Questions remain surrounding the various mechanisms that lead to nanoparticle formation. Mechanisms combining physical and chemical routes, so-called physically stabilised soot inception, are highlighted as a possible “middle way”.
Will using heat pumps for domestic heating increase or decrease inequality? We use the Universal Digital Twin to investigate...
Preprint 281, ''Universal Digital Twin – the impact of heat
pumps on inequality'', has been published!
This paper investigates how using heat pumps for domestic heating would impact fuel poverty and inequality. The analysis integrates a geospatial description of climate observations, gas and electricity infrastructure, energy consumption and fuel poverty from the base world of a Universal Digital Twin based on the World Avatar knowledge graph. Historic temperature data were used to estimate the temporal and geospatial variation of the performance of air source heat pumps in the UK. The corresponding change in gas and electricity consumption that could be achieved using heat pumps instead of gas for domestic heating was estimated. The geospatial impact of the heat pumps was assessed in terms of CO2 savings, and their effect on fuel cost and fuel poverty. Whilst heat pumps would reduce emissions, it is predicted that they would increase fuel costs. It was shown that both local and regional areas of high fuel poverty would experience some of the largest increases in fuel cost. This illustrates the potential for the transition to sustainable heating to exacerbate inequality. The analysis suggests that existing regional inequalities will increase, and that it comes down to a political choice between investments to support the most effective use of heat pumps, and delayed investments to counter inequality. The ability of the World Avatar to integrate the models and data necessary to perform this type of analysis provides a means to generate actionable information, for example, to enable local policy interventions to address the tension between social and environmental goals.
New paper - Modelling investigation of the thermal treatment of ash-contaminated particulate filters
Very excited to share our new paper investigating the effect of thermal treatment of ash-contaminated particulate filters!
This paper investigates the impact of thermal treatment on the pressure drop of particulate filters containing ash deposits. A model has been developed and applied to describe the deposition of soot and ash particles, and estimate the spatial distribution of the deposits in such filters. Phenomenological models have been developed to describe the potential sintering and cracking of the ash deposits caused by thermal treatment of the filter. The model results are in good agreement with experimental measurements of the reduction in the pressure drop in thermally treated filters. It was found that crack formation in the ash layer can lead to significant reduction of the pressure drop at relatively low temperatures. Sintering of ash deposits in the wall and the ash plug also contributes towards a decrease in filter pressure drop at higher temperatures. This work is the first attempt to model the impact of the thermal treatment of ash in particulate filters in order to support the development of future ash management strategies. The cracking of the ash layer during the thermal treatment has been identified to be the most critical effect for pressure drop reduction.
New paper - Universal Digital Twin – A Dynamic Knowledge Graph
Very excited to share our new paper proposing a design for a Universal Digital Twin!
This application of dynamic knowledge graphs presents a candidate to solve problems caused by lack of interoperability between different types of data and models. We believe that it has the potential to unify many different research streams, promoting synergistic effects that would be difficult to achieve in other ways. The paper explains how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted. A preview of some of the areas that we have investigated with our collaborators at CMCL can be found here: https://kg.cmclinnovations.com/.
The Universal Digital Twin is growing
Preprint 279, ''Universal Digital Twin - Integration of national-scale energy systems and climate data'', has been published!
This paper applies a knowledge graph-based approach to unify multiple heterogeneous domains inherent in climate and energy supply research. Existing approaches that rely on bespoke models with spreadsheet-type inputs are non-interpretable, static and make it difficult to combine existing domain specific models. The difficulties inherent to this approach become increasingly prevalent as energy supply models gain complexity while society pursues a net-zero future. 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 integrates data describing the systems of interest and specifies respective links between domains. We represent the UK gas transmission system, and HadUK-Grid climate data set as linked data for the first time, formally associating the data with the statistical output areas used to report governmental administrative data throughout the UK. We demonstrate how computational agents contained within the World Avatar can operate on the knowledge graph, incorporating live feeds of data such as instantaneous gas flow rates, as well as parsing information into interpretable forms such as interactive visualisations. Through this approach, we enable a dynamic, interpretable, modular and cross-domain representation of the UK that enables domain specific experts to contribute towards a national-scale digital twin.
New paper - diradical aromatic soot precursors in flames
Very excited to share our new paper on the molecular species that could lead to soot formation!
Thank you to the whole team; Jacob Martin, Laura Pascazio, Angiras Menon, Jethro Akroyd, Katharina Kaiser, Fabian Schulz, Mario Commodo, Andrea D'Anna, Leo Gross and Markus Kraft. Truly a team effort! Read more in our open-access article.
Preprint 276 published
Preprint 276, ''Universal Digital Twin - Land Use'', has been published!
This paper develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customised vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in a cross-domain use case that combines data about land use with a geospatial analysis of scenarios for energy provision. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed.
2021 Carbon Journal Prize
Former CoMo PhD student Dr Jacob Martin has received the prestigious 2021 Carbon Journal Thesis Prize. Jacob’s thesis, “Investigating the role of curvature on the formation and thermal transformations of soot”, was published in 2020 and supervised by Professor Markus Kraft.
The Carbon Journal Prize is awarded annually to a recent graduate in recognition of an outstanding PhD thesis in carbon material science and technology.
Preprint 274 published
Preprint 274, ''How does blending oxygenated fuels affect soot formation from Jet A2 diffusion flames?'', has been published!
Four oxygenated fuels: ethanol (EtOH), dimethyl carbonate (DMC), dimethoxy-methane (DMM) and polyoxymethylene dimethyl ether (PODE3) were blended with Jet A2 to investigate the sooting behaviour of the fuel mixtures. The smoke point was measured using wick-fed laminar diffusion flames as per the ASTM D1322 standard. The oxygen extended sooting index (OESI) was calculated to determine the sooting tendency of each mixture. Colour-ratio pyrometry and differential mobility spectrometry were used to measure the soot volume fraction (fv) and particle size distribution (PSD). The addition of oxygenated fuels caused a strong reduction in sooting tendency (i.e. OESI) at low blend strengths (5%) and a weaker linear reduction at higher blend strengths (10% and 20%). Each mixture showed a similar reduction at a given mole fraction of oxygenated fuel. The OESI broadly correlated with the soot volume fraction and particle size measurements. Increasing blend strengths resulted in smaller particles at the tip of the flame. The average particle size at the tip was influenced by the oxygen content but not the molecular structure of the oxygenated fuels, whereas the soot volume fraction in the wings was influenced by boththe molecular structure of the oxygenated fuels and the oxygen/carbon ratio of the mixture. For the first time, fv and PSD have been reported for flames produced using Jet A2 blends in an ASTM D1322 lamp. The ability to relate data gathered using the ASTM D1322 standard for the sooting behaviour of different mixtures is going to be increasingly important as the aviation industry seeks to switch to sustainable fuels.
Preprint 272 published
Preprint 272, ''Radial dependence of TiO2 nanoparticles synthesised in jet-wall stagnation flames'', has been published!
The formation of titanium dioxide (TiO2) nanoparticles from titanium tetraisopropoxide (TTIP) in premixed, jet-wall stagnation flames was simulated to investigate the variation of the particle properties as a function of deposition radius. Two different TTIP loadings (280 and 560 ppm) were studied in two flames: a lean flame (equivalence ratio, ϕ=0.35) and a stoichiometric flame (ϕ=1.0). First, the growth of particles was described using a spherical particle model that was fully coupled to the conservation equations of chemically reacting flow and solved in 2D using the finite volume method. Second, particle trajectories were extracted from the 2D simulations and post-processed using a hybrid particle-number/detailed particle model solved using a stochastic numerical method. In the 2D simulations, the particles were predicted to have mean diameters in the range of 3 to 10 nm, which is consistent with, but slightly less than experimental values observed in the literature. Off-centreline particle trajectories experienced longer residence times at higher temperatures downstream of the flame front. Two particle size distribution (PSD) shapes were observed. In the lean flame, a bimodal PSD was observed due to the high rates of inception and surface growth. In contrast, the stoichiometric flame was dominated by coagulation and the particles quickly attained a self-preserving size distribution. The PSDs were found to be different beyond a deposition radius of approximately one and a half times the nozzle radius due to a small degree of aggregation; this may impact the synthesis of nanoparticles using jet-wall stagnation flames for novel applications.
CoMo group open to Feodor Lynen Research Fellows
In 2016, Prof. Markus Kraft was awarded the Friedrich Wilhelm Bessel Award and is therefore eligible to host Feodor Lynen Research Fellows sponsored by the Alexander von Humboldt Foundation. The Feodor Lynen Research Fellowship covers the salary and travel expenses of researchers from Germany to work at the host institution for 6-24 months. In addition, the fellowship enables the successful candidate to apply for alumni sponsorship from the Humboldt Foundation after the end of the fellowship and become part of their international network of academics.
If you are interested in working at the University of Cambridge and in joining the CoMo group as a post-doctoral researcher, please check your eligibility on the official Feodor Lynen Research Fellowship website and familiarise yourself with the application procedure. You will need to write a research proposal that aligns with your professional expertise. The topic might be of computational or experimental nature but should lie within the research areas of the CoMo group.