Welcome from the Computational Modelling Group

A picture showing several members of the CoMo 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 17 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.

Markus Kraft's Signature
Markus Kraft - Head of the CoMo Group

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The World Avatar added to WEF’s list of Global Use Cases

08 September 2022
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The World Avatar project has been selected for the World Economic Forum (WEF)’s list of Global Use Cases as part of its Global Digital Twin Cities initiative.

The initiative offers a potential solution for policymakers worldwide to improve city governance and create a conducive urban ecosystem for industries and people. It is a collaboration between the WEF’s Urban Transformation Platform, The China Academy of Information and Communication Technology (CAICT), and other key stakeholders.

At the beginning of 2021, the WEF and the CAICT launched the three-year initiative to make digital twin technology accessible to the public, promote social equity and inclusiveness, help cities achieve digital and low-carbon transformation, and jointly shape the future of digital twin city development. Ultimately, WEF hopes to render cities more inclusive, sustainable, and liveable through these efforts.

The World Avatar and the WEF

The World Avatar (TWA) project perfectly fits as an example for this WEF initiative. TWA represents the digital world using a dynamic knowledge graph containing concepts and data describing the world. TWA simulates the world’s behaviour with an ecosystem of autonomous computational agents that update the concepts and data so that the digital world remains current in time.

In this manner, it is possible to create digital twins that can describe the behaviour of any complex system and can make data-driven decisions about how to optimise the systems. TWA can improve interoperability across all sectors by providing a solution that enables the integration of distributed heterogeneous models and data. By including a description of the computational agents within the TWA knowledge graph, we can confer the discoverability of the computational capabilities and easily combine heterogeneous pieces of software and data.

Uses of The World Avatar

TWA contains a description of the UK’s electrical power and gas transmission systems. This gives geospatial details of infrastructure and captures relationships between different infrastructure elements and live data feeds describing gas flow into the transmission system. TWA can leverage disparate information from gas transmission systems, gas consumption data, and climate observations to investigate the hypothetical outcome of switching from gas heating to heat pumps.

Using TWA, it was discovered that while a complete switch to heat pumps would reduce the UK’s total carbon dioxide emissions by approximately 10%, it would exacerbate fuel poverty in colder parts of the country. In this way, TWA allows the UK government to examine specific scenarios in enough detail to support local policymaking.

TWA can also contain descriptions of the geometry and features of buildings, including details of individual apartments and the contents of individual rooms. Once the buildings are described in TWA, they can be processed by computational agents acting on the knowledge graph.

For example, TWA has a representation of the CARES lab in Singapore. Data about the building, the laboratory layout, and the locations of sensors in the laboratory are all represented as part of TWA’s knowledge graph. Agents then use real-time data about the ambient humidity and outdoor air temperature to achieve a specified temperature in the lab, and flammable chemicals can be monitored by incorporating sensors into the safety cabinet in which they are stored.

Another fitting application of TWA is the Climate Resilience Demonstrator (CReDo), developed by CARES’ collaborator CMCL Innovations as part of the UK’s National Digital Twin programme. CMCL applied ideas from TWA to create a digital twin that integrated data describing assets from the water, energy, and telecom industries. Data from flood simulations were then combined with asset data to model the potential cascade of failures through the combined asset network in future flood scenarios for cities.

TWA is much more than just these examples. With its dynamic knowledge graph technology and ecosystem of autonomous computational agents, it can create digital twins that can describe the behaviour of any complex system and can make data-driven decisions about how to optimise the systems. WEF’s Global Digital Twin Cities initiative has recognised the value of this approach, and by adding CARES and TWA to its Global Use Cases, it stands as an example of how to reach true interoperability between diverse sectors.

New paper - Universal Digital Twin: Integration of national-scale energy systems and climate data

13 June 2022
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Very excited to share our new paper investigating how our idea for a Universal Digital Twin can integrate national-scale energy systems and climate data!

This article 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 noninterpretable, 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 visualizations. Through this approach, we enable a dynamic, interpretable, modular, and cross-domain representation of the UK that enables domain specific experts to contribute toward a national-scale digital twin.

Preprint 291 published

22 February 2022
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Preprint 291, ''Reviewing the modelling treatment of deposits in particulate filters for internal combustion emissions'', has been published!


Internal combustion in transport vehicles is still one of the biggest contributors to ultrafine particle emissions which have been proven to have many adverse effects on human health and the environment in general. To mitigate this problem a variety of particle filters have been developed and along with these filters a whole range of models aiming to optimise filter performance. This paper reviews a wide variety of particulate filter models for vehicular emission control and presents the volume of work in a unified and consistent notation. Particle filtration models are examined with respect to their filtration efficiency, the way they handle particle deposits within the filter wall, the formation of filter cake and the role of catalytic conversion and the effect of gaseous emission. Further, the impact of the chemical and physical properties of particulate deposits on the filter regeneration process is analysed and reaction pathways and rates are presented. In addition the accumulation of ash deposits and its impact on the filter behaviour is critically reviewed. Finally, various measures are identified that can potentially improve the current particle filter models.

PhD studentship available: Modelling the formation of carbon nanotubes

12 November 2021
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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

10 November 2021
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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

01 November 2021
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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...

28 September 2021
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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 - Universal Digital Twin – A Dynamic Knowledge Graph

06 September 2021
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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/.

CoMo group open to Feodor Lynen Research Fellows

23 November 2020
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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.