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 23 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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Preprint 264 published
Preprint 264, ''The National Digital Twin of the UK – a knowledge-graph approach'', has been published!
It is widely recognized that combatting climate change will require massive changes in infrastructure and energy systems. Digitalization will offer new ways to design and operate infrastructure and will form an important part of the response to this challenge. Digital twins composed of multiple interacting distributed entities that share data and combine to answer complex questions have been identified as a key aspect of this move towards digitalization. However, it is far from clear how to implement such a digital twin. This remains a critical area of research. In this paper we propose a solution to the implementation problem based on a dynamic knowledge graph developed as part of the World Avatar project. We present the concept and preliminary evidence from case studies in Singapore.
Preprint 263 published
Preprint 263, ''Carbonaceous nanoparticle formation in flames'', has been published!
The route by which gas-phase molecules in hydrocarbon flames form solid carbonaceous nanoparticles 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" with reactive aromatics activated by hydrogen of particular interest.
Preprint 262 published
Preprint 262, ''Automated calibration of a poly(oxymethylene) dimethyl ether oxidation mechanism using knowledge-graph technology'', has been published!
In this paper, we develop a knowledge-graph based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene) dimethyl ether (PODEn, where n=3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park Simulator (JPS, theworldavatar.com), an existing cross-domain knowledge-graph. OntoChemExp is fully capable of supporting experimental results in the Process Informatics Model (PrIMe) database. Following this, a set of software agents are developed to perform experimental results retrieval, sensitivity analysis, and calibration tasks. The sensitivity analysis agent is used for both generic sensitivity analyses and reaction selection for subsequent calibration. The calibration process is performed as a sampling task followed by an optimisation task. The agents are designed for use with generic models but are demonstrated with ignition delay time and laminar flame speed simulations. We find that calibration times are reduced while accuracy is increased compared to manual calibration, achieving fittings 92% more accurate. Further, we demonstrate how this workflow is implemented as an extension of the JPS.
PhD studentship available: Modelling the plasma synthesis of graphene
A PhD studentship to start in October 2021 is available in the Computational Modelling Group via the EPSRC Centre for Doctoral Training in Aerosol Science.
The project will develop a Kinetic Monte Carlo model to investigate the processes controlling the plasma synthesis of graphene. The model will provide important understanding to guide the future production of high-value carbon materials. A slide introducing the project is available here.
We are looking for an outstanding student who is interested in working with us to design and implement Kinetic Monte Carlo algorithms to explore the growth of carbon materials. 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 261 published
Preprint 261, ''Structural effects of C3 oxygenated fuels on soot formation in ethylene coflow diffusion flames'', has been published!
This paper investigates how the structure of three C3 oxygenated fuels: dimethyl carbonate (DMC), dimethoxymethane (DMM) and isopropanol (IPA) influences soot formation when the fuels are blended with ethylene in laminar coflow diffusion flames. Up to 20% of the total carbon was substituted with oxygenated fuel. Colour ratio pyrometry was used to measure the soot volume fraction (SVF). IPA caused a strong increase in SVF, whereas DMM and DMC both caused an initial increase followed by a progressive decrease in SVF as the proportion of oxygenated fuel was increased. Differential mobility spectrometry and thermocouple probes were used to measure the particle size distribution and gas temperature in the flames at 5% blend strength. The hottest region of the 5% flames was consistently about 100 K cooler than the corresponding region of the ethylene flame, indicating a thermal effect of the doping. The 5% flames showed an increase in the maximum centre-line average particle size and SVF versus the ethylene flame, with the IPA showing the largest increase. The evolution of the centre-line particle size distributions showed that the 5% flames experienced earlier particle growth compared to the ethylene flame. Consideration of the role of the chemical pathways towards benzene formation suggests that methyl radicals from the decomposition of the oxygenated fuels are responsible for the increase in SVF at 5% doping. The difference in SVF between the IPA versus DMM and DMC flames is thought to be due to the additional presence of C3 species originating from the carbon-carbon bonded backbone of IPA. The difference between the DMC versus DMM flames is thought to arise from carbon dioxide produced during the decomposition of DMC, and a corresponding thermal effect where the pyrolysis region of the 5% DMC flame was observed to be about 50 K cooler than the other flames.
Another blockchain paper wins Highly Cited Paper Award
Last time we won this award, it was also for a blockchain paper.
Preprint 259 published
Preprint 259, "Modelling investigation of the thermal treatment of ash-contaminated particulate filters" has been published!
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.
Prof. Kraft presents a Combustion Webinar
Professor Markus Kraft, head of the CoMo group, recently presented an online webinar titled "Carbonaceous nanoparticle formation in flames" highlighting the group's recent research on soot formation. The Combustion Webinar series was organised by the combustion science community to enable the opportunity to interact technically, which has been difficult recently due to the pandemic. Talks so far have covered fundamental combustion science and diagnostics, as well as the future of the internal combustion engine.
Preprint 255 published
Preprint 255, ''The effect of poly(oxymethylene) dimethyl ethers (PODE3) on soot formation in ethylene/PODE3 laminar coflow diffusion flames'', has been published!
This paper investigates the effect of poly(oxymethylene) dimethyl ether (PODE3) on soot formation in atmospheric pressure ethylene/PODE3 laminar coflow diffusion flames. The flames were fuelled using ethylene/PODE3 mixtures, where up to 20% of the total carbon in the mixture was substituted with PODE3. Flame temperature measurements suggest that differences in the soot formation in the flames are more likely due to a chemical effect rather than a temperature effect. Colour ratio pyrometry and differential mobility spectrometry were used to measure the soot volume fraction (SVF) and the particle size distribution (PSD) of the flames. PODE3 was observed to be effective in reducing soot formation due to its high oxygen content and the absence of carbon-carbon bonds as per previous engine studies. However, for the laboratory flames studied in this work, it was observed that introducing low levels of PODE3 actually increased the amount of soot. When PODE3 was blended with ethylene at 5%, there was an increase of about 10% in the SVF and about 6% in average particle size compared to the pure ethylene flame. Consideration of the chemical pathways suggests that this interaction is specific to C2 chemistry. Only when the amount of PODE3 was increased to 10% did the SVF decrease. A further increase in PODE3 to 20% was observed to inhibit the particle growth, with the maximum average particle size decreasing by about 73%. The results suggest that blending sufficient amounts of PODE3 with fuels could reduce soot formation, but that the use of too little PODE3 could increase soot formation in the cases of fuels that produce a substantial amount of C2 species in fuel-rich regions of an engine. The data set reported with this paper includes, for the first time, PSD data for the International Sooting Flame Workshop coflow laminar diffusion flame (ISF-4 coflow 3).
Prof. Kraft elected Fellow of the Combustion Institute
Head of the CoMo group Professor Markus Kraft has been elected a Fellow of The Combustion Institute for outstanding and influential research in soot chemistry and computational modelling of engine combustion.
To be considered for this award, researchers must be active participants of The Combustion Institute, whether through publishing in affiliated journals, attending the International Symposia on Combustion or attending section meetings.
Professor Kraft has contributed significantly towards the detailed modelling of combustion synthesis of nanoparticles. He has a strong interest in the area of computational modelling and optimisation targeted towards developing carbon abatement and emissions reduction technologies for the automotive, power and chemical industries.
The 27 new Fellows for 2020 were announced on 17th February.
Supercharging decarbonisation through intelligent technologies
Integrating digital tools into the world’s energy systems could reduce carbon emissions by more than 50 per cent, a new review has found.
The review re-assesses the famous marginal abatement cost curve (MACC) popularised by McKinsey and finds that digitalisation of energy systems completely alters the curve, thanks to the creation of novel pathways for the transition to low-carbon energy. If cyber-physical systems are integrated into our energy systems, carbon abatement potential can be expected to increase by 20 per cent, rising to 30 per cent when artificial intelligence (AI) is included.
MACCs illustrate both the cost and potential of various carbon dioxide reduction strategies and are used by policy makers to assess which paths to pursue. The addition of cyber-physical systems –digital technologies that interact with the physical world – is a substantial update to the MACC and further establishes it as an indispensable tool for those working on decarbonisation.
Decarbonising the world’s energy systems is a crucial part of mitigating climate change through the reduction of greenhouse gas emissions. While decarbonisation is non-negotiable if climate breakdown is to be halted, it must be balanced with ensuring economic stability and a smooth transition to sustainable energy.
Digital technologies such as big data, machine learning and the Internet of Things hold immense potential to help us meet this challenge. Their applications range from helping to reduce our power bills by employing smart meters in the home, to assisting with peer-to-peer energy trading between power stations via blockchain.
An international team of researchers from Singapore, Switzerland, the UK and the US found that while existing digital technologies have numerous and effective applications when considered individually, the potential reduction of carbon emissions is multiplied when they are combined. Such combinations are called cyber-physical systems – interacting networks of physical infrastructure and computers that allow for smarter analysis, decision-making and optimisation of energy systems.
Introducing AI into these cyber-physical systems can lead to further carbon savings; up to 30 per cent more than without AI. This combination of technologies creates what is dubbed “intelligent cyber-physical systems”. Benefits include more resilient infrastructure and operational flexibility, among others.
Enhanced renewable energy forecasting is one good example of how an intelligent cyber-physical system can be applied. The wind and solar energy sectors have seen much growth and while the price of these technologies has come down, the intermittent nature of this type of power has limited its application. The integration of backup energy systems (natural gas plants, for example) or energy storage technologies is required. Intelligent cyber-physical technologies, in particular machine learning, could help with this integration through improved forecasting of solar and wind variability.
Other large energy systems such as power plants can also benefit. When applied to carbon capture and storage plants, for example, these technologies can convert operational data into actionable intelligence, thereby reducing costs and improving energy efficiency through improved processes.
Cyber-physical systems, especially those combined with AI, provide the much-needed boost required for countries to meet their decarbonisation and emissions targets. It is now up to policy makers to take this forward by incentivising the deployment of these technologies to combat climate change.
“The Impact of Intelligent Cyber-Physical Systems on the Decarbonization of Energy” (DOI: 10.1039/c9ee01919g) is published in Energy & Environmental Science by researchers from University of Cambridge, Cambridge Centre for Advanced Research and Education in Singapore Ltd, Swiss Academy of Sciences, Princeton University, National University of Singapore and Nanyang Technological University.
Prof. Kraft presents at inaugural Energy and AI conference
Professor Markus Kraft, head of the CoMo group, recently attended The First International Conference on Energy and AI, held in Tianjin, China in early January. Professor Kraft was an invited speaker at the conference and gave a plenary talk on the topic of intelligent decarbonisation. The conference covered such topics as digital twins in energy systems, Internet of Things and cyber-physical systems, all of which have particular relevance for CARES’ J-Park Simulator project.
The conference also saw the launch of a new international journal, Energy and AI. Professor Kraft is on the Editorial Board, which had their first meeting during the conference. An open-access journal by Elsevier, Energy and AI invites submissions of articles, reviews and short communications at the interface of energy and artificial intelligence.
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.