Computational science augments traditional research methods by accelerating and guiding experimental work and providing insight into processes and results. It is used across Shell’s businesses to predict everything from the chemistry of catalysts and batteries to capturing flow through reactors, pipelines and rocks. These are complex simulations which require high performance computing and algorithm optimisation.

A key aim of computational science is to use computer models to predict the performance of materials and systems in specific situations. One of the most striking aspects of computational science projects is their breadth of scale. This multiscale modelling covers interactions at the atomic and molecular levels to the design of reactors in industrial plants.

Our grasp of computational technology helped us to lead the way in technological developments in exploration in the 1960s, 70s and 80s. Demand for computational design and analysis has increased dramatically since mid-2000’s across increasingly varied domains. The growth in computer power from Moore’s law has made realistic catalyst modelling and complex fluid flow studies possible that were unthinkable only 15 years ago. Shell has a diverse team of chemical engineers, mechanical engineers, aerospace engineers, chemists, material scientists, mathematicians, physicists and computational scientists. This expertise in mathematics and computing is what gives us such a strong advantage today in developing and adopting digital technology.

By combining data-based models with physics/chemistry based computational models, we augment the power of both by integrating the speed and agility of AI with the interpretability and explainability of Computational Science, we move towards an era of Augmented Intelligence, where we augment our decision making manifold. Find out more in our recent publication on developing machine learning models for materials datasets.

Find out more

High Performance Computing for a sustainable hydrogen economy

Find out more how we use digital solutions to scale up the share of hydrogen in the energy system.

The road towards faster and sharper insights

Digitalisation is an enabler for the energy transition. The energy company of the future will be powered by sophisticated computational simulation algorithms. Our experts in High Performance Computing (HPC) technologies are pushing the boundaries in the energy sector, helping us run these algorithms faster and more efficiently.

Other fields of application of Computational Science

  • Ferrari race car on track

    Optimising Fuel Formulation

    Shell has a long and highly successful relationship with Scuderia Ferrari. Shell leverages computational science technology to develop an advanced computer system for the Formula One fuel formulation. This system simulates a vast number of possible fuel formulations. This enables researchers to optimise fuel properties and narrow the range of possible solutions to a selection which can be tested using conventional chemical analyses in the Shell Technology Centre Hamburg, Germany. This computational pre-selection helps to accelerate the overall fuel development cycle.

  • Battery model

    Improving battery performance to increase potential for renewable energy and safety

    Chemical storage of electrical energy is an important aspect of meeting modern energy demands. It can mitigate the intermittency and spatial variability of renewable energy availability. Combining traditional physics and chemistry with simulation and advanced imaging technology enables us to compare different kinds of batteries, not only looking at which materials perform better, but also which are more sustainable. We are looking across the end-to-end life of the battery from design and use right through to materials recovery and recycling. Shell is modelling the changes in the physical state and composition of electrodes and electrolytes in batteries as well as performance at pack level. Find out more about the research conducted with University of California Berkeley.

  • Model decarbonisation pathways for hard-to-abate industries.

    Model decarbonisation pathways for hard-to-abate industries

    Shell is helping to decarbonise hard-to-abate sectors of the manufacturing industry with an initial focus on cement and steel in India. ShellComputational Science researchers at our Research Centre in Bangalore, India, in collaboration with Shell TechWorks, are currently developing system-level models to help - through scenario analysis – hard-to-abate industries identify ways of improving energy productivity and reduce faster the levels of their carbon dioxide emissions. These models will provide a range of options and highlight the cost imperatives and emissions impact of the change in the operations. The models will eventually deliver insights on techno-economics factors and carbon trading options as well.

  • Demonstrate Shell E‑Fluids to be best-in-class thermal fluids for next generation EV batteries

    Demonstrate Shell E‑Fluids to be best-in-class thermal fluids for next generation EV batteries

    E-Fluids are a critical enabler to the large-scale deployment of electric-vehicles (EV). Their thermal properties make battery charging and discharging safer and more performant by efficiently cooling the battery packs. Shell applied computational science to demonstrate that Shell E-Fluids offer better active cooling performances than its market competitors. The demonstration of this game-changing performance led to a new patent for Shell and a deeper cooperation with the Austrian EV battery pack manufacturer Kreisel Electric. The computational model for E-Fluids can now be used to develop new formulations for Shell E-Fluids in pushing the performance further. It can also be used to provide modelling service to test fluid dynamics in alternative battery pack arrangements.

Recent Publications

Estimation of Time-lapse Timeshifts using Machine Learning

Yuan S., Duan Y., Hatchell P., Vila J., and Wang K., Society of Exploration Geophysicists Annual Meeting, Houston, TX, Oct. 2020.

Continuous Subsurface Tomography over Cellular Internet of Things

Hadi Jamali-Rad, Vincent van Beveren, Xander Campman, Jo van den Brand, Detlef Hohl, accepted to appear in IEEE Sensors Journal, 2020.

Mimicking coalescence using a pressure-controlled dynamic thin film balance

Emmanouil Chatzigiannakis, Peter Veenstra, Dick ten Boschb  and  Jan Vermant Soft Matter, August 2020, Advance Article.

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