Hartree Engineering & Manufacturing

Engineering & Manufacturing

We are developing software tools and methods for:

High accuracy computer modelling
Uncertainty quantification
Simulating complex multi-scale/multi-physics problems
Coupling new or existing solvers

We are demonstrating these tools through proof-of-concept projects in the following areas:

Complex rheology: understanding material properties through multi-scale modelling
Uncertainty quantification: using cognitive computing to gain a better understanding of errors in the modelling of carbon deposition and unsteady thermal flows
Acoustics: applying multi-physics and cognitive technologies to identify and reduce noise in simulated data

Our Engineering and Manufacturing digital assets will:

Reduce development costs
Improve product performance
Reduce time to market

Digital assets

The work combines existing modelling programs with AI and novel code-coupling technology to make it easier to develop state-of-the-art industrially relevant simulation models.

AI-driven process simulation | Using AI to control systems
Machine learning offers the opportunity to build control systems which can adapt to changing conditions and optimise themselves to meet business requirements – for example reducing energy use, cost, or improving output quality.
Multiphysics and Multiphase Code Coupler | Scalable and flexible code coupling
Industrially relevant systems often require a modelling approach which spans multiple scales (e.g. atomistic and macroscopic) or physical models (e.g. CFD and solid mechanics) to capture all of the relevant behaviour of a virtual product.
Quantification of Uncertainty Toolkit for Engineering (QUTE) | Computing the unknown
Simpler, cheaper and faster methods that can reproduce the results of a more accurate and more computationally demanding model to within a defined tolerance level
Simulation Methods for Engineering Applications | Building the virtual product
Combining our code coupling and cognitive assets allows us to rapidly develop custom simulation models for a wide range of industrial problems

People

David Emerson, Stephen Longshaw, Alex Skillen, Stefano Rolfo, Chris Thompson, Maggie Zimon, Robert Sawko, Lan Hoang, Blair Edwards, Stephen Checkley.