Model-Based Optimisation

The use of computational modelling in engineering has gained increasing momentum over the last three decades mainly driven by the massive advance in computer power. Industry is making use of computational models to increase the speed of its technical development, which is an important factor in the overall competitiveness of a company. Environmental considerations, such as global warming and pollution reduction demand constant development of new products for an ever changing set of constraints defined by the market and government. It is clear that computational modelling will play an increasingly significant role in this optimisation process as it reduces the cost of development.

There are a number of important aspects to this process which require careful attention

  • Robust and comprehensive access to experimental and model data
    • Advanced data models based upon XML and RDF, the language of the world wide web, these can be employed to store experimental and model data in a standardised structure. By adopting these standards, models can consistently access these data for on-going model parameter optimisation against infinitely large databases.
  • Intelligent model parameter estimation
    • Due to limited computational resources or lack of understanding of the underlying physical processes, computational models are built upon assumptions, whilst not ideal this introduces model parameters. This aspect must be embraced as a critical step in the model development process as the methods employed for the optimisation of these parameters with respect to data (experimental or model) have serious implications for the robustness of the model when adopted later on.
  • Inclusion of experimental and parametric uncertainty
    • We also have advanced optimisation technologies that will account for experimental uncertainty during parameter optimisation but also enable for engineers to propagate the uncertainty in model based parameters through the model to the final model results. This enables engineers to have a measure of model confidence.
  • Engineering process or design optimisation
    • Once the model is properly validated and engineers have sufficient confidence in its application, control inputs can be optimised to satisfy multiple engineering constraints whilst maximising efficiency or reducing emissions etc.

A user story outlines how this methodology was carried out for application for developing advanced soot and combustion models for IC Engines. A second example was applied to tackle similar challenges in modelling granulation processes.

How Can We Help?

  • Data standardisation – experimental and model data storage and communication
  • Advanced optimisation routines – accounting for experimental and parametric uncertainty
  • Model repose surfaces, reduced models
  • Bayesian approaches
  • Advanced parameter estimation
  • Engineering process or design optimisation
  • Multi-objective, multi parameter optimisation
  • Model discrimination – quantify usefulness of experiments for identifying model parameters

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