Tag Archives: simulation

Can you trust your digital twin?

Author’s digital twin?

There is about a 3% probability that you have a twin. About 32 in 1000 people are one of a pair of twins. At the moment an even smaller number of us have a digital twin but this is the direction in which computational biomedicine is moving along with other fields. For instance, soon all aircraft will have digital twins and most new nuclear power plants. Digital twins are computational representations of individual members of a population, or fleet, in the case of aircraft and power plants. For an engineering system, its computer-aided design (CAD) is the beginning of its twin, to which information is added from the quality assurance inspections before it leaves the factory and from non-destructive inspections during routine maintenance, as well as data acquired during service operations from health monitoring. The result is an integrated model and database, which describes the condition and history of the system from conception to the present, that can be used to predict its response to anticipated changes in its environment, its remaining useful life or the impact of proposed modifications to its form and function. It is more challenging to create digital twins of ourselves because we don’t have original design drawings or direct access to the onboard health monitoring system but this is being worked on. However, digital twins are only useful if people believe in the behaviour or performance that they predict and are prepared to make decisions based on the predictions, in other words if the digital twins possess credibility. Credibility appears to be like beauty because it is in eye of the beholder. Most modellers believe that their models are both beautiful and credible, after all they are their ‘babies’, but unfortunately modellers are not usually the decision-makers who often have a different frame of reference and set of values. In my group, one current line of research is to provide metrics and language that will assist in conveying confidence in the reliability of a digital twin to non-expert decision-makers and another is to create methodologies for evaluating the evidence prior to making a decision. The approach is different depending on the extent to which the underlying models are principled, i.e. based on the laws of science, and can be tested using observations from the real world. In practice, even with principled, testable models, a digital twin will never be an identical twin and hence there will always be some uncertainty so that decisions remain a matter of judgement based on a sound understanding of the best available evidence – so you are always likely to need advice from a friendly engineer 🙂

Sources:

De Lange, C., 2014, Meet your unborn child – before it’s conceived, New Scientist, 12 April 2014, p.8.

Glaessgen, E.H., & Stargel, D.S., 2012, The digital twin paradigm for future NASA and US Air Force vehicles, Proc 53^rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA paper 2012-2018, NF1676L-13293.

Patterson E.A., Feligiotti, M. & Hack, E., 2013, On the integration of validation, quality assurance and non-destructive evaluation, J. Strain Analysis, 48(1):48-59.

Patterson, E.A., Taylor, R.J. & Bankhead, M., 2016, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103.

Patterson EA & Whelan MP, 2016, A framework to establish credibility of computational models in biology, Progress in Biophysics & Molecular Biology, doi: 10.1016/j.pbiomolbio.2016.08.007.

Tuegel, E.J., 2012, The airframe digital twin: some challenges to realization, Proc 53^rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.

Opal offers validation opportunity for climate models

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Many of us will be familiar with the concept of the carbon cycle, but what about the silicon cycle? Silicon is the second most abundant element in the Earth’s crust. As a consequence of erosion, it is carried by rivers into the sea where organisms, such as sponges and diatoms (photosynthetic algae), convert the silicon in seawater into opal that ends up in ocean sediment when these organisms die. This marine silicon cycle can be incorporated into climate models, since each step is influenced by climatic conditions, and the opal sediment distribution from deep sea sediment cores can be used for model validation.

This approach can assist in providing additional confidence in climate models, which are notoriously difficult to validate, and was described by Katharine Hendry, a Royal Society University Research Fellow at the University of Bristol at a recent conference at the Royal Society. This struck me as an out-of-the box or lateral way of seeking to increase confidence in climate models.

There are many examples in engineering where we tend to shy away from comprehensive validation of computational models because the acquisition of measured data seems too difficult and, or expensive. We should take inspiration from sponges – by looking for data that is not necessarily the objective of the modelling but that nevertheless characterises the model’s behaviour.

Source:

Thumbnail: http://www.aquariumcreationsonline.net/sponge.html

Credibility is in the eye of the beholder

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Last month I described how computational models were used as more than fables in many areas of applied science, including engineering and precision medicine [‘Models as fables’ on March 16th, 2016]. When people need to make decisions with socioeconomic and, or personal costs, based on the predictions from these models, then the models need to be credible. Credibility is like beauty, it is in the eye of the beholder. It is a challenging problem to convince decision-makers, who are often not expert in the technology or modelling techniques, that the predictions are reliable and accurate. After all, a model that is reliable and accurate but in which decision-makers have no confidence is almost useless. In my research we are interested in the credibility of computational mechanics models that are used to optimise the design of load-bearing structures, whether it is the frame of a building, the wing of an aircraft or a hip prosthesis. We have techniques that allow us to characterise maps of strain using feature vectors [see my post entitled ‘Recognising strain‘ on October 28th, 2015] and then to compare the ‘distances’ between the vectors representing the predictions and measurements. If the predicted map of strain is an perfect representation of the map measured in a physical prototype, then this ‘distance’ will be zero. Of course, this never happens because there is noise in the measured data and our models are never perfect because they contain simplifying assumptions that make the modelling viable. The difficult question is how much difference is acceptable between the predictions and measurements . The public expect certainty with respect to the performance of an engineering structure whereas engineers know that there is always some uncertainty – we can reduce it but that costs money. Money for more sophisticated models, for more computational resources to execute the models, and for more and better quality measurements.

Models as fables

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In his book, ‘Economic Rules – Why economics works, when it fails and how to tell the difference‘, Dani Rodrik describes models as fables – short stories that revolve around a few principal characters who live in an unnamed generic place and whose behaviour and interaction produce an outcome that serves as a lesson of sorts. This seems to me to be a healthy perspective compared to the almost slavish belief in computational models that is common today in many quarters. However, in engineering and increasingly in precision medicine, we use computational models as reliable and detailed predictors of the performance of specific systems. Quantifying this reliability in a way that is useful to non-expert decision-makers is a current area of my research. This work originated in aerospace engineering where it is possible, though expensive, to acquire comprehensive and information-rich data from experiments and then to validate models by comparing their predictions to measurements. We have progressed to nuclear power engineering in which the extreme conditions and time-scales lead to sparse or incomplete data that make it more challenging to assess the reliability of computational models. Now, we are just starting to consider models in computational biology where the inherent variability of biological data and our inability to control the real world present even bigger challenges to establishing model reliability.

Sources:

Dani Rodrik, Economic Rules: Why economics works, when it fails and how to tell the difference, Oxford University Press, 2015

Patterson, E.A., Taylor, R.J. & Bankhead, M., A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016

Hack, E., Lampeas, G. & Patterson, E.A., An evaluation of a protocol for the validation of computational solid mechanics models, J. Strain Analysis, 51(1):5-13, 2016.

Patterson, E.A., Challenges in experimental strain analysis: interfaces and temperature extremes, J. Strain Analysis, 50(5): 282-3, 2015

Patterson, E.A., On the credibility of engineering models and meta-models, J. Strain Analysis, 50(4):218-220, 2015

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