The Intelligent Integration of Digital Twins with Physical Experiments

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The development of new technology in the gas turbine industry - from initial research projects to application in production engines - is often associated with large costs and long timescales. It is now a priority in this industry to reduce the expense of the development process, and to make the introduction of performance-enhancing technological innovations a more streamlined and cost-effective procedure. Digital Twins - high resolution, unit-specific computer representations of individual gas turbines used for high-precision CFD simulations - have the potential to make redundant certain stages of testing and component validation in the development process, and it will be this potential about which my investigations will focus. Typically, novel technology is first investigated in a research or academic setting, in which its behaviour can be measured, quantified, and more thoroughly understood. If a technology has potential to improve engine performance in some way, it may be further investigated in company research facilities: dedicated component test rigs may be constructed, and the part being investigated will also be introduced into a development engine and tested under more realistic conditions. Finally, if successful at this stage, the component will be introduced into a production engine, before final tests are run, validating the machine before handover to a customer. In recent years, it has been the case that dedicated component testing and the development engine testing has become increasingly uneconomic, and it has occasionally been the case that such tests overrun to a point even beyond customer handover. Replacing this stage of development with a cheaper, quicker alternative that provides equal or greater utility to company engineers would clearly be of great benefit. My project will investigate how dedicated component test rigs and development engine test rigs may be replaced with a single 'prototype engine' test stage, complimented by the use of a Digital Twin, to improve efficiency and lower costs. Instead of performing exhaustive tests of a single component in isolation, the component would be instead installed into a prototype engine, of which there exists a Digital Twin copy available to generate simulations. Given test data from this adapted prototype engine, and measurements of certain externalities to which performance is known to be sensitive (e.g. atmospheric conditions, fuel quality, machine health indices etc.), a fair comparison could be made against a 'baseline' prototype digital twin simulation. In theory, the impact of the new component could be inferred from differences between the physical measurements and the simulation results. Replacing the component test rig stage in this way could potentially result in reduced overall costs and more rapid integration of new technologies. To be able to achieve this goal, an investigation into historical Digital Twin and physical experimental data is necessary. In particular, the sensitivities to different external conditions, and the reproducibility and reliability of experimental results must be well understood - for the proposal to work, it must be shown how Digital Twin results can be reliably mapped to physical results under variable conditions. The degree of certainty with which predictions can be made and measures of performance can be inferred must be accurately quantified. To do this, I will be making novel use of statistical sensitivity methods (such as Sobol analysis), and will be producing a number of new low-order models for gas turbine components, against which the results of Digital Twin simulations and physical experiments under variable conditions may be compared.