Abstract
The rapid decarbonisation of the power generation and aviation sectors will require a move away from incremental development, exposing designers and researchers to the risk of unexpected results from uncertainty in boundary layer state. This problem already exists for parts developed with fully turbulent assumptions, but in novel design spaces the risk increases for both real components, where previous knowledge of similar designs may be inapplicable, and particularly in experimental testing of scaled models, where reducing Reynolds number can result in a drastic change in flow topology that skews the conclusions of a test. Computational methods struggle to reliably predict boundary layer state so experimental techniques for diagnosing boundary layer state are needed. Infrared thermography (IR) is a non-invasive technique that offers simple, fast visualisation of boundary layer state with no additional instrumentation. IR is relatively uncommon in the literature and there is minimal information available on the best practices for its use. This paper aims to encourage the adoption of IR as a diagnostic tool by demonstrating routes for optimisation and pointing out pitfalls to avoid. A low-order model is developed and used to predict how the signal-to-noise ratio (SNR) of an IR visualisation changes depending on the thermal design of the test piece. It is shown that in low-speed flows with active heating from the surface the SNR is maximised through a suitable choice of surface insulation, while in high-speed flows, where passive temperature differences are used, there is a crossover between heat transfer and recovery temperature effects that results in an SNR of zero, an effect that can arise in both steady-state and transient experiments. Experimental validation of the 1D model in both flow regimes is shown alongside two case studies on the use of IR in sub-scale testing where uncertainty in boundary layer state results in critical differences from the full-scale flow.
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