Towards indirect assessment of surface anomalies on wind turbine rotor blades

TL;DR

This paper explores non-invasive infrared thermography techniques for detecting surface defects on wind turbine blades by combining fluid dynamics, wind tunnel, field measurements, and computational simulations.

Contribution

It introduces a novel approach linking fluid dynamical parameters to infrared thermography for classifying rotor blade surface anomalies.

Findings

IRT data sensitivity correlates with the roughness Reynolds number.

Validated local domain approach enables efficient Navier-Stokes simulations.

Parameter regime relevant for turbines is feasible in experiments.

Abstract

We present results from novel field, lab and computer studies, that pave the way towards non-invasive classification of localised surface defects on running wind turbine rotors using infrared thermography (IRT). In particular, we first parametrise the problem from a fluid dynamical point of view using the roughness Reynolds number ($Re_k$) and demonstrate how the parameter regime relevant for modern wind turbines translate to parameter values that are currently feasible in typical wind tunnel and computer experiments. Second, we discuss preparatory wind tunnel and field measurements, that demonstrate a promising degree of sensitivity of the recorded IRT data w.r.t. the key control parameter ($Re_k$), which is a minimum requirement for the proposed classification technique to work. Third, we introduce and validate a local domain ansatz for future computer experiments, that enables well-resolved Navier-Stokes simulations for the target parameter regime at reasonable computational costs.