## 1 Introduction
### State of the Art  
A measurement method to analyze the laminar-turbulent flow transition on wind turbines is the use of pressure sensors [2]. Pressure sensors have successfully been applied on wind turbines, but the time-consuming installation of these sensors leads to long downtimes of the wind turbine. Doppler Global Velocimetry (DGV) [3,4] and ParticleImage Velocimetry (PIV) [5] are known non-invasive, direct flow field measuring techniques. However, both measurement principles have severe limitations for ground-based boundary layer flow measurements on the rotor blades of operating wind turbines. The large measuring distance (>100 m), the large size of the wind turbine rotor and the large volume of airflow involved result in low contrast-to-noise ratios (CNR) and low spatial resolutions. Both limitations make these methods less suitable for in-process measurements on wind turbines. As an alternative, infrared thermography (IRT) is an optical flow visualization method that relies on the different heat transfer in the different boundary layer flow regimes. It provides fast, non-invasive flow imaging from large distances. In wind tunnel experiments, the capability of IRT to visualize the laminar-turbulent transition [6], laminar separation bubbles [7] or turbulent separation [8] was proven. However, the exposure time for one camera image is limited to cope with the motion blur of the moving rotor blade. [PERSON] et al. addressed the challenges of motion blur and camera noise by employing two integration times. They effectively combined 16,000 images of a turbine rotor blade using a filtering and de-blurring process [9]. [PERSON] et al. employed temperature decline thermography to visualize the boundary layer state on a turbine rotor with reduced noise [10]. IRT has further been demonstrated as a convenient method for determining the laminar-turbulent transition on rotor blades [11]. The laminar-turbulent transition is determined by evaluating the intensity of the thermograms, and [PERSON] et al. [12] and [PERSON] et al. [13] were the first to apply this method for in-process measurements on wind turbines. Using IRT on wind turbines, the CNR of the thermographic images is limited due to the field conditions (for instance low solar radiation) or a small sensitivity of the measurement effect (as seen in the detection of static [14] or dynamic stall [15], or when using small-signal evaluation approaches such as differential images tested on an airfoil [16] and rotor blades of wind turbines [17]). [PERSON] et al. improved the contrast with averaging methods, i.e., non negative Matrix factorization [18] and principle component analysis [19]. Nevertheless, without including previous knowledge, the available CNR directly limits the achievable measurement uncertainty for the determination of the laminar-turbulent transition position on the rotor blade of a wind turbine. However, even though every rotor blade is different and suffers different conditions over its lifespan, they also share many common characteristics and, presumably, behave similarly in the wind. Therefore, it needs to be investigated whether previous knowledge, which means information that has been gained on previous experiments or simulations, can improve the IRT-based measurement of the laminar-turbulent transition position on wind turbine rotor blades. Since such an investigation is missing for the IRT-based flow visualization, understanding how to include previous knowledge into a measurement means reaching out to other fields of research. [PERSON] and [PERSON] used previous knowledge in the form of a physical law as a constraint to improve their measurement [20]. Constraints are a straightforward approach to including previous knowledge, but previous knowledge cannot always be formulated in a suitable form. A more general way is to think of previous knowledge in the form of a probability distribution for the transition to be at a certain position.