In-cloud icing of objects is caused by super-cooled microscopic water droplets carried by the wind. To estimate the icing rate of objects in such conditions, the liquid water content (LWC) of the icing cloud and the median volume diameter (MVD) of the droplets are measured. Mixed-phase clouds also contain ice crystals which must be ruled out in order to avoid overestimation of the icing rate. Typically, cloud droplet instruments are not able to do this. A particle imaging instrument ICEMET (icing condition evaluation method) was used to observe in-cloud icing conditions. This lensless device uses a computational imaging method to reconstruct the shadow images of the microscopic objects. The size, position and shape descriptors of each particle are measured. This data is then used to filter out the ice crystals. The droplet size distribution and the size of the measurement volume are used to determine the LWC and MVD. The performance of the instrument was tested under mixedphase icing conditions in a wind tunnel and on a wind turbine. The measured LWC and MVD values were used to model the ice accretion on a cylinder-shaped object according to ISO 12494:2017 icing standard. In the wind tunnel, the modeled ice mass was compared to the weighed ice mass collected by a cylinder. According to our results, ice accretion rates were overestimeted by 65.6 % on average without filtering out the ice crystals. Thus, the ability to distinguish between droplets and ice crystals is essential for estimating the icing rate properly.

A time-resolved Raman spectrometer is demonstrated based on a 256×8 CMOS SPAD line sensor and a 573 nm fiber-coupled diamond Raman laser delivering pulses with duration below 100 ps FWHM. The collected back scattered light from the sample is dispersed on the line sensor using a custom volume holographic grating having 1800 lines/mm. Efficient fluorescence rejection in the Raman measurements is achieved due to a combination of time gating on sub-100 ps-time scale and a 573 nm excitation wavelength. To demonstrate the performance of the spectrometer, fluorescent oil samples were measured. For organic sesame seed oil having a continuous wave mode fluorescence-to-Raman ratio of 10.5 and a fluorescence lifetime of 2.7 ns, a signal-to-distortion value of 76.2 was achieved. For roasted sesame seed oil having a continuous wave mode fluorescence-to-Raman ratio of 82 and a fluorescence lifetime of 2.2 ns, a signal-to-distortion value of 28.2 was achieved. In both cases, the fluorescence-to-Raman ratio was reduced by a factor of 24-25 owing to time gating. For organic oil, spectral distortion was dominated by dark counts while for the more fluorescent roasted oil, the main source of spectral distortion was timing skew of the sensor. With the presented post-processing techniques, the level of distortion could be reduced by 88-89 % for both samples. Compared to common 532 nm excitation, approximately 73 % lower fluorescence-to-Raman ratio was observed for 573 nm excitation when analyzing the organic sesame seed oil. Index Terms-Fluorescence rejection, Raman laser, Raman spectrometer, Raman spectroscopy, SPAD sensor, time-correlated single photon counting, time gating, timing skew I. INTRODUCTION AMAN spectroscopy is used in a wide range of fields including food and oil industries, mining industry, medical diagnostics, pharmacy, forensic science and archaeometry [1]

An optical cloud droplet and ice crystal measurement system ICEMET (icing condition evaluation method), designed for present icing condition monitoring in field conditions, is presented. The aim in this work has been to develop a simple but precise imaging technique to measure the two often missing parameters needed in icing rate calculations caused by icing clouds-the droplet size distribution (DSD) and the liquid water content (LWC) of the air. The measurement principle of the sensor is based on lens-less digital in-line holographic imaging. Cloud droplets and ice crystals are illuminated by a short laser light pulse and the resulting hologram is digitally sampled by a digital image sensor and the digital hologram is then numerically analyzed to calculate the present DSD and LWC values. The sensor has anti-icing heating power up to 500 W and it is freely rotating by the wind for an optimal sampling direction and aerodynamics. A volume of 0.5 cm 3 is sampled in each hologram and the maximum sampling rate is 3 cm 3 /s. Laboratory tests and simulations were made to ensure the adequate operation of the measurement sensor. Computational flow dynamics simulations showed good agreement with droplet concentration distributions measured from an icing wind tunnel. The anti-icing heating of the sensor kept the sensor operational even in severe icing conditions; the most severe test conditions were the temperature − 15 °C, wind speed 20 m/s and the LWC 0.185 g/m 3. The verification measurements made using NIST traceable monodisperse particle standard glass spheres showed that the ICEMET sensor measurement median diameter 25.54 µm matched well with 25.60 µm ± 0.70 µm diameter confidence level given by the manufacturer.

Computational fluid dynamics and particle tracing simulations are presented for a cloud droplet sensor. Airspeeds and streamlines around the sensor are calculated at several wind speeds and their effect on the droplet sampling are examined. Particle tracing is used to study the effect of different wind speeds and droplet sizes on the sampling of the cloud droplets. Simulated droplet concentrations are confirmed by comparing them with measured wind tunnel data. Results demonstrate clear sampling effects that are functions of both wind speed and droplet size. Optimal compromise between maximal measurement volume and sampling effects is found and a simple approximation for sensor's sampling bias is presented. The results show that CFD simulations can give valuable information about the sampling of droplets in an ideal environment with known droplet concentrations. Even in a wind tunnel, the true test conditions are often impossible to accurately determine. Thus by simulating the sampling effects in different conditions, the sensor can be calibrated for a wide range of naturally occurring cloud conditions.