Contents lists available at ScienceDirect Cold Regions Science and Technology journal homepage: www.elsevier.com/locate/coldregions Measuring thickness of marine ice using IR thermography Taimur Rashid ⁎ , Hassan Abbas Khawaja, Kåre Edvardsen UiT-The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway ARTICLE INFO Keywords: Marine ice Ice thickness Infrared imaging Thermography Marine operations Cold climate ABSTRACT There are several challenges to operating in a cold climate. Marine icing is one of them, and its mitigation is vital for marine operations. The presented work is a laboratory-scale setup to measure marine ice thickness. The developed methodology can be applied towards de-/anti-icing setups. The method described is based on measuring the average surface temperatures of the marine ice. Infrared thermography (IRT) is used to measure the thermal response of ice when subjected to active heating. These tests are performed at various controlled climatic conditions. The surface temperature profiles of marine icing samples are recorded with a calibrated high definition infrared camera. The results show distinct thermal profiles for different ice thicknesses (5, 10 and 15 mm). The thermal profile revealed three parameters, namely: time to respond (t 0 ), rate of change of temperature ∂ ∂ ( ) T t , and time to reach ΔT of 5 °C (t f ). These parameters can be empirically correlated to initial temperature (T 0 ) and ice thickness (t h ). It was found that time to respond (t 0 ) had a strong correlation with ice thickness (t h ); however, the rate of change of temperature ∂ ∂ ( ) T t and time to reach ΔT of 5 °C (t f ) were both dependent on initial temperature (T 0 ) and ice thickness (t h ). The study mentioned above is conceptual proof that ice thickness can be measured with the given setup, taking into account environmental parameters and accurate calibration. 1. Introduction 1.1. Cold climate operations Cold climate operations face several challenges. Recent activity in the Arctic Circle has encouraged researchers to study the various challenges. One of these is marine icing phenomenon. The long-term exposure of the superstructures to marine icing can cause rapid ice accretion, which can be hazardous for both human and machine safety (Ayele & Barabadi, 2016; Makkonen, 1984; Ryerson, 2011; Shellard, 1974). The uncertainty in the prediction of icing makes this issue all the more significant. Icing on ships and offshore structures is caused by atmospheric sources and sea spray, with sea spray being the major contributor to icing. It is generated by the collision of sea waves, the breaking of waves due to strong winds and bursting bubbles that float upon the waves (Lozowski et al., 2000; O'Dowd et al., 2008; Rashid et al., 2016b). The droplets produced from the sea spray travel at a certain trajectory and fall upon different parts of ships such as rails, deck, stairs, etc. These droplets freeze, typically during their trajectory path, due to the low atmospheric temperature (Samuelsen et al., 2017; Stallabrass, 1980). The droplets are known as sea spray, which is the main source of marine icing on various parts of ships (Stallabrass, 1980). Generally, sea spray icing occurs at the lower heights of the ship's surface such as decks, derricks and handrails. The height of the sea spray is approximately 16 m above sea level (Sultana et al., 2018; WMO, 1962). Various empirical models have been developed to predict the marine icing phenomenon. Many of these models are based on the extensive icing data collected between the 1960s and 1980s and are reported by researchers (Brown & Roebber, 1985; Overland et al., 1986; Roebber & Mitten, 1987; Samuelsen et al., 2017; Stallabrass, 1980; Zakrzewski et al., 1989). This study has gained momentum over the past 20 years. Researchers have come up with empirical relationships, including different parameters, to explain and accurately predict marine icing phenomenon, e.g. (Dehghani-Sanij et al., 2017; Samuelsen et al., 2017) These parameters include atmospheric temperature, wind speed, droplets' mass, droplets' travel time, etc. Dehghani et al. (Dehghani et al., 2017) have developed an analytical model for heat conduction through brine-spongy ice, and Fazelpour et al. (Fazelpour et al., 2017) have demonstrated an image-processing method to detect accumulated ice on known structures. https://doi.org/10.1016/j.coldregions.2018.08.025 Received 8 May 2018; Received in revised form 10 July 2018; Accepted 23 August 2018 ⁎ Corresponding author. E-mail address: taimur.rashid@uit.no (T. Rashid). Cold Regions Science and Technology xxx (xxxx) xxx–xxx 0165-232X/ © 2018 Elsevier B.V. All rights reserved. Please cite this article as: Rashid, T., Cold Regions Science and Technology, https://doi.org/10.1016/j.coldregions.2018.08.025