Experimental
Characterization of Frost Growth on a Horizontal Plate Under Natural
Convection

Niroomand, Shirin; Fauchoux, Melanie; Simonson, Carey J.

doi:10.1115/1.4040989

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…The 3D photogrammetry can overcome the difficulties generated by conventional 2D roughness measurements [25,33] using a surface profile from the lateral edge of the flat plate. Figure 6 gives an example conducted by using our wind tunnel system to show the difference of frost roughness observed from a lateral side 2D frost surface profile (Fig.…”

Section: Methodsmentioning

confidence: 99%

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Variation of Frost Roughness on a Flat Plate Under Forced Convection

Tongxin

O’Neal

McClain

2020

Journal of Thermal Science and Engineering Applications

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Experiments were conducted on a cold flat plate to characterize the variation of frost roughness over both time and location on the surfaces. The testing conditions included air temperatures from 8 to 16°C, wall temperatures from −20 to −10°C, relative humidities from 60 to 80%, and air velocities from 0.5 to 2.5 m s−1. A 3-D photogrammetric method was employed to measure the variation in frost root-mean-square height and skewness by location and time. These data were used to develop the equivalent sand-grain roughness for the frost at different locations and time. The experimental results showed that frost roughness varied by location and changed with time. For the environmental conditions in this study, relative humidity and air temperature were the most important factors determining changes in the peak frost roughness. For example, at an air temperature of 12ºC and surface temperature of −15ºC, the frost roughness peaked at about 40 minutes for a relative humidity of 80% and 90 minutes for a relative humidity of 60%. Empirical correlations were provided to describe the relationships between the environmental conditions and the appearance of the peak frost roughness.

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Section: Methodsmentioning

confidence: 99%

“…Conventional methods of frost roughness measurement [25,33] have used lateral side two-dimensional (2D) frost surface profiles. Frosting usually presents nonhomogeneous nucleation along the edges of test surfaces.…”

Section: Introductionmentioning

confidence: 99%

Variation of Frost Roughness on a Flat Plate Under Forced Convection

Tongxin

O’Neal

McClain

2020

Journal of Thermal Science and Engineering Applications

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“…This research fully demonstrates the necessity of image processing in frost detection. Also based on image processing technology, Shirin [14] and Wang [66] used a digital camera to take time-lapse photography of frost on the test sample, as shown in Figure 6b; through the calibration of the frost image, frost thickness was investigated with MATLAB software. Shirin's results show that surface roughness is related to frost density regardless of operating conditions, and frost density decreases with an increase in frost surface roughness.…”

Section: Image Recognition Detectionmentioning

confidence: 99%

“…Zhang et al [13] found that the type of frost crystals can change from feathery to needle-like, flaky, or even irregularly shaped crystals. Different crystal shapes result in frost layers with different surface properties, such as different densities, roughness, thickness, and thermal properties [14].…”

Section: Introductionmentioning

confidence: 99%

A Review of Methods and Techniques for Detecting Frost on Plant Surfaces

Song

et al. 2021

Agriculture

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Severe frost usually has adverse impacts on agricultural production, resulting in crop freeze injury, poor crop yield, and crop quality reduction. Timely and accurate detection of frost plays an important role in cold damage warnings, prevention, and control. Current frost detection methods mostly use physical properties such as light, electricity, and heat, or the judge and quantify using environmental factors such as temperature and wind speed. However, it is difficult to detect and accurately identify the frosting phenomenon in real time during field trials because of the complex environment, different plant types, and interference by many factors during observation. To provide an overview of the analytical tools for scientists, researchers, and product developers, a review and comparative analysis of the available literature on frost mechanisms, correlations, and characteristics are presented in this study. First, the mechanisms of the frost formation process, frost level, and the significance of detection, are introduced. Then, the methods and techniques used to measure frost on plant surfaces are synthetically classified and further compared. Moreover, the key points and difficulties are summarized and discussed. Finally, some constructive methods of frost detection are proposed to improve the frost detection process.

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“…As frost growing, the local temperature of frost, humidity of the trapped air varies and ultimately affects the following frost growth. So frosting is a highly transient coupled heat-mass transfer process, and multiple factors including surface temperature, wettability, roughness [3][4][5][6][7][8][9][10], air temperature, humidity and velocity [11][12][13][14] can affect the process.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulations of Frost Growth Using Mixture Model on Surfaces with Different Wettability

Shahane¹,

Shen²,

Wang³

2021

Preprint

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Frost growth on cold surfaces is a transient process with coupled heat and mass transfer. Due to multiple factors such as humidity, temperature, flow velocity and constantly changing thermal properties as frost grows, precise prediction can be challenging. Especially when the geometry of the frosting surfaces gets complicated, it requires a balance of computing accuracy and efficiency. In this work, a numerical model is developed to predict frost growth considering the effect of the above parameters. Mixture model is adapted to improve the computational efficiency and the unstructured grids add the flexibility to extend the model to complex geometries. The predicted frost growth rate matches well with the experimental data reported in the literature under similar conditions. The model predicts reasonable growth trend of frost as the surface temperature, air temperature, humidity and flow velocity vary. The surface wettability effect is well captured at the early stage of frosting and it shows a higher frost growth rate on surfaces with a higher wettability.

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Experimental Characterization of Frost Growth on a Horizontal Plate Under Natural Convection

Cited by 16 publications

References 35 publications

Variation of Frost Roughness on a Flat Plate Under Forced Convection

Variation of Frost Roughness on a Flat Plate Under Forced Convection

A Review of Methods and Techniques for Detecting Frost on Plant Surfaces

Numerical Simulations of Frost Growth Using Mixture Model on Surfaces with Different Wettability

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