Local variation of frost layer thickness and morphology

doi:10.1016/j.ijthermalsci.2005.05.004

International Journal of Thermal Sciences

Volume 45, Issue 2, February 2006, Pages 116-123

https://doi.org/10.1016/j.ijthermalsci.2005.05.004 Get rights and content

Abstract

Frosting is an important phenomenon encountered in the cryogenic industries in connection with gas coolers, refrigerators, heat pumps, etc. It may adversely affect the performances of those devices. This paper experimentally studied the local frost formation process on a cold surface with natural airflows or forced airflows over it. The frost layer thickness was found to increase stepwise during the frost formation process. This increase pattern was ever indicated only by one literature. The literature attributed the pattern to the melting of frost crystals at the frost surface. However, present observation of the morphology of the frost layer surface suggested that the growth of water drops or ice particles at the initial period caused the first slowly increasing of the frost layer thickness; the following growth of acerose-shaped ice crystals caused the rapidly increasing of the frost layer thickness; thereafter, the column-shaped ice crystals on the surface grew in its length and radius alternatively, which caused the frost layer thickness increasing rapidly and slowly alternatively.

References (20)

Y.B. Lee et al.
Frost formation on a vertical plate in simultaneously developing flow
Experimental Thermal Fluid Sci.
(2002)
H.W. Schneider
Equation of the growth rate of frost forming on cooled surfaces
Internat. J. Heat Mass Transfer
(1978)
L.A. Kennedy et al.
Free convection heat and mass transfer under conditions of frost deposition
Internat. J. Heat Mass Transfer
(1974)
K.S. Lee et al.
A one-dimensional model for frost formation on a cold flat surface
Internat. J. Heat Mass Transfer
(1997)
Y. Hayashi et al.
Study of frost properties correlating with frost formation types
J. Heat Transfer
(1977)
I. Tokura et al.
Study on properties and growth rate of frost layers on cold surfaces
J. Heat Transfer
(1983)
D.L. O'Neal et al.
Measurement of frost growth and density in a parallel plate geometry
ASHRAE Trans.
(1984)
P.L.T. Brian et al.
Frost deposition on cold surfaces
Indust. Engrg. Chem. Fund.
(1970)
A.Z. Sahin
An experimental study on the initiation and growth of frost formation on a horizontal plate
Experimental Heat Transfer
(1994)
G.J. Trammell et al.
A study of frost formed on a flat plate held at sub-freezing temperatures
ASHRAE J.
(July 1968)

There are more references available in the full text version of this article.

Cited by (52)

Capacitive sensing of frost growth dynamics on aluminum surfaces with different wettabilities
2024, International Journal of Heat and Mass Transfer
Frost formation is a critical issue in many industries including air conditioning and refrigeration, aviation, transportation, and cryogenics. Frost buildup on heat exchanger surfaces reduces their efficiency by acting as a thermal resistance and by reducing or blocking the airflow. Defrosting is therefore necessary to maintain system efficiency. However, defrost initiation and cycle duration are critical parameters that govern system performance. Excessively early or late defrosting can result in energy loss. Frost detection is thus essential to carry out defrosting efficiently. To know when to initiate defrost, both the density and thickness of the frost must be known as both affect the frost thermal resistance. However, state of the art approaches are not sensitive to both frost density and thickness. Here, a capacitive sensing method is proposed that is sensitive to both the density and thickness of the frost. The method can achieve precise frost detection, offering insights into the ideal time to initiate defrosting by being sensitive to factors that affect frost specific thermal resistance. Experiments are carried out on plain (hydrophilic), superhydrophobic, and superhydrophilic aluminum surfaces at three different surface temperatures (−6 °C, −10 °C, −16 °C) to investigate the sensing performance. The capacitance measured increases with time as frost forms and grows. This change in capacitance, along with mass and thickness data, was used as a tool to characterize frost growth. The results indicate that decreasing wettability (superhydrophilic to plain (hydrophilic) to superhydrophobic) results in a decreasing trend in mass accumulation, frost density, and change in capacitance and an increasing trend in frost thicknesses and thermal resistance. It was also demonstrated that increasing surface temperature results in decreasing changes in capacitance (measurement sensitivity), mass accumulation, frost thicknesses, and thermal resistance, with an increasing trend in frost density. A strong correlation between frost mass and change in capacitance was observed regardless of surface temperature or wettability. This rendered implications for the best time of defrosting initiation. The proposed approach offers high accuracy, sensitivity, and convenience as a non-invasive method to study dynamic frost growth with insights into more optimal defrosting cycles, contributing to the development of more efficient energy systems.
Experimental investigation and validation on an air-source heat pump frosting state recognition method based on fan current fluctuation signal and machine learning
2024, Energy
Thick frost on the evaporators of air-source heat pumps and refrigeration systems can degrade system performance. Defrosting in time based on precise frosting state recognition method is of great importance. Indirect measuring recognition method with low accuracy is widely used in commercial systems, for cheapness and simpleness, needing improvement urgently. While accurate direct measuring methods are usually expensive and complicated. Therefore, a novel frosting state recognition method with high accuracy is proposed, which can be realized with a simple current sensor. The method is based on the micro fluctuation in the evaporator fan current (rather than current amplitude) caused by the perturbed air due to frost. An experimental setup is built to obtain fan current samples. Combining three feature extraction approaches and three classifiers, four frost state recognition methods using fan current fluctuation are merged. They are compared and studied based on the experimental samples. Results show original signal + 1D-CNN method has the best identification performance, reaching 95.74 ± 1.73 % accuracy at −10 °C evaporator air temperature. It reveals 94.53 ± 1.06 % accuracy in a temperature range of −5∼-20 °C, and 94.73 ± 1.00 % accuracy for another fan with the same model. The method has significant improvement in performance and good potential for further study and application.
Time acceleration methodology for rapid simulation of frost formation with experimental validation
2024, International Journal of Heat and Mass Transfer
In this study, a numerical methodology based on the multiphase flow model was developed for predicting frost growth on a cooled surface. The proposed numerical methodology was validated by comparing the results of numerical simulations with measured data obtained from the present experiment. The slow-time acceleration technique was applied to reduce the computational time required for the simulation. The results demonstrated that the developed methodology accurately predicts the distribution of the ice phase volume fraction, the humid air streamline, temperature, and velocity magnitude. Furthermore, the slow-time acceleration technique significantly reduces the computational time required without sacrificing accuracy. Therefore, the developed methodology can be used to predict frost growth with good accuracy and reduced computational time, which has practical implications for various applications.
Detection of frost growth and distribution on louver and offset strip fins of a microchannel heat exchanger using capacitance sensing approach
2023, International Journal of Heat and Mass Transfer
Frost growth and distribution on a microchannel heat exchanger (MCHX) are measured using a capacitance sensing approach. Capacitive sensors using two electrode layouts are developed to detect the real-time frost growth in the direction along and perpendicular to the airflow. Frosting and defrosting experiments are conducted under different air and coolant temperatures on both louver fins and rectangular offset strip (ROS) fins. The results demonstrate that the frost thickness is measured on the actual heat exchanger surface with an estimated uncertainty of ± 0.128 mm (95% confidence). A delay of frost growth on the trailing edge is captured by the sensor for both fin structures, and a larger frost thickness is obtained under lower air and coolant temperatures. The fin shape effect on the frost distribution profile is also characterized using the sensor. Compared to that on louver fin, the frost thickness at the leading edge of ROS fin is larger, as is the frost thickness difference between the leading and trailing edges. Due to the louver effect, the frost is more uniformly distributed over the fin. Based on the capacitance data at five locations, a frost distribution map is constructed and agrees well with frost imaging. This work demonstrates the development and application of a novel method for frost distribution detection for heat exchangers, providing a powerful tool for the study of the frosting process and defrost control.
Frost layer growth behavior study on a cryogenic surface with water vapor content <1000 ppmv
2023, International Communications in Heat and Mass Transfer
Frosting under cryogenic conditions is common in liquefied natural gas (LNG), cryogenic containers, cryogenic wind tunnels, cryopump and other engineering fields, but the corresponding researches are still insufficient compared with that under general-low temperature conditions. Especially, research on extra-low-content water vapor frosting under cryogenic conditions stays almost blank. A visualization experiment system was built, based on which cryogenic frosting experiments of water-vapor-carrying nitrogen gas flow with different water vapor contents (120 ppmv ≤ ω_in ≤ 780 ppmv) considering the cooling process (from 290 K to 100 K) were carried out. Images through the whole frosting period were recorded and image processing was executed. The evolution of the frost surface was captured and the translation velocity was calculated. Physical properties of the frost layer including thickness (as large as 1.75 mm at 2 h) and roughness (up to 76.2 μm) were obtained and compared with humid air frosting. The frost layer growth pattern transition from needle-like vertical growth to clump-grass-like circular rotational growth was observed in the rapid cooling stage. The results reveal differences in frost formation mechanisms and characteristics from general-low to cryogenic temperatures and can provide a quantitative reference for mass and heat transfer analysis in practical frosting circumstances.
Study on a novel defrost control method based on the surface texture of evaporator image with gray-level cooccurrence matrix, new characterization parameter combination and machine learning
2023, Energy and Buildings
Defrost control using digital image processing is a potential, economical, and energy-saving solution for air-source heat pump or refrigeration system, comparing with currently studied or used direct/indirect measuring methods. However, under complex operating condition of different shooting angles, lighting conditions, and pixel level, which are common in refrigeration system, the accuracy of frost state recognition with existing digital image processing methods decrease dramatically. Therefore, a new method based on optimized gray level co-occurrence matrix and new characterization parameter combination to extract image texture characteristics, combining extreme learning machine algorithm (OGLCM-ELM), is proposed for the first time. An experimental rig for evaporator frosting image under different lighting intensity, shooting angle and pixel level is set up. The collected experimental image data are divided into 3 classifications (Frostless, light frost, and heavy frost) or 2 classifications ((Frostless + light frost, and heavy frost)). The results show for ternary classification, OGLCM-ELM reveals significantly higher recognition accuracy then existing methods, average accuracy is as higher as 98.14%. It is also 5.28% and 3.14% higher than those of OGLCM-SVM, OGLCM-BP. Other performance parameters, precision, recall, F1-score, and calculating time are also totally better than other methods. For binary classification. the average accuracy of OGLCM-ELM even reaches 99% under complex operating condition, indicating it is a practical and potential technology for defrost control.

View all citing articles on Scopus

View full text

Local variation of frost layer thickness and morphology

Abstract

Experimental Thermal Fluid Sci.

Internat. J. Heat Mass Transfer

Internat. J. Heat Mass Transfer

Internat. J. Heat Mass Transfer

Study of frost properties correlating with frost formation types

J. Heat Transfer

Study on properties and growth rate of frost layers on cold surfaces

J. Heat Transfer

Measurement of frost growth and density in a parallel plate geometry

ASHRAE Trans.

Frost deposition on cold surfaces

Indust. Engrg. Chem. Fund.

An experimental study on the initiation and growth of frost formation on a horizontal plate

Experimental Heat Transfer

A study of frost formed on a flat plate held at sub-freezing temperatures

ASHRAE J.