# Experimental Study of Pool Boiling Heat Transfer on Novel Fin Surfaces

Mahyar Ghazvini, Mazen Hafez, Philippe Mandin, Myeongsub Kim

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Mahyar Ghazvini, Mazen Hafez, Philippe Mandin, Myeongsub Kim. Experimental Study of Pool Boiling Heat Transfer on Novel Fin Surfaces. SSRN Electronic Journal, 2022, 10.2139/ssrn.4313622. [hal-05338307](https://hal-univ-brest.fr/hal-05338307v1)

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# Experimental Study of Pool Boiling Heat Transfer on Novel Fin Surfaces

Mahyar Ghazviniª, Mazen Hafezª, Philippe Mandinᵇ, Myeongsub Kimª*

ªOcean and Mechanical Engineering, Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431 *kimm@fau.edu

ᵇInstitut de Recherche Dupuy de Lôme, UMR CNRS 6027, 56100 Lorient, France

## Abstract

One of the most effective cooling techniques to manage extreme heat flux is probably boiling heat transfer because of its high heat transfer capability due to phase change. One method to further enhance the magnitude boiling heat transfer is increasing surface area by using fin structures. These structures have demonstrated superior efficiency compared with flat surface heat sinks. The objective of this study is to experimentally evaluate the performance of two different fin structures such as regular and modified fins in a nucleate boiling facility. A nucleation site is artificially fabricated on top of the regular fin (modified fin) to increase heat transfer performance. Heat transfer rates and heat transfer coefficients of the two fin structures are estimated in atmospheric pressure conditions utilizing DI water and compared with the literature. According to the results, the regular fin heat sink shows a comparatively similar heat flux at low wall superheats ( -30 °C to -10 °C) compared to the modified fin, while the modified fin structure indicates higher heat transfer performance than the regular fin once the wall superheat is higher than -10 °C. According to the boiling curves of two structures under various subcooling levels, the wall temperature of the modified fin is 2-4 °C lower than that of the regular fin. This comes from the additional nucleation site on the hollow fin, a greater rewetting phenomenon, and therefore a promising bubble nucleation, growth, and departure. Also, a multilayer perceptron artificial neural network with a back-propagation training algorithm is implemented to model the bubble departure diameter concerning wall superheat and subcooling level to predict the bubble behavior from the artificial nucleation site. The results show that the network is able to predict the desired parameter with a precision of R=0.92811.

**Keywords:** Thermal Management, Nucleate Boiling, Bubble Growth, Fin Structure.
# 1. Introduction

In the past few years, microelectronic devices have been getting smaller and more powerful which leads to severe heat dissipation in tightly enclosed areas. For example, in the processor Core i7-6700 K by Intel, the dissipated heat is rated at 95–110 W on the surface of a 13.52 × 9.05 mm² [1,2]. To guarantee a functional and prolonged lifetime microelectronic device, we must maintain the operating temperature stable in its optimal condition by actively managing such high heat flux. Nucleate boiling heat transfer (HT) is probably one of the most effective cooling methods for many systems because of a large amount of latent heat associated with the phase change [3]. The potential of innovative cooling systems associated with phase change has already provided considerable energy and cost savings in many thermal management applications [4–10].

Research on the enhancement of boiling HT has become a hot topic as the heat dissipation demand has continuously increased [2,11–13]. One robust method to enhance heat transfer performance of a heated surface is surface modification including chemical corrosion, surface deposition, and machining. The surface modification considerably affects the bubble behavior during boiling, consequently on the heat transfer performance, which shows distinct heat flux compared to conventional plain surfaces. Numerous surface modification techniques have been considered to fulfill the required heat dissipation for high-power devices. Various sizes of micro-pin-fins under distinct subcooling levels were experimentally utilized by Wei and Honda [14–16] in FC72 pool boiling. Based on their results, nucleate boiling can be enhanced by augmenting surface cavities. Additionally, for studying the roughness influences on nucleate boiling, special microstructures were constructed by Chu et al. [17]. They concluded that the roughest surface resulted in the most considerable critical heat flux improvement. Different pin-finned structures were designed by Kim et al. [18] to investigate the fin size's influence on heat flux. An optimum gap size for attaining the best HT was identified which showed 350% improvement of CHF. Also, they introduced an optimum channel width for achieving the greatest heat flux withing various microchannels. Xue et al. [19] experimentally studied the effect of pin-finned structures on boiling heat transfer in the heat fluxes from 7.2 W/cm² to
27.2 W/cm² under microgravity in nucleate boiling. According to the results, pin-finned structures with the dimensions of 30 × 60 µm² indicated a substantial HT improvement in nucleate boiling compared to a smooth surface under microgravity conditions. Another study was implemented by Walunj and Sathyabhama [27] to evaluate the influence of various microchannels including stepped, parabolic, and rectangular geometries on HT and bubble behavior.

Even though pin-finned structures have been widely used in HT enhancements, some modifications could be performed to enhance HT performance further. One advanced method is to artificially fabricate cavities on a surface [21–25]. These cavities play a huge role in enhancing HT due to increasing nucleation sites on a surface in boiling. It should be noted that all cavities cannot produce bubbles since there is a minimum cavity radius at [3]. Not all the cavities generate bubbles, and, therefore, a minimum cavity radius exists at certain heat flux. In other words, a cavity is called active nucleation site once the radius of the cavity is higher than the minimum radius. Also, the number of cavities which can generate bubbles per unit area is called the active nucleation site density. This parameter along with the bubble departure diameter is tightly connected to the boiling HT coefficient or the boiling heat flux [3].

The bubble behavior's change induces the HT enhancement of fin structures, which is the result of the surface modification [20]. Since bubble behaviors in nucleate boiling significantly affect the heat dissipation from a heated surface, studying bubble behaviors with some key parameters such as bubble departure frequency, bubble departure diameter, and growth time require special attention. Many studies focused on understanding bubble behaviors to provide an acceptable physical mechanism and theoretical prediction. Bosnjakovic [4] studied the bubble growth for the first time in superheated liquid. Later, some scholars experimented on the bubble behavior on pin-finned surfaces in nucleate boiling. Liu et al. [21] investigated the effects of microcavities in different pin-finned surfaces of HTC in nucleate boiling. Based on the results, the HTC increased considerably by augmenting the number of microcavities. Xu et al. [22] experimentally investigated the bubble behaviors on vertical pin-finned surfaces under subcooled boiling. They observed
distinct bubble growth modes on various surfaces, along with the effect of pin-finned surfaces on the bubble growth and departure. The results in the literature suggest that the bubble departure diameter and heat flux on pin-finned structures is more significant than on the smooth surface.

In this study, we introduce a new fin structure with an artificial nucleation site to further enhance the HT performance of the heated surface. We mainly investigate the heat flux and the bubble dynamics parameters of the new fin structure, including bubble growth time and bubble departure diameter. The results are compared with those from a regular fin structure to understand the effect of the additional nucleation sites on heat transfer. Also, a multilayer perceptron (MLP) artificial neural network (ANN) with a back-propagation (BP) training algorithm is implemented to model the bubble departure diameter with respect to wall superheat and subcooling level to predict the bubble behavior from the artificial nucleation site. This modeling approach would help understand bubble behaviors in nucleate boiling and consequently achieve a universal theoretical prediction of bubble growth in boiling. The mean square error (MSE) value of the model is calculated to evaluate the model's accuracy.

## 2. Materials and Methods

The experimental setup consists of a boiling chamber, test surfaces, data acquisition system, high-speed camera, light source, and heaters. DI water was used as a working fluid at atmospheric pressure, as shown in Figure 1. The boiling chamber (120×120×120 mm³) was made from transparent polymethylmethacrylate (PMMA, McMaster-Carr, Elmhurst, IL) sheets (6 × 6 inch, 3.6 mm thick) was filled with 1 L DI water. Two K-type thermocouples (OMEGA Engineering) were utilized to measure the liquid temperature. Also, two cartridge heaters (McMaster-Carr, 400W power, 2-inch length) were immersed from the top to the chamber to regulate the liquid temperature. The heater kept the working fluid at different subcooling levels from 25 K to near saturation temperature. A 50W LED dual gooseneck light source (AmScope, Irvine, CA) was utilized for the illumination, and a
high-speed camera (Fastec IL5, Fastec Imaging Corp., San Diego, CA) was used to capture images from the bubble cycle on the heated surfaces. The high-speed camera recorded images of the boiling process at the maximum frequency of 100 frames per second. Also, Nikon AF micro-NIKKOR 60mm f/2.8D lens (B&H Photo Electronic Corp.) was mounted on the camera to achieve clear close-up images. The images were then exported for processing, in which the segmentation and bubble behavior information extraction was completed using a MATLAB algorithm. The DC power supply connected to the heaters in order to regulate the heat flux of the liquid and surface temperatures. A low-thermal conductive silicone sealant (0.18 W/(m·K)) was used on the non-structured area of the heated surface to secure that only the fin area (10×4 mm²) was the effective heat transfer surface. The wall temperature was measured by a K-type thermocouple pasted to the backside of the surface with a high thermal conductivity adhesive. The entire test sections were attached to a low thermal conductive material (polycarbonate base), as presented in Figure 2. Table 1 presents the test conditions of the pool boiling.

The DC power supply provides the heat flux which is measured as,

$$q = \frac{VI}{A} \qquad (1)$$

where *I* and *V* indicate the current and voltage through the heated surface, and *A* refers to the effective HT area. The wall superheat is,

$$\Delta T_{sat} = T_{w} - T_{sat} \qquad (2)$$

where *T*<sub>sat</sub> and *T*<sub>w</sub> indicate the saturation temperature of the working fluid and wall surface temperature, respectively.

A 3D optical profilometer (Keyence VR-6000) was used to perform non-contact surface and roughness measurements. This profilometer can measure surface data with a resolution of 0.1 µm, allowing feature measurement which cannot be done with probe-type instrument. Figure 3 shows the 3D profiles of the regular and modified fin surfaces.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Schematic diagram of the experimental setup.</figcaption></figure>

<table><caption>**Table 1.** Test conditions of the pool boiling experiments.</caption><thead><tr><th>Test surfaces</th><th>Liquid subcooling levels (°C)</th><th>Pressure (atm)</th><th>Maximum heat flux (W/mm²)</th></tr></thead><tbody><tr><td>Regular fin surface</td><td>20, 15, 10</td><td>1.0</td><td>3.024</td></tr><tr><td>Modified fin surface</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Schematic of the test section.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3. 3D profiles of (a) the regular and (b) modified fin surfaces.</figcaption></figure>

# 3. Results and discussion

## 3.1. Heat transfer performance of fins in pool boiling

Pool boiling curves relating to the heat flux and wall superheat (Eq. 2) are used to characterize the heat transfer performance of the tested surfaces. Figure 4 shows the boiling curves for the two different surfaces under three liquid subcooling levels. In all subcooling levels, the surface with an artificial nucleation site shows lower wall temperature at the same heat flux or higher heat flux at a given temperature. It can be found that in the convection boiling region at a temperature range from 70 to 100 °C (i.e., ΔT$_{sat}$ = -30~0 °C), there is no significant difference in heat flux between the surfaces. On the other hand, the heat transfer performance of the modified fin is better than the regular fin when the nucleate boiling region begins (105~115°C). The boiling curve of the modified fin is moved to the left of the curves of the regular fin, and the wall temperature of the modified fin is 2-4 °C lower than that of the regular fin at the same heat flux.
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Boiling curves of two different surfaces under various subcooling levels (a) 35 °C, b) 25 °C, and c) 15 °C).</figcaption></figure>
## 3.2. Bubble departure diameter

To investigate the bubble behavior in nucleate boiling on the two fin structures under various conditions, the experiments were performed at different subcooling levels of 10 °C, 15 °C, and 20 °C. Subcooling level was considered to have smooth bubble growth in nucleate boiling since chaotic bubble formation at high heat fluxes hinders precise observance of the bubble behavior. Also, the entire experiments were performed under a heat flux of less than 4 W/mm², ensuring continuous single bubble generation.

Our particular interest is the investigation of the bubble departure diameter (BDD) ($D_d$) that explains the influences of wall temperature and subcooling level on the HT performance, and nucleate boiling HTC can be determined by using this parameter [3]. The bubble departure diameter is also a critical parameter that determines heat transfer coefficients using different semi-empirical or empirical correlations [3]. It is generally understood that the vapor bubble's final diameter at the time of departure is BDD [3]. The BDD is measured by analyzing images of transient bubble formation using a high-speed camera. Then, image processing software is applied for measuring the BDD during nucleate boiling. Figure 5 depicts the influence of wall temperature and subcooling levels on the bubble departure diameter for the regular fin. The diameter increases by increasing wall temperature and subcooling levels–it is approximately 2.56 mm for wall temperature at 106 °C and subcooling level at 10 °C. Figure 6 shows 3D scattered and fitted surface plots for the bubble departure diameter in the regular fin. In Figures 5 and 6, one interesting observation is that the bubble departure diameter increases linearly with wall temperature when decreasing the subcooling level. On the other hand, the effects of wall temperature on the BDD at different subcooling levels in the modified fin are presented in Figure 7. It can be seen that, like the regular fin, the BDD increases by increasing the wall temperature and reducing the subcooling level, showing approximately 3.3 mm at a wall temperature of 106 °C and a subcooling level of 10 °C.
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Bubble departure diameter on the regular fin with respect to wall temperature at three different subcooling levels.</figcaption></figure>

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 6.** a) 3D scattered and b) fitted surface plots for the bubble departure diameter on the regular fin.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 7.** Bubble departure diameter on the modified fin with respect to wall temperature at three different subcooling levels.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 8.** a) 3D scattered and b) fitted surface plots for the bubble departure diameter on the modified fin.</figcaption></figure>

### 3.3. Comparison between regular and modified fins

To better understand the effect of an artificial nucleation site on the fin, the BDD and bubble growth time in the regular and modified fins are compared at a wall temperature of 105 °C and a subcooling level of 10 °C, as shown in Figures 9 and 10. In Figure 9, although the data for the bubble diameter seem scattered along the wall temperature, the size of the BDD increases with increasing the wall temperature. Also, the bubble departure diameter for the modified fin is considerably higher than that of the regular fin at a given time and wall temperature. This is most likely because the artificial nucleation site on top of the modified fin facilitates bubble initiation and growth because of increasing the boiling surface area. Particularly, the difference in the departure diameter between the modified
and regular fins is much larger when the wall temperature is above 100 °C than when it is less than 100 °C.

The bubble growth rate refers to the variations of bubble diameter with time, which is another significant parameter associated with the boiling heat transfer coefficient. Figure 10 shows the changes in bubble diameter from its initiation to departure along with time, which indicates the bubble growth time. In this figure, the bubble diameter increases dramatically in the early stage of the bubble growth (< 0.5 ms) because of the nucleation of the bubble on the artificial nucleation site, called the inertia-controlled phase of bubble growth. Within this stage, a roughly linear relationship between the bubble diameter and time can be observed. At > 0.5 ms, the initiated bubble grows up at a slower rate compared to the initial stage and keeps a stable growth for a longer time (> 3 ms). In Figure 10, at the end stage of the bubble growth, the bubble leaves the nucleation site under the buoyancy force with its largest diameter (3.33 mm for the modified fin vs. 2.56 mm for the regular fin). The results in Figures 9 and 10 suggest that the bubble initiation, growth, and departure from the modified fin take place at a faster rate than those of the regular fin. The fast ebullition cycle of the bubble leads to bubble generation at a higher frequency. A lower local wall temperature can be achieved for the surface with a higher bubble frequency since fast bubble nucleation can remove high amount of heat from the heated surface.

<figure><img src="image_13.png" /><figcaption>**Figure 9.** Bubble departure diameter of both surfaces at subcooling level of 10 °C.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Figure 10.** Bubble growth curves for different surfaces at wall temperature of 105 °C and subcooling level of 10 °C.</figcaption></figure>

**3.4. The bubble growth model**

Over the past decades, in order to characterize the bubble cycle in nucleate boiling, some
mathematical models were proposed by different scholars. One of the first classical models
introduced by Bosnjakovic [4] and Jakob [5],
$$ \frac{dR}{dt} = \frac{k (T_o - T_s)}{h_{lv} \rho_v \sqrt{\pi \alpha_l t}} $$
focused on the bubble
growth in the homogeneous superheated liquid, where $\alpha_l$, $T_o-T_s$, $h_{lv}$, $\rho_v$, and $k$ are referred
to the liquid thermal diffusivity, the liquid superheat, the latent heat, the vapor density, and
the liquid thermal conductivity, respectively. Next, different adjustments have been
implemented to model the actual bubble process under various conditions due to the ideal
conditions of the mentioned model. As a consequence, different improved models were
introduced by various researchers, as provided in Table 2. Figure 11 shows the bubble
growth models developed by the traditional models while comparing the experimental data
of the current study for the two different fin surfaces. It shows that the outcomes of Mikic
[23] and Forster & Zuber [24] models comparatively predict the bubble diameter on the
regular fin surface at the initial stage, although the growth rate of the experimental result
is significantly higher than that of the classical models' growth rate. This is because the classical
models were founded upon plain surfaces while the HT from the pin-finned surfaces was not taken into account. As time passes, the bubble behavior on the regular fin can be predicted relatively better by the model proposed by Cole and Shulman [25]. On the other hand, none of the classical models predict bubble growth on the modified fin surface. Furthermore, the bubble diameter based on the classical models constantly enlarged with time. As it is clear, no model can predict the bubble behavior on the modified fin which has artificial nucleation site. Thus, a new model is needed to introduce in order to describe the bubble behavior. In the bubble formation on the modified fin which has an artificial nucleation site, not only larger bubble can be created, but also the nucleation site remains strenuously active which can produce continuous bubbles, thus, sustainable bubble generation can be achieved from the artificial nucleation site without any suppression. This phenomenon leads to a lower wall temperature and higher heat transfer coefficient.

<figure><img src="image_15.png" /><figcaption>**Figure 11.** The comparison of the bubble growth models, including the traditional models and the experimental data of the current study for the two different fin structures.</figcaption></figure>

<table><caption>**Table 2.** Classical models for the prediction of the bubble growth curve.</caption><thead><tr><td>Ref.</td><td>Author</td><td>Correlations</td></tr></thead><tbody><tr><td>[25]</td><td>Cole and Shulman</td><td>D = 5 Ja<sup>0.75</sup>√α<sub>l</sub>t</td></tr></tbody></table>
[23]

Mikic

$$r^+ = \frac{2}{3}[(r^+ + 1)^{1.5} - (r^+)^{1.5} - 1]$$

[24]

Foster and Zuber

$$D = 3.544J\alpha\sqrt{\alpha_l t}$$

### 3.5. Modeling of bubble departure diameter by MLP network

In the last few decades, advanced computer methods have been emerged due to the technological advancements. These methods can be applied to various engineering applications. The reason is because performing a two-phase experiment or CFD simulation is often time-consuming and expensive. Also, predicting bubble dynamics parameters BDD in nucleate boiling is complex due to numerous independent variables involved in the process, such as liquid and wall temperatures, each valid within a finite range of values. Thus, an advanced computational method should be introduced to interpret the relationship between their parameters. The utilization of soft computing can be mentioned as an encouraging method. Recently, novel advancements of soft computing methods have been observed in numerous engineering and scientific applications, including Genetic Algorithms (GA), Genetic Programming (GP), and Artificial Neural Networks (ANNs). Among the mentioned methods, ANN, which mimics the way human brain learns, utilizes data to perform required tasks without supervising it [26]. An ANN is a major modeling technique of data in different engineering problems, including heat transfer and boiling phenomena [27]. ANN is developed based on the operation of the brain's neural network. The network's processing units are neurons connected through communication links, each with an associated weight. A standard neural network has considerable amounts of neurons and their connections [28]. The fundamental elements of an ANN are transfer function, learning algorithm, a network architecture, and changing these elements makes ANN methods distinct from other artificial neural networks.

In the current study, a MLP-neural network with a back-propagation (BP) training algorithm is utilized to optimize the ANN-based modeling. In the BP training algorithm, the weights of the neuron connections are adjusted based on the discrepancy between the desired network outputs and the predicted outputs [29]. An MLP network composes of one
input layer, one output layer, and one or more hidden layers (Figure 12). The inputs and outputs' number of the problem determines the number of neurons in the input and output layers, whereas the hidden layers' number and the neurons' number in each hidden layer can be selected by designer's choice. Moreover, the designer can choose the neurons' transfer function in the hidden and output layers. Thus, the performance of this method is susceptible to these selectable parameters, and, in particular, the results are sensitive to the number of neurons in each hidden layer and the transfer function in each layer. The network's architecture can be optimized to produce the minimum error for the best performance. In this way, the network's performance is determined based on the MSE values between the desired output and the predicted output from the networks.

<figure><img src="image_16.png" /><figcaption>**Figure 12.** Example schematic of a neural network structure.</figcaption></figure>

To model the bubble departure diameter with the neural network, the obtained experimental dataset is divided randomly such that 70% of the dataset is utilized for training, 15% for testing, and the last 15% for validation. To train the network, the Levenberg-Marquardt algorithm is applied because it is commonly used and very robust in mathematics and computing, showing that on many occasions Levenberg-Marquardt algorithm gives the best solution whilst starting far off the ultimate solution [30]. The input variables and the number of samples for each case are presented in Table 3. All datasets are normalized before being applied to the neural network. The normalization commonly expedites the learning process, leading to a quicker convergence. Figure 13 shows the typical structure of the 1HL network applied in this study, where *W*, *b*, and *N1* indicate the weight, bias, and number of neurons for the neural network.
<table><caption>**Table 3.** Inputs and the number of samples for each output parameter.</caption><thead><tr><td>Output</td><td>Inputs</td><td>Number of samples</td></tr></thead><tbody><tr><td>Bubble departure diameter (mm)</td><td>Wall temperature (°C)</td><td>Subcooling level (°C)</td><td>46</td></tr></tbody></table>

<figure><img src="image_17.png" /><figcaption>**Figure 13.** A typical structure of the 1HL network.</figcaption></figure>

Figures 14 shows the regression diagrams for the best MLP network with one hidden layer for bubble departure diameter, rely upon the parameters in Table 3. In these diagrams, the MLP network with one hidden layer can approximate the desired outputs based on the values of the correlation coefficient. The correlation coefficient (R) provides the ability of the suggested models in predicting the bubble departure diameter data with values from 0 to 1. Based on Figure 14, the R values of the regression diagrams for the bubble departure diameter are 0.93186 and 0.92812 for training and all datasets, respectively. Most of the data points for both test and validation are condensed near the line that indicates the precise estimation of the suggested models. The R values for the BDD for the test and validation are 0.94369 and 0.96332, respectively. These results suggest that the MLP neural network can accurately predict the bubble departure diameter in nucleate boiling from a heated surface. The developed ANN model enables the accurate prediction of the BDD at various subcooling levels and wall temperatures.
<figure><img src="image_18.png" /><figcaption>**Figure 14.** Regression diagrams of the MLP network for bubble departure diameter with the 1HL network.</figcaption></figure>

Additionally, Figure 15 shows the error histogram for the BDD with the 1HL neural network. The error histogram is a graph of the errors between estimated values from the neural network and target values. This indicates how predicted values are close to the target values, and how the errors from the neural network are distributed. For achieving a precise
model, the error distribution diagram is needed to obey the normal distribution diagram.
Based on Figure 15, the errors are normally distributed and considerably low. In other
words, the error frequency accumulates mainly in the zero-axis error range, which makes
a symmetric graph and shows that the methods have an excellent performance in estimating
the behavior of the bubble departure diameter. Note that the "zero error" line divides
positive and negative values. The direction of the bias can be identified by the sign of the
error. Positive error indicates the outputs are smaller than the targets and negative error
indicates that the targets are larger than the outputs.

<figure><img src="image_19.png" /><figcaption>Figure 15. Error analysis for bubble departure diameter with the 1HL network.</figcaption></figure>

4. Conclusion

The objective of this study is to experimentally evaluate the performance of two different
fin structures such as regular and modified fins in a nucleate boiling facility. A nucleation
site is artificially fabricated on top of the regular fin (modified fin) to increase heat transfer
performance. Heat transfer rates and heat transfer coefficients of the two fin structures are estimated in atmospheric pressure conditions utilizing DI water and compared with the literature. Distinct bubble dynamics parameters including bubble growth rate and bubble departure diameter are estimated by using these two surfaces, and the obtained results are compared with each other to delineate the effect of using artificial nucleation site on the heat transfer performance using high-speed imaging. Also, a MLP-ANN with a back-propagation training algorithm is applied for modeling the bubble departure diameter at different wall superheat and subcooling levels. The mean square error of the model is calculated to evaluate the accuracy of the models. This approach would help understand the bubble behavior in nucleate boiling and, consequently, achieve a universal theoretical prediction of bubble growth in boiling. In summary,

1) The modified fin shows better heat transfer performance than the regular fin, with a higher bubble departure diameter and growth rate.

2) The local wall temperature in the modified fin is lower since the fast bubble nucleation remove great amounts of heat at a higher bubble frequency thanks to the artificial nucleation site.

3) The bubble departure diameter for the modified fin is 29% larger than the regular fin at a subcooling level of 10 °C and wall superheat of 6 °C.

4) The MLP neural network can accurately predict the BDD in nucleate boiling from a heated surface. The results show that the network can predict the desired parameter with a precision of 0.92811. Moreover, the modeling results show that the residuals are scattered around the zero axis, which shows the model's accuracy.

5) By comparing the two fin surfaces, the surface with an artificial nucleation site shows a lower wall temperature at the same heat flux. It can be found that in the convection boiling region, there is no significant difference between the wall temperatures for both surfaces. The heat transfer performance of the modified fin is better than the regular fin in the nucleate boiling region.

## Acknowledgment

This work was financially supported by the National Science Foundation (#1917272).
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