Numerical Investigation of Nanofluid’s Heat Transfer Performance in

Passive Residual Heat Removing System of AP1000 Nuclear Reactor

MANTASHA PONKTY1, ANAMIKA PUJA2, ABDUS SATTAR MOLLAH1,*

1Department of Nuclear Science and Engineering,

Military Institute of Science and Technology,

Dhaka,

BANGLADESH

2Department of Energy and Nuclear Engineering,

Ontario Tech University,

Oshawa,

CANADA

*Corresponding Author

Abstract: - The Passive Heat Removal system (PHRS) is designed to remove the residual heat from the core in

case of a station blackout, failure of emergency core cooling system, or failure of feedwater supply through the

Passive Residual Heat Removal Heat Exchanger (PRHR HX). PRHR HX consists of a C-shaped tube bundle as

a heat exchanger and the In-Containment Refueling Water Storage Tank (IRWST) as a heat sink. A

temperature distribution of this passive heat removal system of an AP1000 Reactor is generated using

COMSOL Multiphysics and the heat transfer coefficient is calculated to illustrate the effectiveness of the

PHRS. A comparison of the heat transfer coefficient between the IRWST filled with water and nanofluid has

been generated using the PRHR HX design. Thermophysical properties of nanofluids have been calculated in

the process of calculating the heat transfer coefficient. Numerical results show the difference in temperature

reduction of Al2O3, TiO2, and Ag as opposed to water in the IRWST. Time-dependent heat conduction of water

and nanofluid results contribute to the effective analysis of passive heat removal systems and provide

information for the safe operation of AP1000 reactors. By the end of 2024/2025, two VVER-1200 power

stations with a combined capacity of 2400 MW will be operating in Bangladesh. For safety and

licensing reasons, heat transfer simulation of VVER-1200 can be performed using COMSOL

software.

Key-Words: - Passive Heat, Nanofluid, Heat Transfer, PRHR Hx, AP1000, COMSOL, CFD, IRWST, Mesh.

Received: September 12, 2023. Revised: May 17, 2024. Accepted: June 13, 2024. Published: July 31, 2024.

1 Introduction

Global public anxiety over the safety of nuclear

reactor operations has skyrocketed in the wake of

the Fukushima Daiichi tragedy in 2011. Due to the

loss of power to pump cooling water, the accident's

leftover heat from the core cannot be dissipated in

time. Therefore, the primary focus in recent years

has been on creating novel and sophisticated

reactors with passive safety features. Without any

further active controls or activities, the passive

safety systems can function. The fundamental laws

of physics serve as the engine for the continuous

operation of passive safety measures. There are

numerous designs of large Gen-II/III+ Pressurized

Water Reactors on the market. Technologies such as

the Korean APR-1400, European EPR, American

AP1000, Russian VVER-1200, NHR-200-II in

China and others, [1], [2], [3], [4]. Many modern

pressurized water reactors (PWRs) have used the

passive residual heat removal heat exchanger

(PRHR HX) with a C-tube bundle in recent years.

The PRHR HX is a crucial piece of equipment in the

passive safety system that is housed in the in-

containment refilling water storage tank (IRWST) of

the AP1000 (a two-loop advanced passive power

plant) and CAP1400 (a two-loop advanced passive

power plant) in China. For the reactor's safety, the

Passive Residual Heat Removal System's (PRHRS)

efficient and dependable operation is crucial. The

PRHR HX's (passive residual heat removal heat

exchanger) heat transfer effect is negatively

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Mantasha Ponkty, Anamika Puja,

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E-ISSN: 2224-3410

Volume 21, 2024

impacted by the stratification that can occur in the

passive heat sink tank (IRWST) of the PRHRS. The

Westinghouse AP1000 was the main focus of the

authors' attention in this paper, [2], [3], [4].

AP1000 Pressurized Water Reactors use passive

safety systems to enhance plant safety and these

systems use only natural forces such as gravity,

natural circulation, and compressed gas to provide

the driving forces for the systems to adequately cool

the reactor core following an accident. As a part of

the Passive Safety System (PSS), the Passive

Residual Heat Removal System (PRHRS) removes

the decay heat from the core by natural circulation.

PRHRS is composed of a heat source and heat sink

with a piping system between two components

allowing water to circulate due to the buoyancy

effect, [5], [6], [7]. In-Containment Refueling Water

Storage Tank (IRWST) works as a heat sink and the

primary coolant water is the heat source in this

system releasing heat through the C-shaped tube

bundle heat exchanger immersed into the IRWST. A

passive residual heat removal heat exchanger is a

key equipment in the passive residual heat removal

system, which plays an important role in the safety

of the reactor in any emergency or accidental

scenario.

Authors [8] analyzed the heat transfer and flow

calculation of the PRHR Hx of the AP1000 reactor.

In this study, RELAP5 is applied to calculate heat

distribution, and ANSYS FLUENT codes are used

to obtain the CFD calculations. To increase the heat

transfer capacity of the system, the implementation

of nanofluid in the IRWST has good research

potential due to its proven effectiveness in heat-

exchanging applications.

To study the heat transfer enhancement, three

nanofluids are considered in this research- Ag-

water, TiO2-water, and Al2O3-water nanofluids.

These nanofluids show better thermal properties

compared to their base-fluid water. Authors [9]

studied the thermal conductivity enhancement of

aqueous solution of silver nanoparticles. Authors

[10] experimentally researched thermo-physical

properties and heat transfer characteristics of low-

volume fraction water-based silver nanofluid.

Thermal conductivity and viscosity of TiO2-water

nanofluid are measured, [11]. Heat transfer

enhancement by applying TiO2 nanofluid is

experimentally proved by researchers, [12]. Authors

[13] studied the properties of both water and

ethylene glycol-based Al2O3 nanofluid depending on

the temperature and concentration as well as their

effect on free convective heat transfer. Finally, the

established and proposed correlations of nanofluid

properties and enhancement ratios of thermal

characteristics of water-based composite nanofluids

discussed in different studies are reviewed by

researchers, [14].

With the advancement of science, the

production of nanoparticles from various materials

has become possible. One of the characteristics of

materials in the scale of nano is the large surface-to-

volume ratio, which gives them special abilities.

Nanofluid is a term proposed by researcher [15] as a

new kind of heat transfer fluid that contains small

quantities of metallic or non-metallic nanoparticles.

These particles were scattered homogeneously and

permanently in a continuous phase. Other

researchers [16] explored the theoretical approach of

nanoparticles and their properties in terms of using

them in heat exchangers for enhanced efficiency.

Researcher [17] numerically experimented with

the flow and heat transfer of nanofluid for the

application of solar collectors. Two types of

nanofluids were chosen in the study- CuO and

Al2O3 in volume fractions of 0.5% and 1%. With

COMSOL Multiphysics simulations, it was possible

to prove the result to accord with the theory of

Nanofluid’s enhanced heat transfer behavior.

In recent years, there have been a significant

number of studies on Passive Heat Removing

Systems. Authors [18] numerically investigated the

behavior of the ultimate heat sink whereas other

researchers [19], [20] showed heat transfer and fluid

flow studies in both numerical and experimental

approaches. To improve the heat transform

performance, researchers [21] suggested an

outstanding idea of change in PRHR Hx tube design

and introduced a spiral shape instead of regular C-

shape submerged tubes. In our study, nanofluid is

applied as the ultimate heat sink, keeping the regular

C-shape of the PRHR Hx tubes to compare the

performance of the heat-removing system with the

regular setup of the water heat sink.

The application of nanofluid in the Passive Heat

System of nuclear power plants is a potential

research area that can bring out the possibility of

elevating heat transfer in a passive approach.

Adequate research can help to find out the most

suitable nanofluid with optimized concentration

which can be used to achieve the goal.

Numerous studies involving nuclear reactors have

made use of COMSOL Multiphysics to carry out

multiphysics simulations like thermal hydraulics. A

potent tool for modeling and evaluating a broad

range of events in numerous domains is COMSOL

Multiphysics [22], [23], [24], [25], [26], [27], [28],

[29], [30], [31], [32], [33], [34], [35], [36], [37],

[38], [39]. Here are a few real-world uses for

COMSOL in engineering simulation, [39]:

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 Mechanical Engineering: COMSOL can

simulate stress, strain, and deformation in

mechanical components, structural mechanics,

geomechanics, and nonlinear material models

analyze fluid-structure interaction, and design for

optimal performance.

 Nuclear Engineering: COMSOL excels at

simulating coolant flow, heat transfer, and fuel

behavior within the reactor core. This can

optimize fuel design, predict core performance,

and aid in safety analysis based on thermal

hydraulic simulations. Modeling neutron flux, fuel

burnup, and control rod behavior is crucial for

reactor operation and refueling strategies.

COMSOL can be a valuable tool for these

simulations. Simulating accident scenarios like

loss-of-coolant accidents helps assess safety

margins and design emergency cooling systems.

 Biomedical Engineering: From designing

artificial hearts (left ventricular assist devices) to

studying blood flow patterns, COMSOL helps

optimize medical devices.

 Microfluidics: Design and analyze microfluidic

devices used in lab-on-a-chip technologies and

medical diagnostics

 Chemical Engineering: Model and simulate

chemical reactions, mixing processes, and mass

transfer in reactors and other chemical equipment

 Acoustics: Analyze sound propagation, noise

cancellation, and vibration in various applications.

These are just a few examples, and COMSOL's

versatility extends to many other fields including

aerospace engineering, heat transfer analysis, and

material science, [40].

The purpose of this work is to utilize COMSOL

Multiphysics tools for the assessment of heat

transfer with nanofluids for the passive heat transfer

system of the AP1000 reactor.

2 Model

A series of procedures known as the modeling

workflow must be followed to set up and operate a

finite element model in COMSOL Multiphysics®

software. The following steps make up the modeling

workflow:

 Constructing the geometry.

 Configuring the environment for the model.

 Describing the characteristics of the materials.

 Assembling the mesh.

 Defining the boundary conditions in physics.

 Putting the simulation into action.

 Modifying the outcomes after they have been

obtained.

2.1 Geometry

The geometry option in the Comsol multiphysics

software [39] is used to develop a geometry of

IRWST of AP1000 under the option model builder.

The passive residual heat removing heat exchanging

system of the AP1000 reactor design consists of a

D-shaped IRWST with a height of 14 m and volume

of 2100 m3 and 689 submerged C-shaped tubes of

5.5 m in length with 19mm in inner radius. To avoid

complications in the calculation, a reduced and

simplified version of the passive heat-removing

system was designed for our study. Figure 1 shows

the 3D model of simplified IRWST with a single C-

tube. The tank dimensions are reduced to 500 mm in

height and 300 mm in radius. The C-tube diameter

is given 54 mm to avoid meshing complications.

Figure 2 shows the finer mesh of the whole 3D

geometry. The flow study and outlet temperature

calculation were obtained from the 2D version of

the 3D model. After careful examination, every

Material property has been chosen in the Settings

box for that material.

Fig. 1: 3D Model geometry

Fig. 2: Mesh of the geometry

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2.2 Mesh Quality Analysis

To generate an unstructured tetrahedral mesh,

choose User-controlled mesh from the Sequence

Type list in the Mesh Settings box. The 3D

geometry was studied under 3 different meshing

conditions to obtain a comparison between the

precision of the results. The physics-controlled

mesh conditions were generated automatically by

COMSOL. Following are the statistics of each mesh

condition over the 3D geometry, [39]. The data on

mesh quality and statistics are given in Table 1.

Table 1. Mesh Statistics

Fine mesh

Quality measure

Skewness

Num. of elements

173071

Min element

quality

0.0782

Avg element

quality

0.6107

Element volume

ratio

6.614e-5

Finer mesh

Quality measure

Skewness

Num. of elements

836380

Min element

quality

0.1927

Avg element

quality

0.6642

Element volume

ratio

6.128e-5

Extra fine

mesh

Quality measure

Skewness

Num. of elements

1441882

Min element

quality

0.1882

Avg element

quality

0.6717

Element volume

ratio

1.208e-5

Figure 3, Figure 4 and Figure 5 show the 3D

geometry under the selected mesh conditions and

properties.

Fig. 3: Fine Mesh

Fig. 4: Finer Mesh

Fig. 5: Extra Fine Mesh

2.3 Boundary Conditions

After that, we check the physics domain settings and

establish the heat transfer problem's boundary

conditions [39]. The activation temperature for

PHRHx of AP1000 design is 297° C and the

primary mass flow rate inside the C tubes is 65 kg/s,

[23]. The boundary conditions differ from the real

scale due to the simplification of the model. Table 2

and Table 3 show the initial and boundary

conditions used in both 3D and 2D geometry:

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Table 2. Boundary Conditions for Heat Transfer

Study (3D)

Table 3. Boundary Conditions for Flow Study (2D)

3 Methodology

3.1 Simulation

A time-dependent heat transfer study in liquids was

performed on the 3D geometry to obtain the selected

surface temperature reduction in 100 sec. Figure 6

shows the geometry for the calculation of the heat

transfer coefficient. The temperatures were used to

calculate the local heat transfer coefficient using

theoretical equations. The geometry holds 2

domains- the first one is a primary coolant as the

heat source and the other one is the IRWST domain

as the heat sink.

Figure 7 shows the selected line for obtaining

the heat transfer coefficient. Water and nanofluid

properties were added to the material properties of

the IRWST domain. To reduce further

computational load, a stationary study of heat

transfer and laminar flow was done on the 2D

geometry to obtain the outlet temperatures.

Boundary conditions were adjusted according to the

simplifications of the model and studies.

To initiate a simulation, select Compute from

the Study option by right-clicking on it in the Model

Builder window. A simulation's solution sequence is

automatically defined by the Study node based on

the chosen study type and physics. In this instance,

the simulation can be completed in a matter of

seconds. Two convergence graphs are produced

during the solution process to display the

convergence progress of the various solver

algorithms used in the study.

Fig. 6: Selected Surface to Calculate Heat Transfer

Coeff

Fig. 7: Selected Line to Obtain Outlet Temperature

Heat Transfer equations used by COMSOL

Multiphysics, [39]: (1)

(2)

Where ρ is the fluid density (SI unit:

kg/m3), Cp (SI unit: J/(kg·K)) is the heat capacity at

constant pressure, and T (SI unit: K) is the

temperature. u is a velocity field, k is the thermal

conductivity (SI unit: J/K), and Q (SI unit: W/m)

represents a general heat source.

3.2 Calculation of Nanofluid Properties

The thermophysical properties of nanofluid were

required to calculate the final heat transfer

coefficient. To obtain these properties some

experimental and theoretical correlations were

implemented. These properties depend on three

The initial temperature of

IRWST

20°C

The initial temperature of

outer temperature at the

primary length of the C-tube

20°C

Temperature of primary

coolant domain

177°C

The initial temperature of

IRWST

20°C

The initial temperature of

outer temperature at the

primary length of the C-

tube

20°C

Temperature of primary

coolant inlet

297°C

Inlet velocity

0.001 m/s

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main factors- (1) Properties of base fluid (bf) with

respect to temperature, (2) Properties of

nanoparticles (np) and (3) The volume fraction of

nanoparticles mixed in the base fluid. The properties

of base fluid water and the Ag, TiO2, and Al2O3

nanoparticles are given in Table 4, [17]. For our

study, the different volume fractions of three

different nanofluids were considered which are

mentioned in Table 5.

Table 4. Properties of Base fluid and Nanoparticles

at 300 K

(kg/m3)

(J/kgK)

(W/mK)

 (10-5)

(K-5)

Water

997

4179

0.613

10500

235

429

1.89

TiO2

4250

686.2

8.95

0.9

Al2O3

3970

765

0.85

Table 5. Percentage of Nanoparticles in Base Fluid

Nanoparticle

Volume Fraction Considered (Φ)

0.25%, 0.5%, 1%

TiO2

1%, 2%, 3%

Al2O3

1%, 3%, 6%

The thermal conductivity (k), viscosity (μ),

density (ρ), and specific heat (Cp) for each

nanofluid were obtained from the following

equations (3), (4), (5), and (6) respectively.

  󰇟󰇛󰇜

󰇠 (3)

[Wasp]  󰇛  󰇜 (4)

  󰇛  󰇜 (5)

 󰇛󰇜

  (6)

3.3 Calculation of Dimensionless Numbers

When the valve of the passive heat removal system

opens after 120 sec of reactor shutdown, primary

coolant starts to flow in the pipelines connecting to

the reactor core and the IRWST. There is a natural

circulation of coolant between the core and the

submerged heat exchanger pipes due to the

temperature and velocity difference. The primary

coolant brings residual heat that is still produced in

the core to the heat exchanger tubes and the IRWST

water absorbs the heat from the outer surface of the

pipes. Thus a pool boiling phenomenon occurs in

the IRWST which takes about 5 hours to start

boiling its water inventory. Another natural

circulation of water in the passive heat removing

system can be considered in the case of IRWST

water boiling.

The calculations of a natural convection heat

transfer coefficient calculator usually involve the

use of dimensionless number correlations, namely

the correlations between the Nusselt number (Nu)

and the Rayleigh number (Ra), Grashof number

(Gr), and/or Prandtl number (Pr), where Ra = GrPr.

The box on the left provides definitions for the

Nusselt, Grashof, and Prandtl numbers. The flow

regime created by natural convection and the

ensuing heat transfer is described by the

Rayleigh number (Ra). Considering the natural

circulation of the heat sink, the following equations

are implemented to calculate Ra, Nu and finally the

heat transfer coefficient.

 󰇛

󰇜

 (7)

 󰇝  

󰇟󰇛󰇜󰇠󰇞 (8)

  

 (9)

Where:

- Thermal expansion coefficient of fluid (1/K)

L – characteristic linear dimension (m).

Pr – Prandtl Number of the fluid

v – Kinematic Viscosity (m2/s)

Ts – Temperature of the solid surface (K)

To – Room temperature of the fluid (K)

h- Heat Transfer Coefficient (W/m2 K)

4 Results and Discussion

The heat transfer coefficient was calculated over the

selected surface shown in Figure 6. For each case of

nanofluids, a comparison between the heat transfer

coefficients depending on the volume

concentrations of nanoparticles was plotted in

Figure 8, Figure 9 and Figure 10. For all cases, the

heat transfer coefficient is found to increase with the

increment of the volume fraction of the particles.

The heat transfer coefficients in cases of Ag-water

nanofluid show comparatively low variance as it is

studied in lower mass fraction. On the other hand,

for Al2O3-water nanofluid, the highest coefficient is

found due to the higher thermal conductivity of

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Al2O3 nanoparticles and its higher mass fraction

used in this study which is 6%.

Fig. 8: Local Heat Transfer Comparison for Different

Concentrations of Ag-water nf

Fig. 9: Local Heat Transfer Comparison for Different

Concentrations of TiO2-water nf

Fig. 10: Local Heat Transfer Comparison for

Different Concentrations of Al2O3-water nf

The heat transfer performance is enhanced by

using nanofluid instead of pure water. The ratio of

the heat transfer coefficient of the nanofluids to the

base fluid is also studied. Figure 11, Figure 12 and

Figure 13 show a comparison of the ratio of heat

transfer coefficient of nanofluids in different

concentrations. For Ag-water nanofluid the highest

ratio is found to be about 1.75 for using the highest

concentration considered which only 1% of Ag

nanoparticles is. For TiO2-water nanofluid the

highest ratio is 1.6 for the highest concentration 3%.

Al2O3 showed the best heat transfer coefficient ratio

among all the selected nanofluids. For 1%

concentration its highest ratio is about 3.2 and for

6% concentration it makes the heat transfer about 6

times higher than pure water.

Fig. 11: h(nf)/h(bf) of Ag for Different Volume

Fraction

Fig. 12: h(nf)/h(bf) of TiO2 for Different Volume

Fraction

Fig. 13: h(nf)/h(bf) of Al2O3 for Different Volume

Fraction

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Fig. 14: Comparison Between Heat Transfer

Coefficient of Water and Nanofluids

For a better approach to comparing heat transfer

performance between water and selected nanofluids,

Figure 14 is plotted with the heat transfer coefficient

of water and 1% of each nanofluid with respect to

time. 1% Al2O3 nanofluid shows the highest heat

transfer coefficient resulting in a lower outlet

temperature of the heat exchanger tube.

From the flow study in 2D geometry, the outlet

temperatures were obtained for the cases of water

and selected nanofluids as the IRWST inventory.

Table 6 gives the simulated outlet temperature when

the primary coolant temperature at the inlet is

considered to be the activation temperature of the

system, i.e., 297°C.

Table 6. Outlet Temperatures

IRWST inventory

Outlet temperature

(°C)

Water

231.1639

0.25%

231.1028

Ag-water

0.5%

231.086

231.0524

231.064

TiO2-water

231.01

230.9489

231.0591

Al2O3-water

230.9192

230.7028

The Mesh Independence Test and Convergence

Plot are shown in Figure 15 for water. On the other

hand, the mesh independence of 6% Ag nanofluid is

shown in Figure 16.

Fig. 15: Mesh Independent Test for Water

Fig. 16: Mesh-independent Test for 6% Ag

Nanofluid

The convergence plot for the stationary study is

shown in Figure 17. The convergence plot for the

Time Dependent Study is illustrated in Figure 18.

Water as the heatsink study was chosen to

demonstrate the convergence as a general

representation of the overall computational works.

Fig. 17: Convergence Plot for Stationary Study

erat

ure

(K)

Fine mesh

Extra fine mesh

Finer mesh

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Fig. 18: Convergence Plot for Time Dependent

Study

5 Conclusion

This paper deals with a concept of using nanofluid

in the IRWST instead of pure water and an

investigation on the improved heat transfer

coefficient is conducted to illustrate the reduced

outlet temperature of the heat exchanger. To avoid

computational complications single tube design has

been implemented, but the simulation shows an

increase in local heat transfer coefficient using the

AlO3 nanofluid. The enhanced thermophysical

properties of nanofluid can be a beneficial tool for

the passive decay heat removal system for an

AP1000 nuclear reactor but the availability and

affordability of nanofluid is to be considered. The

PRHR HX is an essential part of the PHRS for

removing decay heat from the core in case of an

emergency and system failure. Using nanofluid to

increase the heat transfer efficacy can be a way of

enhancing overall safety measurement. Artificial

Intelligence (AI) can be integrated with a lot of

fascinating studies with nanofluids. Artificial

intelligence is outside the scope of this study.

This study can be integrated with artificial

intelligence (AI) in the future to provide a

potent and perceptive research endeavor.

Two VVER-1200 power plants with a combined

capacity of 2400 MW will be in operation in

Bangladesh by the end of 2024/2025. COMSOL

software can be used to simulate the heat transfer of

VVER-1200 for safety and licensing purposes but

there are challenges to address and future directions

to explore.

 COMSOL simulations need validation against

actual VVER-1200 data or established reactor

physics codes. Access to such data or codes might

be limited due to proprietary restrictions.

 Close collaboration with Rosatom (Russia), the

VVER-1200 technology provider, would be

beneficial. Rosatom might have access to

validated simulation data or codes that could be

used for COMSOL validation in Bangladesh.

 Developing programs to transfer knowledge and

train Bangladeshi researchers and engineers in

nuclear simulation techniques and COMSOL

software would create a skilled workforce for

future nuclear projects.

References:

[1] IAEA Passive Safety Systems and Natural

Circulation in Water Cooled Nuclear Power

Plants, IAEA-TECDOC-1624, IAEA, Vienna

2009, [Online]. https://www-

pub.iaea.org/MTCD/publications/PDF/te_162

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Mantasha Ponkty, Anamika Puja,

Abdus Sattar Mollah

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Mantasha Ponkty: Conceptualization,

Methodology, Software, Investigation, and

Writing - original draft, Review

- Anamika Puja: Modelling, Investigation, Data

curation, Writing and Review

- Abdus Sattar Mollah: Conceptualization, Review

& Editing, Supervision

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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WSEAS TRANSACTIONS on ADVANCES in ENGINEERING EDUCATION

DOI: 10.37394/232010.2024.21.11

Mantasha Ponkty, Anamika Puja,

Abdus Sattar Mollah

E-ISSN: 2224-3410

Volume 21, 2024