CFD Based Performance Prediction Model

for Paddle Wheel Aerator

Priyambodo Nur Ardi Nugroho

, Muhammad Anis Mustaghfirin

Dwi Sasmita Aji Pambudi

, Eky Novianarenti

, Dyah Arum Wulandari

and Shuichi Torii

Shipbuilding Engineering Department, Politeknik Perkapalan Negeri Surabaya, Indonesia

Department of Mechanical Engineering, Universitas Negeri Jakarta, Indonesia

Department of Advanced Mechanical System Engineering, Kumamoto University, Japan

dyaharum@unj.ac.id, torii@mech.kumamoto-u.ac.jp

Keywords: Paddlewheel Aerator, Performance Prediction Model, Computational Fluid Dynamics (CFD), Water Quality,

Aeration Process.

Abstract: The intensive aquaculture industry is mainly affected by the ability to maintain water quality, and low

dissolved oxygen could be improved through the aeration process. The paddlewheel aerator is one of the

supporting devices required for the intensive aquaculture pond system. The paddlewheel aerator still has a

low aeration performance, resulting in higher operational costs. This paper presents an analytical model to

predict the optimal performance of a paddle wheel aerator. The model considers the most influential factors

affecting the performance of the paddlewheel aerator. Then, Computational Fluid Dynamics (CFD) is

employed to simulate and validate the developed analytical model. The simulation results demonstrate that

the model is accurate enough to estimate the paddlewheel aerator's optimal operational condition.

1 INTRODUCTION

Aquaculture has become an important global source

of food and commercial products. According to a

technical report by Gillet (2008) for the Food and

Agriculture Organization of the United Nations, the

annual worldwide production of shrimp, both farmed

and caught, is approximately 6 million tonnes, with

more than 40% coming from farming. Aquaculture is

generally divided into marine and inland categories.

Unlike marine aquaculture, which takes place in finite

spaces surrounded by an open environment such as a

river or sea, inland aquaculture occurs in isolated

coastal vessels or ponds with little connection to the

external environment. In these isolated spaces,

biological conditions can deviate from the natural

environment, leading to technical problems such as

https://orcid.org/0000-0001-7111-5866

https://orcid.org/0000-0002-9669-1015

https://orcid.org/0000-0002-4869-4326

https://orcid.org/0009-0002-7869-1692

https://orcid.org/0000-0001-5803-0227

https://orcid.org/0000-0001-9327-741X

low dissolved oxygen (DO) levels in the water, which

can pose a challenge for aquatic organisms in shrimp

culture (Itano, 2018).

Aeration is the mechanism by which a certain

amount of oxygen is added to the water to provide

sufficient oxygen. Aeration is achieved by increasing

the contact of water and air using an aerator. One type

of aerator widely used in pond culture is the paddle

wheel aerator (Laksitanonta, 2003). The paddlewheel

aerator is the most suitable due to its aeration

mechanism and the sizeable drive power (Romaire,

2007). Several parameters affect the aeration rate,

including water and air surface contact, differential

oxygen concentration, membrane surface coefficient,

and turbulence (Boyd, 1988). A single paddle wheel

aerator's performance in shrimp ponds has been

examined (Ong, 2005). On the other hand, a

comprehensive review of the state-of-the-art

Nugroho, P., Mustaghﬁrin, M., Pambudi, D., Novianarenti, E., Wulandari, D. and Torii, S.

CFD Based Performance Prediction Model for Paddle Wheel Aerator.

DOI: 10.5220/0012114600003680

In Proceedings of the 4th International Conference on Advanced Engineering and Technology (ICATECH 2023), pages 103-106

ISBN: 978-989-758-663-7; ISSN: 2975-948X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

103

paddlewheel aerators in aquaculture is also available

(Saucedo-Teran, 2019).

Some researchers have examined the aeration

efficiency of a single paddle wheel aerator in

aquaculture (Duan, 2015). The paddle wheel's shape,

size, and speed can impact aeration efficiency

(Moulick, 2002). Low aeration efficiency can result

in the need for higher drive horsepower due to higher

drag, which can lead to increased operating costs,

including electricity and fuel consumption. Various

models of paddle wheel aerators are available in the

market, with Taiwan-designed aerators being widely

used due to their affordability and lightweight design,

as depicted in Figure 1 (Wyban, 1989). However,

their efficiency is relatively low, with the paddle

wheel aerator having a Standard Aeration Efficiency

(SAE) value of 1.063 kg O

kW h

-1

, which is lower

than other designs (Peterson, 2002).

Previous research has used computational fluid

dynamics (CFD) modelling to examine the

performance of paddle wheel aerators in aquaculture,

providing insights into the aerator's flow pattern and

oxygen transfer efficiency (Akintoye, 2018). Other

studies have also employed CFD simulations to

analyze the flow pattern of a paddle wheel aerator

tank (Kiatkittipong, 2007) and to investigate flow and

mixing patterns in an aerated paddlewheel tank

(Prasertsri, 2009). The current study aimed to predict

the optimal performance of a paddle wheel aerator

based on numerical analysis of the current model

design, serving as a baseline for future improvement.

Figure 1: Paddle Wheel Aerator based on Taiwan design.

2 MATERIALS AND METHODS

A hydrodynamic model was performed using the

continuity and momentum equations for Newtonian

incompressible fluids to represent the flow structure

through a paddlewheel aerator. This study refers to

previous studies about the hydrodynamic

characteristics of a paddle wheel aerator using CFD

simulation (Yu, 2021). Recently, a numerical study

based on CFD simulation to investigate the

hydrodynamic characteristics of the impeller of a

paddle wheel aerator have been conducted (Miao,

2018). Reynolds terms were resolved to fluctuate

time-averaged values and averaged in the

longitudinal direction to yield the depth-integrated

three-dimensional Reynolds equation (Schlichting,

1979). The paddlewheel aerator's thrust effects are

considered a mass force to produce the acceleration

and are included in the Reynolds equation (Peterson,

2002). The governing equations can be deduced in the

x, y, and z directions to give the following equations

(Chen, 1994).

𝜕𝜂

𝜕𝑡

𝜕𝐻𝑈

𝜕𝑥

𝜕𝐻𝑉

𝜕𝑦

𝜕𝐻𝑊

𝜕𝑧

(1)



















=𝐻𝐹



−

𝑔𝐻





−







+𝜀

𝐻

































(2)



















=𝐻𝐹



−

𝑔𝐻





−







+𝜀

𝐻

































(3)



















=𝐻𝐹



−

𝑔𝐻





−







+𝜀

𝐻

































(4)

Where η is water surface elevation; t is time; x,

y, and z are Cartesian coordinates; U, V, and W are

depth-averaged velocity components in the x-, y-, and

z- directions; and H is total depth. The paddlewheel

aerator pushes the water mass forward, creating

horizontal circulation. On the other hand, it splashes

water particles vertically. Figure 2 shows the

operation of the paddlewheel aerator in a fish pond.

Water particles are projected and dispersed in an area

of constant height and width at the front of the

impeller. The blades of the paddlewheel aerator have

some holes in the body to improve reaeration.

However, the swept mass of the blades may not be

equal to the mass pushed forward by the paddlewheel

aerator, resulting in a mass imbalance. To correct this,

a simple mass correction factor can be used.

ICATECH 2023 - International Conference on Advanced Engineering and Technology

104

Figure 2. Pushing and splashing of water particles by

paddlewheel operating in the pond.

Figure 3 shows the computational domain after

running approximately 10,000 iterations. This study

aimed to create a relationship between laboratory data

and a computational fluid dynamics model. The

Ansys Fluent software was used to perform a series

of simulation experiments on the developed model.

The Reynolds-averaged Navier-Stokes equations

were applied to the stable, incompressible, three-

dimensional flow of the pond. The transport

equations of the Standard k – ε turbulence model were

utilized to calculate the turbulence kinetic energy k

and its dissipation rate ε.

Figure 3. Computational Domain.

3 RESULTS AND DISCUSSION

Figure 4 provides a visualization of the velocity

vector changes in the computational domain,

observed from the x-y side view. The highest

recorded velocity vector was found to be 5 m/s, while

the average velocity, based on the velocity volume

represented in Figure 5, was approximately 2 m/s.

The results were confirmed through experimental

analysis. It is important to note that various factors,

such as wind, pond shape, floor topography, turbulent

diffusivity, and paddlewheel aerator arrangement,

influence the pond's flow characteristics.

Figure 4: Velocity Vector.

A physical experimental analysis is necessary to

separate the individual effects of wind, pond shape,

floor topography, turbulent diffusivity, and the

arrangement of paddlewheel aerators on the flow

characteristics in the pond. However, controlling and

quantifying all these effects simultaneously is not

easy. Therefore, laboratory experiments using

proportional models are needed to control the

parameters and obtain data for analysis. It is

suggested that further research should be conducted

to verify the model prediction and clarify the flow

characteristics of paddle wheel aerators using fluid

dynamics experiments in a controlled laboratory, as

previously recommended (Kang, 2004).

Figure 5: Velocity Volume.

4 CONCLUSIONS

In conclusion, optimizing various factors affecting

aeration can lead to improved efficiency, ultimately

reducing operating costs and improving overall

aquaculture productivity. Computational fluid

dynamics models have proven to be useful tools in

CFD Based Performance Prediction Model for Paddle Wheel Aerator

105

predicting the performance of paddle wheel aerators

and can aid in the development of more efficient

designs. However, laboratory experiments using

proportional models are still necessary to fully

understand the flow characteristics and optimize the

design of paddle wheel aerators. Future research

should focus on combining both computational and

experimental approaches to improve the

understanding and optimization of aeration in

aquaculture.

ACKNOWLEDGEMENTS

This research was supported by the Ministry of

Research, Technology and Higher Education

Indonesia and The Indonesia Endowment Funds for

Education (LPDP) through an applied scientific

research funding program. Such funding is crucial in

advancing research and promoting innovation, and it

is a positive step toward promoting sustainable

aquaculture practices. The results of this study can

provide valuable insights into the aquaculture

industry, which can help them optimize their

production processes and increase their productivity

while minimizing their environmental impact.

REFERENCES

Akintoye, H. A., Ogunyemi, S. O., & Ogunbiyi, O. A.

(2018). CFD modelling of paddle wheel aerator

performance in aquaculture. Aquacultural Engineering,

81, 77-86.

Boyd, C. E. (1988). Pond water aeration systems.

Aquaculture Engineering, 18, 9-40.

Chen, Y., & Falconer, R. (1994). Modified forms of the

third-order convection, second-order diffusion scheme

for the advection-diffusion equation. Advances in

Water Resources, 17(3), 147-170.

Duan, J., & Wang, Y. (2015). Study on the aeration

efficiency of the single paddle wheel aerator in

aquaculture. Journal of Aquaculture Research &

Development, 6(2), 2-7.

Gillet, R. (2008). Global study of shrimp fisheries. FAO

fisheries technical paper 475.

Kang, Y. H., Lee, M. O., Choi, S. D., & Sin, Y. S. (2004).

2-D hydrodynamic model simulating paddlewheel-

driven circulation in rectangular shrimp culture ponds.

Aquaculture, 231(1-4), 163-179.

Kiatkittipong, W., Reungsang, A., & Chansiripornchai, N.

(2007). CFD simulation of fluid flow pattern in paddle

wheel aerator tank. Chemical Engineering Journal,

133(1-3), 259-266.

Laksitanonta, S., Singh, S., & Singh, G. A. (2003). A

review of aerators and aeration practices in Thai

Aquaculture. Agricultural Mechanization in Asia,

Africa and Latin America, 34, 64-71.

Miao, J., Wang, Y., Wang, J., & Yan, H. (2018). Numerical

study on hydrodynamic characteristics of the paddle

wheel aerator impeller based on CFD simulation.

Journal of Aquaculture Research & Development, 9(6),

1-5.

Moulick, S., Mal, B. C., & Bandyopadhyay, D. (2002).

Prediction of aeration performance of paddlewheel

aerators. Aquaculture Engineering, 25(3), 217-237.

Ong, B. K., Wong, K. W., & Ho, Y. B. (2005). Performance

of single paddle wheel aerator in shrimp ponds.

Aquacultural Engineering, 33(3-4), 277-288.

Peterson, E. L., & Walker, M. B. (2002). Effect of speed on

Taiwanese paddlewheel aeration. Aquaculture

Engineering, 26(2), 129-147.

Prasertsri, P., Thiansem, S., & Thammasorn, T. (2009).

Simulation of flow and mixing in the paddlewheel

aerated tank using CFD. Chemical Engineering

Journal, 150(1), 70-78.

Romaire, R. P., & Merry, G. E. (2007). Effect of

paddlewheel aeration on water quality in crawfish

pond. Applied Aquaculture, 19(1-2), 61-75.

Saucedo-Teran, R. A., & Magana-Esparza, D. (2019).

Paddlewheel aerators in aquaculture: a review of the

state-of-the-art. Aquaculture International, 27(4),

1199-1222.

Schlichting, H. (1979). Boundary Layer Theory. 6

edition.

McGraw-Hill, New York. 742.

Tomoaki Itano, Taishi Inagaki, Choji Nakamura, Ren

Hashimoto, Naohiro Negoro, Jinsuke Hyodo, Syuta

Honda (2018). Water circulation induced by

mechanical aerators in a rectangular vessel for shrimp

aquaculture. Aquacultural Engineering, 81, 59-66.

Wang, J., Li, Y., Li, W., Chen, J., Yang, Y., & Wang, S.

(2020). Numerical simulation of flow and mass transfer

in the paddlewheel aerated tank based on the LBM

method. Aquacultural Engineering, 89, 102014.

Wang, Y., Wang, J., Miao, J., & Liu, B. (2019). Numerical

simulation on flow characteristics and oxygen transfer

of the paddle wheel aerator. Journal of Aquaculture

Research & Development, 10(2), 1-5.

Wyban, J. A., Pruder, G. D., & Leber, K. M. (1989).

Paddlewheel effect on shrimp growth, production, and

crop value in commercial earthen ponds. Journal of the

World Aquaculture Society, 20(1), 18-23.

Yu, D., Li, D., Li, D., Li, Y., & Li, L. (2021). Study on the

hydrodynamic characteristics of paddle wheel aerator

based on CFD simulation. Journal of Aquaculture

Research & Development, 12(6), 1-9.

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