553

1 INTRODUCTION

One of the principal challenges in global fisheries is

advancing sustainable fishing practices that minimize

environmental degradation while enhancing societal

advantages One of the most significant environmental

concerns is the high level of fuel consumption in

capture fisheries, which contributes to greenhouse gas

emissions. Many countries subsidize fuel, further

complicating efforts to curb emissions. Given the

economic reliance on fuel in the fishing industry,

finding fuel-efficient methods for seafood production

has been a long-standing priority. Over the years,

various technical innovations have been introduced,

such as modernizing fishing fleets, investing in energy-

efficient propellers and gears, replacing engines, and

improving vessel hull designs [1–3].

The design of a ship’s hull plays a crucial role in

hydrodynamics, stability, safety, and operational

efficiency. However, additional variables, including

hull biofouling, can modify the topographical

characteristics of the hull surface, leading to increased

hydrodynamic drag and elevated fuel expenditure

[4,5].

To address these challenges, regulatory bodies like

the International Maritime Organization (IMO) have

implemented strict energy efficiency measures. The

2014 amendment to MARPOL ANNEX IV introduced

the Energy Efficiency Design Index (EEDI) for new

CFD Analysis of Cobalt-Based Ceramic Coatings

for Energy Optimization in the Fishing Naval Industry

D.S. Sanz

, S. García

, J. García

, D. Boullosa-Falces

& A. Trueba

University of Cantabria, Santander, Spain

University of the Basque Country, Portugalete, Spain

ABSTRACT: The rising cost of fossil fuels has created a significant economic challenge for the fishing fleet, whose

performance heavily relies on marine diesel consumption. The increase in operational costs due to fuel price

surges negatively impacts the profitability of ships, particularly in the fishing industry, where profit margins are

often tight. Given this issue, it is crucial to explore solutions that reduce fuel consumption without compromising

the operational efficiency of ships. In this context, cobalt-based ceramic coatings, designed and tested in

accordance with the ASTM-D3623 procedure, emerge as an innovative and promising alternative. These coatings

reduce biofouling adhesion, a buildup of marine organisms on the ship’s hull that increases frictional resistance

to movement, consequently leading to higher fuel consumption. By decreasing hydrodynamic resistance, ships

require less energy for propulsion, thereby optimizing fuel consumption. Additionally, these coatings provide

anticorrosive protection, extending the service life of ships and reducing maintenance costs. The cobalt-based

coating has been tested under controlled laboratory conditions and subjected to hydrodynamic shear forces

representative of ship navigation at a speed of 10 knots. This article evaluates via CFD the impact of these coatings

on the drag resistance of a trawler ship, demonstrating that the increase in hull roughness due to biofouling

adhesion on the cobalt-based ceramic coating after one month of navigation results in a 0.02% increase in drag.

In contrast, the Intersleek 1001 coating leads to a 6.35% increase in drag under the same conditions.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 19

Number 2

June 2025

DOI: 10.12716/1001.19.02.27

554

ships and the Ship Energy Efficiency Management Plan

(SEEMP) for existing vessels. Moreover, IMO has set

ambitious targets, aiming for a 40% reduction in CO2

emissions by 2030 and a 70% reduction by 2050

compared to 2008 levels. These regulations push the

shipping industry to optimize vessel efficiency and

minimize environmental impact [6].

Computational Fluid Dynamics (CFD) has become

an essential tool for improving ship hull design,

allowing researchers to simulate hydrodynamic

conditions and predict the impact of hull roughness

and biofouling on frictional resistance. Several studies

have demonstrated CFD’s effectiveness in this area. [7]

employed roughness functions in CFD simulations,

though they did not incorporate Reynolds-averaged

Navier-Stokes (RANS) calculations, which are crucial

for accurately modelling turbulent flows. Izaguirre-

Alza et al. used Star-CCM+ software to validate surface

roughness effects but relied on built-in roughness

functions [8].

Recent CFD advancements have aimed at

improving predictive accuracy. [9] introduced a CFD

model incorporating fouling control coatings,

validating results against experimental data. Later,

they applied a RANS model with a modified roughness

function to predict efficiency losses due to hull

deterioration. Other researchers, such as [10,11],

analysed biofouling effects on propeller efficiency,

while [12] developed a methodology using

OpenFOAM to estimate frictional resistance.

As the shipping industry seeks energy-efficient

solutions to comply with environmental regulations,

advanced CFD models will play a key role in

optimizing hull design, reducing fuel consumption,

and minimizing greenhouse gas emissions [1].

Biofouling refers to the unwanted adhesion and

accumulation of biotic deposits on surfaces submerged

in seawater [13–15]. These deposits primarily consist of

organic biofilms formed by microorganisms embedded

in a self-produced polymeric matrix, which can also

trap inorganic particles such as salts or corrosion by

products [16]. Over time, microbial biofouling

facilitates the settlement of larger macro-organisms,

leading to macro-fouling. This phenomenon presents a

significant challenge to various sectors of the blue

economy, particularly maritime transportation, where

biofouling can increase hydrodynamic drag, reducing

ship performance and elevating fuel consumption by

up to 30% to maintain operational efficiency. The

subsequent rise in fuel demand has both economic and

environmental consequences, as fuel costs account for

nearly 50% of marine transportation expenses and

increased combustion results in higher greenhouse gas

emissions [17].

To mitigate the adverse effects of biofouling in

fishing vessels, marine antifouling coatings have been

developed to inhibit biofouling growth either by

releasing active biocidal agents or by providing a

smooth, non-stick surface that prevents the settlement

of fouling organisms. These coatings significantly

reduce frictional resistance, thereby improving vessel

efficiency, lowering fuel consumption, and decreasing

greenhouse gas emissions. Given the high dependency

of fishing fleets on fuel, enhancing hydrodynamic

efficiency is crucial for achieving more sustainable and

cost-effective operations. However, different fouling

control coatings vary in surface roughness and

durability, influencing their effectiveness in

minimizing frictional resistance over time. Hence,

accurately assessing the impact of different coatings on

vessel performance is essential for optimizing their

application in the fishing industry. Studies suggest that

full-scale ship resistance and power requirements for

various antifouling coatings can be predicted using

laboratory drag measurements and boundary layer

similarity laws. Moreover, empirical correlations have

been established to estimate frictional resistance in

different fouling conditions, providing valuable

insights for coating selection and maintenance

strategies [18].

Biofouling and corrosion, though distinct processes,

often coexist and exacerbate material degradation in

marine environments, particularly in fishing vessels

that operate under diverse oceanographic conditions.

Biofouling involves the colonization of submerged

surfaces by microorganisms such as bacteria and algae,

which form biofilms that serve as substrates for larger

fouling organisms like barnacles and mussels [19]. The

development of biofilms is influenced by multiple

factors, including water temperature, nutrient

availability, flow dynamics, and surface composition.

Smooth, defect-free surfaces generally exhibit lower

biofouling susceptibility compared to rough or

irregular surfaces. Furthermore, nutrient-rich waters

encountered in fishing grounds facilitate microbial

proliferation, enhancing biofilm growth and

subsequent macro-fouling, which significantly affects

vessel performance by increasing drag and fuel

consumption [20].

Corrosion, on the other hand, is the deterioration of

materials through chemical or electrochemical

reactions with their environment. In marine settings,

saltwater exposure accelerates corrosion, leading to

significant structural damage over time. Corrosion

manifests in various forms, such as metal oxidation,

steel degradation, and material fatigue, and is driven

by factors including oxygen exposure, moisture, acidic

conditions, and saline environments. The

consequences of marine corrosion for fishing vessels

include reduced hull integrity, increased maintenance

costs, and potential safety hazards. For instance,

corrosion-induced weakening of hull structures can

elevate the risk of leaks, compromising vessel safety

and operational efficiency. Therefore, corrosion

prevention strategies are crucial for ensuring the

longevity and reliability of fishing fleets. Common

mitigation measures include the use of corrosion-

resistant materials, protective coatings, and

comprehensive corrosion monitoring systems [21]. The

interaction between biofouling and corrosion is

intricate, as biofouling can accelerate corrosion

through microbial metabolic activity. Certain

microorganisms produce acidic compounds that

degrade material surfaces, while biofilms create

microenvironments that promote localized corrosion.

Additionally, biofouling can trap moisture and

corrosion-inducing agents, further exacerbating

material degradation. Consequently, integrated

antifouling and anticorrosion strategies are essential

for comprehensive fishing vessel maintenance. The

development of advanced coatings that

simultaneously provide antifouling and anticorrosion

protection is a promising approach to mitigating these

555

challenges and improving the sustainability of fishing

operations [13,22,23].

Among these advanced coatings, cobalt-based

enamels have demonstrated exceptional mechanical

properties, including high resistance to erosion and

corrosion [18]. Research has shown that these coatings

exhibit high hardness and durability, making them

highly effective in protecting fishing vessels against

biofouling and material degradation. The surface of

cobalt-based enamels is highly hydrophilic, preventing

the adhesion of organic molecules—a critical initial

step in biofilm formation. Additionally, the vitreous

structure of ceramic glazes provides a smooth surface

with an average roughness height of approximately

0.189 μm, reducing microbial adhesion and facilitating

the removal of biofouling through hydrodynamic

forces, thereby improving vessel efficiency and

reducing fuel consumption.

One significant advantage of cobalt-based enamels

is their environmental compatibility. Unlike traditional

antifouling coatings that may contain toxic heavy

metals such as copper, cobalt-based enamels do not

release harmful substances into marine ecosystems.

This environmentally friendly characteristic aligns

with sustainability goals, making them a viable

alternative for long-term applications in fishing fleets.

Furthermore, these coatings exhibit remarkable

durability, maintaining their protective properties

even in highly corrosive marine environments, which

is essential for vessels operating in harsh fishing

grounds [24]. Biofouling and corrosion are major

concerns for fishing vessels, impacting fuel efficiency,

structural integrity, and environmental sustainability.

The use of advanced antifouling and anticorrosion

coatings, such as cobalt-based enamels, represents a

promising solution for mitigating these challenges.

Continued research and innovation in coating

technology will play a pivotal role in enhancing the

performance and longevity of fishing vessels while

reducing operational costs and environmental impacts,

contributing to the decarbonization of the fishing

industry.

A Computational Fluid Dynamics (CFD)-based

Reynolds Averaged Navier-Stokes (RANS) model was

employed to analyse the hydrodynamic performance

of a fishing vessel by modifying experimental

variables. OpenFOAM (Open Field Operation and

Manipulation), an open-source, C++-based software,

was utilized due to its high accuracy in simulating

rough hull conditions and its capability to integrate

custom algorithms.

This study examined the impact of hull biofouling

on surface roughness under both static and dynamic

navigation conditions. Laboratory tests were

conducted on a cobalt-based ceramic coating and a

reference Intersleek 1001 coating to assess their

performance. The experimentally obtained roughness

data were then incorporated into the CFD model to

evaluate the reduction in frictional resistance achieved

through cobalt-based ceramic coatings. The findings

demonstrate the potential of advanced coatings to

enhance fuel efficiency and decrease greenhouse gas

emissions in fishing vessels, contributing to the

decarbonization of the fishing industry.

2 MATERIALS AND METHODS

2.1 CFD simulations

In this study, OpenFOAM is utilized as the primary

Computational Fluid Dynamics (CFD) simulation tool.

As an open-source C++ library, it adheres to an object-

oriented programming paradigm and is distributed

under a GPL license. OpenFOAM provides a wide

range of solvers for simulating laminar and turbulent

flows, single and multiphase systems, and adaptive

meshing. Its modular architecture allows for extensive

customization, making it particularly suitable for

analyzing multi-phase turbulent flows involving

floating bodies.

2.1.1 Solver numerical model

The hydrodynamic simulations in this study were

performed using the interFoam solver in OpenFOAM,

designed for incompressible, two-phase flow. The

solver employs the Navier-Stokes Equation (1) and

continuity equations Equation (2) to model laminar

flow of Newtonian fluids.

· p µ g



    





+  = − +  +







(1)

·0



=

(2)

Here, υ is the velocity, p is the pressure, ρ is density, μ

is the dynamic viscosity, g is the acceleration due to

gravity and ∇2 is the Laplace operator.

Specifically, interFoam solves the Reynolds-

Averaged Navier-Stokes (RANS) equations for two

immiscible phases, air and water, using finite volume

discretization and the Volume of Fluid (VOF) method

to capture the free surface. The VOF method treats air

and water as a single effective fluid, with the volume

fraction of water (α) in each computational cell

indicating the phase distribution: α = 1 for water, α = 0

for air, and 0 < α < 1 for mixed cells. The air-water

interface is approximated by the iso-surface α = 0.5. The

local density (ρ) and viscosity (μ) of the fluid, with

μwater = 1.00E-3 Pa·s and μair = 1.48E-5 Pa·s, are

determined for each computational cell using

Equations (3) and (4).

( )

water air

   

= + −

(3)

( )

water air

   

= + −

(4)

2.1.2 Fishing Vessels model, settings, and simulation

mesh

The hull model selected for this study corresponds

to a factory trawler with a length between

perpendicular of 48.17 meters, a beam of 12.00 meters,

and a depth of 10.16 meters. It has a draft of 4.49

meters, a wetted surface area of 780.82 m², and a

displacement volume of 1479.38 m³ (Table 1). Las

formas del modelo de buque se muestran en la Figure

1. The body plan of the ship model is presented in

Figure 1.

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Table 1. Full-scale trawler ship model specifications

Specification

Trawler ship

Length between perpendicular

Lpp (m)

48.17

Breadth

B (m)

12.00

Depth

D (m)

10.16

Draft

T (m)

4.49

Wetted surface area

S (m2)

780.82

Displacement volume

V (m3)

1479.38

Figure 1. Body plan of the trawler ship model

In this study, the nutkRoughWallFunction model

was implemented in OpenFOAM to account for

surface roughness effects in viscous resistance

predictions. Wall functions, derived from empirical

equations, were applied to define near-wall boundary

conditions for momentum and turbulence transport

equations. The wall function model in OpenFOAM

was based on the formulations proposed by [25] and

[26]. To ensure the precise application of wall functions

in mesh design, the y+ parameter, representing the

non-dimensional boundary layer thickness, had to

remain within a valid range. This constraint-imposed

limitations on the size of the mesh cells adjacent to the

hull surface, requiring their height to exceed the

roughness value for proper recognition in the

simulation.

The roughness model incorporated the sand-grain

roughness height on the hull surface to improve the

accuracy of viscous resistance predictions.

Additionally, the roughness parameter influenced

turbulence properties. The nutkRoughWallFunction

boundary condition provided a turbulent kinematic

viscosity definition to account for roughness effects,

relying on turbulence kinetic energy.

OpenFOAM supported three turbulence models: k-

ε, k-ω, and SST k-ω. In this study, turbulence was

modeled using the Reynolds-Averaged Stress (RAS)

SST k-ω two-equation model, with second-order

upwind discretization applied to ensure numerical

stability and accuracy.

To achieve accurate numerical simulation results,

the mesh previously utilized in the investigations of

[18] was adopted. The computational domain

underwent five successive refinements in the vicinity

of the hull to ensure adequate resolution, allowing for

the precise capture of pressure and viscous forces. The

snappyHexMesh utility was then employed to

integrate the hull geometry into the simulation

domain. This process included one additional

refinement near the hull, two refinements directly on

the hull surface, and the generation of boundary layers

to maintain the required non-dimensional wall

distance (y⁺). The final mesh configuration, consisting

of 6.95 million cells, resulted in a y⁺ value of

approximately 100. All simulations were conducted

assuming a static hull, with no mesh motion or

morphing applied.

2.1.3 Frictional resistance calculation using CFD

prediction

The CFD model was employed to numerically

predict viscous and pressure resistance, thereby

determining the total drag along the X-axis for a

navigation speed of 10 knots. To transform the

experimentally obtained average roughness heights

into equivalent sand-grain roughness heights, the

conversion equation proposed by [27] (Equations (5))

was applied. The conversion equation used was:

5.863·

kk=

(5)

Here, ks is the equivalent sand grain roughness height,

and ka is the average roughness height.

By incorporating Equation (5) into the CFD

computational model, it was possible to avoid directly

modeling the rough surface caused by biofouling,

thereby reducing the computational effort required by

the designer. This approach proved particularly

advantageous when analyzing multiple roughness

levels, as was the case in the present study. Moreover,

accurately discretizing surface roughness would have

necessitated a significantly refined mesh, leading to a

considerable increase in computational cost and

simulation time. This methodology was feasible

because the correlation between the roughness

function and the roughness Reynolds number was well

established. Consequently, this correlation was

incorporated into the OpenFOAM solver through the

wall function and turbulence model, enabling an

efficient and accurate representation of roughness

effects.

CFD simulations were performed on a workstation

equipped with a 13th Gen Intel® CoreTM i9-13900KF

processor clocked at 3.00 GHz and 128 GB of installed

RAM. For each simulation, 6 cores were employed.

2.2 Experimental Phase

2.2.1 Preparation and composition of experimental panels

The preparation of experimental steel panels coated

with ceramic enamel followed a standardized

multistep procedure to ensure optimal coating

performance. The ceramic coating was applied to an

S235JR steel panel measuring 150 × 400 mm with a

thickness of 3 mm. Prior to the application of the

ceramic coating, the steel surfaces underwent abrasive

blast cleaning to achieve a surface roughness

corresponding to grades Sa 2.5 or Sa 3 in accordance

with ISO 8503. Surface cleanliness was ensured

following ISO 8501 standards, and contamination

levels were controlled so as not to exceed rating 2 as

defined in ISO 8502-3. Subsequently, the application of

a groundcoat layer was carried out

The enamel coating process consisted of sequential

application of a primer (100 g/m²), groundcoat (450–

500 g/m²), and covercoat (700–800 g/m²). Thermal

treatment was carried out in a muffle furnace at 840 °C

for the primer and groundcoat layers, and at 820 °C for

the covercoat. Firing duration was adjusted according

to substrate thickness to achieve proper sintering

without compromising structural integrity. The

enamel slurry was prepared using 100% TSSG00505/1

enamel frit combined with 40% water by weight. The

557

mixture was homogenized for 10 minutes and sieved

through a 60-mesh screen to ensure particle size

uniformity. The final slurry exhibited a specific gravity

in the range of 1.75–1.77 g/ml, suitable for achieving

consistent coating thickness.

The qualitative chemical composition of the enamel

included more than 30% SiO₂ as the primary glass-

forming oxide. Additionally, 5–15% of Na₂O and B₂O₃

acted as fluxing agents, while minor constituents (<5%)

such as Li₂O, K₂O, CaO, MnO, CoO, CuO, Al₂O₃, TiO₂,

ZrO₂, and F provided specific functional and structural

properties to the final enamel layer.

2.2.2 Static and Dynamic Testing

The experimental test was conducted in accordance

with ASTM D4939-89 at the Biofouling Laboratory of

Cantabria University. This facility’s strategic location

allowed for the direct intake of seawater from the Bay

of Santander (43°28′N, 3°48′W) using a Grundfos

SP 14-4R stainless steel submersible pump, which

transported the water to the laboratory.

Throughout the experimental phase, a reference

panel coated with the commercial antifouling paint

Intersleek 1001 was evaluated in parallel with the

cobalt-based ceramic coating. The test panels were

fully immersed in seawater under static conditions

(static test) inside an experimental reactor to facilitate

biofouling development. These panels were mounted

on a drum using plastic supports.

After a one-month static exposure, the dynamic test

was initiated. This phase involved rotating the drum,

where the test panels were mounted, at a speed of 10

knots. This setup was designed to replicate the

hydrodynamic shear stress experienced on the hull of

a fishing vessel during navigation (Figure 2).

Figure 2. Experimental reactor: (a) static test, (b) Dynamic

test.

2.2.3 Surface Roughness Characterization of Ceramic

Coating

The surface roughness of the cobalt-based ceramic

coating was measured three times throughout the

experimental phase, following the guidelines

established in ASME/ANSI B46.1-2009. The first

measurement was taken prior to experimentation, the

second after one month of seawater immersion in the

reactor (static test), and the third following a one-

month dynamic test. The initial roughness

characterization of both the ceramic coating and the

Intersleek 1001 marine paint was carried out using a

contact profilometer (Taylor-Hobson, Surtronic 3+).

After the static and dynamic exposures, when the

surfaces were covered with biofouling, roughness

measurements were performed using the TQC surface

roughness tester (DC9000 Series), which is accurate to

±5 microns or <2%.

3 RESULTS AND DISCUSION

3.1 Experimental study

The results obtained during the experimental phase are

presented in Table 2. Before exposure in sea water, the

cobalt-based ceramic coating exhibited significantly

lower roughness values (Ka = 0.25 μm, Ks = 1.44 μm)

compared to Intersleek 1001 (Ka = 5.12 μm, Ks = 30.02

μm), representing a 1948.0% higher roughness for

Intersleek 1001.

After 30 days of static seawater exposure, both

coatings experienced an increase in roughness due to

biofouling accumulation. The cobalt-based coating

reached Ka = 10.21 μm and Ks = 59.86 μm, whereas

Intersleek 1001 showed lower roughness values (Ka =

7.83 μm, Ks = 45.91 μm), indicating 23.31% less

biofouling adhesion compared to the ceramic coating.

The roughness data obtained for the cobalt-based

ceramic coating exhibited highly similar values

compared to those reported by [4,5] in his studies on a

coating with similar characteristics, showing a

roughness variation of only 1.47%.

Under dynamic conditions, after 30 days of

exposure simulating navigation at 10 knots, the

roughness of the cobalt-based ceramic coating

significantly decreased (Ka = 0.31 μm, Ks = 1.82 μm),

approaching its initial values. In contrast, the Intersleek

1001 coating maintained a higher roughness after the

dynamic phase (Ka = 6.47 μm, Ks = 37.93 μm),

suggesting a lower self-cleaning efficiency compared

to the ceramic coating, un 1987.10% mas de rugosidad,

with a 1987.10% higher roughness.

Table 2. Ceramic coatings roughness under different

experimental conditions.

Condition

Coating

μm

No exposure

Blue (Co)

0.25± 0.09

1.44±0.55

Intersleek 1001

5.12± 1.24

30.02±7.27

30-days exposure

Blue (Co)

10.21±1.79

59.86±7.92

(Static Conditions)

Intersleek 1001

7,83±1.18

45.91±7.05

30-days exposure

Blue (Co)

0.31±0.10

1.82±0.32

(Dynamic Conditions)

Intersleek 1001

6.47±0.93

37.93±6.86

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3.2 CFD simulation at different fouling levels

Each simulation required 50637.4 seconds of physical

time to complete, producing a computational mesh of

7986487 cells, ensuring high-resolution domain

discretization.

The CFD simulation results for a trawler hull model

coated with the evaluated coatings under different

exposure conditions are summarized in Table 3. Before

seawater exposure, the hull coated with the cobalt-

based ceramic exhibited a viscous resistance (Rv) of

1.47 kN and a total resistance (Rtot) of 5.45 kN. In

contrast, the Intersleek 1001-coated hull showed

slightly higher resistance values, with Rv = 1.50 kN and

Rtot = 5.75 kN, representing a 5.58% increase in total

resistance. Figure 3, 4 and 5 presents the results of the

CFD simulation for the six different cases outlined in

Table 3.

Figure 3. Velocity distribution on the hull surface under no-

exposure condition: (a) Cobalt-based ceramic coating, (b)

Intersleek 1001 coating.

Figure 4. Velocity distribution on the hull surface under 30-

days exposure in static conditions: (a) Cobalt-based ceramic

coating, (b) Intersleek 1001 coating.

Figure 4. Velocity distribution on the hull surface under 30-

days exposure in dynamic conditions: (a) Cobalt-based

ceramic coating, (b) Intersleek 1001 coating.

After 30 days of static seawater exposure,

biofouling accumulation led to an increase in hull

resistance for both coatings. The trawler coated with

the cobalt-based ceramic reached Rv = 1.73 kN and Rtot

= 6.01 kN, reflecting a 10.40% rise in total resistance.

The hull coated with Intersleek 1001 also experienced

an increase, with Rv = 1.64 kN and Rtot = 5.89 kN,

corresponding to an 8.09% increase in total resistance.

Under dynamic conditions, after 30 days of

simulated navigation at 10 knots, the cobalt-based

ceramic coating effectively restored its initial resistance

values (Rv = 1.47 kN, Rtot = 5.45 kN), indicating

successful biofouling removal and a negligible

resistance variation of 0.02%. Conversely, the hull

coated with Intersleek 1001 maintained higher

resistance values (Rv = 1.51 kN, Rtot = 5.79 kN), leading

to a 6.35% increase in total resistance, suggesting lower

self-cleaning efficiency compared to the ceramic

coating.

Table 3. Simulation results for ceramic and commercial

coatings.

Condition

Coating

Viscous

resistance

Total

resistance

Rtot

Difference

No exposure

Blue (Co)

1.47 E+04

5.45 E+04

0,00

Intersleek 1001

1.50 E+04

5.75 E+04

5.58

30-days

exposure

Blue (Co)

1.73 E+04

6.01 E+04

10.40

(Static

Conditions)

Intersleek 1001

1.64 E+04

5.89 E+04

8.09

30-days

exposure

Blue (Co)

1.47 E+04

5.45 E+04

0.02

(Dynamic

Conditions)

Intersleek 1001

1.51 E+04

5.79 E+04

6.35

4 CONCLUSIONS

This study investigated the hydrodynamic

performance and biofouling behavior of a cobalt-based

ceramic coating compared to Intersleek 1001

antifouling paint through both experimental and

computational approaches. The results provide

valuable insights into the effectiveness of ceramic

coatings in reducing biofouling accumulation and

minimizing drag resistance in trawler hulls.

Experimental roughness measurements demonstrated

that, prior to seawater exposure, the cobalt-based

ceramic coating exhibited significantly lower

roughness values than Intersleek 1001. However, after

30 days of static seawater exposure, the ceramic coating

showed a greater increase in roughness due to

biofouling adhesion, with roughness values 23.31%

higher than those observed for Intersleek 1001. Despite

this, the roughness values obtained for the ceramic

coating closely aligned with previous studies,

confirming the reproducibility of the experimental

findings.

Under dynamic conditions, simulating navigation

at 10 knots, the ceramic coating displayed remarkable

self-cleaning properties. After 30 days of dynamic

testing, its roughness values returned to near-initial

conditions, whereas the Intersleek 1001-coated surface

retained a significantly higher roughness (1987.10%

greater than that of the ceramic coating). These

findings suggest that the cobalt-based ceramic coating

offers superior biofouling removal capabilities when

subjected to hydrodynamic shear stress.

CFD simulations further validated the experimental

findings by quantifying the impact of surface

roughness on the total drag resistance of a trawler hull.

Prior to exposure, the ceramic-coated hull exhibited

lower total resistance than the Intersleek 1001-coated

hull. After 30 days of static exposure, biofouling-

induced roughness increased the total resistance of

both coatings; however, dynamic conditions effectively

restored the ceramic coating’s initial resistance values,

559

whereas the Intersleek 1001-coated hull retained a

6.35% increase in resistance.

Overall, the results highlight the potential of cobalt-

based ceramic coatings as a viable alternative to

conventional antifouling paints, offering both lower

initial roughness and enhanced self-cleaning

performance under operational conditions.

ACKNOWLEDGEMENTS

The authors acknowledge Vibrantz Technologies for their

invaluable collaboration in the manufacturing of the

experimental panels. Their support has been instrumental in

making this research possible.

FUNDING

This work was supported by the Government of Cantabria

and the European Union Next GenerationEU/PRTR [project

Environmentally friendly bioactive coatings for energy

improvement and emissions reduction in the fishing

shipbuilding industry - BIO-ENER.].

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