• International Journal of Technology (IJTech)
  • Vol 13, No 3 (2022)

Experimental and Computational Fluid Dynamics Investigations into the Effect of Loading Condition on Resistance of Hard-Chine Semi Planning Crew Boat

Experimental and Computational Fluid Dynamics Investigations into the Effect of Loading Condition on Resistance of Hard-Chine Semi Planning Crew Boat

Title: Experimental and Computational Fluid Dynamics Investigations into the Effect of Loading Condition on Resistance of Hard-Chine Semi Planning Crew Boat
Soegeng Riyadi, Wasis Dwi Aryawan, I Ketut Aria Pria Utama

Corresponding email:


Published at : 01 Jul 2022
Volume : IJtech Vol 13, No 3 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i3.4597

Cite this article as:
Riyadi, S., Aryawan, W.D., Utama, I.K.A.P., 2022. Experimental and Computational Fluid Dynamics Investigations into the Effect of Loading Condition on Resistance of Hard-Chine Semi Planning Crew Boat. International Journal of Technology. Volume 13(3), pp. 518-532

1,242
Downloads
Soegeng Riyadi Department of Naval Architecture, Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
Wasis Dwi Aryawan Department of Naval Architecture, Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
I Ketut Aria Pria Utama Department of Naval Architecture, Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
Email to Corresponding Author
Abstract
Experimental and Computational Fluid Dynamics Investigations into the Effect of Loading Condition on Resistance of Hard-Chine Semi Planning Crew Boat

Nowadays, the issue of energy efficiency in the maritime transportation sector has been strongly associated with the decreasing use of fossil energy and greenhouse gas emissions. Crew boats are one of the ship modes which consumes a lot of fuel in maritime transportation. This affects the number of exhaust gases released into the atmosphere. A study into the estimation of crew boat resistance was carried out experimentally using a towing tank, numerically using a CFD methodology, and then compared with Savitsky's method. Measurements were taken in calm waters under even keel and trim scenarios, considering load variation had been adjusted for. Determining the correct load position affected the LCG (Longitudinal Center of Gravity) and VCG (Vertical Center of Gravity)   parameters, affected trim, and decreased crew boat resistance. Overall results showed that the experimental test, CFD method, and the empirical estimation from Savitsky were in good agreement with average errors up to 3%. The calculation results demonstrated that trim had a greater influence on decreasing resistance up to 3.062% than even keel position. Furthermore, the shifting of LCG had a more significant effect than that of VCG in the context of resistance changes.

Computational Fluid Dynamics (CFD); Crew boat; Energy efficiency; Loading condition; Resistance; Semi-planning.

Introduction

    Crew boats are very important to the shipping industry as they provide the connection between a base onshore and offshore installations, such as drilling rigs, or designated anchorages that serve hundreds of ships at a time (Karanassos, 2016). Companies that operate fleets of offshore structure platforms need boats to transport employees and operators to and from the platforms regularly. These crew boats are also employed for modest constructions or minor changes on the platform thus they are utilized to transport teams of workers and their equipment (El-Reedy, 2021).
    Currently, energy efficiency in the transportation sector is an absolute necessity. The contribution of energy demands in the transportation sector is about 21% of the total energy needed in the world, whereas sea transportation energy contributes approx.  6% of the total transportation energy demand (British Petroleum, 2020). The impact of the energy used in maritime transportation is directly proportional to the production of exhaust gases and pollution, which are both current problems in the environment. Crew boats compete directly with helicopters, therefore they are built with a high-speed planning hull and lightweight aluminium to overcome resistance (Latorre, 2003) and some crew boats are intended to carry passengers and cargo while operating in a semi-planning mode. Crew boat as the object of this research is a type of marine transportation that focuses its operations on speed and energy efficiency. Energy efficiency in marine transportation is very dependent on operational optimization among the hull of the ship, engine, propulsion system, and the routes.

    Trim optimization was one of the effective measures to reduce fuel consumption (Molland et al., 2014). Reichel et al. (2014) studied the physics behind the changing of propulsive power when trimming a vessel to detect the origin of the changes. Islam and Soares (2019) presented trim optimization at different speeds and drafts could be a convenient and effective way for vessels to improve efficiency. Le et al. (2021) described the physical phenomena of a ship's resistance shifting as the trim state changed. The residuary resistance coefficient acting on the hull resistance was found to be the major effect resulting in changing propulsive power when a vessel was trimmed. Residual resistance is made up of wave resistance, which refers to the energy loss produced by the vessel's waves, and viscous pressure resistance (Molland et al., 2017).
    One of the powerful tools to investigate the problems above was by using the computational fluid dynamics (CFD) method (Bertram, 2011).  Sherbaz and Duan (2014) demonstrated that trim had a pronounced increasing effect on resistance during bow trim at MOERI container ships (KCS). The effect on resistance is varying during stern trim and the optimum trim point is 0.02 m trim by stern. The study of viscous and wave-making components reveals that viscous resistance changes slightly with a change in trim whereas trim had a dominant effect on the wave-making resistance. Kazemi and Salari (2017) provided a computational and experimental hydrodynamic study for a hard-chine planning boat under a variety of loading variables and speeds. The comparison of numerical and laboratory findings revealed a high degree of agreement between them. Furthermore, CFD applications are used in the evaluation of water flows regarding the effects on ships (Suastika et al., 2017; Utama et al., 2021a), they presented that the CFD calculations and model testing correlate quite well for overall ship resistance in calm water.
    CFD simulation was utilized in this research to determine resistance, and tank tests were performed to validate the results. The main objective was to analyze the drag on the crew boat due to the center of mass movement. The stages were as follows; firstly, comparing the geometric properties of the crew boat model using CFD and experimental data. Secondly, the CFD solver was briefly introduced, followed by the numerical setup consisting of mesh generation and boundary conditions. Thirdly, the CFD verification technique was applied in the validation of the numerical approach, and it was accomplished by comparing the resistance values obtained from CFD, Savitsky, and experimental methods. Finally, by following verification and validation, CFD analysis was performed on 3 (three) longitudinal loads and 3 (three) vertical loads at the cargo deck and there were differences in trim on the crew boat. Calculation of the trim effect to identify the optimal load configuration in terms of reducing ship resistance was carried out and discussed.

Conclusion

The current study has provided a computational and experimental hydrodynamic analysis of a crew boat under a variety of loading situations and speeds to validate and verify the results. The great degree of agreement between model testing and CFD predictions for total ship resistance in calm water has resulted in a high degree of confidence in the CFD results. The impact of longitudinal and vertical load variations was investigated on a model scale, with the findings of the tank test serving as confirmation of the results of the CFD output model construction. With the speed at Fr. 0.117, 0.467, and 0.701, the discrepancies differed, respectively 3.22%, 4.48%, and -2.04%. The initial conditions for the LC1-LC2, LC3-LC4, and LC5-LC6 pairings were 0.40 deg., 0.69 deg., and 0.21 deg., respectively, due to the influence of weight on the crew boat. There were three sets of CT lines since each pair of LC groups produced comparable CT. The differences to LC1 used as a reference are 0.0136 and -0.0172 at Fr=0.117, and 0.0039 and -0.0049 at Fr=0.701. The impact of changing placement has been less as Fr increases. At Fr=0117, the effect of VCG has changed in each LC with the same LCG having the least effect, 0.059% to 0.085%. The optimal condition for investigating operational speed, Fr=0.700, was obtained in the LC5-LC6 pair since CT is lowered between 0.908% and 3.062% of the reference LC. This may also be observed in the LC pair's wave elevation for the smaller spray wave and stern wave. A similar effect may be achieved by using hydrostatic pressure spray. Consequently, it was discovered that shifting the position of the crew boat to the front resulted in less resistance than shifting the position to the back of the ship. The implementation of the investigation findings has been enabling the ship's crew to make better decisions about how to set the ship's speed and load position. Thus, by implementing this, it can serve as an operational guide for reducing total ship resistance and hence exhaust gas emissions.

Acknowledgement

    The authors wished to thank the Ministry of Research, Technology, and BRIN for the Doctoral Program Research Grant of the year 2020 under contract number 1238/PKS/ITS/2020. The authors also thanked Mr. Langgeng Condro, Mr. Achmad Sutiyo, and Mr. Rudie Aminudin for their help in experimental model tests.

References

Abouelfadl, A.H., Abdelraouf, E.E.Y., 2016. The Impact of Optimizing Trim on Reducing Fuel Consumption. Journal of Shipping and Ocean Engineering, Volume 6, pp. 179–184

Anderson, J.D., 1995. Computational Fluid Dynamics: The Basics with Applications. Mechanical Engineering Editions. New York, USA: McGraw-Hill, pp. 526–532

Bardina, J.E., Huang, P.G., Coakley, T.J., 1997. Turbulence Modeling Validation. In: 28th Fluid Dynamics Conference. https://doi.org/10.2514/6.1997C2121

Bentley Systems Inc., 2018. User Manual: MAXSURF Resistance. MAXURF Connect Edition V22. USA: Bentley Systems Inc.

Bertram, V., 2011. Practical Ship Hydrodynamics. 2nd Edition. USA: Elsevier Science

British Petroleum, 2020. Carbon Emissions from Energy Use are The Largest Source of Greenhouse Gas Emissions, Energy Outlook 2020 Edition. Available at: https://www.bp.com/content/dam/bp/businesssites/en/global/corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2020.pdf.

Diez, M., Lee, E.J., Powers, A.M., Fullerton, A.M., Lewis, R.R, Harrison, E.L, Stern, F., 2020. FSI and MDO for Weight Reduction of a Grillage Panel of a Fast Deep-V Planing Hull Subject to Slamming in Waves. In: 33rd Symposium on Naval Hydrodynamics

El-Reedy, M.A., 2021. Offshore Projects and Engineering Management. 1st Edition. USA: Gulf Professional Publishing

Ghadimi, P., Tavakoli, S., Dashtimanesh, A., 2017. Calm Water Performance of Hard-Chine Vessels in Semi-Planing and Planing Regimes. Polish Maritime Research, Volume 23(4), pp. 23–45

Islam, H., Soares, C.G., 2019. Effect of Trim on Container Ship Resistance at Different Ship Speeds and Drafts. Ocean Engineering, Volume 183, pp. 106–115

International Maritime Organization (IMO), 2016. Module 4 – Ship Board Energy Management. In IMO Train the Trainer (TTT) Course on Energy Efficient Ship Operation, pp. 17–19. Available online at https://wwwcdn.imo.org/localresources/en/OurWork/Environment/Documents/Airpollution/M4 energy management onboard final.pdf.

International Towing Tank Conference (ITTC), 2017. ITTC-Recommended Procedures and Guidelines: Uncertainty Analysis in CFD Verification and Validation Methodology and Procedures. In: 28th International Towing Tank Conference, pp. 17–22 

International Towing Tank Conference (ITTC), 2014a. ITTC-Recommended Procedures and Guidelines: Practical Guidelines for Ship Resistance CFD No. 7.5–03–02–04. In: 27th International Conference Towing Tank, pp. 1–12

International Towing Tank Conference (ITTC), 2014b. ITTC-Recommended Procedures and Guidelines: Example for Uncertainty Analysis of Resistance Tests in Towing Tanks (7.5-02-02-02.1), In: 27th International Conference Towing Tank. pp. 1–11

International Towing Tank Conference (ITTC), 2014c. ITTC-Recommended Procedures and Guidelines: General Guideline for Uncertainty Analysis in Resistance Tests 7.5-02-02-02 (Revision 02). In: 27th International Conference Towing Tank

Karanassos, H.A., 2016. Commercial Ship Surveying: On/Off Hire Condition Surveys and Bunker Surveys. UK: Elsevier

Kazemi, H., Salari, M., 2017. Effects of Loading Conditions on Hydrodynamics of a Hard-Chine Planing Vessel Using CFD and a Dynamic Model. International Jounal of Maritime Technology, Volume 7, pp. 11–18

Latorre, R., 2003. Naval Architecture. In: Encyclopedia of Physical Science and Technology, Third Edition, Academic Press, USA, pp. 343–360

Le, T.-H., Vu, M.T., Bich, V.N., Phuong, N.K., Ha, N.T.H., Chuan, T.Q., Tu, T.N., 2021. Numerical Investigation on the Effect of Trim on Ship Resistance by RANSE Method. Applied Ocean Research, Volume 111, p. 102642

Luhulima, R. B., Sutiyo, Alia, M. R., Utama, I.K.A.P., 2021. Experimental Investigation into the Resistance Characteristics of Trimaran and Pentamaran Configurations. International Journal of Technology, Volume 12(5), pp. 291–319

Lyu, X., Tu, H., Xie, D., Sun, J., 2018. On Resistance Reduction of a Hull by Trim Optimization. Brodogr. Brodogradnja, Volume 69(1), pp. 1–13

Marintek, 2021. Marine Customer Cases. NUMECA International. Available online at https://www.numeca.com/marine-case/id/129, Accessed on September 29, 2021

Menter, F.R., 1994. Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications. AIAA Journal, Volume 32(8), pp. 1598–1605

Molland, A.F., Turnock, S.R., Hudson, D.A., 2017. Components of Hull Resistance. In: Ship Resistance and Propulsion, 2nd Edition,  Cambridge University Press, Cambridge, pp. 12–69

Molland, A.F., Turnock, S.R., Hudson, D.A., Utama, I.K.A.P., 2014. Reducing Ship Emissions: A Review of Potential Practical Improvements in the Propulsive Efficiency of Future Ships. Transactions of The Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, Volume 156 (A2), p. 175

Reichel, M., Minchev, A., Larsen, N.L., 2014. Trim Optimisation-Theory and Practice. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, Volume 8, pp. 387–392

Savitsky, D., 2003. On the Subject of High-Speed Monohulls. In: Presented to the Greek Section Of the Society Of Naval Architects and Marine Engineers, Grecee, pp. 1–44

Sherbaz, S., Duan, W., 2014. Ship Trim Optimization: Assessment of Influence of Trim on Resistance of MOERI Container Ship. The Scientific World Journal, Volume 2014. https://doi.org/10.1155/2014/603695

Suastika, K., Hidayat, A., Riyadi, S., 2017. Effects of the Application of a Stern Foil on Ship Resistance: a Case Study of an Orela Crew Boat. International Journal of Technology, Volume 8(7), pp. 1266–1275

Utama, I.K.A.P., Sutiyo, Suastika, I.K., 2021a. Experimental and Numerical Investigation into the Effect of the Axe-Bow on the Drag Reduction of a Trimaran Configuration. International Journal of Technology, Volume 12 (3), pp. 527–538

Utama, I.K.A.P., Purnamasari, D., Suastika, I.K., Nurhadi, Thomas, G.A., 2021b. Toward Improvement of Resistance Testing Reliability. Journal of Engineering and Technological Sciences, Volume 53(2), pp. 201–210

Download PDF
Who cite this paper
Table of Contents
Article
Abstract
Introduction
Conclusion
Acknowlegement
References