DOI:
https://doi.org/10.14483/23448393.22627Published:
2026-04-08Issue:
Vol. 31 No. 1 (2026)Section:
Mechanical EngineeringRegression Analysis of Principal Ship Attributes for Purse Seiners in the Concept Design Stage
Análisis de regresión de los atributos principales de barcos pesqueros de cerco en la etapa de diseño conceptual
Keywords:
ship dimensions, fishing vessels, hull statistics, ship concept design, regression analysis (en).Keywords:
Dimensiones de barcos, embarcaciones pesqueras, estadísticas de cascos de barcos, diseño conceptual del buque, análisis de regresión (es).Downloads
Abstract (en)
Context: In the ship concept design stage, naval architects must estimate the principal attributes of a vessel before the final definition of hull proportions and detailed arrangements. These estimates are essential for assessing feasibility, performance, and operational capability. However, reliable vessel-specific predictive tools are often unavailable for certain vessel categories.
Method: A database of 130 American-type semi-industrial purse with RSW systems was employed to develop practical regression-based power-law models for estimating key ship attributes directly from principal hull dimensions.
Results: The results show that the main volumetric and power attributes exhibit strong and consistent scaling relationships with the principal dimensions, yielding moderate to high determination coefficients, while the fuel and water capacities report a weak geometric dependence. The proposed regression equations demonstrate improved predictive performance when compared to existing formulations reported in the literature for semi-industrial purse seiners.
Conclusions: This work provides a set of vessel-specific, empirically grounded regression tools tailored to American-type semi-industrial purse seiners, which can be directly applied during the ship concept design stage to support rapid and informed decision-making. This study complements previous analyses focused on hull geometric ratios by addressing a distinct and application-oriented design problem that deals with attribute estimation rather than geometric proportion selection.
Abstract (es)
Contexto: En la etapa de diseño conceptual del buque, los arquitectos navales deben estimar los atributos principales de una embarcación antes de la definición final de las proporciones del casco y de realizar arreglos detallados. Estas estimaciones son esenciales para evaluar la factibilidad, el desempeño y la capacidad operativa. Sin embargo, no se suele disponer de herramientas predictivas confiables y específicas para determinadas categorías de embarcaciones.
Método: Se empleó una base de datos de 130 embarcaciones cerqueras semiindustriales de tipo americano con sistemas RSW para desarrollar modelos prácticos basados en regresión de tipo ley de potencia, a fin de estimar los atributos clave del buque directamente de las dimensiones principales del casco.
Resultados: Los resultados muestran que los principales atributos volumétricos y de potencia presentan relaciones de escalamiento fuertes y consistentes con las dimensiones principales, con coeficientes de determinación moderados a altos, mientras que las capacidades de combustible y agua reportan una débil dependencia geométrica. Las ecuaciones de regresión propuestas demuestran un mejor desempeño predictivo en comparación con las formulaciones existentes que figuran en la literatura para cerqueros semiindustriales.
Conclusiones: Este trabajo proporciona un conjunto de herramientas de regresión específicas para este tipo de embarcaciones, las cuales han sido empíricamente fundamentadas y adaptadas a los cerqueros semiindustriales tipo americano, y pueden aplicarse directamente durante la etapa de diseño conceptual del buque para una toma de decisiones rápida e informada. Al abordar un problema de diseño distinto y orientado a la aplicación, este estudio complementa análisis previos que emplean las relaciones geométricas del casco, centrándose en la estimación de atributos más que en la selección de proporciones geométricas.
References
[1] B. Rinauro, E. Begovic, F. Mauro, and G. Rosano, “Regression analysis for container ships in the early design stage,” Ocean Eng., vol. 292, art. 116499, Jan. 2024, https://doi.org/10.1016/j.oceaneng.2023.116499
[2] A. Lepore, L. Mocerino, B. Palumbo, E. Rizzuto, E. Rossi, and L. Vitiello, “A supervised statistical learning approach to the preliminary design of cruise ship Gross Tonnage,” Appl. Ocean Res., vol. 143, art. 103837, Feb. 2024, https://doi.org/10.1016/j.apor.2023.103837
[3] T. Cepowski and P. Chorab, “Determination of design formulas for container ships at the preliminary design stage using artificial neural network and multiple nonlinear regression,” Ocean Eng., vol. 238, art. 109727, Oct. 2021, https://doi.org/10.1016/j.oceaneng.2021.109727
[4] D. de la Torre, “Effect of ship size on the hull dimension ratios of purse seiners,” Ing. Investig., vol. 45, no. 1, art. e116364, 2025, https://doi.org/10.15446/ing.investig.116364
[5] M. Kalajdžić and N. Momčilović, “A step toward the preliminary design of seagoing multi-purpose cargo vessels,” Brodogradnja, vol. 71, no. 2, pp. 75–89, Jun. 2020, https://doi.org/10.21278/brod71205
[6] S. Gurgen, I. Altin, and M. Ozkok, “Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network,” Ships Offshore Struct., vol. 13, no. 5, pp. 459–465, Jul. 2018, https://doi.org/10.1080/17445302.2018.1425337
[7] X. Peng, H. Lei, and W. Zhong, “Research on estimation method of gross registered tonnage of ships in Grand Canal,” in Modeling Risk Management for Resources and Environment in China, First., D. D. Wu and Y. Zhou, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 101–108, https://doi.org/10.1007/978-3-642-18387-4
[8] D. G. . M. Watson, Practical ship design, vol. 1. Oxford: Elsevier Science LTD, 1998.
[9] S&P Global, “IHS Fairplay World Shipping Encyclopedia version 12.01,” 2011. [Online]. Available: https://www.spglobal.com/marketintelligence/en/mi/products/maritime-portal-desktop.html
[10] A. Papanikolaou, Ship design. Methodologies of preliminary design, 1st ed. Dordrecht: Springer Netherlands, 2014, https://doi.org/10.1007/978-94-017-8751-2
[11] H. Bin, L. Xiuwen, and R. Yuqing, “Statistics and analysis of main parameters of ocean fishing vessels in China,” in 2020 5th Int. Conf. Electromech, Control Tech. Trans. (ICECTT), IEEE, May 2020, pp. 594–598, https://doi.org/10.1109/ICECTT50890.2020.00135
[12] O. E. Surov, M. V. Kitaev, E. E. Solovieva, V. A. Veselov, and D. V. Tyuftyaev, “Analysis of main dimensions and characteristics of fishing vessels,” Russ. J. Water Transp., vol. 2, no. 72, pp. 41–53, Sep. 2022, https://doi.org/10.37890/jwt.vi72.273
[13] Ministerio de la Producción, Gobierno del Perú, “Embarcaciones pesqueras.” [Online]. Available: https://consultasenlinea.produce.gob.pe/ConsultasEnLinea/consultas.web/embarcacion
[14] Dirección General de Capitanías y Guardacostas del Perú, “Consulta datos de la nave.” [Online]. Available: https://www.dicapi.mil.pe/consultas/naves-artefactos-navales
[15] H. M. Gaspar, “Data-driven ship design,” in 17th Int. Conf. Comput. IT Appl. Marit. Ind., 2015, pp. 426–439. http://www.shiplab.hials.org/wp-content/uploads/2019/05/40_Gaspar.pdf
[16] L. J. C. Valerio Mena, V. N. Acosta Pastor, and V. A. Loarte Vicuña, “Determinación numérica de la resistencia al avance de buques,” TECNIA, vol. 9, no. 2, pp. 61–68, Dec. 1999, https://doi.org/10.21754/tecnia.v9i2.324
[17] N. Hamlin, “Ship geometry,” in Principles of Naval Architecture, 2nd ed., vol. I, E. V. Lewis, Ed. Jersey City, NJ, USA: The Society of Naval Architects and Marine Engineers, 1988, p. 19.
[18] B. Tibbitts, “Naval surface ships,” in Ship Design and Construction, T. Lamb, Ed. Jersey City, NJ, USA: The Society of Naval Architects and Marine Engineers, 2003, pp. 28_1-28_11.
[19] J. Fox, Applied regression analysis & generalized linear models, 3rd ed. Thousand Oaks, CA, USA: SAGE Publications, 2016.
[20] D. Doane and L. Seward, Applied statistics in business and economics, 3rd ed. New York: McGraw-Hill, 2011.
[21] N. Bialystocki and D. Konovessis, “On the estimation of ship’s fuel consumption and speed curve: A statistical approach,” J. Ocean Eng. Sci., vol. 1, no. 2, pp. 157–166, Apr. 2016, https://doi.org/10.1016/j.joes.2016.02.001
[22] H. Schneekluth and V. Bertram, Ship design for efficiency and economy, 2nd ed. Oxford, UK: Butterworth-Heinemann, 1998.
[23] C. B. Barrass, Ship design and performance for masters and mates. Oxford, UK: Elsevier Butterworth-Heinemann, 2004.
[24] P. Gale, “Concept design,” in Ship Design and Construction, J. Lamb, Ed. Jersey City, NJ, USA: The Society of Naval Architects and Marine Engineers, 2003, pp. 5–9.
[25] K. Rawson and E. Tupper, Basic ship theory. Vol. I, 5th ed. Oxford, UK: Butterworth-Heinemann, 2001.
[26] M. L. Hakim, B. Nugroho, I. K. Suastika, and I. K. A. P. Utama, “Alternative empirical formula for predicting the frictional drag penalty due to fouling on the ship hull using the design of experiments (DOE) method,” Int. J. Technol., vol. 12, no. 4, art. 829, Oct. 2021, https://doi.org/10.14716/ijtech.v12i4.4692
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