Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Marine Engineering Innovation and Research
Budhi Santoso
Department of Naval Architecture, Politeknik Negeri Bengkalis, Bengkalis, 28711
Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Performance Mapping and Fuel Efficiency Analysis of an LSCS Piston Diesel Engine under Injector Pressure Variation for Marine Applications Benedicta Dian Alfanda; Budhi Santoso; Dwi Sasmita Aji Pambudi; Mahasin Maulana Ahmad; Kiki Dwi Wulandari; Widya Emilia Primaningtyas; Ratna Muninggar; Syifa Atho Illah Firmansyah
International Journal of Marine Engineering Innovation and Research Vol. 11 No. 2 (2026)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v11i2

Abstract

Fuel efficiency and stable engine performance are important issues in small diesel engines used for marine-related applications, such as auxiliary power units, generator sets, and small vessel systems. This study investigates the effect of injector pressure variation on the performance and fuel efficiency of a four-stroke single-cylinder diesel engine equipped with a Lateral Swirl Combustion System (LSCS) piston. The injector pressure was varied at 170, 180, and 190 bar, while the engine was tested at different speeds of 1000, 1200, 1300, and 1400 rpm under electrical loads of 2000, 3000, and 4000 W. The evaluated performance parameters included output power, torque, brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), and performance map characteristics. The results show that injector pressure significantly influenced the combustion performance and fuel utilization of the LSCS piston diesel engine. Among the tested conditions, the 190 bar injector pressure produced the best overall performance, with a maximum power of 2.72 kW, torque of 18.65 N·m, BSFC of 371.06 g/kWh, and BMEP of 1.95 bar. Compared with the 180bar setting, the 190bar injector pressure increased power by 1.87%, increased torque by 1.96%, reduced BSFC by 7.32%, and increased BMEP by 2.09%. The performance map also indicated that the 190bar setting provided the most favorable operating region, characterized by relatively low BSFC and higher BMEP. These findings suggest that injector pressure optimization combined with LSCS piston geometry can improve combustion effectiveness and fuel efficiency in small diesel engines for marine-related applications.
Intelligent Optimization of Fast Boat Hull Form for Resistance Reduction Using CFD and Surrogate-Assisted Algorithms Romadhoni; Budhi Santoso; Muhammad Alimul Hafiz
International Journal of Marine Engineering Innovation and Research Vol. 11 No. 1 (2026)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v11i1

Abstract

High-speed fast boats operating at high Froude numbers experience rapidly increasing resistance due to coupled viscous and wave-making effects. This study proposes a surrogate-assisted hull-form optimization framework that combines Reynolds-averaged Navier-Stokes (RANS) CFD (SST k-ω) with Gaussian Process Regression (Kriging) to minimize the total resistance of a fast monohull while maintaining displacement and geometric feasibility. The hull geometry was parameterized using four variables: deadrise angle, prismatic coefficient, longitudinal centre of buoyancy (LCB), and bow entrance angle. A set of candidate designs was evaluated by CFD, a surrogate model was trained and validated (R² = 0.985, prediction error < 2%), and global optimization was carried out using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). CFD verification of the best design shows a consistent resistance reduction across Fn = 0–0.75, with a maximum reduction of 14.4% at Fn = 0.65 compared to the baseline hull. The optimized hull exhibits reduced bow pressure peaks and delayed flow separation at the transom. The surrogate-assisted strategy reduces the number of CFD evaluations and lowers the overall computational effort by about 78% while preserving prediction accuracy. R^2=0.992.
Co-Authors Benedicta Dian Alfanda Dwi Sasmita Aji Pambudi Kiki Dwi Wulandari Mahasin Maulana Ahmad Muhammad Alimul Hafiz Ratna Muninggar Romadhoni Syifa Atho Illah Firmansyah Widya Emilia Primaningtyas