Water lubrication systems have emerged as a promising pollution-free solution for underwater rotor-bearing solutions. Further, texture water-lubricated bearings such as Herringbone Groove Journal Bearings (HGJBs) enhance the stability and performance of underwater vehicle rotors. The present study presents a comprehensive investigation into optimizing HGJB design parameters using Artificial Neural Networks (ANN) and Genetic Algorithm (GA) based techniques. The influence of bearing parameters such as eccentricity ratio, speed, groove depth, groove angle, and the number of grooves on the dynamic characteristics is analyzed. A key novelty of this research is the integration of ANN-based performance prediction with GA-driven optimization, ensuring an efficient and accurate determination of optimal design parameters. The optimized HGJB configuration is compared with the traditional Plain Journal Bearings (PJBs), demonstrating superior stiffness characteristics and enhanced vibration suppression capabilities. The results reveal that HGJBs provide improved dynamic performance, making them highly suitable for critical applications. Additionally, various weighted optimization strategies are explored to balance stiffness and damping, offering tailored solutions for different operational requirements. The findings contribute significantly to the design and performance enhancement of water-lubricated bearings, paving the way for their broader adoption in advanced engineering applications.
LundJW. Review of the concept of dynamic coefficients for fluid film journal bearings. J Tribol1987; 109: 37–41.
2.
SunJChanglinG. Hydrodynamic lubrication analysis of journal bearing considering misalignment caused by shaft deformation. Tribol Int2004; 37: 841–848.
3.
ChenSKChouHCKangY. Stability analysis of hydrodynamic bearing with herringbone grooved sleeve. Tribol Int2012; 55: 15–28.
4.
OhdarSK. A comprehensive review on advancements in water-lubricated bearings : Surface texture, tribological performance, and thermal effects. Proc Inst Mech Eng J: J Eng Tribol2025; 239: 955–984.
5.
AdatepeHByklogluASofuogluH. An investigation of tribological behaviors of dynamically loaded non-grooved and micro-grooved journal bearings. Tribol Int2013; 58: 12–19.
6.
Tala-IghilNFillonM. A numerical investigation of both thermal and texturing surface effects on the journal bearings static characteristics. Tribol Int2015; 90: 228–239.
7.
ShenCKhonsariMM. Numerical optimization of texture shape for parallel surfaces under unidirectional and bidirectional sliding. Tribol Int2015; 82: 1–11.
8.
GropperDWangLHarveyTJ. Hydrodynamic lubrication of textured surfaces: a review of modeling techniques and key findings. Tribol Int2016; 94: 509–529.
9.
MiyanagaNTomiokaJ. Effect of support stiffness and damping on stability characteristics of herringbone-grooved aerodynamic journal bearings mounted on viscoelastic supports. Tribol Int2016; 100: 195–203.
10.
SunJShuLSongX, et al.Multi-objective optimization design of engine crankshaft bearing. Ind Lubr Tribol2016; 68: 86–91.
11.
LvFRaoZTaN, et al.Mixed-lubrication analysis of thin polymer film overplayed metallic marine stern bearing considering wall slip and journal misalignment. Tribol Int2017; 109: 390–397.
12.
ShindeABPawarPM. Multi-objective optimization of surface textured journal bearing by Taguchi based grey relational analysis. Tribol Int2017; 114: 349–357.
13.
YamadaHTauraHKanekoS. Numerical and experimental analyses of the dynamic characteristics of journal bearings with square dimples. J Tribol2018; 140: 011703.
14.
LvFJiaoCTaN, et al.Mixed-lubrication analysis of misaligned bearing considering turbulence. Tribol Int2018; 119: 19–26.
15.
GongJJinYLiuZ, et al.Study on influencing factors of lubrication performance of water-lubricated micro-groove bearing. Tribol Int2019; 129: 390–397.
16.
JangJYKhonsariMM. Performance and characterization of dynamically-loaded engine bearings with provision for misalignment. Tribol Int2019; 130: 387–399.
ShiJCaoHChenX. Effect of angular misalignment on the dynamic characteristics of externally pressurized air journal bearing. Proc Inst Mech Eng Part J J Eng Tribol2020; 234: 205–228.
19.
XieZZhangYZhouJ, et al.Theoretical and experimental research on the micro interface lubrication regime of water lubricated bearing. Mech Syst Signal Process2021; 151: 107422.
20.
LiYLiRYeY, et al.Numerical analysis on the performance characteristics of a new gas journal bearing by using finite difference method. Adv Mech Eng2021; 13: 168781402110280.
21.
KhamariDSKumarJBeheraSK. Numerical investigation of influence sensitivity of a gas foil bearing parameters on the dynamic coefficients. J Brazilian Soc Mech Sci Eng2021; 43: 167.
22.
MengFZhangYSuL, et al.Dynamic characteristics of compound textured journal bearing. Proc Inst Mech Eng Part J J Eng Tribol2021; 235: 1312–1334.
23.
XuBGuoHWuX, et al.Static and dynamic characteristics and stability analysis of high-speed water-lubricated hydrodynamic journal bearings. Proc Inst Mech Eng Part J J Eng Tribol2022; 236: 701–720.
24.
XieZWangXZhuW. Theoretical and experimental exploration into the fluid structure coupling dynamic behaviors towards water-lubricated bearing with axial asymmetric grooves. Mech Syst Signal Process2022; 168: 108624.
25.
HongTXingGYZuoHY, et al.Increasing operational stability of journal bearing in hydraulic suspension micro-pump by herringbone grooved structure. Sci China Technol Sci2024; 67: 853–862.
26.
JangJYKhonsariMMSotoC, et al.Effect of varnish on the performance and stability of journal bearings. Tribol Int2024; 198: 109897.
27.
SharmaSJamwalGAwasthiRK. Dynamic and stability performance improvement of the hydrodynamic bearing by using triangular-shaped textures. Proc Inst Mech Eng Part J J Eng Tribol2020; 234: 1436–1451.
28.
YadavSKThakreGDKhatriCB. Improvement in textured hole-entry hybrid journal bearing system by using multi-objective genetic algorithm. J Brazilian Soc Mech Sci Eng2022; 44: 1–19.
29.
B B, N S, P RB, et al.Hydrodynamic bearing performance trade-off study and fuzzy based multi-objective optimisation on a offset surface textured journal bearing. Adv Mater Process Technol2022; 8: 989–1006.
30.
CartwrightSRothwellBCFigueredoG, et al.A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings. Tribol Int2024; 196: 109670.
31.
GaneshaAGirishHPaiR, et al.A data-driven exploration of multi pad bidirectional adjustable bearings and deep learning model-based optimization. Tribol Int2024; 194: 109570.
32.
HessNShangL. Development of a machine learning model for elastohydrodynamic pressure prediction in journal Bearings. J Tribol2022; 144: 081603.
33.
LiSBabinAShutinD, et al.Active hybrid journal bearings with lubrication control: Towards machine learning. Tribol Int2022; 175: 107805.
34.
RavindraKGRudreshBMKumarMKP, et al.Optimization of design parameters to evaluate the performance of journal bearing: ANN approach. Mater Today Proc2022; 54: 171–178.
35.
RossopoulosGNPapadopoulosCI. A journal bearing performance prediction method utilizing a machine learning technique. Proc Inst Mech Eng Part J J Eng Tribol2022; 236: 1993–2003.
36.
YangJPalazzoloA. Computational fluid dynamics based mixing prediction for tilt pad journal bearing TEHD modeling - part II: implementation with machine learning. J Tribol2021; 143: 1–18.
37.
MishraHPBeheraSK. A methodology for performance prediction: hydrodynamic investigation of spiral grooved thrust bearing. Lubr Sci2023; 35: 399–419.
38.
HirsGG. The load capacity and stability characteristics of hydrodynamic grooved journal bearings. ASLE Trans1965; 8: 296–305.
