The mechanical features of the bolted connection interface significantly influence the dynamic behavior of the rod-fastening combined rotor (RFCR). Key factors such as preload, surface roughness, and polish govern the mechanical characteristics of this connection interface. However, accurately estimating the connection interface parameters proves challenging due to nonlinear behaviors such as loosening and slippage of the rods, which arise from manufacturing and assembly errors, and vibrations during rotor operation. To evaluate the dynamic characteristics of the RFCR effectively, it is crucial to create a stochastic model that accounts for uncertainties and then identify the system parameters accordingly. Based on experimental data, this study thoroughly investigates the inherent characteristics of the RFCR. Initially, the impact of preload magnitude and its detuning on the vibration characteristics of the rotor system is experimentally analyzed. Subsequently, the rotor structure is modeled using a stochastic general node approach, and the uncertainty of the model parameters is addressed through Bayesian identification. The suggested model's efficacy and dependability are evaluated by comparing experimental findings. This research demonstrates that the proposed methodology not only enhances the accuracy of rotating machinery system analysis but also contributes to the advancement of dynamic theory and its application in systems with multiple uncertain parameters.
LiuYLiuHWangX, et al. Nonlinear dynamic characteristics of a three-dimensional rod-fastening rotor bearing system. Proc IMechE, Part C: J Mechanical Engineering Science2015; 229: 882–894.
2.
BogradSReussPSchmidtA, et al. Modeling the dynamics of mechanical joints. Mech Syst Signal Process2011; 25: 2801–2826.
3.
LiYZhuZWenC, et al. Rub-impact dynamic analysis of a dual-rotor system with bolted joint structure: theoretical and experimental investigations. Mech Syst Signal Process2024; 209: 111144.
4.
LiYZhuZWenC, et al. Dynamic modelling and parametric analysis of a bolted joint rotor-bearing system considering stick-slip behavior at mating interface. Int J Non Linear Mech2024; 159: 104631.
5.
LiYLongTLuoZ, et al. Numerical and experimental investigations on dynamic behaviors of a bolted joint rotor system with pedestal looseness. J Sound Vib2024; 571: 118036.
6.
DuDSunWCuiB, et al. Traveling-wave vibrations of disc-drum rotors with PSC under mistuning-coupled conditions. Int J Mech Sci2023; 250: 108326.
7.
HanZWangDWangY, et al. Dynamic effects of the initial skewness of the inertial principal axis on the rotor with bolted joint. Mech Syst Signal Process2023; 200: 110564.
8.
AldanaSMooreKJ.Dynamic interactions between two axially aligned threaded joints undergoing loosening. J Sound Vib2022; 520: 116625.
9.
YangSZhouCHeJ, et al. Dynamic characteristics of a rod-fastening rotor system with tie rod bolt loosening. Nonlinear Dyn2023; 111: 18697–18723.
10.
LiuYLiuHFanB.Nonlinear dynamic properties of disk-bolt rotor with interfacial cutting faults on assembly surfaces. J Vib Control2018; 24: 4369–4382.
11.
BeaudoinM-ABehdinanK.Analytical lump model for the nonlinear dynamic response of bolted flanges in aero-engine casings. Mech Syst Signal Process2019; 115: 14–28.
12.
HongJYangZWangY, et al. Combination resonances of rotor systems with asymmetric residual preloads in bolted joints. Mech Syst Signal Process2023; 183: 109626.
13.
ZhuoMYangLXiaK, et al. Thermal effects on the tensile forces of rods in rod-fastened rotor of heavy-duty gas turbine. Proc IMechE, Part C: J Mechanical Engineering Science2019; 233: 2753–2762.
14.
WuXJiaoYChenZ, et al. Establishment of a contact stiffness matrix and its effect on the dynamic behavior of rod-fastening rotor bearing system. Arch Appl Mech2021; 91: 3247–3271.
15.
ZhaoRXuYLiZ, et al. Numerical and experimental study of the preload induced period-1 and chaotic vibration of a rotor system considering contact effects. Appl Math Model2023; 121: 653–667.
16.
ZhaoRXuYZhaoZ, et al. Multi-scale contact induced period-doubling vibrations in rotor systems: Numerical and experimental studies. Mech Syst Signal Process2023; 195: 110251.
17.
SunYXiaoHXuJ.Investigation into the interfacial stiffness ratio of stationary contacts between rough surfaces using an equivalent thin layer. Int J Mech Sci2019; 163: 105147.
18.
JamshidiHAhmadianH.A modified rough interface model considering shear and normal elastic deformation couplings. Int J Solids Struct2020; 203: 57–72.
19.
GhaedniaHWangXSahaS, et al. A review of elastic–plastic contact mechanics. Appl Mech Rev2017; 69: 060804.
20.
MitraMEpureanuBI.Dynamic modeling and projection-based reduction methods for bladed disks with nonlinear frictional and intermittent contact interfaces. Appl Mech Rev2019; 71: 050803.
21.
ShiBYangJWiercigrochM.Vibrational energy transfer in coupled mechanical systems with nonlinear joints. Int J Mech Sci2023; 260: 108612.
22.
HammamiCBalmesEGuskovM.Numerical design and test on an assembled structure of a bolted joint with viscoelastic damping. Mech Syst Signal Process2016; 70–71: 714–724.
23.
JamiaNJalaliHTaghipourJ, et al. An equivalent model of a nonlinear bolted flange joint. Mech Syst Signal Process2021; 153: 107507.
24.
BiswasSChatterjeeA.A two-state hysteresis model for bolted joints, with minor loops from partial unloading. Int J Mech Sci2018; 140: 506–520.
25.
IbrahimRAPettitCL.Uncertainties and dynamic problems of bolted joints and other fasteners. J Sound Vib2005; 279: 857–936.
26.
NarendraKSParthasarathyK.Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw1990; 1: 4–27.
27.
WuJZhangYChenL, et al. A Chebyshev interval method for nonlinear dynamic systems under uncertainty. Appl Math Model2013; 37: 4578–4591.
28.
WuJLuoZZhengJ, et al. Incremental modeling of a new high-order polynomial surrogate model. Appl Math Model2016; 40: 4681–4699.
29.
MaXZhangZHuaH.Uncertainty quantization and reliability analysis for rotor/stator rub-impact using advanced kriging surrogate model. J Sound Vib2022; 525: 116800–116816.
30.
KhodaparastHHGoversYDayyaniI, et al. Fuzzy finite element model updating of the DLR AIRMOD test structure. Appl Math Model2017; 52: 512–526.
31.
GoversYLinkM.Stochastic model updating—Covariance matrix adjustment from uncertain experimental modal data. Mech Syst Signal Process2010; 24: 696–706.
32.
KhodaparastHHMottersheadJEFriswellMI.Perturbation methods for the estimation of parameter variability in stochastic model updating. Mech Syst Signal Process2008; 22: 1751–1773.
33.
HuaXGNiYQChenZQ, et al. An improved perturbation method for stochastic finite element model updating. Int J Numer Methods Eng2008; 73: 1845–1864.
34.
SilvaTANMaiaNMMLinkM, et al. Parameter selection and covariance updating. Mech Syst Signal Process2016; 70–71: 269–283.
35.
BehmaneshIMoaveniBLombaertGPapadimitriouC.Hierarchical Bayesian model updating for structural identification. Mech Syst Signal Process2015; 64–65: 360–376.
36.
JalaliHKhodaparastHHMadineiH, et al. Stochastic modelling and updating of a joint contact interface. Mech Syst Signal Process2019; 129: 645–658.
37.
Abu HusainNHaddad KhodaparastHOuyangH. Parameter selection and stochastic model updating using perturbation methods with parameter weighting matrix assignment. Mech Syst Signal Process2012; 32: 135–152.
38.
BeckJLAuS-K.Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation. J Eng Mech2002; 128: 380–391.
39.
BeckJLYuenK-V.Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech2004; 130: 192–203.
40.
LamH-FYangJAuS-K.Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm. Eng Struct2015; 102: 144–155.
41.
LamHFPengHYAuSK.Development of a practical algorithm for Bayesian model updating of a coupled slab system utilizing field test data. Eng Struct2014; 79: 182–194.
42.
KatafygiotisLSLamH.Tangential-projection algorithm for manifold representation in unidentifiable model updating problems. Earthq Eng Struct Dyn2002; 31: 791–812.
43.
ChingJChenY-C.Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J Eng Mech2007; 133: 816–832.
44.
FangCLiuH-JLamH-F, et al. Practical model updating of the Ting Kau Bridge through the MCMC-based Bayesian algorithm utilizing measured modal parameters. Eng Struct2022; 254: 113839.
45.
WangCLiZJiX, et al. Dynamic uncertainty analysis of rod-fastening rotor systems with rubbing effects using a Chebyshev interval method. Eur J Mech A-Solids2025; 113: 105725.
46.
HastingsWK.Monte Carlo sampling methods using Markov chains and their applications. Biometrika1970; 57: 97–109.
