Abstract

An improved Wiener path integral (WPI) approach is developed for predicting the stochastic response probability density functions (PDFs) of nonlinear systems under non-white excitation. Specifically, the excitation process is modeled as the output of a filter whose input is Gaussian white noise, the joint response PDF of the nonlinear system is expressed as a functional integral of the stochastic action over the space of all possible trajectories between the initial and final states, and the second-order variation of the stochastic action is recast to a quadratic form and taken into account in the estimation of the joint response PDF. Compared to the standard WPI approach where the second-order variation of the stochastic action is regarded as a constant, the improved WPI approach developed herein considers the fluctuations of the second-order variation of the stochastic action in the computational domain, thus improving the accuracy of the stochastic response estimation. Two numerical examples are illustrated, and the nonstationary response PDFs estimated by the improved WPI approach agree well with the Monte Carlo simulation results.

References

1.
Roberts
,
J. B.
, and
Spanos
,
P. D.
,
2003
,
Random Vibration and Statistical Linearization
,
Dover Publications
,
Mineola, NY
.
2.
Li
,
J.
, and
Chen
,
J.
,
2009
,
Stochastic Dynamics of Structures
,
Wiley (Asia) Pte Ltd
,
2 Clementi Loop, Singapore
.
3.
Grigoriu
,
M.
,
2012
,
Stochastic Systems: Uncertainty Quantification and Propagation
,
Springer
,
London
.
4.
Naess
,
A.
, and
Moe
,
V.
,
2000
, “
Efficient Path Integration Method for Nonlinear Dynamic Systems
,”
Probab. Eng. Mech.
,
15
(
2
), pp.
221
231
.10.1016/S0266-8920(99)00031-4
5.
Iourtchenko
,
D. V.
,
Mo
,
E.
, and
Naess
,
A.
,
2006
, “
Response Probability Density Functions of Strongly Non-Linear Systems by the Path Integration Method
,”
Int. J. Non-Linear Mech.
,
41
(
5
), pp.
693
705
.10.1016/j.ijnonlinmec.2006.04.002
6.
Kumar
,
P.
, and
Narayanan
,
S.
,
2010
, “
Modified Path Integral Solution of Fokker–Planck Equation: Response and Bifurcation of Nonlinear Systems
,”
ASME J. Comput. Nonlinear Dynam.
,
5
(
1
), p.
011004
.10.1115/1.4000312
7.
Di Paola
,
M.
, and
Alotta
,
G.
,
2020
, “
Path Integral Methods for the Probabilistic Analysis of Nonlinear Systems Under a White-Noise Process
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. B. Mech. Eng.
,
6
(
4
), p.
040801
.10.1115/1.4047882
8.
Di Paola
,
M.
, and
Santoro
,
R.
,
2008
, “
Path Integral Solution for Non-Linear System Enforced by Poison White Noise
,”
Probab. Eng. Mech.
,
23
(
2–3
), pp.
164
169
.10.1016/j.probengmech.2007.12.029
9.
Pirrotta
,
A.
, and
Santoro
,
R.
,
2011
, “
Probabilistic Response of Nonlinear Systems Under Combined Normal and Poisson White Noise Via Path Integral Method
,”
Probab. Eng. Mech.
,
26
(
1
), pp.
26
32
.10.1016/j.probengmech.2010.06.003
10.
Di Matteo
,
A.
, and
Pirrotta
,
A.
,
2017
, “
Path Integral Method for Nonlinear Systems Under Levy White Noise
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. B. Mech. Eng.
,
3
(
3
), p.
030905
.10.1115/1.4036703
11.
Tai
,
W. C.
,
2022
, “
Efficient Path Integration of Nonlinear Oscillators Subject to Combined Random and Harmonic Excitation
,”
ASME J. Comput. Nonlinear Dynam.
,
17
(
6
), p.
061005
.10.1115/1.4053936
12.
Mo
,
E.
, and
Naess
,
A.
,
2009
, “
Nonsmooth Dynamics by Path Integration: An Example of Stochastic and Chaotic Response of a Meshing Gear Pair
,”
ASME J. Comput. Nonlinear Dynam.
,
4
(
3
), p.
034501
.10.1115/1.3124780
13.
Chai
,
W.
,
Naess
,
A.
, and
Leira
,
B. J.
,
2015
, “
Stochastic Dynamic Analysis and Reliability of a Vessel Rolling in Random Beam Seas
,”
J. Ship Res.
,
59
(
2
), pp.
113
131
.10.5957/jsr.2015.59.2.113
14.
Gaidai
,
O.
,
Dimentberg
,
M.
, and
Naess
,
A.
,
2018
, “
Rotating Shaft's Non-Linear Response Statistics Under Biaxial Random Excitation, By Path Integration
,”
Int. J. Mech. Sci.
,
142-143
, pp.
121
126
.10.1016/j.ijmecsci.2018.04.043
15.
Zhu
,
H. T.
,
Xu
,
Y. G.
,
Yu
,
Y.
, and
Xu
,
L. X.
,
2021
, “
Stationary Response of Nonlinear Vibration Energy Harvesters by Path Integration
,”
ASME J. Comput. Nonlinear Dynam.
,
16
(
5
), p.
051004
.10.1115/1.4050612
16.
Kougioumtzoglou
,
I. A.
, and
Spanos
,
P. D.
,
2012
, “
An Analytical Wiener Path Integral Technique for Non-Stationary Response Determination of Nonlinear Oscillators
,”
Probab. Eng. Mech.
,
28
, pp.
125
131
.10.1016/j.probengmech.2011.08.022
17.
Psaros
,
A. F.
,
Brudastova
,
O.
,
Malara
,
G.
, and
Kougioumtzoglou
,
I. A.
,
2018
, “
Wiener Path Integral Based Response Determination of Nonlinear Systems Subject to Non-White, Non-Gaussian, and Non-Stationary Stochastic Excitation
,”
J. Sound Vib.
,
433
, pp.
314
333
.10.1016/j.jsv.2018.07.013
18.
Petromichelakis
,
I.
,
Psaros
,
A. F.
, and
Kougioumtzoglou
,
I. A.
,
2020
, “
Stochastic Response Determination of Nonlinear Structural Systems With Singular Diffusion Matrices: A Wiener Path Integral Variational Formulation With Constraints
,”
Probab. Eng. Mech.
,
60
, p.
103044
.10.1016/j.probengmech.2020.103044
19.
Mavromatis
,
I. G.
,
Psaros
,
A. F.
, and
Kougioumtzoglou
,
I. A.
,
2023
, “
A Wiener Path Integral Formalism for Treating Nonlinear Systems With Non-Markovian Response Processes
,”
J. Eng. Mech.
,
149
(
1
), p.
04022092
.10.1061/JENMDT.EMENG-6873
20.
Katsidoniotaki
,
M. I.
,
Psaros
,
A. F.
, and
Kougioumtzoglou
,
I. A.
,
2022
, “
Uncertainty Quantification of Nonlinear System Stochastic Response Estimates Based on the Wiener Path Integral Technique: A Bayesian Compressive Sampling Treatment
,”
Probab. Eng. Mech.
,
67
, p.
103193
.10.1016/j.probengmech.2021.103193
21.
Psaros
,
A. F.
, and
Kougioumtzoglou
,
I. A.
,
2020
, “
Functional Series Expansions and Quadratic Approximations for Enhancing the Accuracy of the Wiener Path Integral Technique
,”
J. Eng. Mech.
,
146
(
7
), p.
04020065
.10.1061/(ASCE)EM.1943-7889.0001793
22.
Zhao
,
Y.
,
Psaros
,
A. F.
,
Petromichelakis
,
I.
, and
Kougioumtzoglou
,
I. A.
,
2022
, “
A Quadratic Wiener Path Integral Approximation for Stochastic Response Determination of Multi-Degree-of-Freedom Nonlinear Systems
,”
Probab. Eng. Mech.
,
69
, p.
103319
.10.1016/j.probengmech.2022.103319
23.
Spanos
,
P. D.
,
1986
, “
Filter Approaches to Wave Kinematics Approximation
,”
Appl. Ocean Res.
,
8
(
1
), pp.
2
7
.10.1016/S0141-1187(86)80025-6
24.
Chai
,
W.
,
Naess
,
A.
, and
Leira
,
B. J.
,
2015
, “
Filter Models for Prediction of Stochastic Ship Roll Response
,”
Probab. Eng. Mech.
,
41
, pp.
104
114
.10.1016/j.probengmech.2015.06.002
25.
Chaichian
,
M.
, and
Demichev
,
A.
,
2001
,
Path Integrals in Physics, Vol. I: Stochastic Processes and Quantum Mechanics
,
Institute of Physics Publishing
,
Bristol, UK
.
26.
Ewing
,
G. M.
,
1985
,
Calculus of Variations With Applications
,
Dover Publications
,
Garden City, NY
.
27.
Gel'fand
,
I. M.
, and
Yaglom
,
A. M.
,
1960
, “
Integration in Functional Spaces and Its Applications in Quantum Physics
,”
J. Math. Phys.
,
1
(
1
), pp.
48
69
.10.1063/1.1703636
28.
Shampine
,
L. F.
,
Gladwell
,
I.
, and
Thompson
,
K.
,
2003
,
Solving ODEs With MATLAB
,
Cambridge University Press
,
Cambridge, UK
.
29.
Agarwal
,
R. P.
, and
O' Regan
,
D.
,
2009
,
Ordinary and Partial Differential Equations: With Special Functions, Fourier Series, and Boundary Value Problems
,
Springer
,
New York
.
30.
Strang
,
G.
,
2016
,
Introduction to Linear Algebra
, 5th ed.,
Wellesley-Cambridge Press
,
Wellesley, MA
.
You do not currently have access to this content.