Fuzzy-stochastic coupled models for broadband noise radiation from flexible
Abstract
Broadband noise radiation from flexible panels is governed by the coupling of random excitation fields with uncertain structural and boundary parameters. This paper develops a fuzzy-stochastic vibro-acoustic framework that separates (i) aleatory uncertainty in the broadband pressure field from (ii) epistemic uncertainty in panel properties and mount conditions. The panel dynamics are modeled in the frequency domain using a modal or finite-element representation of the thin-plate operator, while acoustic radiation is evaluated through a baffle-mounted radiation model leading to radiated sound power spectra. Random excitation is represented by the pressure cross spectral density, enabling direct propagation of spectral statistics to displacement and velocity cross-spectra via linear transfer functions. Epistemic uncertainty in Young's modulus, thickness, and loss factor is represented by fuzzy numbers and propagated through α-cuts, yielding interval-valued parameter sets at each α level. The coupling is implemented using an α-cut outer loop with a stochastic inner solver that computes mean and variance of radiated sound power; α-level interval extrema then provide fuzzy envelopes of stochastic response metrics. Verification is performed through modal truncation, frequency-grid stability, and α-grid refinement. Numerical demonstrations using representative datasets show that epistemic uncertainty can induce wide bands in band-integrated sound power level (≈9.9 dB in the 100–1,000 Hz band at α = 0), and that percentile metrics (e.g., 95th percentile under a lognormal approximation) provide conservative bounds for design decision-making. The proposed framework offers a transparent and computationally tractable route to uncertainty-aware broadband vibro-acoustic prediction for panels in vehicles, buildings, and machinery enclosures.
Copyright (c) 2026 Suleiman Ibrahim Mohammad, Yogeesh Nijalingappa, Basem Abu Zneid, Shashikumar Honnavalli Channabasavaiah, Asokan Vasudevan, Jayaprakasha Pathiyappanapallya Chandraiah

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1]Fahy F, Gardonio P. Sound and Structural Vibration: Radiation, Transmission and Response, 2nd ed. Academic Press; 2007. doi: 10.1016/B978-0-12-373633-8.X5000-5
[2]Skudrzyk E. The Foundations of Acoustics: Basic Mathematics and Basic Acoustics. Springer; 1971. doi: 10.1007/978-3-7091-8255-0
[3]Williams EG, Maynard JD. Numerical Evaluation of the Rayleigh Integral for Planar Radiators Using the FFT. Journal of the Acoustical Society of America. 1982; 72(6): 2020–2030. doi: 10.1121/1.388633
[4]Zadeh LA. Fuzzy Sets. Information and Control. 1965; 8(3): 338–353. doi: 10.1016/S0019-9958(65)90241-X
[5]Dubois D, Prade H. Fuzzy Sets and Systems: Theory and Applications. Academic Press; 1980.
[6]Hanss M. Applied Fuzzy Arithmetic: An Introduction with Engineering Applications. Springer; 2005. doi: 10.1007/b138914
[7]Kwakernaak H. Fuzzy Random Variables—I. Definitions and Theorems. Information Sciences. 1978; 15(1): 1–29. doi: 10.1016/0020-0255(78)90019-1
[8]Puri ML, Ralescu DA. Fuzzy Random Variables. Journal of Mathematical Analysis and Applications. 1986; 114(2): 409–422. doi: 10.1016/0022-247X(86)90093-4
[9]Ghanem RG, Spanos PD. Spectral Stochastic Finite-Element Formulation for Reliability Analysis. Journal of Engineering Mechanics. 1991; 117(10): 2351–2372. doi: 10.1061/(ASCE)0733-9399(1991)117:10(2351)
[10]Ghanem RG, Spanos PD. Stochastic Finite Elements: A Spectral Approach. Springer; 1991. doi: 10.1007/978-1-4612-3094-6
[11]Xiu D, Karniadakis GE. Modeling Uncertainty in Flow Simulations via Generalized Polynomial Chaos. Journal of Computational Physics. 2003; 187(1): 137–167. doi: 10.1016/S0021-9991(03)00092-5
[12]Xiu D, Karniadakis GE. The Wiener–Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing. 2002; 24(2): 619–644. doi: 10.1137/S1064827501387826
[13]Bendat JS, Piersol AG. Random Data: Analysis and Measurement Procedures, 4th ed. Wiley; 2010. doi: 10.1002/9781118032428
[14]Bendat JS, Piersol AG. Engineering Applications of Correlation and Spectral Analysis, 2nd ed. Wiley; 1993.
[15]Gelman A, Carlin JB, Stern HS, et al. Bayesian Data Analysis. Chapman & Hall/CRC; 1995. doi: 10.1201/9780429258411
[16]Robert CP, Casella G. Monte Carlo Statistical Methods. Springer; 2005. doi: 10.1007/978-1-4757-4145-2
[17]Jaynes ET. Probability Theory: The Logic of Science. Cambridge University Press; 2003. doi: 10.1017/CBO9780511790423
[18]Thomson WT, Dahleh MD. Theory of Vibration with Applications, 5th ed. CRC Press; 1997. doi: 10.1201/9780203718841
[19]Deb K. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley; 2001.
[20]Yang S, Meng D, Wang H, et al. A Novel Learning Function for Adaptive Surrogate-Model-Based Reliability Evaluation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2024; 382(2264): 20220395. doi: 10.1098/rsta.2022.0395
[21]Hong L, Li H, Fu J. A Novel Surrogate-Model Based Active Learning Method for Structural Reliability Analysis. Computer Methods in Applied Mechanics and Engineering. 2022; 394: 114835. doi: 10.1016/j.cma.2022.114835
[22]Yang S, Meng D, Yang H, et al. Adaptive Kriging-Assisted Enhanced Sparrow Search with Augmented-Lagrangian First-Order Reliability Method for Highly Efficient Structural Reliability Analysis. Reliability Engineering & System Safety. 2026; 267: 111916. doi: 10.1016/j.ress.2025.111916
[23]Yang S, Meng D, Alfouneh M, et al. A Robust-Weighted Hybrid Nonlinear Regression for Reliability Based Topology Optimization with Multi-Source Uncertainties. Computer Methods in Applied Mechanics and Engineering. 2025; 447: 118360. doi: 10.1016/j.cma.2025.118360
[24]Reynders EPB, Van Hoorickx C. Uncertainty Quantification of Diffuse Sound Insulation Values. Journal of Sound and Vibration. 2023; 544: 117404. doi: 10.1016/j.jsv.2022.117404
[25]Yuan X, Huo R, Pei Q, et al. Uncertainty Quantification for the 3D Half-Space Sound Scattering Problem of IGABEM Based on the Catmull-Clark Subdivision Surfaces. Engineering Analysis with Boundary Elements. 2025; 176: 106222. doi: 10.1016/j.enganabound.2025.106222
[26]Zhang J, Wang Y, Wu P. Equivalence Analysis of Acoustic Excitation and Random Vibration Exerted on Spacecraft Units. Journal of Physics: Conference Series. 2023; 2569: 012007. doi: 10.1088/1742-6596/2569/1/012007
[27]Zhang H, Ding Y, He L, et al. The Vibro-Acoustic Characteristics Analysis of the Coupled System between Composite Laminated Rotationally Stiffened Plate and Acoustic Cavities. Applied Sciences. 2024; 14(3): 1002. doi: 10.3390/app14031002




