Modeling physical uncertainties in dynamic stall induced fluid–structure interaction of turbine blades using arbitrary polynomial chaos

• Published in: Computers & structures Vol. 85; no. 11; pp. 866 - 878
• Main Authors: Witteveen, Jeroen A.S., Sarkar, Sunetra, Bijl, Hester
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2007; Elsevier Science
• Subjects: Arbitrary uncertainties; Computational techniques; Dynamic stall; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Mathematical methods in physics; Physics; Polynomial chaos expansion; Solid dynamics (ballistics, collision, multibody system, stabilization...); Solid mechanics; Stall flutter; Structural and continuum mechanics; Structural nonlinearity; Uncertainty quantification; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Uncertainty quantification; Structural nonlinearity; Stall flutter; Polynomial chaos expansion; Dynamic stall; Arbitrary uncertainties; Chaos; Torsion pendulum; Stall; Fluid structure interaction; Modeling; Uncertain system; Turbine blade; Dynamic model; Deterministic approach; Monte Carlo method; Fluid dynamics; Aeroelastic flutter; Statistical moment; Aerodynamic force; Unsteady flow; Torsional vibration; Orthogonal polynomial; Critical flow; Instability; Aeroelasticity; Non linear effect
• ISSN: 0045-7949; 1879-2243
• DOI: 10.1016/j.compstruc.2007.01.004

A nonlinear dynamic problem of stall induced flutter oscillation subject to physical uncertainties is analyzed using arbitrary polynomial chaos. A single-degree-of-freedom stall flutter model with torsional oscillation is considered subject to nonlinear aerodynamic loads in the dynamic stall regime and nonlinear structural stiffness. The analysis of the deterministic aeroelastic response demonstrated that the problem is sensitive to variations in structural natural frequency and structural nonlinearity. The effect of uncertainties in these parameters is studied. Arbitrary polynomial chaos is employed in which appropriate expansion polynomials are constructed based on the statistical moments of the uncertain input. The arbitrary polynomial chaos results are compared with Monte Carlo simulations.