Analytical Linearization of a State-Space Viscous Vortex Particle Method for Rotary-Wing Simulations

This work presents an extension of previous studies on the analytical linearization of state-space vortex particle methods (VPM) by incorporating viscous effects. The developed framework couples a panel method to capture blade surface and near-wake aerodynamics with a viscous vortex particle method (VVPM) for modeling the far-wake. The resulting formulation yields a nonlinear time-periodic (NLTP) system described by ordinary differential equations (ODEs) in first-order form. To enable linear analysis, the NLTP system is linearized into a linear time-periodic (LTP) representation using two techniques: finite differencing and a novel analytical linearization approach. Harmonic decomposition is then applied to transform the LTP system into a higher-order linear time-invariant (LTI) model, enabling the use of time-invariant analysis tools. The methodology is implemented in MATLAB® and applied to a representative utility helicopter rotor blade. Validation is performed against experimental data, and the linearized models are evaluated through time and frequency domain comparisons with the nonlinear system. Results demonstrate that the linearized models accurately capture unsteady wake dynamics, particularly under low to mid-frequency excitation. Moreover, the analytical linearization method significantly reduces computational cost – achieving an efficiency improvement of O(n2), where n is the number of system states – compared to finite-difference approaches.

Authors

Publication Details

Published
2025-05-20
DOI
https://doi.org/10.4050/f-0081-2025-110
Primary Topic
Aerodynamics and Fluid Dynamics Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analytical Linearization of a State-Space Viscous Vortex Particle Method for Rotary-Wing Simulations

Umberto Saetti, Hussien Hussien
Aerodynamics and Fluid Dynamics Research
article

Analytical Linearization of a State-Space Viscous Vortex Particle Method for Rotary-Wing Simulations

Umberto Saetti, Hussien Hussien
article en

Abstract

This work presents an extension of previous studies on the analytical linearization of state-space vortex particle methods (VPM) by incorporating viscous effects. The developed framework couples a panel method to capture blade surface and near-wake aerodynamics with a viscous vortex particle method (VVPM) for modeling the far-wake. The resulting formulation yields a nonlinear time-periodic (NLTP) system described by ordinary differential equations (ODEs) in first-order form. To enable linear analysis, the NLTP system is linearized into a linear time-periodic (LTP) representation using two techniques: finite differencing and a novel analytical linearization approach. Harmonic decomposition is then applied to transform the LTP system into a higher-order linear time-invariant (LTI) model, enabling the use of time-invariant analysis tools. The methodology is implemented in MATLAB® and applied to a representative utility helicopter rotor blade. Validation is performed against experimental data, and the linearized models are evaluated through time and frequency domain comparisons with the nonlinear system. Results demonstrate that the linearized models accurately capture unsteady wake dynamics, particularly under low to mid-frequency excitation. Moreover, the analytical linearization method significantly reduces computational cost – achieving an efficiency improvement of O(n2), where n is the number of system states – compared to finite-difference approaches.

Openalex Percentile: Top 78%
Aerodynamics and Fluid Dynamics Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.