Flutter prediction for unmanned long endurance aircraft using virtual structural model and experimental modes

Aeroelastic stability is an important consideration in the design and certification processes of modern, flexible aircraft. This includes demonstration of freedom from flutter at all combinations of airspeed and altitude within the flight envelope. This paper presents the analysis and clearance of flutter characteristics for an unmanned medium altitude long endurance aircraft by two distinct methods. The traditional method based on a theoretical Finite Element (FE) model was adopted prior to the conduct of Ground Vibration Tests (GVT) where as the more recent method utilized a Virtual Structural Model (VSM) built on the experimental modal parameters that were obtained from the GVT. The Direct Matrix Abstraction Program (DMAP) feature of MSC NASTRAN was used to create the VSM whose nodes correspond to the GVT accelerometer locations, and to insert the GVT modes into the model. In both approaches the unsteady airloads were generated using the Doublet Lattice Method (DLM). The GVT/VSM based analyses closely mirrored the original FE model analysis and confirmed the existence of sufficient flutter margins, thereby enabling the flight certification of the aircraft. The VSM route to flutter analysis is outlined in this paper as an alternative to using a GVT-tuned FE model. This is beneficial (i) if no FE model is available as it happens with bought out aircraft, or (ii) if the configurations of interest are too few to justify the effort of FE model tuning.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering
Published
2025-07-21
DOI
https://doi.org/10.1177/09544100251362517
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Flutter prediction for unmanned long endurance aircraft using virtual structural model and experimental modes

Subhaschandra Kattimani, Ashok K Sampath
Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering
Structural Health Monitoring Techniques
article

Flutter prediction for unmanned long endurance aircraft using virtual structural model and experimental modes

Subhaschandra Kattimani, Ashok K Sampath
article en

Abstract

Aeroelastic stability is an important consideration in the design and certification processes of modern, flexible aircraft. This includes demonstration of freedom from flutter at all combinations of airspeed and altitude within the flight envelope. This paper presents the analysis and clearance of flutter characteristics for an unmanned medium altitude long endurance aircraft by two distinct methods. The traditional method based on a theoretical Finite Element (FE) model was adopted prior to the conduct of Ground Vibration Tests (GVT) where as the more recent method utilized a Virtual Structural Model (VSM) built on the experimental modal parameters that were obtained from the GVT. The Direct Matrix Abstraction Program (DMAP) feature of MSC NASTRAN was used to create the VSM whose nodes correspond to the GVT accelerometer locations, and to insert the GVT modes into the model. In both approaches the unsteady airloads were generated using the Doublet Lattice Method (DLM). The GVT/VSM based analyses closely mirrored the original FE model analysis and confirmed the existence of sufficient flutter margins, thereby enabling the flight certification of the aircraft. The VSM route to flutter analysis is outlined in this paper as an alternative to using a GVT-tuned FE model. This is beneficial (i) if no FE model is available as it happens with bought out aircraft, or (ii) if the configurations of interest are too few to justify the effort of FE model tuning.

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering
National Institute of Technology Karnataka (IN)
Openalex Percentile: Top 81%
Structural Health Monitoring Techniques
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.