PSI - Issue 80

Tafara E. Makuni et al. / Procedia Structural Integrity 80 (2026) 105–116 Tafara Makuni / Structural Integrity Procedia 00 (2019) 000 – 000

106

2

Pressure (Pa) Density (kgs -1 ) Velocity (ms -1 )

p ρ

U σ

Stress Strain

ε δ

Displacement Freestream

∞

1D 2D 3D

One dimensional Two dimensional Three dimensional x-direction (m) y-direction (m)

x y z

z-direction (m) ANN Artificial Neural Network AV Air vehicle CAD Computer Aided Design CFD

Computational Fluid Dynamics

C D C L C M C P DT EV FE D

Drag coefficient Lift coefficient Moment coefficient Pressure coefficient

Drag (N)

Digital Twin

Evektor

Finite Element

FEA ICL

Finite Element Analysis Imperial College London

L

Lift (N)

ML NN

Machine Learning Neural Network

POD PLS

Proper orthogonal decomposition

Partial least-squared

PT Re SG

Pressure tap

Reynolds number

Strain gauge SHM Structural Health Monitoring SUM Structural Usage Monitoring UAV Unmanned Aerial Vehicle

1. Introduction The emergence of advanced digital technologies, particularly digital twins, DTs, offers the potential to transform aircraft operation and maintenance practices by aligning them closely with the real-time operating conditions and actual loads experienced by the aircraft (Shkarayev, et al., 2002). A DT is a virtual model of a physical entity that accurately describes the current state of that entity and can predict the future performance of that entity for a variety of scenarios. At the heart of digital twinning is establishing a close relationship between a physical and virtual reality