Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

@article{Iacobello2022LoadEI,
  title={Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data},
  author={Giovanni Iacobello and Frieder Kaiser and David E. Rival},
  journal={Physics of Fluids},
  year={2022}
}
X iv :2 10 5. 04 52 0v 4 [ ph ys ic s. fl udy n] 2 6 O ct 2 02 1 Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data Giovanni Iacobello, 2, a) Frieder Kaiser, b) and David E. Rival Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario, K7L 3N6, Canada Department of Mechanical Engineering Sciences, University of Surrey, Guildford, GU2 7XH, UK 

Figures from this paper

References

SHOWING 1-10 OF 35 REFERENCES
Exploring the signature of distributed pressure measurements on non-slender delta wings during axial and vertical gusts
For a broad range of aerodynamic bodies, vortex structures arising from perturbations such as gusts cause characteristic surface pressure signatures that are coupled to the observed aerodynamic
Combustion noise is scale-free: transition from scale-free to order at the onset of thermoacoustic instability
We investigate the scale invariance of combustion noise generated from turbulent reacting flows in a confined environment using complex networks. The time series data of unsteady pressure, which is
Unsupervised modelling of a transitional boundary layer
Abstract A data-driven approach for the identification of local turbulent-flow states and of their dynamics is proposed. After subdividing a flow domain in smaller regions, the $K$-medoids clustering
Cluster-based feedback control of turbulent post-stall separated flows
TLDR
The objective of the present work is not necessarily to suppress flow separation but to minimize the desired cost function to achieve enhanced aerodynamic performance.
Network structure of two-dimensional decaying isotropic turbulence
TLDR
The vortex interactions in two-dimensional decaying isotropic turbulence are examined and it is found that the vortical-interaction network can be characterized by a weighted scale-free network.
Cluster-based reduced-order modelling of a mixing layer
TLDR
A novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows that generalises the Ulam–Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron–Frobenius operator.
Leading-edge flow sensing for detection of vortex shedding from airfoils in unsteady flows
Sensing of vortex shedding in unsteady airfoil flows can be beneficial in controlling and positively harnessing their effects for increased aerodynamic performance. The time variation of the
Lagrangian network analysis of turbulent mixing
TLDR
It is shown that the time-varying network is able to clearly describe the particle swarm dynamics, in a parametrically robust and computationally inexpensive way, and offers a powerful resource for Lagrangian analysis of turbulent flows.
Suppression of thermoacoustic instability by targeting the hubs of the turbulent networks in a bluff body stabilized combustor
Abstract In the present study, we quantify the vorticity interactions in a bluff body stabilized turbulent combustor during the transition from combustion noise to thermoacoustic instability via
Cluster-based network modeling—From snapshots to complex dynamical systems
TLDR
Cluster-based network modeling (CNM) is proposed, a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge that complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.
...
1
2
3
4
...