Performance improvements and new solution strategies of Actran/TM for nacelle simulations

Antwerpen, Bernard Van; Detandt, Yves; Copiello, Diego; Rosseel, Eveline; Gaudry, Eloi

doi:10.2514/6.2014-2315

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…Since the number of liner dofs is generally large, the reduced-order model is constructed by introducing a pellicular basis constituted of a set of acoustic modes in a pellicular domain that is related to liner dofs. All this methodology has been implemented in a commercial software [19,21,22], which will be used for the numerical simulations presented in Section VIII B. Definition of the geometry of the acoustic problem For liner performance assessment, an aeroacoustic modeling of the acoustic radiation problem of a nacelle intake is used.…”

Section: Construction Of the Nominal Computational Modelmentioning

confidence: 99%

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Dangla

Soize

Cunha

et al. 2020

Aiaa Aviation 2020 Forum

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This paper is devoted to the uncertainty quantification for 3D acoustic performance model of nacelle liners (acoustic treatments). Uncertainties are taken into account in order to increase the robustness of the predictions. A full computational acoustic propagation model based on the convected Helmholtz equation in presence of a non-homogeneous flow velocity field computed by solving the Linearized Euler Equations (LEE) is used. A reduced-order computational model is deduced in order to implement the probabilistic model of uncertainties. The model uncertainties induced by modeling errors have been taken into account for the acoustic propagation model and the liner model, using the nonparametric probabilistic approach. In addition, the uncertainties on the acoustic excitation induced by the fan have been introduced using the parametric probabilistic approach. The developed methodology is applied to a 3D nacelle intake and allows for computing the confidence regions of the random far-field radiated pressure in terms of random SPL (Sound Pressure Level), which are compared to experiments for several flight conditions and frequencies. Nomenclature r(x, y, z), t, ω = Space three-dimensional vector, time scalar variable, and angular frequencỹ X, X 0 , X = Total, static, and fluctuating thermodynamic variable, function of space and time M ∞ , M = Uniform and local Mach number c α , ϕ α (x, y), k z α , k xy α = Amplitude, eigenfunction, axial, and transversal wave numbers of a duct mode α S duct = Cross section of the duct domain Ω duct ρ, ρ 0 , ρ ∞ , ρ = Total, steady-flow, uniform, and fluctuating fluid mass densitỹ v, v 0 , v ∞ , v = Total, steady-flow, uniform, and fluctuating fluid velocitỹ p, p 0 , p = Total, steady-flow, and fluctuating pressure fields Ω i , Ω o , Ω duct = Inner, outer, and duct domains of the intake acoustic problem ∂Ω i , ∂Ω o , ∂Ω duct = Inner, outer, duct domain boundaries Γ io , Γ h , Γ, Γ f , Γ ho , Γ duct , = Interface between inner and outer domains, hardwall, liner, fan, outer hardwall, and duct surfaces n io , n h , n, n f , n ho , n duct = Outward unit normal of surfaces Γ io , Γ

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Section: Construction Of the Nominal Computational Modelmentioning

confidence: 99%

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Dangla

Soize

Cunha

et al. 2020

Aiaa Aviation 2020 Forum

View full text Add to dashboard Cite

show abstract

“…offs[69]. An Intel Skylake node with 32 cores 190 GB of RAM was used with 2 parallel tasks and 16 threads on each.…”

mentioning

confidence: 99%

Noise predictions of a Mach 0.9 round jet using tailored adjoint Green’s functions

Spieser

Bogey

Bailly

2023

Journal of Sound and Vibration

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Combining Sparse Approximate Factorizations with Mixed-precision Iterative Refinement

Amestoy

Buttari

Higham

et al. 2023

ACM Trans. Math. Softw.

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The standard LU factorization-based solution process for linear systems can be enhanced in speed or accuracy by employing mixed precision iterative refinement. Most recent work has focused on dense systems. We investigate the potential of mixed precision iterative refinement to enhance methods for sparse systems based on approximate sparse factorizations. In doing so we first develop a new error analysis for LU- and GMRES-based iterative refinement under a general model of LU factorization that accounts for the approximation methods typically used by modern sparse solvers, such as low-rank approximations or relaxed pivoting strategies. We then provide a detailed performance analysis of both the execution time and memory consumption of different algorithms, based on a selected set of iterative refinement variants and approximate sparse factorizations. Our performance study uses the multifrontal solver MUMPS, which can exploit block low-rank (BLR) factorization and static pivoting. We evaluate the performance of the algorithms on large, sparse problems coming from a variety of real-life and industrial applications showing that mixed precision iterative refinement combined with approximate sparse factorization can lead to considerable reductions of both the time and memory consumption.

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Performance improvements and new solution strategies of Actran/TM for nacelle simulations

Cited by 9 publications

References 5 publications

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Stochastic computational model of 3D acoustic noise predictions for nacelle liners

Noise predictions of a Mach 0.9 round jet using tailored adjoint Green’s functions

Combining Sparse Approximate Factorizations with Mixed-precision Iterative Refinement

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