Enabling off-design linearised aerodynamics analysis using Krylov subspace recycling technique. (25th November 2016)

• Record Type: Journal Article
• Authors: Xu, Shenren; Timme, Sebastian; Badcock, Kenneth J.
• Is Part Of: Computers & fluids. Volume 140 (2016)
• Page Start: 385
• Page End: 396
• Publication Date: 2016-11-25
• Subjects: Linearized computational fluid dynamics -- Shock buffet -- Flutter -- GCRO–DR -- Krylov subspace recycling; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2016.10.018
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: A novel algorithm for solving large sparse linear systems of equations is proposed. It combines an ILU preconditioner based on blended Jacobian and GCRO–DR solver. The solver implemented in DLR–TAU is applied to linearised aerodynamics analysis. Superior performance regarding memory and runtime is shown on challenging cases. Our work enabled rapid aeroelastic analysis using CFD at off-design conditions. Abstract: The major computational challenge, when using frequency domain linearised computational fluid dynamics in the analysis of aeroelastic problems such as aircraft flutter, gust response or shock buffet, are the excessive memory and CPU time requirements to solve the large sparse linear systems of equations. To address these issues found with the generalised minimal residual linear equation solver, the generalised conjugate residual solver with deflated restarting is adopted here in which an invariant Krylov subspace is recycled both between restarts when solving a single linear frequency domain problem and for a sequence of equations when varying the system matrix and forcing terms. The proposed method is implemented in an industrial code and applied to three test cases including the forced excitation and buffet onset of a pitch-plunge aerofoil, a realistic passenger aircraft in inviscid transonic flow and a generic half wing-body model at a pre-buffet condition. The memory requirements for the problems investigated are reduced by up to an order of magnitude, while the CPU times are reduced by up to a factor of three.