A. K. Alekseev

Comparison of advanced large-scale minimization algorithms for the solution of inverse ill-posed problems

We compare the performance of several robust large-scale minimization algorithms for the unconstrained minimization of an ill-posed inverse problem. The parabolized Navier–Stokes equation model was used for adjoint parameter estimation. The methods compared consist of three versions of nonlinear conjugate-gradient (CG) method, quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS), the limited-memory quasi-Newton (L-BFGS) [D.C. Liu and J. Nocedal, On the limited memory BFGS method for large scale minimization, Math. Program. 45 (1989), pp. 503–528], truncated Newton (T-N) method [S.G. Nash, Preconditioning of truncated Newton methods, SIAM J. Sci. Stat. Comput. 6 (1985), pp. 599–616, S.G. Nash, Newton-type minimization via the Lanczos method, SIAM J. Numer. Anal. 21 (1984), pp. 770–788] and a new hybrid algorithm proposed by Morales and Nocedal [J.L. Morales and J. Nocedal, Enriched methods for large-scale unconstrained optimization, Comput. Optim. Appl. 21 (2002), pp. 143–154]. For all the methods employed and tested, the gradient of the cost function is obtained via an adjoint method. A detailed description of the algorithmic form of minimization algorithms employed in the minimization comparison is provided. For the inviscid case, the CG-descent method of Hager [W.W. Hager and H. Zhang, A new conjugate gradient method with guaranteed descent and efficient line search, SIAM J. Optim. 16 (1) (2005), pp. 170–192] performed the best followed closely by the hybrid method [J.L. Morales and J. Nocedal, Enriched methods for large-scale unconstrained optimization, Comput. Optim. Appl. 21 (2002), pp. 143–154], while in the viscous case, the hybrid method emerged as the best performed followed by CG [D.F. Shanno and K.H. Phua, Remark on algorithm 500. Minimization of unconstrained multivariate functions, ACM Trans. Math. Softw. 6 (1980), pp. 618–622] and CG-descent [W.W. Hager and H. Zhang, A new conjugate gradient method with guaranteed descent and efficient line search, SIAM J. Optim. 16 (1) (2005), pp. 170–192]. This required an adequate choice of parameters in the CG-descent method as well as controlling the number of L-BFGS and T-N iterations to be interlaced in the hybrid method.

Reduced order modeling based on POD of a parabolized Navier-Stokes equations model II: Trust region POD 4D VAR data assimilation

A reduced order model based on Proper Orthogonal Decomposition (POD) 4D VAR (Four-dimensional Variational) data assimilation for the parabolized Navier-Stokes (PNS) equations is derived. Various approaches of POD implementation of the reduced order inverse problem are studied and compared including an ad-hoc POD adaptivity along with a trust region POD adaptivity. The numerical results obtained show that the trust region POD 4D VAR provides the best results amongst all the POD adaptive methods tested in all error metrics for the reduced order inverse problem of the PNS equations.

Calculation of Uncertainty Propagation using Adjoint Equations

The uncertainty of flow parameters depending on the error of input data (initial, boundary conditions, coefficients) may be efficiently calculated using adjoint equations. This approach is extremely effective for uncertainty estimation at certain checkpoints because it requires only a single (adjoint) system of equations to be solved in addition to the system describing the flow-field. The fields of adjoint “temperature”, adjoint “density”, etc. are then used to calculate the transfer of uncertainty from all input data.

Estimation of goal functional error arising from iterative solution of Euler equations

Estimation of the error arising in the cost (goal) functional due to stopping the iterative process is considered for a steady problem solved by temporal relaxation. The functional error is calculated using an iteration residual along with related adjoint parameters. Numerical tests demonstrate the applicability of this approach for the steady 2D Euler equations.