Strapdown calibration and alignment study. Volume 2 - Procedural and parametric trade-off analyses document Final report

Parametric and procedural tradeoffs for alignment and calibration of inertial sensing uni

Strapdown calibration and alignment study. Volume 2 - Procedural and parametric trade- off analyses document

Techniques for laboratory calibration and alignment of strapdown inertial sensing unit - procedural and parametric trade-off analyse

Agricultural Precautionary Wealth

Using panel data, the relationship between income uncertainty and the stock of wealth through precautionary saving is examined. Evidence from Kansas data is consistent with the precautionary saving motive in that farm households facing greater uncertainty in income maintain larger stocks of wealth in order to smooth consumption. These results are found by regressing net worth against measures of permanent income (life-cycle income), measures of uncertainty, and demographic variables.precautionary saving, precautionary wealth, risk, Risk and Uncertainty,

First-Order Vortex Lattice Melting and Magnetization of YBa2_2Cu3_3O$_{7-\delta}

We present the first non-mean-field calculation of the magnetization $M(T)$ of YBa$_2$Cu$_3$O$_{7-\delta}$ both above and below the flux-lattice melting temperature $T_m(H)$. The results are in good agreement with experiment as a function of transverse applied field $H$. The effects of fluctuations in both order parameter $\psi({\bf r})$ and magnetic induction $B$ are included in the Ginzburg-Landau free energy functional: $\psi({\bf r})$ fluctuates within the lowest Landau level in each layer, while $B$ fluctuates uniformly according to the appropriate Boltzmann factor. The second derivative $(\partial^2 M/\partial T^2)_H$ is predicted to be negative throughout the vortex liquid state and positive in the solid state. The discontinuities in entropy and magnetization at melting are calculated to be $\sim 0.034\, k_B$ per flux line per layer and $\sim 0.0014$~emu~cm$^{-3}$ at a field of 50 kOe.Comment: 11 pages, 4 PostScript figures in one uuencoded fil

Strapdown calibration and alignment study. Volume 1 - Development document Final report

Calibration and alignment techniques for inertial sensing uni

Multigrid solvers for the de Rham complex with optimal complexity in polynomial degree

The Riesz maps of the $L^2$ de Rham complex frequently arise as subproblems in the construction of fast preconditioners for more complicated problems. In this work we present multigrid solvers for high-order finite element discretizations of these Riesz maps with the same time and space complexity as sum-factorized operator application, i.e.~with optimal complexity in polynomial degree in the context of Krylov methods. The key idea of our approach is to build new finite elements for each space in the de Rham complex with orthogonality properties in both the $L^2$- and $H(\mathrm{d})$-inner products ($\mathrm{d} \in \{\mathrm{grad}, \mathrm{curl}, \mathrm{div}\})$ on the reference hexahedron. The resulting sparsity enables the fast solution of the patch problems arising in the Pavarino, Arnold--Falk--Winther and Hiptmair space decompositions, in the separable case. In the non-separable case, the method can be applied to an auxiliary operator that is sparse by construction. With exact Cholesky factorizations of the sparse patch problems, the application complexity is optimal but the setup costs and storage are not. We overcome this with the finer Hiptmair space decomposition and the use of incomplete Cholesky factorizations imposing the sparsity pattern arising from static condensation, which applies whether static condensation is used for the solver or not. This yields multigrid relaxations with time and space complexity that are both optimal in the polynomial degree

A scalable and robust vertex-star relaxation for high-order FEM

Pavarino proved that the additive Schwarz method with vertex patches and a low-order coarse space gives a $p$-robust solver for symmetric and coercive problems. However, for very high polynomial degree it is not feasible to assemble or factorize the matrices for each patch. In this work we introduce a direct solver for separable patch problems that scales to very high polynomial degree on tensor product cells. The solver constructs a tensor product basis that diagonalizes the blocks in the stiffness matrix for the internal degrees of freedom of each individual cell. As a result, the non-zero structure of the cell matrices is that of the graph connecting internal degrees of freedom to their projection onto the facets. In the new basis, the patch problem is as sparse as a low-order finite difference discretization, while having a sparser Cholesky factorization. We can thus afford to assemble and factorize the matrices for the vertex-patch problems, even for very high polynomial degree. In the non-separable case, the method can be applied as a preconditioner by approximating the problem with a separable surrogate. We demonstrate the approach by solving the Poisson equation and a $H(\mathrm{div})$-conforming interior penalty discretization of linear elasticity in three dimensions at $p = 15$