Navier-Stokes calculations for the vortex of a rotor in hover

An efficient finite-difference scheme for the solution of the incompressible Navier-Stokes equation is used to study the vortex wake of a rotor in hover. The solution Procedure uses a vorticity-stream function formulation and incorporates an asymptotic far-field boundary condition enabling the size of the computational domain to be reduced in comparison to other methods. The results from the present method are compared with experimental data obtained by smoke flow visualization and hot-wire measurements for several rotor blade configurations

Theory of minimum effort control

Optimum control theory formulations for solving problems in optimum guidance for interplanetary manned space flight mission

Uncertainties of predictions from parton distribution functions II: the Hessian method

We develop a general method to quantify the uncertainties of parton distribution functions and their physical predictions, with emphasis on incorporating all relevant experimental constraints. The method uses the Hessian formalism to study an effective chi-squared function that quantifies the fit between theory and experiment. Key ingredients are a recently developed iterative procedure to calculate the Hessian matrix in the difficult global analysis environment, and the use of parameters defined as components along appropriately normalized eigenvectors. The result is a set of 2d Eigenvector Basis parton distributions (where d=16 is the number of parton parameters) from which the uncertainty on any physical quantity due to the uncertainty in parton distributions can be calculated. We illustrate the method by applying it to calculate uncertainties of gluon and quark distribution functions, W boson rapidity distributions, and the correlation between W and Z production cross sections.Comment: 30 pages, Latex. Reference added. Normalization of Hessian matrix changed to HEP standar

Optimal Topological Test for Degeneracies of Real Hamiltonians

We consider adiabatic transport of eigenstates of real Hamiltonians around loops in parameter space. It is demonstrated that loops that map to nontrivial loops in the space of eigenbases must encircle degeneracies. Examples from Jahn-Teller theory are presented to illustrate the test. We show furthermore that the proposed test is optimal.Comment: Minor corrections, accepted in Phys. Rev. Let

Neutrino Dimuon Production and the Strangeness Asymmetry of the Nucleon

We have performed the first global QCD analysis to include the CCFR and NuTeV dimuon data, which provide direct constraints on the strange and anti-strange parton distributions, $s(x)$ and $\bar{s}(x)$. To explore the strangeness sector, we adopt a general parametrization of the non-perturbative $s(x), \bar{s}(x)$ functions satisfying basic QCD requirements. We find that the strangeness asymmetry, as represented by the momentum integral $[S^{-}]\equiv \int_0^1 x [s(x)-\bar{s}(x)] dx$, is sensitive to the dimuon data provided the theoretical QCD constraints are enforced. We use the Lagrange Multiplier method to probe the quality of the global fit as a function of $[S^-]$ and find $-0.001 < [S^-] < 0.004$. Representative parton distribution sets spanning this range are given. Comparisons with previous work are made.Comment: 23 pages, 4 figures; expanded version for publicatio

Collider Inclusive Jet Data and the Gluon Distribution

Inclusive jet production data are important for constraining the gluon distribution in the global QCD analysis of parton distribution functions. With the addition of recent CDF and D0 Run II jet data, we study a number of issues that play a role in determining the up-to-date gluon distribution and its uncertainty, and produce a new set of parton distributions that make use of that data. We present in detail the general procedures used to study the compatibility between new data sets and the previous body of data used in a global fit. We introduce a new method in which the Hessian matrix for uncertainties is ``rediagonalized'' to obtain eigenvector sets that conveniently characterize the uncertainty of a particular observable.Comment: Published versio

Multivariate Fitting and the Error Matrix in Global Analysis of Data

When a large body of data from diverse experiments is analyzed using a theoretical model with many parameters, the standard error matrix method and the general tools for evaluating errors may become inadequate. We present an iterative method that significantly improves the reliability of the error matrix calculation. To obtain even better estimates of the uncertainties on predictions of physical observables, we also present a Lagrange multiplier method that explores the entire parameter space and avoids the linear approximations assumed in conventional error propagation calculations. These methods are illustrated by an example from the global analysis of parton distribution functions.Comment: 13 pages, 5 figures, Latex; minor clarifications, fortran program made available; Normalization of Hessian matrix changed to HEP standar