Minimum drag profiles in infinite cavity flows

The problem considered is that of minimizing the drag of a symmetric plate in infinite cavity flow under the constraints of fixed arclength and fixed chord. The flow is assumed to be steady, irrotational, and incompressible. The effects of gravity and viscosity are ignored.

Using complex variables, expressions for the drag, arclength, and chord, are derived in terms of two hodograph variables, Γ (the logarithm of the speed) and β (the flow angle), and two real parameters, a magnification factor and a parameter which determines how much of the plate is a free-streamline.

Two methods are employed for optimization:

(1) The parameter method. Γ and β are expanded in finite orthogonal series of N terms. Optimization is performed with respect to the N coefficients in these series and the magnification and free-streamline parameters. This method is carried out for the case N = 1 and minimum drag profiles and drag coefficients are found for all values of the ratio of arclength to chord.

(2) The variational method. A variational calculus method for minimizing integral functionals of a function and its finite Hilbert transform is introduced, This method is applied to functionals of quadratic form and a necessary condition for the existence of a minimum solution is derived. The variational method is applied to the minimum drag problem and a nonlinear integral equation is derived but not solved.

Estudo de lacunas de rapidez e caracterização de processos difrativos em eventos de minimum bias a 7TeV no CMS/LHC

Neste trabalho estudamos as características das distribuições da lacuna de rapidez em amostras de eventos de minimum bias de colisões pp a ps=7 TeV no CMS/LHC. Tais eventos são constituídos por processos difrativos, além de processos de QCD mole. São investigados o tamanho e a localização das lacunas, assim como as correlações entre as distribuições obtidas a partir dos objetos reconstruídos no detector e as distribuições obtidas a partir das partículas geradas via simulação Monte Carlo. Uma boa compreensão dessas distribuições pode, eventualmente, possibilitar a caracterização de eventos difrativos nos dados.