Numerical prediction of turbulent boundary layer noise from a sharp-edged flat plate

An efficient hybrid uncorrelated wall plane waves–boundary element method (UWPW-BEM) technique is proposed to predict the flow-induced noise from a structure in low Mach number turbulent flow. Reynolds-averaged Navier-Stokes equations are used to estimate the turbulent boundary layer parameters such as convective velocity, boundary layer thickness, and wall shear stress over the surface of the structure. The spectrum of the wall pressure fluctuations is evaluated from the turbulent boundary layer parameters and by using semi-empirical models from literature. The wall pressure field underneath the turbulent boundary layer is synthesized by realizations of uncorrelated wall plane waves (UWPW). An acoustic BEM solver is then employed to compute the acoustic pressure scattered by the structure from the synthesized wall pressure field. Finally, the acoustic response of the structure in turbulent flow is obtained as an ensemble average of the acoustic pressures due to all realizations of uncorrelated plane waves. To demonstrate the hybrid UWPW-BEM approach, the self-noise generated by a flat plate in turbulent flow with Reynolds number based on chord Re_c = 4.9 × 10⁵ is predicted. The results are compared with those obtained from a large eddy simulation (LES)-BEM technique as well as with experimental data from literature.     

Interpretation of heart rate variability via detrended fluctuation analysis and alphabeta filter

Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.     

Strongly singular and hypersingular integrals for aeroacoustic incident fields

Using the Burton and Miller formulation to predict the scattering of flow‐induced noise by a body immersed in the flow requires the near‐field pressure and pressure gradient incident on the body. In this paper, Lighthill's acoustic analogy is used to derive formulations for the near‐field pressure and pressure gradient at any point within the flow noise source region, including points on the body. These near‐field formulations involve strongly singular and hypersingular volume and surface integrals. To evaluate these singular integrals, an effective singularity regularization technique is derived. An analytical source distribution is used to demonstrate the accuracy of the method. A cell‐averaged representation of this analytical source distribution, similar to the data stored by computational fluid dynamics solvers, is also created. A piecewise linear, continuous source distribution is generated from these cell‐average values, producing a C⁰ distribution. A k‐exact reconstruction technique is then used to create high‐order polynomials of the solution variables for each volume cell. These high‐order polynomials are constructed from its cell average value and the average values of the nearby cells. The source distribution created using the k‐exact reconstruction is discontinuous across cell boundaries but exhibits a smooth polynomial distribution within each cell. The near‐field pressure and pressure gradient predicted using these reconstructed source distributions are compared with the results obtained using the analytical distribution. Copyright © 2014 John Wiley & Sons, Ltd.     

A parallel multi-configuration self-consistent field algorithm

A scalable multi-configuration self-consistent field (MCSCF) algorithm is described. The method for optimizing the orbital and configurational parameters is based upon the two-step Newton–Raphson approach with an augmented orbital Hessian matrix. A single copy of the two-electron integrals in the molecular orbital basis is distributed over the memory of all processors. Storage of the augmented Hessian is avoided by re-computing its elements as needed. A replicated data approach is used to parallelize the configuration interaction step. Scalability to 1024 processors is demonstrated.