Engineering topics

Thorp, S. A.

Publications and source records attributed to Thorp, S. A..

3 records

NASA NTRS2002

Fan Noise Source Diagnostic Test Computation of Rotor Wake Turbulence Noise

An important source mechanism of fan broadband noise is the interaction of rotor wake turbulence with the fan outlet guide vanes. A broadband noise model that utilizes computed rotor flow turbulence from a RANS code is used to predict fan broadband noise spectra. The noise model is employed to examine the broadband noise characteristics of the 22-inch Source Diagnostic Test fan rig for which broadband noise data were obtained in wind tunnel tests at the NASA Glenn Research Center. A 9-case matrix of three outlet guide vane configurations at three representative fan tip speeds are considered. For all cases inlet and exhaust acoustic power spectra are computed and compared with the measured spectra where possible. In general, the acoustic power levels and shape of the predicted spectra are in good agreement with the measured data. The predicted spectra show the experimentally observed trends with fan tip speed, vane count, and vane sweep. The results also demonstrate the validity of using CFD-based turbulence information for fan broadband noise calculations.

Nallasamy, M.↗

NASA NTRS1998

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Decker, A. J.↗

NASA NTRS1998

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Decker, A. J.↗