Assessment of the contribution of surface roughness to airframe noise

The generation of sound by turbulent boundary-layer flow at low Mach number over a rough wall is investigated by applying a theoretical model that describes the scattering of the turbulence near field into sound by roughness elements. Attention is focused on the numerical method to approximately quantify the absolute level of far-field radiated roughness noise. Models for the source statistics are obtained by scaling smooth-wall data by the increased skin friction velocity and boundary-layer thickness for a rough surface. Numerical integration is performed to determine the roughness noise, and it reproduces the spectral characteristics of the available empirical formula and experimental data. Experiments are conducted to measure the radiated sound from two rough plates in an open jet The measured noise spectra of the rough plates are above that of a smooth plate in 1-2.5 kHz frequency and exhibit reasonable agreement with the predicted level. Estimates of the roughness noise for a Boeing 757 sized aircraft wing with idealized levels of surface roughness show that hi the high-frequency region the sound radiated from surface roughness may exceed that from the trailing edge, and higher overall sound pressure levels are observed for the roughness noise. The trailing edge noise is also enhanced by surface roughness somewhat A parametric study indicates that roughness height and roughness density significantly affect the roughness noise with roughness height having the dominant effect The roughness noise directivity varies with different levels of surface roughness. Copyright © 2007 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Effects of surface roughness on airframe noise

The generation of sound by turbulent boundary layer flow at low Mach number over a rough wall is investigated by applying the theoretical model which describes the scattering of the turbulence near field into sound by roughness elements. Attention is focused on the numerical method to approximately quantify the absolute level of the roughness noise radiated to far field. Empirical models for the source statistics are obtained by scaling smooth-wall data through increased skin friction velocity and boundary layer thickness for the rough surface. Numerical integration is performed to determine the roughness noise, and it reproduces the spectral characteristics of the available empirical formula and experimental data. Experiments are conducted to measure the radiated sound from two rough plates in an open jet by four 1/2'' free-field condenser microphones. The measured noise spectra of the rough plates are above that of a smooth plate in 1-2.5 kHz frequency and exhibits encouraging agreement with the predicted spectra. Also, a phased microphone array is utilized to localize the sound source, and it confirms that the rough plates generate higher source strengthes in this frequency range. A parametric study illustrates that the roughness height and roughness density significantly affect the far-field radiated roughness noise with the roughness height having the dominant effect. The estimates of the roughness noise for a Boeing 757 sized aircraft wing show that in high frequency region the sound radiated from surface roughness may exceed that from the trailing edge, and higher overall sound pressure levels for the roughness noise are also observed.

Targeted regeneration of bone in the osteoporotic human femur

We have recently developed image processing techniques for measuring the cortical thicknesses of skeletal structures in vivo, with resolution surpassing that of the underlying computed tomography system. The resulting thickness maps can be analysed across cohorts by statistical parametric mapping. Applying these methods to the proximal femurs of osteoporotic women, we discover targeted and apparently synergistic effects of pharmaceutical osteoporosis therapy and habitual mechanical load in enhancing bone thickness. © 2011 Poole et al.

Assessing the road-damaging potential of heavy vehicles

The relative influence of various heavy vehicle design features on road-damaging potential is discussed. Testing procedures that could be used to measure the road-damaging potential of heavy vehicles are examined. A validated vehicle simulation is used to examine some of the characteristics of dynamic tyre forces generated by typical leaf sprung and air sprung articulated heavy vehicles for typical highway conditions. The proposed EC suspension test is simulated and the results compared with dynamic tyre forces generated under highway conditions. It is concluded that the road-damaging potential of a vehicle cannot be assessed by the simplistic parametric measurement of the proposed EC test. It is questionable whether a vehicle that passes the test will be any more 'road friendly' than one that fails.

Spatial repeatability of dynamic tyre forces generated by heavy vehicles

Road damage due to heavy vehicles is thought to be dependent on the extent to which lorries in normal traffic apply peak forces to the same locations along the road. A validated vehicle simulation is used to simulate 37 leaf-sprung articulated vehicles with parametric variations typical of vehicles in one weight class in the highway vehicle fleet. The spatial distribution of tyre forces generated by each vehicle is compared with the distribution generated by a reference vehicle, and the conditions are established for which repeated heavy loading occurs at specific points along the road. It is estimated that approximately two-thirds of vehicles in this class (a large proportion of all heavy vehicles) may contribute to a repeated pattern of road loading. It is concluded that dynamic tyre forces are a significant factor influencing road damage, compared to other factors such as tyre configuration and axle spacing.

Probabilistic inference for fast learning in control

A novel framework is provided for very fast model-based reinforcement learning in continuous state and action spaces. It requires probabilistic models that explicitly characterize their levels of condence. Within the framework, exible, non-parametric models are used to describe the world based on previously collected experience. It demonstrates learning on the cart-pole problem in a setting where very limited prior knowledge about the task has been provided. Learning progressed rapidly, and a good policy found after only a small number of iterations.