Learning legged locomotion

Legged locomotion of biological systems can be viewed as a self-organizing process of highly complex system-environment interactions. Walking behavior is, for example, generated from the interactions between many mechanical components (e.g., physical interactions between feet and ground, skeletons and muscle-tendon systems), and distributed informational processes (e.g., sensory information processing, sensory-motor control in central nervous system, and reflexes) [21]. An interesting aspect of legged locomotion study lies in the fact that there are multiple levels of self-organization processes (at the levels of mechanical dynamics, sensory-motor control, and learning). Previously, the self-organization of mechanical dynamics was nicely demonstrated by the so-called Passive Dynamic Walkers (PDWs; [18]). The PDW is a purely mechanical structure consisting of body, thigh, and shank limbs that are connected by passive joints. When placed on a shallow slope, it exhibits natural bipedal walking dynamics by converting potential to kinetic energy without any actuation. An important contribution of these case studies is that, if designed properly, mechanical dynamics can generate a relatively complex locomotion dynamics, on the one hand, and the mechanical dynamics induces self-stability against small disturbances without any explicit control of motors, on the other. The basic principle of the mechanical self-stability appears to be fairly general that there are several different physics models that exhibit similar characteristics in different kinds of behaviors (e.g., hopping, running, and swimming; [2, 4, 9, 16, 19]), and a number of robotic platforms have been developed based on them [1, 8, 13, 22]. © 2009 Springer London.

Fine scale structures from deformation of aluminium containing small alumina particles

Microstructures and mechanical properties have been studied in aluminium containing a fine dispersion of alumina particles, deformed by cold-rolling to strains between 1.4 and 3.5. The microstructure was characterised by TEM. The deformation structures evolved very rapidly, forming a nanostructured material, with fine subgrains about 0.2 μm in diameter and a fraction of high-angle boundaries which was already high at a strain of 1.4, but continued to increase with rolling strain. The yield stress and ductility of the rolled materials were measured in tension, and properties were similar for all materials. Yield stress measurements were correlated with estimates made using microstructural models. The role of small particles in forming and stabilising the deformation structure is discussed. This nanostructured cold-deformed alloy has mechanical properties which are usefully enhanced at comparatively low cost. This gives it, and similar particle-strengthened alloys, good potential for commercial exploitation. © 2002 Acta Materialia Inc. Published by Elsevier Science Ltd. All rights reserved.

Measurement and simulation of surface roughness noise using phased microphone arrays

A turbulent boundary-layer flow over a rough wall generates a dipole sound field as the near-field hydrodynamic disturbances in the turbulent boundary-layer scatter into radiated sound at small surface irregularities. In this paper, phased microphone arrays are applied to the measurement and simulation of surface roughness noise. The radiated sound from two rough plates and one smooth plate in an open jet is measured at three streamwise locations, and the beamforming source maps demonstrate the dipole directivity. Higher source strengths can be observed on the rough plates which also enhance the trailing-edge noise. A prediction scheme in previous theoretical work is used to describe the strength of a distribution of incoherent dipoles and to simulate the sound detected by the microphone array. Source maps of measurement and simulation exhibit satisfactory similarities in both source pattern and source strength, which confirms the dipole nature and the predicted magnitude of roughness noise. However, the simulations underestimate the streamwise gradient of the source strengths and overestimate the source strengths at the highest frequency. © 2008 Elsevier Ltd. All rights reserved.