Analysis of the piston ring/liner oil film development during warm-up for an SI-engine

A one-dimensional ring-pack lubrication model developed at MIT is applied to simulate the oil film behavior during the warm-up period of a Kohler spark ignition engine [1]. This is done by making assumptions for the evolution of the oil temperatures during warm-up and that the oil control ring during downstrokes is fully flooded. The ring-pack lubrication model includes features such as three different lubrication regimes, i.e. pure hydrodynamic lubrication, boundary lubrication and pure asperity contact, non-steady wetting of both inlet and outlet of the piston ring, capability to use all ring face profiles that can be approximated by piece-wise polynomials and, finally, the ability to model the rheology of multi-grade oils. Not surprisingly, the simulations show that by far the most important parameter is the temperature dependence of the oil viscosity. This dependence is subsequently examined further by choosing different oils. The baseline oil is SAE 10W30 and results are compared to those using the SAE 30 and the SAE 10W50 oils.

Fibre optics monitoring of clay cuttings and embankments along London's ring motorway

Although a wide range of techniques exist for slope monitoring, the task of monitoring slopes is sometimes complicated by the extensive nature and unpredictability of slope movements. The Brillouin optical time-domain reflectometer (BOTDR) is a distributed optical fiber strain measurement technology utilising Brillouin scattering. This method measures continuous strain along a standard optical fibre over a distance up to 10 km and hence has potential to detect deformations and diagnose problems along large sections of slopes and embankments. This paper reports the demonstration of BOTDR method for monitoring surface ground movements of clay cuttings and embankments along London's ring M25 motorway. A field trial investigating varying methods of onsite fibre optic installations was conducted. The surrounding ground was artificially moved by excavating a 3 m deep trench perpendicular to the instrumented sections. Results obtained from onsite installations after slope movement demonstrate a half-pipe covered fibre optic installed on wide (200mm) Tensar ™SS20 geogrid gives the most consistent recorded strain change profile. Initial conclusions suggest this method best represents induced ground motion at the surface and hence is recommended for implementation in future sitework. Copyright ASCE 2008.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.