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.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.