The influence of particle shape on the stress-strain and creep response of a fine silica sand

The influence of particle shape on the stress-strain response of fine silica sand is investigated experimentally. Two sands from the same source and with the same particle size distribution were examined using Fourier descriptor analysis for particle shape. Their grains were, on average, found to have similar angularity but different elongation. During triaxial stress path testing, the stress-strain behavior of the sands for both loading and creep stages were found to be influenced by particle elongation. In particular, the behavior of the sand with less elongated grains was more like that of rounded glass beads during creep. The results highlight the role of particle shape in stress transmission in granular packings and suggest that shape should be taken more rigorously into consideration in characterizing geomaterials. © 2005 Taylor & Francis Group.

Model based control for closed loop testing of 1-D engine simulation models

1-D engine simulation models are widely used for the analysis and verification of air-path design concepts and prediction of the resulting engine transient response. The latter often requires closed loop control over the model to ensure operation within physical limits and tracking of reference signals. For this purpose, a particular implementation of Model Predictive Control (MPC) based on a corresponding Mean Value Engine Model (MVEM) is reported here. The MVEM is linearised on-line at each operating point to allow for the formulation of quadratic programming (QP) problems, which are solved as the part of the proposed MPC algorithm. The MPC output is used to control a 1-D engine model. The closed loop performance of such a system is benchmarked against the solution of a related optimal control problem (OCP). As an example this study is focused on the transient response of a light-duty car Diesel engine. For the cases examined the proposed controller implementation gives a more systematic procedure than other ad-hoc approaches that require considerable tuning effort. © 2012 IFAC.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.