Three different groups of materials were chosen to investigate the uniaxial compaction of particulate solids. Dentritic and cubic sodium chloride were chosen as plastically deforming, dicalcium phosphcte and sugar as fragmentary and styrocell, homopolymer and copolyrinier as non-compactable materials. The uniaxial compaction of the materials was continuously followed by measurement of the applied force, the force transmitted radially to the die wall and the upper punch displacement. The data obtained was presented in the form of Mohr circles, stress pathways (shear-mean compaction stress planes) and a three dimensional representation in mean compaction stress, shear stress and volume change. The yield loci evaluated from Mohr circles and shear-mean compaction stress relationships of compactable and non-compactable materials were found to be similar in shape. The unloading stress profiles were however more informative. All unloading shear-mean compaction stress curves of the compactable materials cross the mean compaction stress axis to give negative values of shear stress and reach a minimum value of ^t_min' which was material and compaction pressure dependent. The unloading curves of non-compactable materials gave approximately zero shear. The parameters evaluated from the characteristic stress profiles were correlated to the tensile strength and hardness of compacts. Mathematical expressions have been proposed for the shear-mean compaction stress relationships of the materials investigated. The materials were characterised before and after compaction in terms of specific surface area, porosity and mechanical strength of compacts with compaction pressure.

Mean-risk stochastic programs model uncertainty by including risk measures in the objective function. This allows for modeling risk averseness for many problems in science and engineering. This dissertation addresses gaps in the literature on stochastic programs with mean-risk objectives. This includes a need for a computational study of the few available algorithms for this class of problems. The study was aimed at implementing and performing an empirical investigation of decomposition algorithms for stochastic linear programs with absolute semideviation (ASD) and quantile deviation (QDEV) as mean-risk measures. Specifically, the goals of the study were to analyze for specific instances how algorithms perform across different levels of risk, investigate the effect of using ASD and QDEV as risk measures, and understand when it is appropriate to use the risk-averse approach over the risk-neutral one. We derive two new subgradient based algorithms for the ASD and QDEV models, respectively. These algorithms are based on decomposing the stochastic program stage-wise and using a single (aggregated) cut in the master program to approximate the mean and deviation terms of the mean-risk objective function. We also consider a variant of each of the algorithms from the literature in which the mean-risk objective function is approximated by separate optimality cuts, one for the mean and one for the deviation term. These algorithms are implemented and applied to standard stochastic programming test instances to study their comparative performance. Both the aggregated cut and separate cut algorithms have comparable computational performance for ASD, while the separate cut algorithm outperforms its aggregate counterpart for QDEV. The computational study also reveals several insights on mean-risk stochastic linear programs. For example, the results show that for most standard test instances the risk-neutral approach is still appropriate. We show that this is the case due to the test instances having random variables with uniform marginal distributions. In contrast, when these distributions are changed to be non-uniform, the risk-averse approach is preferred. The results also show that the QDEV mean-risk measure has broader flexibility than ASD in modeling risk.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2008. / Principal faculty advisor: John A. Pelesko, Dept. of Mathematical Sciences. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 127-136).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 125-134).

Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 32-33).

Thesis (MSc)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

University, Diss., 2000--Osnabrück.

Universiẗat, Diss., 2000--München.