The MIIS Eprints Archive: No conditions. Results ordered -Date Deposited. 2018-03-22T19:56:23ZEPrintshttp://www.maths-in-industry.org/images/sitelogo.gifhttp://www.maths-in-industry.org/miis/2008-10-07Z2015-05-29T19:48:20Zhttp://www.maths-in-industry.org/miis/id/eprint/156This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1562008-10-07ZContaminant Transport in Municipial Water SystemsOur first task from CHAL was to examine the dynamic (forward) algorithm used in the software package EPANET, and ensure that it is computing physically reasonable solutions for networks that experience rapid "flow reversals", in which the flow in a pipe section changes direction over short time intervals.

We begin with an overview of the physics of flow in water distributor networks, and the implementation used in the EPANET flow solver. The bulk of our results from the workshop are concerned with analysing simple flow networks and using the results to draw conclusions about the well-posedeness of both the forward and inverse problems.John Stockie2008-10-07Z2015-05-29T19:48:21Zhttp://www.maths-in-industry.org/miis/id/eprint/157This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1572008-10-07ZOptimal Control for Multi-variable ProblemsChemex Laboratories measures concentrations of various minerals in land samples provided by their clients. The problem presented by Chemex is that they would like to be able to detect when their measurement apparatus needs re-calibration due to a shift in process parameters, so as to minimize rework.

Three very contrasting approaches to the problem were taken during the workshop. They are: considering the process as a Markov Chain; use of Bayesian Belief Networks; and Multivariate control chart implementation. Preliminary data analysis for data provided by Chemex was also performed in order to initially assess some of the models considered.Luz PalaciosRita Aggarwala2008-10-07Z2015-05-29T19:48:22Zhttp://www.maths-in-industry.org/miis/id/eprint/158This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1582008-10-07ZDensity Driven Turbulent Mixing at Batch InterfacesModels are developed for the turbulent mixing and growth at a batch interface. These models depend crucially on the choice of diffusion coefficient .

The model where is the harmonic average of the mixing coefficients of the two pure fluids is analysed in detail, since this is likely to be a good approximation when the density difference between the two fluids is small.

When the density difference is large, the laminar flow regime fingering will occur and there will be a relatively sharp interface between the fluids. However, in the turbulent case, as gravity drives the denser fluid into the less dense one the invading fluid is immediately mixed by turbulent diffusion. This means that sharp interfaces do not exist. Instead there will be a finite mixing region where the volume fraction of each fluid changes from to . In this case will depend upon the relative concentration of the fluids. This approach leads to a degenerate diffusion problem.Tim MyersJim Brannan2008-10-07Z2015-05-29T19:48:23Zhttp://www.maths-in-industry.org/miis/id/eprint/159This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1592008-10-07ZEfficient Portfolio SelectionMerak believed that an efficient frontier analysis method that combined the robustness of the Monte Carlo approach with the confidence of the Markowitz approach would be a very powerful tool for any industry. However, it soon became clear that there are other ways to address the problem that do not require a Monte Carlo component.

Three subgroups were formed, and each developed a different approach for solving the problem. These were the Portfolio Selection Algorithm Approach, the Statistical Inference Approach, and the Integer Programming Approach.Min TsaoRita AggarwalaHassan AuragMarc Paulhus2008-10-07Z2015-05-29T19:48:25Zhttp://www.maths-in-industry.org/miis/id/eprint/160This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1602008-10-07ZDynamics of Large Mining ExcavatorsIn this report, parametric and non-parametric regression models and their suitability to determining the mass in the bucket of large mining excavators are compared. Although the problem of determining the force on the bucket teeth could be modelled in a similar fashion, it is not clear that there is a single force acting on the teeth and not a more complex contact between the bucket and the ground, so the report focuses on the problem of determining the payload mass.

The parametric model is found to be the better approach, because although a detailed model of each type of machine must be developed, an exact measurement of the model parameters is not required, and the model requires far less data for training that the non-parametric one.James Watmough2008-10-07Z2015-05-29T19:48:26Zhttp://www.maths-in-industry.org/miis/id/eprint/161This item is in the repository with the URL: http://www.maths-in-industry.org/miis/id/eprint/1612008-10-07ZClassification of Chemical Compound Pharmacophore StructuresA pharmacophore is a structural abstraction of the interactions between various functional group types in a compound. They are described by a spatial representation of these groups as centres (or vertices) of geometrical polyhedra, together with pairwise distances between distances.

We provide an analysis that facilitates counting 3 and 4 centre pharmacophores, including a mathematical model for distance interval ratios, triangle and other inequality requirements for feasible triangles and tetrahedra, and symmetries.

Beside spatial symmetries and and distance similarities for each edge of the polyhedra, there does not appear to be any other relevant structural similarity feature between two pharmacophores that can be used to reduce the classification of a typical compound.Andrej BonaClaude Laflamme