System modelling for Rankine Cycle waste heat recovery from a spark ignition engine

Paper presented at the 5th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 1-4 July, 2007.The Rankine Cycle can be used to convert low grade heat, including the dual heat sources of a spark ignition engine, into useful energy. To maximise the shaft power output, by optimising the capture of waste heat, requires detailed consideration of the cycle thermodynamics. A spreadsheet model for each proposed Rankine Cycle configuration has been developed and the results verified by comparison with a standard industry process engineering software package. The spreadsheet model results may be more reliable because the thermodynamic data are based on real data rather than equations of state. Comparisons of the proposed cycles are given for hexane as the working fluid.cs201

pi-NN Coupling Constants from NN Elastic Data between 210 and 800 Mev

High partial waves for $pp$ and $np$ elastic scattering are examined critically from 210 to 800 MeV. Non-OPE contributions are compared with predictions from theory. There are some discrepancies, but sufficient agreement that values of the $\pi NN$ coupling constants $g_0^2$ for $\pi ^0$ exchange and $g^2_{c}$ for charged $\pi$ exchange can be derived. Results are $g^2_0 = 13.91 \pm 0.13 \pm 0.07$ and $g^2_c = 13.69 \pm 0.15 \pm 0.24$, where the first error is statistical and the second is an estimate of the likely systematic error, arising mostly from uncertainties in the normalisation of total cross sections and $d\sigma/d\Omega$.Comment: 21 pages of LaTeX, UI-NTH-940

Fast divide-and-conquer algorithms for preemptive scheduling problems with controllable processing times – A polymatroid optimization approach

We consider a variety of preemptive scheduling problems with controllable processing times on a single machine and on identical/uniform parallel machines, where the objective is to minimize the total compression cost. In this paper, we propose fast divide-and-conquer algorithms for these scheduling problems. Our approach is based on the observation that each scheduling problem we discuss can be formulated as a polymatroid optimization problem. We develop a novel divide-and-conquer technique for the polymatroid optimization problem and then apply it to each scheduling problem. We show that each scheduling problem can be solved in O(Tfeas(n) log n) time by using our divide-and-conquer technique, where n is the number of jobs and Tfeas(n) denotes the time complexity of the corresponding feasible scheduling problem with n jobs. This approach yields faster algorithms for most of the scheduling problems discussed in this paper