Dispersion analysis of the least‐squares finite‐element shallow‐water system

The frequency or dispersion relation for the least‐squares mixed formulation of the shallow‐water equations is analysed. We consider the use of different approximation spaces corresponding to co‐located and staggered meshes, respectively. The study includes the effect of Coriolis, and the dispersion properties are compared analytically and graphically with those of the mixed Galerkin formulation. Numerical solutions of a test problem to simulate slow Rossby modes illustrate the theoretical results. Copyright © 2003 John Wiley & Sons, Ltd.     

Searching for new conditions for fermion N-representability

New elements of the dual cone of the set of fermion N-representable 2-density operators are proposed. So far, the explicit form of the corresponding necessary conditions for N-representability is obtained for N = 3. In this case the new condition is stronger than the known B- and C-conditions for 3-representability. The results provide evidence that in the spectral decomposition of the N-representable 2-density operator there exists an intrinsic relation between the eigenvalue and the corresponding eigenfunction.     

Kinetics of the reactions of the IO radical with dimethyl sulfide,methanethiol, ethylene,and propylene

The reactions of IO radicals with CH₃SCH₃, CH₃SH, C₂H₄, and C₃H₆ have been studied using the discharge flow method with direct detection of IO radicals by mass spectrometry. The absolute rate constants obtained at 298 K are the following: IO + CH₃SCH₃ → products (1): k₁ = (1.5 ± 0.2) × 10^?14; IO + CH₃SH → products (2): k₂ = (6.6 ± 1.3) × 10^?16; IO + C₂H₄ →products (3): k₃ < 2 × 10^?16; IO + C₃H₆ → products (4): k₄ < 2 × 10^?16 (units are cm³ molecule^?1 s^?1). CH₃S(O)CH₃ and HOI were found as products of reactions (1) and (2), respectively. The present lower value of k₁ compared to our previous determination is discussed.     

Ephemeral and/or coloured nuonic hydrogen atoms

The unexpected and, in conventional terms, yet unexplained experimental results, obtained in systematic measurements of muon transfer from muonic hydrogen to sulphur and oxygen of sulphur dioxide, seem to violate the principle of reproducibility of muon transfer data. With the hypothesis of ephemeral muonic hydrogen atoms, the number of different hydrogen atoms can be reduced from four to two. This hypothesis does, however, not help to interpret the transfer data to helium, neon and argon, where the muonic hydrogen atoms seem to wear colours.     

The ALTO project at IPN-Orsay

The availability of both a tandem deuteron beam and a linac electron beam, the latter converted into Bremsstrahlung, at the new ALTO facility at IPN-Orsay offers a unique opportunity to compare the performance of a laser ion guide under different regimes. The ALTO accelerator has delivered its first electron beam at the end of 2005 and a design for a gas-cell prototype is being studied.     

Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components