A note on the heat-transfer to nonspherical particles from plasma

Heat transfer from plasma to a nonspherical partical in the free-molecular regime is studied in the present paper under thin plasma sheath condition. Analytical expressions for the floating potential charge and heat fluxes of an ellipsoid particle of revolution are derived and curves are given for key parameters for arbitrary plasma flow direction. On the basis of these results, an equivalent sphere with the same surface area as the nonspherical particle is suggested to be used for calculating the total heat flux of nonspherical particle in engineering application with acceptable accuracy. Furthermore, the effects of particle rotation, which occurs in most aerosol systems, on the heat transfer are also discussed.

Basic equations for suspension flows with interphase mass-transfer

In the case of suspension flows, the rate of interphase momentum transfer M(k) and that of interphase energy transfer E(k), which were expressed as a sum of infinite discontinuities by Ishii, have been reduced to the sum of several terms which have concise physical significance. M(k) is composed of the following terms: (i) the momentum carried by the interphase mass transfer; (ii) the interphase drag force due to the relative motion between phases; (iii) the interphase force produced by the concentration gradient of the dispersed phase in a pressure field. And E(k) is composed of the following four terms, that is, the energy carried by the interphase mass transfer, the work produced by the interphase forces of the second and third parts above, and the heat transfer between phases. It is concluded from the results that (i) the term, (-alpha-k-nabla-p), which is related to the pressure gradient in the momentum equation, can be derived from the basic conservation laws without introducing the "shared-pressure presumption"; (ii) the mean velocity of the action point of the interphase drag is the mean velocity of the interface displacement, upsilonBAR-i. It is approximately equal to the mean velocity of the dispersed phase, upsilonBAR-d. Hence the work terms produced by the drag forces are f(dc) . upsilonBAR-d, and f(cd) . upsilonBAR-d, respectively, with upsilonBAR-i not being replaced by the mean velocity of the continuous phase, upsilonBAR-c; (iii) by analogy, the terms of the momentum transfer due to phase change are upsilonBAR-d-GAMMA-c, and upsilonBAR-d-GAMMA-d, respectively; (iv) since the transformation between explicit heat and latent heat occurs in the process of phase change, the algebraic sum of the heat transfer between phases is not equal to zero. Q(ic) and Q(id) are composed of the explicit heat and latent heat, so that the sum Q(ic) + Q(id)) is equal to zero.

Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.