13,868 research outputs found
Joint PDF modelling of turbulent flow and dispersion in an urban street canyon
The joint probability density function (PDF) of turbulent velocity and
concentration of a passive scalar in an urban street canyon is computed using a
newly developed particle-in-cell Monte Carlo method. Compared to moment
closures, the PDF methodology provides the full one-point one-time PDF of the
underlying fields containing all higher moments and correlations. The
small-scale mixing of the scalar released from a concentrated source at the
street level is modelled by the interaction by exchange with the conditional
mean (IECM) model, with a micro-mixing time scale designed for geometrically
complex settings. The boundary layer along no-slip walls (building sides and
tops) is fully resolved using an elliptic relaxation technique, which captures
the high anisotropy and inhomogeneity of the Reynolds stress tensor in these
regions. A less computationally intensive technique based on wall functions to
represent boundary layers and its effect on the solution are also explored. The
calculated statistics are compared to experimental data and large-eddy
simulation. The present work can be considered as the first example of
computation of the full joint PDF of velocity and a transported passive scalar
in an urban setting. The methodology proves successful in providing high level
statistical information on the turbulence and pollutant concentration fields in
complex urban scenarios.Comment: Accepted in Boundary-Layer Meteorology, Feb. 19, 200
A non-hybrid method for the PDF equations of turbulent flows on unstructured grids
In probability density function (PDF) methods of turbulent flows, the joint
PDF of several flow variables is computed by numerically integrating a system
of stochastic differential equations for Lagrangian particles. A set of
parallel algorithms is proposed to provide an efficient solution of the PDF
transport equation, modeling the joint PDF of turbulent velocity, frequency and
concentration of a passive scalar in geometrically complex configurations. An
unstructured Eulerian grid is employed to extract Eulerian statistics, to solve
for quantities represented at fixed locations of the domain (e.g. the mean
pressure) and to track particles. All three aspects regarding the grid make use
of the finite element method (FEM) employing the simplest linear FEM shape
functions. To model the small-scale mixing of the transported scalar, the
interaction by exchange with the conditional mean model is adopted. An adaptive
algorithm that computes the velocity-conditioned scalar mean is proposed that
homogenizes the statistical error over the sample space with no assumption on
the shape of the underlying velocity PDF. Compared to other hybrid
particle-in-cell approaches for the PDF equations, the current methodology is
consistent without the need for consistency conditions. The algorithm is tested
by computing the dispersion of passive scalars released from concentrated
sources in two different turbulent flows: the fully developed turbulent channel
flow and a street canyon (or cavity) flow. Algorithmic details on estimating
conditional and unconditional statistics, particle tracking and particle-number
control are presented in detail. Relevant aspects of performance and
parallelism on cache-based shared memory machines are discussed.Comment: Accepted in Journal of Computational Physics, Feb. 20, 200
Resource dedication problem in a multi-project environment
There can be different approaches to the management of resources within
the context of multi-project scheduling problems. In general, approaches to multiproject scheduling problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project problem environment is defined here as the resource dedication problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this problem
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