TY - Generic
T1 - Comparison between experiments and CFD predictions of mixed convection flows in an atrium
T2 - Proceedings of the 10th International Conference on Indoor Air Quality and Climate
Y1 - 2005/
SP - 2849
EP - 2853
A1 - Buvaneswari Jayaraman
A1 - Elizabeth U. Finlayson
A1 - Emily E. Wood
A1 - Tracy L. Thatcher
A1 - Phillip N. Price
A1 - Richard G. Sextro
A1 - Ashok J. Gadgil
KW - airflow
KW - cfd
KW - large indoor space
KW - mixed convection
KW - pollutant dispersion
AB - This paper compares results from a computational fluid dynamics (CFD) simulation of airflow and pollutant dispersion under mixed-convection conditions with experimental data obtained in our 7m x 9m x 11m high experimental facility. A tracer gas was continuously released from a 1 m2 horizontal source 0.5 m above the floor. Path-integrated concentrations were measured along multiple short and long sampling paths in three horizontal planes. A steady state CFD analysis was used to model these experiments. The Reynolds Averaged Navier-Stokes (RANS) equations were solved for the flow and temperature field using the commercial CFD software, StarCD. CFD results were compared with the measured path-integrated concentrations. Accuracy of CFD predictions was found to improve with inclusion of thermal effects, and further by using a low-Re turbulence model.
JF - Proceedings of the 10th International Conference on Indoor Air Quality and Climate
PB - Tsinghua University Press
CY - Beijing, China
VL - 3(3)
U1 - 3

ER -
TY - JOUR
T1 - An algorithm for real-time tomography of gas concentrations, using prior information about spatial derivatives
JF - Atmospheric Environment
Y1 - 2001/06//
SP - 2827
EP - 2835
A1 - Phillip N. Price
A1 - Marc L. Fischer
A1 - Ashok J. Gadgil
A1 - Richard G. Sextro
KW - Air Flow
KW - computed tomography
KW - Concentration mapping
KW - Optical remote sensing
KW - pollutant dispersion
AB - We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster.
VL - 35
IS - 16
JO - Atmospheric Environment
ER -