Abstract: In this paper effects of inclination angle on natural
convection flow in an open cavity has been analyzed with Lattice
Boltzmann Method (LBM).The angle of inclination varied from θ= -
45° to 45° with 15° intervals. Study has been conducted for Rayleigh
numbers (Ra) 104 to 106. The comparisons show that the average
Nusselt number increases with growth of Rayleigh number and the
average Nusselt number increase as inclination angles increases at
Ra=104.At Ra=105 and Ra=106 the average Nusselt number enhance
as inclination angels varied from θ= -45° to θ= 0° and decrease as
inclination angels increase in θ= 0° to θ= 45°.
Abstract: In this paper Lattice Boltzmann simulation of
turbulent natural convection with large-eddy simulations (LES) in a
square cavity which is filled by water has been investigated. The
present results are validated by finds of other investigations which
have been done with different numerical methods. Calculations were
performed for high Rayleigh numbers of Ra=108 and 109. The results
confirm that this method is in acceptable agreement with other
verifications of such a flow. In this investigation is tried to present
Large-eddy turbulence flow model by Lattice Boltzmann Method
(LBM) with a clear and simple statement. Effects of increase in
Rayleigh number are displayed on streamlines, isotherm counters and
average Nusselt number. Result shows that the average Nusselt
number enhances with growth of the Rayleigh numbers.
Abstract: Soil organic carbon (SOC) plays a key role in soil
fertility, hydrology, contaminants control and acts as a sink or source
of terrestrial carbon content that can affect the concentration of
atmospheric CO2. SOC supports the sustainability and quality of
ecosystems, especially in semi-arid region. This study was
conducted to determine relative importance of 13 different
exploratory climatic, soil and geometric factors on the SOC contents
in one of the semiarid watershed zones in Iran. Two methods
canonical discriminate analysis (CDA) and feed-forward back
propagation neural networks were used to predict SOC. Stepwise
regression and sensitivity analysis were performed to identify
relative importance of exploratory variables. Results from sensitivity
analysis showed that 7-2-1 neural networks and 5 inputs in CDA
models output have highest predictive ability that explains %70 and
%65 of SOC variability. Since neural network models outperformed
CDA model, it should be preferred for estimating SOC.