A. Goleij, H. Jalilvand, M. R. Pormajidian, M. Tabari, K. Mohammadi Samani,
Volume 11, Issue 41 (fall 2007)
Abstract
In order to investigate the success of natural regeneration and to determine the best area for regeneration settlement, 12 gaps with the areas ranging from 50-100, 150-300, and 400-600 m2 and 4 replicates equal elevation level were selected. For measuring frequency, height, and collar diameter of regenerated seedlings, a certain number of 2 m2 subplots were carried out inside the gaps, along the bigger diameter, and related to each gap’s area. Results showed that the number of seedling varies from 5 (in big gaps) up to 28 (in small and moderate gaps) per square meter. Furthermore, there was not a significant difference between the number of seedlings in small and moderate gaps. In contrast, the number of seedlings in small and moderate gaps was significantly different from those in large gaps (at 1% Probability). This finding demonstrates that natural regeneration would be limited in large gaps (400-600 m2) but it shows a better result in smaller gaps, associated with single- tree harvests. The final result of this study shows that the most appropriate area for selective cuting in such an area is up to at most 300 m2.
H. Goleij, J. Ahadiyan, M. Ghomeshi, H. Arjmandi,
Volume 18, Issue 69 (fall 2014)
Abstract
While the mass density current penetrates the stagnant fluid, a plunge point occurs. In this regard, the boundary of the dense fluid with ambient fluid is determined at the plunge point height. In this research, the hydraulic parameters of the dense flow and the bed slope of the stagnant fluid which have a significant effect on the plunge point have been investigated under the two turbulence models: the k- and the RNG at the Flow-3D model. To achieve the purpose of this research, a physical model was set up at the hydraulics laboratory of Shahid Chamran University (SCU), Ahwaz, Iran. Then, using the Flow-3D model with both the k- and the RNG turbulence model, the height of the plunge point was simulated according to the same experimental condition. Findings showed that the predicted depth under the RNG model is closer to the results of the physical model. For example, the k- and RNG model for the 12% slope can estimate the plunge point depth by 30% and 12.28% respectively more than the experimental data. However, for all the slopes, the k-e model can on average overestimate by 27% and RNG model 10.5% more than the results of experimental data. The statistical analysis showed that the RNG model predicts the plunge point depths with a satisfactory precision.
