A stochastic simulation of nonisothermal nucleation

2008 ◽  
Vol 128 (16) ◽  
pp. 164519 ◽  
Author(s):  
Jonathan C. Barrett
Keyword(s):  
Stochastic Simulation ◽  
Nonisothermal Nucleation

Stochastic simulation of cell cycle

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
P. Luvsantseren ◽  
K. Lochin ◽  
E. Purevjav
Keyword(s):  
Cell Cycle ◽  
Stochastic Simulation

Stochastic simulation for determining the design flood of cascade reservoir systems

2012 ◽  
Vol 43 (1-2) ◽  
pp. 54-63 ◽  
Author(s):  
Baohong Lu ◽  
Huanghe Gu ◽  
Ziyin Xie ◽  
Jiufu Liu ◽  
Lejun Ma ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Southern China ◽  
Flow Simulation ◽  
Simulation Method ◽  
Simulation Approach ◽  
Design Flood ◽  
Design Values ◽  
Stochastic Simulation Model ◽  
The Impact

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

Random numbers for stochastic simulation

1984 ◽  
Vol 6 (4) ◽  
pp. 5-6 ◽  
Author(s):  
Daniel Guinier
Keyword(s):  
Stochastic Simulation ◽  
Random Numbers
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