By Wu J., Dong Q., Liu M.
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Additional info for 3D simulation on the unit duct in the shell side of the ROD baffle heat exchanger
1). Implementation and Experiments We answer the following questions through experiments: (a) How well does the model work in achieving our goal of performing optimal amount of bandwidth testing in bandwidth estimation? (b) How well does the model deliver the final QoS result to end-users? For example, if a user specifies its target transmission time limit associated with a confidence goal (say “meet the time limit by 95% time”), can the model really achieve this level of guarantee? Finally, we are interested in the complexity of the model.
This way, unreliable channels can also participate in improving the overall performance. Mathematical Model and Algorithm Suppose the bandwidth populations of all K channels, X1 , X2 , . . , XK , conform to the normal distribution, X ∼ N (µ, Σ) Where: 2 2 2 σ11 σ12 · · · σ1K X1 µ1 σ2 2 2 X2 µ2 21 σ22 · · · σ2K X= ··· ,µ = ··· ,Σ = ··· ··· ··· ··· 2 2 2 σK1 σK2 · · · σKK XK µK Instead of simply dropping the unreliable channels, as in Section 2, we introduce a reliability factor β varying between 0 and 1 for each channel.
90. 90). 95 is much larger than V2 . 90). To accommodate dropping channels during transmission, we propose the following method. Suppose we have K channels available, when a new sample is obtained on each channel we calculate K V values using K different αs(α1 , α2 . . αK ) for each connection, where each αi is used to calculate what the V value would be on this channel if finally ichannels are used for real transmission. We have, αi = α1/i is the confidence level specified by the end-user. 1 below.
3D simulation on the unit duct in the shell side of the ROD baffle heat exchanger by Wu J., Dong Q., Liu M.