Water Hammer and Hydrodynamics’ Instability - Advances in Control and Automation of Water Systems - page 72

4.3 results And disscusions
Like all fluid research, however, data are obtained at a finite number of locations and
generalizing the findings requires assumptions, with uncertainties spread across the
system.

tABle 4.2 Model Summary and Parameter Estimates (Water hammer condition).

Model Un-standardized

Coefficients

Standardized Coefficients t Sig.

Std. Error Beta

1 (Constant) 28.762 29.73 - 0.967 0.346

flow 0.031 0.01 0.399 2.944 0.009

distance -0.005 0.001 -0.588 -4.356 0

T-time, 0.731 0.464 0.117 1.574 0.133

2 (Constant) 14.265 29.344 - 0.486 0.632

flow 0.036 0.01 0.469 3.533 0.002

distance -0.004 0.001 -0.52 -3.918 0.001

3 (Constant) 97.523 1.519 - 64.189 0

4 (Constant) 117.759 2.114 - 55.697 0

distance -0.008 0.001 -0.913 -10.033 0

5 (Constant) 14.265 29.344 - 0.486 0.632

flow 0.036 0.01 0.469 3.533 0.002

distance -0.004 0.001 -0.52 -3.918 0.001

Regression Equation defined in stage (1) has been accepted, because its coefficients are meaningful:

pressure = 28.762 + .031 flow - .005 Dostane + .731 Time (4.33)
At worst cases, tests can lead to physically doubtful conclusions limited by the scope
of the test program. Neither laboratory models nor field testing can substitute for the
careful and correct application of a proven hydraulic transient computer model. If a
system is faced to large changes in velocity and pressure in short time periods, then
transient analysis is required.

4.3.1 regression modeling results has been compared with research field
tests model
Assumption: p = f (V, T, L), V-velocity (flow), T-time, and L-distance are the most
important requested variables. Regression software fitted the function curve (Figure
4.7) and have provided regression analysis. Results are shown in Table 4.1 “Model
Summary and Parameter Estimates” (Table 4.2).