As shown in Figure 5, empirical curves are used to simulate experimental data using IBM SPSS 22 software (Allen et al. 2014). Equations (10) and (11) are valid for 0.05 ≤ Ht′/P′ < ∼0.75–0.8, and the coefficients of A, B, C, and D are tabulated in Table 3. After applying Equation (10) for compound labyrinth weir and Equation (11) for compound linear weir, the data display a well-behaved nature when the Cdc (α°) curves have been generalised to Ht′/P′ = 1.0, as shown in Figure 5 with dashed lines. Equations (10) and (11) were selected instead of polynomial formulas because the data have a good correlation representation (R2 ≥ 0.98). In addition, extrapolation performance remains well-behaved up to Ht′/P 2.0. Equations (10) and (11) reflect a good choice for the designer in the case of using compound labyrinth weirs with different sidewall angles:
formula
(10)
formula
(11)
Table 3

Fitting coefficients valid for 0.05 ≤ Ht′/P′ < ∼0.75–0.8

Crest shapeFitting coefficientsSidewall angle (α)
10°12°15°20°35°90°
Quarter-round crest 0.601 0.653 0.697 0.732 0.749 0.765 0.543 2.716 
0.254 0.268 0.28 0.346 0.376 0.466 0.804 0.061 
0.353 0.384 0.446 0.435 0.475 0.544 0.642 1.127 
3.262 2.923 2.886 3.313 3.255 4.916 −4.25 0.632 
R2 0.985 0.986 0.991 0.992 0.982 0.979 0.978 0.987 
Crest shapeFitting coefficientsSidewall angle (α)
10°12°15°20°35°90°
Quarter-round crest 0.601 0.653 0.697 0.732 0.749 0.765 0.543 2.716 
0.254 0.268 0.28 0.346 0.376 0.466 0.804 0.061 
0.353 0.384 0.446 0.435 0.475 0.544 0.642 1.127 
3.262 2.923 2.886 3.313 3.255 4.916 −4.25 0.632 
R2 0.985 0.986 0.991 0.992 0.982 0.979 0.978 0.987 
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