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 ≤ *H*_{t}′/*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 *C*_{dc} (*α°*) curves have been generalised to *H*_{t}′/*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 (*R*^{2} ≥ 0.98). In addition, extrapolation performance remains well-behaved up to *H*_{t}′/*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:

(10)

(11)

Table 3

Crest shape . | Fitting coefficients . | Sidewall angle (α) . | |||||||
---|---|---|---|---|---|---|---|---|---|

6° . | 8° . | 10° . | 12° . | 15° . | 20° . | 35° . | 90° . | ||

Quarter-round crest | A | 0.601 | 0.653 | 0.697 | 0.732 | 0.749 | 0.765 | 0.543 | 2.716 |

B | 0.254 | 0.268 | 0.28 | 0.346 | 0.376 | 0.466 | 0.804 | 0.061 | |

C | 0.353 | 0.384 | 0.446 | 0.435 | 0.475 | 0.544 | 0.642 | 1.127 | |

D | 3.262 | 2.923 | 2.886 | 3.313 | 3.255 | 4.916 | −4.25 | 0.632 | |

R^{2} | 0.985 | 0.986 | 0.991 | 0.992 | 0.982 | 0.979 | 0.978 | 0.987 |

Crest shape . | Fitting coefficients . | Sidewall angle (α) . | |||||||
---|---|---|---|---|---|---|---|---|---|

6° . | 8° . | 10° . | 12° . | 15° . | 20° . | 35° . | 90° . | ||

Quarter-round crest | A | 0.601 | 0.653 | 0.697 | 0.732 | 0.749 | 0.765 | 0.543 | 2.716 |

B | 0.254 | 0.268 | 0.28 | 0.346 | 0.376 | 0.466 | 0.804 | 0.061 | |

C | 0.353 | 0.384 | 0.446 | 0.435 | 0.475 | 0.544 | 0.642 | 1.127 | |

D | 3.262 | 2.923 | 2.886 | 3.313 | 3.255 | 4.916 | −4.25 | 0.632 | |

R^{2} | 0.985 | 0.986 | 0.991 | 0.992 | 0.982 | 0.979 | 0.978 | 0.987 |

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