A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Roughness and Demand Estimation in Water Distribution Networks Using Head Loss Adjustment
AbstractTo estimate pipe roughness and nodal demand parameters in water distribution networks, a method based on head loss adjustment is proposed. By using weighted least squares (WLS), model-simulated head losses are adjusted to minimize the sum of the squares of the corrections (differences between simulated and adjusted values) of simulated head losses under the constraints of head and flow measurements. Pipe roughness coefficients (Hazen-Williams C-factors) are computed by using the ratio of the simulated to adjusted head losses and refined by using boundary-fence and gross-error detection techniques. With the adjusted C-factors and head losses, pipe flows are computed to rectify nodal demands, which are limited in their boundary fences. After the demand multipliers are computed and filtered by using gross-error detection techniques, a large number of nodal demands make an advance. The loop of model simulation, head loss adjustment, and roughness and demand calibrations runs iteratively from different starting points of C-factor under multiple loading conditions, then realistic roughness and demands are achieved. The method was verified on two benchmark networks—a hypothetical network and a real network—by using noisy head and flow measurements. The results show that the method is effective for dual estimation of roughness and demand parameters.
Roughness and Demand Estimation in Water Distribution Networks Using Head Loss Adjustment
AbstractTo estimate pipe roughness and nodal demand parameters in water distribution networks, a method based on head loss adjustment is proposed. By using weighted least squares (WLS), model-simulated head losses are adjusted to minimize the sum of the squares of the corrections (differences between simulated and adjusted values) of simulated head losses under the constraints of head and flow measurements. Pipe roughness coefficients (Hazen-Williams C-factors) are computed by using the ratio of the simulated to adjusted head losses and refined by using boundary-fence and gross-error detection techniques. With the adjusted C-factors and head losses, pipe flows are computed to rectify nodal demands, which are limited in their boundary fences. After the demand multipliers are computed and filtered by using gross-error detection techniques, a large number of nodal demands make an advance. The loop of model simulation, head loss adjustment, and roughness and demand calibrations runs iteratively from different starting points of C-factor under multiple loading conditions, then realistic roughness and demands are achieved. The method was verified on two benchmark networks—a hypothetical network and a real network—by using noisy head and flow measurements. The results show that the method is effective for dual estimation of roughness and demand parameters.
Roughness and Demand Estimation in Water Distribution Networks Using Head Loss Adjustment
Gao, Tiejun (author)
2017
Article (Journal)
English
Roughness and Demand Estimation in Water Distribution Networks Using Head Loss Adjustment
| British Library Online Contents | 2017
Pipe Roughness Estimation in Water Distribution Networks Using Head Loss Adjustment
| Online Contents | 2017
Pipe Roughness Estimation in Water Distribution Networks Using Head Loss Adjustment
| British Library Online Contents | 2017