Browsing by Author "Mushumbusi, Philbert"

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CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines
(ETASR, 2024-09) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana
This study utilizes Computational Fluid Dynamics (CFD) to generate pressure and flow rate values for the analysis of flow characteristics and the diagnosis of leaks in inclined pipelines. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) solver in OpenFOAM software was modified to incorporate the effect of pipe orientation angle. Subsequently, the SIMPLE solver was employed to simulate the flow of water through the pipe. Leakage rates were observed to vary in magnitude with respect to leak position and pipe orientation angle, except that leaks close to the flow inlet and pipes with a greater inclination were associated with higher leakage rates. A mathematical leak model is proposed based on non- dimensional flow variables and pipe orientation angle. To generate sufficient pressure values and leakage rates, the CFD simulation was performed 70 times. These values were then incorporated into the mathematical model for the leak location to be predicted. The proposed method is applicable to the detection of leakages of varying sizes in pipelines with different orientations. Therefore, knowing the pipe orientation angle and measurements of inlet flow rate, outlet flow rate, and pressure drop, the model can be used to precisely locate leaks in a pipeline.
Modelling and analysis of hydraulic transients in water pipelines using physics-informed neural network
(Elsevier B. V., 2025-02-18) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana Grace
Hydraulic transients remain a challenge in fluid flow systems, spanning basic pipelines to complex networks. While advances in transient analysis methods have been made, most approaches require full boundary condition data or rely on computationally intensive mesh based techniques. In response to these limitations, PINNs have emerged as a promising alternative for predicting pressure and flow rate transients in pipeline systems without requiring complete knowledge of boundary conditions. The PINN model was trained both with and without initial and boundary condition data, achieving results that matched the Method of Characteristics reference with remarkable accuracy. Notably, the model effectively captured pressure and flow rate traces, even when tested on data from unmonitored locations. This demonstrate the robustness of PINN in addressing incomplete data challenges, enhance mesh free computation, and optimising transient analysis. These findings highlight PINN as a powerful tool for improving field data accuracy and computational efficiency in hydraulic systems, paving the way for their broader application in fluid flow networks.
Modelling and analysis of hydraulic transients in water pipelines using physics-informed neural network
(Elsevier, 2025-03-20) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana Grace
Hydraulic transients remain a challenge in fluid flow systems, spanning basic pipelines to complex networks. While advances in transient analysis methods have been made, most approaches require full boundary condition data or rely on computationally intensive mesh- based techniques. In response to these limitations, PINNs have emerged as a promising alternative for predicting pressure and flow rate transients in pipeline systems without requiring complete knowledge of boundary conditions. The PINN model was trained both with and without initial and boundary condition data, achieving results that matched the Method of Characteristics reference with remarkable accuracy. Notably, the model effectively captured pressure and flow rate traces, even when tested on data from unmonitored locations. This demonstrate the robustness of PINN in addressing incomplete data challenges, enhance mesh- free computation, and optimising transient analysis. These findings highlight PINN as a powerful tool for improving field data accuracy and computational efficiency in hydraulic systems, paving the way for their broader application in fluid flow networks.