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Backfill process has become standard practice in mining industry where the backfill slurry is transported from surface to underground via a pipeline system. Paste backfill is one of the types of backfill slurries which in recent years has gained popularity due to its reduced water content, fast solidification time, and environmentally friendly reputation. However, wear and erosion of the pipe have been a major issue in some paste backfill pipeline operations. Paste backfill behaves as a non‐Newtonian fluid and can be modelled as a Herschel‐Bulkley fluid. To better understand the flow behaviour and wear rate of paste backfill in underground pipeline systems, experimental and numerical studies were carried out. The former focuses on the slump test and L‐pipe flow test to characterize paste backfill properties, while the latter aims to develop a three‐dimensional mathematical model to evaluate flow and wear characteristics in pipe elbows. To ensure robust and accurate solutions, the model was verified with analytical solutions and validated against experimental data. The numerical results suggest that elbow design and paste backfill property significantly affect secondary flow generation which is further reflected in the pipe wear rate. Thicker paste backfill slurry flowing in the 5D elbow yields the lowest wear rate which is beneficial for practical application, albeit it comes at a higher pressure drop.

Pulsating heat pipes (PHPs) are devices that their performance strongly depends on many factors such as filling ratio, working fluid, internal diameter, and etc. Therefore, variety of such parameters must be considered in experimental data or an accurate model must be used to characterize the behaviors of PHPs. In this study, a two layers neural network model is used to predict the behaviors of the PHPs. The effects of filling ratio and heat power input and working fluid on thermal resistance of PHPs are considered. The obtained results are in good agreement with available data and can be appropriate for predicting the trend of effective parameters on PHPs performance.

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