Destruction of preferential accumulation using Lorentz force interactions.

The effect of electric charge, residing on particles, upon the phenomenon of preferential accumulation is investigated using direct numerical simulations of forced isotropic turbulence. It is well known that particles with a certain range of Stokes numbers preferentially accumulate, or de-mix, due to action of turbulent motion. Here it is shown that charged particles interact with each other through an electric field generated by non-uniformity of particle distribution. This interaction mitigates preferential accumulation at a bulk charge density level that is practically relevant and commensurate with the first few centimetres of a spray plume, the region where non-homogeneous particle concentration typically forms. It is suggested that use of electric charge could be useful in improving mixture preparation for spray combustion applications

Consistent Signal Parameter Estimation with 1-Bit Dithered Sampling

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Distributed self-tuning of sensor networks

This work is motivated by the need for an ad hoc sensor network to autonomously optimise its performance for given task objectives and constraints. Arguing that communication is the main bottleneck for distributed computation in a sensor network we formulate two approaches for optimisation of computing rates. The first is a team problem for maximising the minimum communication throughput of sensors and the second is a game problem in which cost for each sensor is a measure of its communication time with its neighbours. We investigate adaptive algorithms using which sensors can tune to the optimal channel attempt rates in a distributed fashion. For the team problem, the adaptive scheme is a stochastic gradient algorithm derived from the augmented Lagrangian formulation of the optimisation problem. The game formulation not only leads to an explicit characterisation of the Nash equilibrium but also to a simple iterative scheme by which sensors can learn the equilibrium attempt probabilities using only the estimates of transmission and reception times from their local measurements. Our approach is promising and should be seen as a step towards developing optimally self-organising architectures for sensor networks

Analytical modelling of the hydraulic effect of hydrate deposition on transportability and plugging location in subsea gas pipelines.

Accurate prediction of the hydraulic effect of hydrate deposition and plug location is critical to the safety and operability of natural gas transport pipelines, especially for gas-dominant subsea pipelines where maintenance and intervention activities are more difficult. To achieve this, the present work improved an existing two-phase pressure drop relation due to friction, by incorporating the hydrates deposition rate into the equation. In addition, a model has been developed to predict the pipeline plugging time. The transient pressure drop predictions in the present study for all six cases at high and low velocities are within 4% mean relative error. Similar predictions by Di Lorenzo et al. are within 40% maximum relative error, while the mean relative error of the transient pressure drop predictions by Zhang et al. was 7.43%. In addition, the plugging flowtime model underpredicts the plugging time by a mean relative error of 9%

Modelling hydrate deposition in gas-dominant subsea pipelines in operating and shutdown scenarios.

This study addresses a significant research gap related to hydrate formation in subsea gas pipelines, with a specific focus on deposition rates during shutdown scenarios, which has received limited attention in previous studies. Past research has employed various methodologies - including experimental, analytical and computational fluid dynamics (CFD) approaches - to predict hydrate formation conditions, but none have tackled the prediction of hydrate deposition during shutdowns. In this study, we employ a multiple linear regression modeling approach using the MATLAB regression learner app. Four distinct regression models were developed using data generated from 81 CFD simulations, utilising a 10 m length by 0.0204 m diameter 3D horizontal pipe model in Ansys Fluent, as previously developed. Through cross-validation against experimental data, the standard linear regression model emerged as the most reliable choice for predicting hydrate deposition rates, providing predictions within ±10% uncertainty bounds of experimental results, up to pressures of 8.8 MPa at hydrate-forming temperatures. The uniqueness of this new model lies in its ability to estimate the risk of hydrate deposition in subsea gas pipelines, especially with low gas flow rates and during shutdown periods, which are critical for maintenance planning. Furthermore, by estimating depositional volumes, the model predicts hydrate slurry volumes at receiving facilities, contributing to energy sustainability and benefiting gas transport pipeline operators, particularly in aging gas fields with declining production

Computational fluid dynamics simulation of natural gas hydrate sloughing and pipewall shedding temperature profile: implications for CO2 transportation in subsea pipeline.

The continuous flow assurance in subsea gas pipelines relies heavily on the assessment of temperature profile during hydrate sloughing and pipewall shedding caused by hydrates, with similar implications for carbon dioxide (CO2) transportation under hydrate-forming conditions. Hydrate sloughing is the peeling off of some hydrate deposits from the pipeline inner surface. Similarly, pipewall shedding by hydrates involves the direct interaction of hydrates with the pipeline inner surface, resulting in the detachment or removal of hydrate deposits from the pipewall. While sloughing occurs within the deposit of hydrates, pipewall shedding is related to direct interaction of the gas phase with the thin layer of hydrates on the pipewall. In this study, a computational fluid dynamics (CFD) simulation approach is employed using a validated CFD model from the literature for predicting hydrate deposition rates (Umuteme et al., 2022), by applying a subcooling temperature to the pipe wall at hydrates-forming condition. We have deduced the presence of hydrates based on the stable temperature profile of natural gas hydrates along the pipeline model. The study shows that the simulated temperature contours align well with the reported hydrate deposition profile in gas pipelines (Di Lorenzo et al., 2018). The conversion of the consumption rate of natural gas to hydrates was achieved using the equation proposed in the literature (Umuteme et al., 2022). Two shear stress regimes have been identified for hydrate sloughing and pipewall shedding in this study, with the latter resulting in higher shear stress on the pipewall. Presently, there is a growing concern regarding the potential leakage of CO2 in pipelines (Lu et al., 2020; Wang et al., 2022; Wareing et al., 2016), which may escalate due to pipewall corrosion caused by hydrates (Obanijesu, 2012). The findings in this research can provide further knowledge that can enhance the safe transportation of CO2 in pipelines under stable hydrate forming conditions

Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation.

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely prediction of pipeline leakage. However, most of these studies rely on a large training data set for training accurate models. The cost of collecting experimental data for model training is huge, while simulation data is computationally expensive and time-consuming. To tackle this problem, the present study proposes a novel data sampling optimisation method, named adaptive particle swarm optimisation (PSO) assisted surrogate model, which was used to train the machine learning models with a limited dataset and achieved good accuracy. The proposed model incorporates the population density of training data samples and model prediction fitness to determine new data samples for improved model fitting accuracy. The proposed method is applied to 3-D pipeline leakage detection and characterisation. The result shows that the predicted leak sizes and location match the actual leakage. The significance of this study is two-fold: the practical application allows for pipeline leak prediction with limited training samples and provides a general framework for computational efficiency improvement using adaptive surrogate modelling in various real-life applications

An improved computational fluid dynamics (CFD) model for predicting hydrate deposition rate and wall shear stress in offshore gas-dominated pipeline.

Gas hydrates in pipelines is still a flow assurance problem in the oil and gas industry, and requires a proactive hydrate plugging risk predicting model. As an active area of research, this work has developed a 3D 10m length by 0.0204m diameter horizontal pipe CFD model based on the eulerian-eulerian multiphase modelling framework to predict hydrate deposition rate in a gas-dominated pipeline. The proposed model simulates the conditions for hydrate formation with user defined functions (UDFs) for both energy and mass sources implemented in ANSYS Fluent, a commercial CFD software. The empirical hydrate deposition rates predicted by this model at varying subcooling temperatures and gas velocities are consistent with experimental results within ±10% uncertainty bound. At lower gas velocity of 4.7m/s, the model overpredicted the hydrate deposition rates of the experimental results in Aman et al. (2016) by 9–25.7%, whereas the analytical model of Di Lorenzo et al. (2018) underpredicted the same experimental results by a range of 27–33%. Consequently, the CFD model can enhance proactive hydrate plugging risk predictions earlier than the analytical model, especially at low gas productivity. Similarly, at a velocity of 8.8m/s and subcooling temperatures of 2.5K, 7.1K and 8.0K, the CFD model underpredicted the hydrate deposition rates of the regressed experimental results in Di Lorenzo et al. (2014a) by 14%, 6% and 4% respectively, and overpredicted the results by 1% at a subcooling temperature of 4.3K. From the CFD model results, we also suggest that hydrate sloughing shear stress is relatively constant, and the wall shedding shear stress by hydrate vary during deposition. Finally, the CFD model also predicted the phase change during hydrate formation, agglomeration, and deposition