A new lattice Boltzmann model for interface reactions between immiscible fluids

In this paper, we describe a lattice Boltzmann model to simulate chemical reactions taking place at the interface between two immiscible fluids. The phase-field approach is used to identify the interface and its orientation, the concentration of reactant at the interface is then calculated iteratively to impose the correct reactive flux condition. The main advantages of the model is that interfaces are considered part of the bulk dynamics with the corrective reactive flux introduced as a source/sink term in the collision step, and, as a consequence, the model’s implementation and performance is independent of the interface geometry and orientation. Results obtained with the proposed model are compared to analytical solution for three different benchmark tests (stationary flat boundary, moving flat boundary and dissolving droplet). We find an excellent agreement between analytical and numerical solutions in all cases. Finally, we present a simulation coupling the Shan Chen multiphase model and the interface reactive model to simulate the dissolution of a collection of immiscible droplets with different sizes rising by buoyancy in a stagnant fluid

Split operator finite element method for modelling pulmonary gas exchange

A pulmonary gas exchange model which contains six species of interest in three different regions is considered. The model leads to a system of coupled non-linear parabolic partial differential equations and is solved using a transport-reaction split operator scheme. A Galerkin weighted residual finite element method solves the transport algorithm while a simple forward time step is used for the reaction algorithm. Using different time scales for the transport and reaction algorithms, we obtain a reasonable approximation of gas exchange

Effects of pore-scale velocity and pore-scale physical processes on contaminant biodegradation during transport in groundwater: modeling and experiments

Contamination of surface and ground water has emerged as one of the most important environmental issues in developed and developing countries. Bioremediation of groundwater takes advantage of bacteria present in the environment to transform toxic compounds to non-toxic metabolites. This biotechnology holds the potential for fast, inexpensive, and effective water decontamination. However, it is still poorly understood and usually not fully controlled due to the lack of information describing the natural phenomena involved. Therefore, a better understanding of the phenomena involved during bioremediation of groundwater could help in the design and implementation of more efficient technologies. The main objective of the present research is to assess how pore-scale physical factors, such as pore-scale velocity, affect the degradation potential of contaminants during transport in groundwater. The target chemicals studied were chlorinated ethenes because they are commonly found in contaminated groundwater sites. To achieve the research objective, the following were employed: a mathematical model that links pore scale processes to the macro-scale representation of contaminant transport; development of numerical tools to solve the mathematical model; and experimental elucidation of the influence of pore-scale flow velocity on the biodegradation of contaminants using column experiments. Results from the mathematical model and experiments were used to elucidate the inter-relationship between physical and biological phenomena at the micro scale. The influence of flow velocity through the porous media (a physical factor) on the biological structure (microbial community in the porous media) was assessed. The results of this investigation contribute to the bioremediation of contaminated groundwater understanding with new insights on the importance of physical transport factors on the biodegradation potential. For example, flow velocity is shown to have an important effect on the degradation potential of chlorinated ethenes. Additionally, the mathematical model and numerical tools have potential application to many other reactive transport problems, including: adsorption onto activated carbon, reaction in packed beds of catalyst, chemical transport in streambeds, and separation in chromatographic columns

Loss allocation in a distribution system with distributed generation units

In Denmark, a large part of the electricity is produced by wind turbines and combined heat and power plants (CHPs). Most of them are connected to the network through distribution systems. This paper presents a new algorithm for allocation of the losses in a distribution system with distributed generation. The algorithm is based on a reduced impedance matrix of the network and current injections from loads and production units. With the algorithm, the effect of the covariance between production and consumption can be evaluated. To verify the theoretical results, a model of the distribution system in Brønderslev in Northern Jutland, including measurement data, has been studied

Data generation and model usage for machine learning-based dynamic security assessment and control

The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants. This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation. To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Bayesian Consistent Dual Ensemble Kalman Filter for State-Parameter Estimation in Subsurface Hydrology

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The Joint-EnKF directly updates the augmented state-parameter vector while the Dual-EnKF employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. In this paper, we reverse the order of the forecast-update steps following the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem, based on which we propose a new dual EnKF scheme, the Dual-EnKF$_{\rm OSA}$. Compared to the Dual-EnKF, this introduces a new update step to the state in a fully consistent Bayesian framework, which is shown to enhance the performance of the dual filtering approach without any significant increase in the computational cost. Numerical experiments are conducted with a two-dimensional synthetic groundwater aquifer model to assess the performance and robustness of the proposed Dual-EnKF$_{\rm OSA}$, and to evaluate its results against those of the Joint- and Dual-EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, further providing reliable estimates of their uncertainties. Compared with the standard Joint- and Dual-EnKFs, the proposed scheme is found more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameters estimates

Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training

In biomedical engineering, earthquake prediction, and underground energy harvesting, it is crucial to indirectly estimate the physical properties of porous media since the direct measurement of those are usually impractical/prohibitive. Here we apply the physics-informed neural networks to solve the inverse problem with regard to the nonlinear Biot's equations. Specifically, we consider batch training and explore the effect of different batch sizes. The results show that training with small batch sizes, i.e., a few examples per batch, provides better approximations (lower percentage error) of the physical parameters than using large batches or the full batch. The increased accuracy of the physical parameters, comes at the cost of longer training time. Specifically, we find the size should not be too small since a very small batch size requires a very long training time without a corresponding improvement in estimation accuracy. We find that a batch size of 8 or 32 is a good compromise, which is also robust to additive noise in the data. The learning rate also plays an important role and should be used as a hyperparameter.Comment: arXiv admin note: text overlap with arXiv:2002.0823

Elimination of the reaction rate 'scale effect': application of the Lagrangian reactive particle-tracking method to simulate mixing-limited, field-scale biodegradation at the Schoolcraft (MI, USA) site

This is the peer reviewed version of the following article: [Ding, D., Benson, D. A., Fernàndez‐Garcia, D., Henri, C. V., Hyndman, D. W., Phanikumar, M. S., & Bolster, D. (2017). Elimination of the reaction rate “scale effect”: Application of the Lagrangian reactive particle‐tracking method to simulate mixing‐limited, field‐scale biodegradation at the Schoolcraft (MI, USA) site. Water Resources Research, 53, 10,411–10,432. https://doi.org/10.1002/2017WR021103], which has been published in final form at https://doi.org/10.1002/2017WR021103. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Measured (or empirically fitted) reaction rates at groundwater remediation sites are typically much lower than those found in the same material at the batch or laboratory scale. The reduced rates are commonly attributed to poorer mixing at the larger scales. A variety of methods have been proposed to account for this scaling effect in reactive transport. In this study, we use the Lagrangian particle-tracking and reaction (PTR) method to simulate a field bioremediation experiment at the Schoolcraft, MI site. A denitrifying bacterium, Pseudomonas Stutzeri strain KC (KC), was injected to the aquifer, along with sufficient substrate, to degrade the contaminant, carbon tetrachloride (CT), under anaerobic conditions. The PTR method simulates chemical reactions through probabilistic rules of particle collisions, interactions, and transformations to address the scale effect (lower apparent reaction rates for each level of upscaling, from batch to column to field scale). In contrast to a prior Eulerian reaction model, the PTR method is able to match the field-scale experiment using the rate coefficients obtained from batch experiments.Peer ReviewedPostprint (author's final draft