Deep Learning: a Study on Marine Renewable Energy and Sustainability Education in an Irish Context

[EN] In 2020-2022 a select group of Irish educators, marine renewable energy proponents and sustainability stakeholders entered into dialogue with a view to enhancing post-primary educational resources. The paucity of educational resources in this field was highlighted, most notably those in the Irish language. This dialogue led to the development, and piloting of a bilingual (Irish and English) cross-curricular programme of learning - a Transition Year Unit - targeted at students aged 15-17. This study aims to evaluate the effectiveness of the learning unit with respect to enhancing knowledge in these novel areas. The methodology is a mixed methods case study. Data gathering processes include student questionnaires, stakeholder focus groups, and expert interviews. The main finding is the importance of stakeholder input into curriculum development to ensure the effectiveness of the Transition Year Unit of Learning, and to enhance learner engagement.. Furthermore, the study recommends that one output include a web-based ‘Deep Learning’ educational platform to include downloadable resources and to enhance impact nationwide be widely disseminated. Keywords: Global citizenship, marine renewable energy education, sustainability education, Transition Year Programme.Logue, P.; Nash, R.; Walsh, M.; Faney, M.; Ó Donnghaile, D. (2023). Deep Learning: a Study on Marine Renewable Energy and Sustainability Education in an Irish Context. Editorial Universitat Politècnica de València. 1047-1054. https://doi.org/10.4995/HEAd23.2023.161501047105

Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations

The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes. We first develop a methodology to generate a synthetic dataset based on paraboloid surfaces discretized on unstructured meshes. We then train a GNN based model and perform generalization tests. Our results demonstrate the efficiency of a GNN based approach for interface reconstruction in multi-phase flow simulations in the industrial context.Comment: 12 pages, fullpaper of ECCOMAS202

A mathematical formulation for reactive transport in porous media adapted to CO2 sequestration

ABSTRACT Carbon capture and storage (CCS) is currently one of the major options to reduce greenhouse gas emissions from power plants. However, the implementation of CCS has been slowed down by uncertainties about the long term evolution of injected carbon into deep saline aquifers. Reactive transport numerical models [1] are used to predict temperature and pressure variations, brine and gas phases displacement, and chemical effects of gas-water-rock interactions. One of the main challenges of these models is to accurately represent the coupling between transport phenomena and mass transfer occurring in sub-surface porous media. In this work, we present a new mathematical formulation for reactive transport in porous media. This fully implicit multi-component, multi-phase flow formulation is able to deal with phase appearance and disappearance combined with stoichiometric mass transfer. Our formulation is currently restricted to advective transport and chemical equilibrium equations, however an extension to diffusion processes and kinetic equations is considered. The novelty of our work consists in the extension of concepts used so far to deal only with phase equilibrium We implement our mathematical formulation in a three-dimensional multi-phase flow code using the HPC numerical framework Arcan

HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization

Due to domain shift, machine learning systems typically fail to generalize well to domains different from those of training data, which is what domain generalization (DG) aims to address. Although various DG methods have been developed, most of them lack interpretability and require domain labels that are not available in many real-world scenarios. This paper presents a novel DG method, called HMOE: Hypernetwork-based Mixture of Experts (MoE), which does not rely on domain labels and is more interpretable. MoE proves effective in identifying heterogeneous patterns in data. For the DG problem, heterogeneity arises exactly from domain shift. HMOE uses hypernetworks taking vectors as input to generate experts' weights, which allows experts to share useful meta-knowledge and enables exploring experts' similarities in a low-dimensional vector space. We compare HMOE with other DG algorithms under a fair and unified benchmark-DomainBed. Our extensive experiments show that HMOE can divide mixed-domain data into distinct clusters that are surprisingly more consistent with human intuition than original domain labels. Compared to other DG methods, HMOE shows competitive performance and achieves SOTA results in some cases

DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)

This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn the physics of the problem without the need for exact solutions, in contrast to most previous data-driven processes where the distance with the available solutions is minimized

Analysis and numerical computation of geochemical systems with kinetic precipitation and dissolution reactions involving several minerals

In this paper, we study underground media where chemical interactions involve aqueous and mineral species. In most kinetic models, a mineral species is involved in a single precipitation or dissolution reaction. In this work, we consider more general precipitation and dissolution kinetic reactions, which can include several minerals, which can each participate in several reactions. We propose a differential inclusion model, with reaction rates defined by set-valued functions, in order to ensure positive quantities of species. We also develop a regularized model, with reaction rates defined by regularized Heaviside functions and blendings if several minerals dissolve in the same reaction. We prove that the unique solution of this system of ODEs converges towards a solution of the differential inclusion model. We run numerical experiments by using a classical ODEs solver for computing the regularized solution. We get good numerical approximations of the quantities of species and the discontinuous reaction rates

Using Graph Neural Network for gas-liquid interface reconstruction in Volume Of Fluid methods

The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes. We first develop a methodology to generate a synthetic dataset based on paraboloid surfaces discretized on unstructured meshes. We then train a GNN based model and perform generalization tests. Our results demonstrate the efficiency of a GNN based approach for interface reconstruction in multi-phase flow simulations in the industrial context

DS-GPS : A Deep Statistical Graph Poisson Solver

International audienc

DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)

International audienceThis paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn the physics of the problem without the need for exact solutions, in contrast to most previous data-driven processes where the distance with the available solutions is minimized

Modeling of the transient interstitial diffusion of implanted atoms during low-temperature annealing of silicon substrates

It has been shown that many of the phenomena related to the formation of "tails" in the low-concentration region of ion-implanted impurity distribution are due to the anomalous diffusion of nonequilibrium impurity interstitials. These phenomena include boron implantation in preamorphized silicon, a "hot" implantation of indium ions, annealing of ion-implanted layers et cetera. In particular, to verify this microscopic mechanism, a simulation of boron redistribution during low-temperature annealing of ion-implanted layers has been carried out under different conditions of transient enhanced diffusion suppression. Due to the good agreement with the experimental data, the values of the average migration length of nonequilibrium impurity interstitials have been obtained. It has been shown that for boron implanted into a silicon layer preamorphized by germanium ions the average migration length of impurity interstitials at the annealing temperature of 800 Celsius degrees be reduced from 11 nm to approximately 6 nm due to additional implantation of nitrogen. The further shortening of the average migration length is observed if the processing temperature is reduced to 750 Celsius degrees. It is also found that for implantation of BF2 ions into silicon crystal, the value of the average migration length of boron interstitials is equal to 7.2 nm for thermal treatment at a temperature of 800 Celsius degrees.Comment: 10 pages, 6 figures, RevTe