A mathematical framework for modelling and evaluating natural gas pipeline networks under hydrogen injection

This article presents the framework of a mathematical formulation for modelling and evaluating natural gas pipeline networks under hydrogen injection. The model development is based on gas transport through pipelines and compressors which compensate for the pressure drops by implying mainly the mass and energy balances on the basic elements of the network. The model was initially implemented for natural gas transport and the principle of extension for hydrogen-natural gas mixtures is presented. The objective is the treatment of the classical fuel minimizing problem in compressor stations. The optimization procedure has been formulated by means of a nonlinear technique within the General Algebraic Modelling System (GAMS) environment. This work deals with the adaptation of the current transmission networks of natural gas to the transport of hydrogen-natural gas mixtures. More precisely, the quantitative amount of hydrogen that can be added to natural gas can be determined. The studied pipeline network,initially proposed by Abbaspour et al. (2005) is revisited here for the case of hydrogen-natural gas mixtures. Typical quantitative results are presented, showing that the addition of hydrogen to natural gas decreases significantly the transmitted power : the maximum fraction of hydrogen that can be added to natural gas is around 6 mass percent for this example

Leakage Management and Pipe System Efficiency. Its Influence in the Improvement of the Efficiency Indexes

[EN] Water is one of the most valuable resources for humans. Worldwide, leakage levels in water distribution systems oscillate between 10% and 55%. This causes the need for constant repairs, economic losses, and risk to the health of users due to possible pathogenic intrusion. There are different methods for estimating the level of leakage in a network, depending on parameters such as service pressure, orifice size, age and pipe material. Sixty-two water distribution networks were analyzed to determine the leakage method used, the calibration method, and the percentage of existing leaks. Different efficiency indicators were proposed and evaluated using this database. Several cases of installation of pumps working as turbines (PATs) in water distribution networks were analyzed in which the use of these recovery systems caused a pressure drop, reducing the level of leaks and recovering energy.Macías Ávila, CA.; Sánchez-Romero, F.; López Jiménez, PA.; Pérez-Sánchez, M. (2021). Leakage Management and Pipe System Efficiency. Its Influence in the Improvement of the Efficiency Indexes. Water. 13(14):1-25. https://doi.org/10.3390/w13141909S125131

Thin plate buckling mitigation and reduction challenges for naval ships

Thin plate buckling or distortion on ship structures is an ongoing issue for shipbuilders. It has been identified that a significant number of factors can be put in place based on prior knowledge and good practice. Additionally, research work aimed at reducing thin plate distortion has been relatively prolific, particularly in the area of simulation modelling. However, the uptake in the research findings by industry has been relatively low. A number of these findings are discussed and their application considered. For any further reductions in thin plate distortion to be generated there is a clear need for better interaction between the research institutes and the industry

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet

Phase-field simulation of hydraulic fracturing with a revised fluid model and hybrid solver

With an intrinsic advantage in describing complex fracture networks, the phase field method has demonstrated promising potential for the simulation of hydraulic fracturing processes in recent literatures. We critically examine the existing phase-field hydraulic fracturing models, and propose a hybrid solution scheme with a revised fluid model. Specifically, the formation deformation and phase field are solved using the finite element method (FEM), while the fluid flows are solved using the finite volume method (FVM). The proposed hybrid scheme is validated with the analytical solution for the toughness-dominated fracture propagation and is tested on the complex hydraulic fracturing process in a naturally fractured formation. Demonstrated by numerical examples, the proposed hybrid phase-field framework has several advantages: (1) it captures the effect of fluid pressure inside the fracture and reservoir more accurately than existing models; (2) it provides a sharper capture of formation fractures; (3) it avoids the nonphysical oscillation of fluid pressure when using a pure FEM solver; and (4) it has a superior performance in mesh and time step convergence

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free parameters that need to be inferred from data collected in scientific experiments. A fundamental question is how to design the experiments so that the collected data are most useful. The field of Bayesian experimental design advocates that, ideally, we should choose designs that maximise the mutual information (MI) between the data and the parameters. For implicit models, however, this approach is severely hampered by the high computational cost of computing posteriors and maximising MI, in particular when we have more than a handful of design variables to optimise. In this paper, we propose a new approach to Bayesian experimental design for implicit models that leverages recent advances in neural MI estimation to deal with these issues. We show that training a neural network to maximise a lower bound on MI allows us to jointly determine the optimal design and the posterior. Simulation studies illustrate that this gracefully extends Bayesian experimental design for implicit models to higher design dimensions.Comment: Accepted at the thirty-seventh International Conference on Machine Learning (ICML) 2020. Camera-ready versio