Relevant bibliographies by topics / Gas network modelling

Academic literature on the topic 'Gas network modelling'

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Published: 14 March 2023

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Journal articles on the topic "Gas network modelling"

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Ekhtiari, Ali, Damian Flynn, and Eoin Syron. "Green Hydrogen Blends with Natural Gas and Its Impact on the Gas Network." Hydrogen 3, no. 4 (October 27, 2022): 402–17. http://dx.doi.org/10.3390/hydrogen3040025.

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With increasing shares of variable and uncertain renewable generation in many power systems, there is an associated increase in the importance of energy storage to help balance supply and demand. Gas networks currently store and transport energy, and they have the potential to play a vital role in longer-term renewable energy storage. Gas and electricity networks are becoming more integrated with quick-responding gas-fired power plants, providing a significant backup source for renewable electricity in many systems. This study investigates Ireland’s gas network and operation when a variable green hydrogen input from excess wind power is blended with natural gas. How blended hydrogen impacts a gas network’s operational variables is also assessed by modelling a quasi-transient gas flow. The modelling approach incorporates gas density and a compressibility factor, in addition to the gas network’s main pressure and flow rate characteristics. With an increasing concentration of green hydrogen, up to 20%, in the gas network, the pipeline flow rate must be increased to compensate for reduced energy quality due to the lower energy density of the blended gas. Pressure drops across the gas pipeline have been investigated using different capacities of P2H from 18 MW to 124 MW. The results show significant potential for the gas network to store and transport renewable energy as hydrogen and improve renewable energy utilisation without upgrading the gas network infrastructure.
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Lin, Jiwei, and Tso-Chien Pan. "Modelling of multi-sectoral critical infrastructure interdependencies for vulnerability analysis." Disaster Prevention and Resilience 1, no. 1 (2022): 2. http://dx.doi.org/10.20517/dpr.2021.05.

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Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.
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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.
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Song, Wenhui, Jun Yao, Kai Zhang, Yongfei Yang, and Hai Sun. "Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach." Advances in Geo-Energy Research 6, no. 4 (July 25, 2022): 359–60. http://dx.doi.org/10.46690/ager.2022.04.11.

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Hagey, L., and H. de Lasa. "C1–C4 Hydrocarbons from synthesis gas Reaction network modelling." Chemical Engineering Science 54, no. 15-16 (July 1999): 3391–97. http://dx.doi.org/10.1016/s0009-2509(98)00476-x.

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Parkinson, J. S., and R. J. Wynne. "Systems Modelling and Control Applied to a Low-Pressure Gas Distribution Network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 206, no. 1 (February 1992): 35–44. http://dx.doi.org/10.1243/pime_proc_1992_206_196_02.

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A control system has been designed and implemented to provide more effective energy management of low-pressure gas distribution networks. The key to this is the provision of a control scheme that maintains low pressures across a network. The work was approached from first principles and a modelling technique has been developed which provides reduced order models that adequately describe the characteristics of multi-feed gas networks. The models were then used for the control system design, which in this case also included the selection of the optimal measurement points for most effective control. Following extensive design studies a relatively straightforward control scheme resulted which has been implemented and proved to be effective.
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Sprengel, Jurgen, Pedro Milano, Ryan Sfand, and Doug Kolak. "Modelling of High Point Vents in water gathering systems – a new approach using Simcenter Flomaster." APPEA Journal 62, no. 1 (May 13, 2022): 106–15. http://dx.doi.org/10.1071/aj21135.

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The cost-efficient extraction of coal seam gas (CSG) is the ultimate objective of every CSG project. One challenge is that gas holdups in water gathering lines can significantly obstruct water removal by causing higher back pressure on the wells, thus resulting in lower dewatering rates with subsequent delays in expected gas production. Gas holdups originate from entrained gas entering the wellhead pumps and from dissolved gas bubbling off in water gathering pipelines. Gas holdups are mechanically removed by High Point Vents (HPVs) installed at pipeline high points, with the efficiency of gas removal directly impacting project economics. A new hydraulic modelling approach enables the realistic simulation of two-phase flow regimes in a single-phase solver by utilising hydraulic components designed to separate gas and water. This modelling process offers major advantages over the traditional gas water ratio approach, as it enables the addition of individual HPVs to be analysed for their impact on the pressure reduction in water gathering pipelines. Modelling can be performed for any network complexity in either steady-state or transient simulation, enabling the analysis of the entire water gathering network over its life span of several decades. An operating envelope of well and pipeline pressure can be generated for an idealistic gas-free network, showing the lowest pressure and a network with fully developed gas holdups and resulting in the maximum possible back pressure. The paper includes a benchmarking study of an existing complex water gathering system confirming the practicality and accuracy of this game changing modelling approach.
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Ziehn, T., R. M. Law, P. J. Rayner, and G. Roff. "Designing optimal greenhouse gas monitoring networks for Australia." Geoscientific Instrumentation, Methods and Data Systems 5, no. 1 (January 19, 2016): 1–15. http://dx.doi.org/10.5194/gi-5-1-2016.

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Abstract. Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
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Ziehn, T., R. M. Law, P. J. Rayner, and G. Roff. "Designing optimal greenhouse gas monitoring networks for Australia." Geoscientific Instrumentation, Methods and Data Systems Discussions 5, no. 2 (August 5, 2015): 247–83. http://dx.doi.org/10.5194/gid-5-247-2015.

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Abstract. Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimize the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to 5 new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
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Jin, Tianying, Luis F. Ayala H., and M. Thaddeus Ityokumbul. "Network modelling and prediction of retrograde gas behaviour in natural gas pipeline systems." International Journal of Engineering Systems Modelling and Simulation 8, no. 3 (2016): 169. http://dx.doi.org/10.1504/ijesms.2016.077646.

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Dissertations / Theses on the topic "Gas network modelling"

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CAVANA, MARCO. "Gas network modelling for a multi-gas system." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850606.

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Swan, Dominic. "Stochastic load modelling in gas network analysis and design." Thesis, University of Exeter, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303776.

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Khalaf, Gholam Hossein. "Neuro-fuzzy control modelling for gas metal arc welding process." Thesis, Loughborough University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263585.

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Qadrdan, Meysam. "Modelling of an integrated gas and electricity network with significant wind capacity." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/24178/.

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The large scale integration of wind generation capacity into an electricity network poses technical as well as economic challenges. In this research, three major challenges introduced by wind including non-correlated power output from geographically dispersed wind farms, wind variability and wind uncertainty were studied. In order to address each of the aforementioned challenges an appropriate modelling approach and case studies were used. The impacts of power output from dispersed wind farms on the Great Britain transmission reinforcement were studied using an optimal DC load flow combined with a power generation model. It was shown that Western and Eastern HVDC links play a crucial role to bypass the Scotland to England transmission bottleneck. The impacts of wind variability on the GB gas and electricity network were investigated through application of the Combined gas and Electricity Network (CGEN) Model. Additional gas storage capacity was shown to be an efficient option to compensate for wind variability. Two-stage and multi-stage stochastic programming models were developed to examine the impact of wind forecast uncertainty on the GB electricity and gas networks. Stochastic modelling approaches were shown to be efficient methods for scheduling and operating the system under wind uncertainty. The key contributions of this thesis are the investigation of the impacts of wind generation variability on the gas network, and development of twostage and multi-stage stochastic programming models of integrated gas and electricity network.
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Asgari, Hamid. "Modelling, Simulation and Control of Gas Turbines Using Artificial Neural Networks." Thesis, University of Canterbury. Mechanical Engineering, 2014. http://hdl.handle.net/10092/9355.

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This thesis investigates novel methodologies for modelling, simulation and control of gas turbines using ANNs. In the field of modelling and simulation, two different types of gas turbines are modelled and simulated using both Simulink and neural network based models. Simulated and operational data sets are employed to demonstrate the capability of neural networks in capturing complex nonlinear dynamics of gas turbines. For ANN-based modelling, the application of both static (MLP) and dynamic (NARX) networks are explored. Simulink and NARX models are set up to explore both steady-state and transient behaviours. To develop an offline ANN-based system identification methodology for a low-power gas turbine, comprehensive computer program code including 18720 different ANN structures is generated and run in MATLAB to create and train different ANN models with feedforward multi-layer perceptron (MLP) structure. The results demonstrate that the ANN-based method can be applied accurately and reliably for the system identification of gas turbines. In this study, Simulink and NARX models are created and validated using experimental data sets to explore transient behaviour of a heavy-duty industrial power plant gas turbine (IPGT). The results show that both Simulink and NARX models successfully capture dynamics of the system. However, NARX approach can model gas turbine behaviour with a higher accuracy compared to Simulink approach. Besides, a separate complex model of the start-up operation of the same IPGT is built and verified by using NARX models. The models are set up and verified on the basis of measured time-series data sets. It is observed that NARX models have the potential to simulate start-up operation and to predict dynamic behaviour of gas turbines. In the area of control system design, a conventional proportional-integral-derivative (PID) controller and neural network based controllers consisting of ANN-based model predictive (MPC) and feedback linearization (NARMA-L2) controllers are designed and employed to control rotational speed of a gas turbine. The related parameters for all controllers are tuned and set up according to the requirements of the controllers design. It is demonstrated that neural network based controllers (in this case NARMA-L2) can perform even better than conventional controllers. The settling time, rise time and maximum overshoot for the response of NARMA-L2 is less than the corresponding factors for the conventional PID controller. It also follows the input changes more accurately than the PID. Overall, it is concluded from this thesis that in spite of all the controversial issues regarding using artificial neural networks for industrial applications, they have a high and strong potential to be considered as a reliable alternative to the conventional modelling, simulation and control methodologies. The models developed in this thesis can be used offline for design and manufacturing purposes or online on sites for condition monitoring, fault detection and trouble shooting of gas turbines.
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Straubhaar, Benjamin. "Pore network modelling of condensation in gas diffusion layers of proton exchange membrane fuel cell." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/19261/1/Straubhaar_B.pdf.

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A Proton Exchange Membrane Fuel Cell (PEMFC) is a device converting hydrogen into electricity thanks to an electrochemical reaction called reverse electrolysis. Like every fuel cell or battery, PEMFCs are made of a series of layers. We are interested in the gas diffusion layer (GDL) on the cathode side. The GDL is made of carbon fibers treated hydrophobic. It can be seen as a thin porous medium with a mean pore size of few tens of microns. A key question in this system is the management of the water produced by the reaction. In this context, the main objective of the thesis is the development of a numerical tool aiming at simulating the liquid water formation within the GDL. A pore network approach is used. We concentrate on a scenario where liquid water forms in the GDL by condensation. Comparisons between simulations and experiments performed with a two-dimensional microfluidic device are first presented for different wettability conditions, temperature distributions and inlet relative humidity in order to validate the model. A sensitivity study is then performed to better characterize the parameters controlling the water invasion. Finally, simulations are compared with in situ experimental water distributions obtained by X-ray micro-tomography as well as with experimental distributions from the literature obtained by neutron imaging.
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Håkansson, David. "Aerothermal and Kinetic Modelling of a Gas Turbine Dry Low Emission Combustion System." Thesis, KTH, Strömningsmekanik och Teknisk Akustik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298477.

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Growing environmental concerns are causing a large transformation within the energy industry. Within the gas turbine industry, there is a large drive to develop improved modern dry-low emission combustion systems. The aim is to enable gas turbines to run on green fuels like hydrogen, while still keeping emission as NOx down. To design these systems, a thorough understanding of the aerothermal and kinetic processes within the combustion system of a gas turbine is essential. The goal of the thesis was to develop a one-dimensional general network model of the combustion system of Siemens Energy SGT-700, which accurately could predict pressure losses, mass flows, key temperatures, and emissions. Three models were evaluated and a code that emulated some aspects of the control system was developed. The models and the code were evaluated and compared to each other and to test data from earlier test campaigns performed on SGT-700 and SGT-600. Simulations were also carried out with hydrogen as the fuel.  In the end, a model of the SGT-700 combustion chamber was developed and delivered to Siemens Energy. The model had been verified against test data and predictions made by other Siemens Energy thermodynamic calculation software, for a range of load conditions. The preforms of the model, when hydrogen was introduced into the fuel mixture, were also tested and compared to test data
En växande medvetenhet kring klimatfrågan, har medfört stora förändringar i energibranschen. I och med detta behöver även gasturbinindustrin förbättra de nuvarande dry-low emissions systemen och göra det möjligt för gasturbiner att förbränna gröna bränslen som väte. Samtidigt måste också utsläppen av NOx hållas nere. För att kunna utforma dessa system behövs en fullständig förståelse för de aerotermiska och kinetiska processerna i en gasturbins förbränningskammare. Målet med detta examensarbete var att utveckla en endimensionell generell nätverksmodell för förbränningssystemet i Siemens Energys SGT-700. Modellen skulle noggrant kunna förutsäga tryckförluster, massflöden, viktiga temperaturer samt utsläpp. Tre modeller utvärderades och en kod som emulerade vissa aspekter av styrsystemet utvecklades också. Modellerna och koden utvärderades och jämfördes mot varandra och även mot testdata från tidigare testserier som utfördes på SGT-700 och SGT-600. Simuleringar utfördes också med väte som bränsle. Slutligen levererades en modell av SGT-700 förbränningskammaren till Siemens Energy. Modellen har verifierats för en rad olika lastfall, mot testdata och data som genererats av andra termodynamisk beräkningsprogram som utvecklats av Siemens Energy. Hur modellen uppförde sig när väte var introducerat in i olika lastfall jämfördes också mot testdata
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MORETTI, Linda. "Analysis and modelling of innovative technologies on natural gas transportation and distribution networks." Doctoral thesis, Università degli studi di Cassino, 2022. https://hdl.handle.net/11580/90999.

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Renewable energy sources (RESs), such as wind, solar and biomass, are the keystone of the energy policy of the EU to fulfil the target of a carbon neutral economy by 2050. However, the integration of a significant share of RESs pose significant challenges to the EU energy systems as it requires, on the one hand, storing large energy volumes to match intermittent renewable supply with the pattern of energy consumption and, on the other hand, transporting renewable energy from where it can be most efficiently and feasibly produced to where it is consumed. To overcome such challenges, it will be effective to consider the reuse of the existing natural gas (NG) infrastructure. The latter will play a crucial role in the development of a decarbonized energy system based on a large usage of RESs due to its widespread presence and its capacity to provide a cost-effective option for transporting and storing large amounts of energy for long-term period exploiting the NG transportation and distribution networks, as well as the storage complexes of the existing NG infrastructure. This thesis aims to address, with a multi-thematic approach, the issue of innovative uses of, as well as the development of innovative technologies on, NG transport and distribution networks. To this aim, a number of five case studies were investigated to: i) evaluate the fault management strategies for NG distribution networks to minimize the disservice and to define possible structural improvement measures; ii) assess the technical feasibility of the Power to Gas concept to store intermittent RESs; iii) analyse the impact of hydrogen injection on NG networks; iv) evaluate the effectiveness of the equilibrium gasification models as a modelling tool for the design and optimization of biomass gasification systems integrated into polygenerative plants coupled with energy networks. Results of this thesis provide useful insights to researchers, designers and policy makers, filling some of the gaps highlighted in the existing scientific literature in all the analysed areas.
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Bagudu, Usman. "Pore network modelling of gas flow processes in porous media with special application to CO2 sequestration." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2991.

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This thesis describes the development of a pore network model and its application to the analysis of the underlying physical mechanisms governing gas flow behaviour in porous media. The main focus of the study is CO2 and CH4 injection for EOR and storage applications as well as the evolution of solution gas following depressurization of hydrocarbon-saturated porous media. The model incorporates algorithms that dynamically track interface movements during both steady and unsteady-state flow under the coupled influence of capillary, gravity, and viscous forces. The model has been validated against laboratory experiments and the roles played by key system parameters have been identified. For injection processes, simulation results show that gravity-driven regimes fall into two broad categories of quasi-stable and migratory regimes, depending on the governing Bond number. The transition from non-dispersive to dispersive migratory flow was found to be largely independent of injection rate but a strong function of pore size distribution variance and system connectivity. CO2 and CH4 regimes in brine were found to exhibit striking similarities, suggesting that CH4-brine relative permeability curves could be used to accurately parameterize simulation models of CO2 storage in aquifers. Decreasing the interfacial tension was found to dampen viscous fingering but exacerbates gravity override which suggests that standard laboratory methods for analysing CO2 EOR processes are likely to overestimate displacement efficiency. Other sensitivity studies highlight the pore to core scale variables that control caprock sealing mechanisms, and residual and solubility trapping during CO2 injection for storage, and their implications at the reservoir scale. For depressurization, simulations performed on a pore network anchored to measured petrophysical properties of a 0.23mD fractured chalk core from a North Sea reservoir show a very weak correlation between depletion rate and critical gas saturation, contrary to conventional belief. Depressurization oil recovery efficiency was found to increase with increase in initial water saturation but the presence of fractures caused the critical saturation to decrease by approximately 60%.
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Carrère, Pierre. "Modelling and numerical simulation of water transfer in Proton Exchange Membrane Fuel Cells." Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0123.

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La gestion de l’eau dans les piles à combustible à membrane d’échange de proton (PEMFC) est une problématique principale pour assurer leur efficacité et leur durabilité. La couche de diffusion côté cathode est considérée comme l’un des composants critiques concernant cette problématique. Dans ce contexte, l’objectif principal de la thèse est d’améliorer la compréhension des mécanismes intervenants dans la formation et le transport de l’eau dans la couche dediffusion côté cathode. Pour ce faire, un modèle en réseau de pores d’injection mixte liquidevapeur (MIPNM) est développé. Ce nouveau modèle permet de simuler la formation et le transport de l’eau dans la couche de diffusion côté cathode sur une plus large gamme de conditions de fonctionnement de la pile (température, densité de courant et humidité relative dans le canal) qu’avec les modèles des travaux précédents. Différents régimes de formation et de transport del’eau sont identifiés et décrits. Dans une seconde partie, le travail de thèse se focalise sur l’impact du traitement hydrophobe de la couche de diffusion. Les couches de diffusion actuellement commercialisées sont rendues hydrophobes en déposant une couche de polytétrafluoroéthylène (PTFE) sur les fibres de carbones hydrophiles. Il a été observé que le revêtement peut être non uniforme sur des couches de diffusion neuves et que le revêtement peut se dégrader au cours dufonctionnement de la pile. L’impact de ces deux phénomènes sur la distribution de l’eau liquide et sur l’accès du gaz réactif jusqu’à la couche catalytique est étudié en utilisant le modèle MIPNM pour des réseaux à mouillabilité mixte. Dans une troisième partie, un travail visant à l’amélioration de l’efficacité des piles est réalisé. Le but est d’optimiser l’accès du gaz réactif jusqu’à la couche catalytique en modifiant la microstructure des couches de diffusion. Ce travail est réalisé en couplant le modèle en réseau de pore avec un algorithme génétique. En complément, la modification des propriétés de mouillabilité des couches de diffusion est étudiée dans le but d’améliorer l’accès du gaz réactif. Enfin, un modèle 1D de tout l’assemblage anode-cathode est développé pour prendre en compte à la fois les conditions de fonctionnement à la cathode et à l’anode. Ce modèle 1D est couplé au MIPNM afin d’évaluer l’impact des conditions de fonctionnement côté anode sur la distribution d’eau liquide dans la couche de diffusion côté cathode
Water management is considered as a key issue in order to improve Proton Exchange Membrane Fuel Cells efficiency and durability. One of the critical components regarding this issue is the athode Gas Diffusion Layer (GDL). In this context, the main goal of the PhD work is to improve the understanding of the mechanisms responsible for the liquid water formation and transport in the cathode GDL. To this end, a Mixed liquid-vapour Injection Pore Network Model (MIPNM) is developed. This new model enables one to simulate the liquid water formation and transport in the cathode GDL for a larger range of operating conditions (temperature, current density and channel relative humidity) than in previous works. Different regimes of water formation and transport are identified and described. In a second part, the PhD work focus on the impact of the GDL hydrophobic treatment. Currently commercialized GDLs are rendered hydrophobic by coating Polytetrafluoroethylene (PTFE) onto the hydrophilic carbon fibres. It has been reported that the coating can be nonuniform on fresh GDLs and also that the coating can be altered during the operation of the fuel cell. The impact of these two phenomena on the liquid water distribution and on the reactant gas access to the catalyst layer is studied using the MIPNM for mixed wettability networks. In a third part, a work aiming at the improvement of PEMFC efficiency is developed. The goal is to optimise the reactant gas access to the catalyst layer by modifying the microstructure of GDLs. This is performed by coupling the PNM with a genetic algorithm. In a complementary study, the improvement of the reactant gas access is studied through modifications of the GDL wettability properties. Finally, a 1D model of the whole anode-cathode assembly is developed so as to take into account both anode and cathode operating conditions. This 1D model is coupled with the MIPNM in order to assess the impact of the anode operating conditions on the liquid water distribution in the cathode GDL
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Books on the topic "Gas network modelling"

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Modelling distributed energy resources in energy service networks. London: Institution of Engineering and Technology, 2013.

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G, Kroeger Peter, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Regulatory Applications., and Brookhaven National Laboratory, eds. THATCH, a computer code for modelling thermal networks of high-temperature gas-cooled nuclear reactors. Washington, DC: Division of Regulatory Applications, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1991.

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G, Kroeger Peter, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Regulatory Applications., and Brookhaven National Laboratory, eds. THATCH, a computer code for modelling thermal networks of high-temperature gas-cooled nuclear reactors. Washington, DC: Division of Regulatory Applications, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1991.

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Khare, Mukesh, and S. M. Shiva Nagendra. Artificial Neural Networks in Vehicular Pollution Modelling. Springer Berlin / Heidelberg, 2010.

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Khare, Mukesh, and S. M. Shiva Nagendra. Artificial Neural Networks in Vehicular Pollution Modelling. Springer London, Limited, 2006.

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Khare, Mukesh, and S. M. Shiva Nagendra. Artificial Neural Networks in Vehicular Pollution Modelling (Studies in Computational Intelligence). Springer, 2006.

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Delgado Martín, Jordi, Andrea Muñoz-Ibáñez, and Ismael Himar Falcón-Suárez. 6th International Workshop on Rock Physics: A Coruña, Spain 13 -17 June 2022: Book of Abstracts. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.000005.

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[Abstract] The 6th International Workshop on Rock Physics (6IWRP) was held A Coruña, Spain, between 13th and 17th of June, 2022. This meeting follows the track of the five successful encounters held in Golden (USA, 2011), Southampton (UK, 2013), Perth (Australia, 2015), Trondheim (Norway, 2017) and Hong Kong (China, 2019). The aim of the workshop was to bring together experiences allowing to illustrate, discuss and exchange recent advances in the wide realm of rock physics, including theoretical developments, in situ and laboratory scale experiments as well as digital analysis. While rock physics is at the core of the oil & gas industry applications, it is also essential to enable the energy transition challenge (e.g. CO2 and H2 storage, geothermal), ensure a safe and adequate use of natural resources and develop efficient waste management strategies. The topics of 6IWRP covered a broad spectrum of rock physics-related research activities, including: • Experimental rock physics. New techniques, approaches and applications; Characterization of the static and dynamic properties of rocks and fluids; Multiphysics measurements (NMR, electrical resistivity…); Deep/crustal scale rock physics. • Modelling and multiscale applications: from the lab to the field. Numerical analysis and model development; Data science applications; Upscaling; Microseismicity and earthquakes; Subsurface stresses and tectonic deformations. • Coupled phenomena and rock properties: exploring interactions. Anisotropy; Flow and fractures; Temperature effects; Rock-fluid interaction; Fluid and pressure effects on geophysical signatures. • The energy transition challenge. Applications to energy storage (hydrogen storage in porous media), geothermal resources, energy production (gas hydrates), geological utilization and storage of CO2, nuclear waste disposal. • Rock physics templates: advances and applications. Quantitative assessment; Applications to reser voir characterization (role of seismic wave anisotropy and fracture networks). • Advanced rock physics tools. Machine learning; application of imaging (X-ray CT, X-ray μCT, FIB-SEM…) to obtain rock proper ties. This book compiles more than 50 abstracts, summarizing the works presented in the 6IWRP by rock physicists from all over the world, belonging to both academia and industry. This book means an updated overview of the rock physics research worldwide.
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Book chapters on the topic "Gas network modelling"

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Hante, Falk M. "Mixed-Integer Optimal Control for PDEs: Relaxation via Differential Inclusions and Applications to Gas Network Optimization." In Mathematical Modelling, Optimization, Analytic and Numerical Solutions, 157–71. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0928-5_7.

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Sacco, T., G. Sansavini, M. Compare, and E. Zio. "Robust portfolio modelling methodological approach to GB gas grid risk analysis via a simplified network version." In Risk, Reliability and Safety: Innovating Theory and Practice, 1736–43. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-262.

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Biesinger, Andreas, Ruben Pesch, Mariela Cotrado, and Dirk Pietruschka. "Increased Efficiency Through Intelligent Networking of Producers and Consumers in Commercial Areas Using the Example of Robert Bosch GmbH." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 105–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_9.

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AbstractEnergy-efficient heating and cooling systems as well as intelligent systems for energy distribution are urgently required in order to be able to meet the ambitious goals of the European Union to reduce greenhouse gas emissions. The present article is intended to show that intelligent system extensions for the area of heating, cooling and electricity production for the industrial sector can lead to significant increase in efficiency. For this purpose, a simulation study for the expansion of a combined heat and power (CHP) plant with 2 MW thermal output using a 1.4 MW absorption chiller has been carried out. This shows that a heat-controlled CHP unit can significantly increase its running time. A system model was created for the initial situation and validated with existing measurement data. In the second step, this model was expanded to include the ACM module. The simulation was able to prove that in the event of a system expansion, the run time of the CHP unit can be increased by 35%. In addition to then increase of energy efficiency in the supply system, the analysis also focuses on the efficiency of the energy distribution via thermal networks in an industrial environment. The presented paper therefore also highlights the optimization potentials in the operation of thermal supply networks for industrial applications. For this purpose, a mathematical model has been developed which in addition to the components of the thermal network itself also comprises the producers and consumers. The specific construction of thermal networks for the supply of industrial properties requires adapted solutions for the simulation of such systems. Therefore, amongst other things, in the paper, solutions are shown for the modelling of direct flow local heating networks as well as for the operation of a cascade-controlled pump group.
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Yang, Dong, Yingli Liu, and Tao Du. "Network Flow Modelling for Optimizing Fire Smoke Control in Complex Urban Traffic Link Tunnels: Incorporating Heat Loss and Gas Species Generation Rate Calculation into Models." In The Proceedings of 11th Asia-Oceania Symposium on Fire Science and Technology, 993–1007. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9139-3_72.

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Kumpulainen, Pekka, Timo Korpela, Yrjö Majanne, and Anna Häyrinen. "Modelling of NOx Emissions in Natural Gas Fired Hot Water Boilers." In Engineering Applications of Neural Networks, 100–108. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23983-5_10.

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Gugat, Martin, and Michael Herty. "A New Model for Transient Flow in Gas Transportation Networks." In Mathematical Modelling, Optimization, Analytic and Numerical Solutions, 147–56. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0928-5_6.

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Bales, Pia, Oliver Kolb, and Jens Lang. "Hierarchical Modelling and Model Adaptivity for Gas Flow on Networks." In Lecture Notes in Computer Science, 337–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01970-8_33.

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Phukon, Neha, Mrigakshee Sarmah, and Bimlesh Kumar. "Process Modelling of Gas–Liquid Stirred Tank with Neural Networks." In Environmental Pollution, 501–11. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5792-2_40.

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van Helden, Jacques, Ariane Toussaint, and Denis Thieffry. "Bacterial Molecular Networks: Bridging the Gap Between Functional Genomics and Dynamical Modelling." In Bacterial Molecular Networks, 1–11. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_1.

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Otto-Sobotka, Fabian, Radoslava Mirkov, Benjamin Hofner, and Thomas Kneib. "Modelling Flow in Gas Transmission Networks Using Shape-Constrained Expectile Regression." In Advances in Contemporary Statistics and Econometrics, 261–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73249-3_14.

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Conference papers on the topic "Gas network modelling"

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Tayarani-Bathaie, Sina S., Zakieh Sadough, and Khashayar Khorasani. "Dynamic Neural Network-based Fault Diagnosis of Gas Turbine Engines." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-064.

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van Westering, Werner, Hans Hellendoorn, Berend Brasjen, and Ruud van der Linden. "Natural gas distribution network modelling and leak minimization." In 2014 IEEE 11th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/icnsc.2014.6819641.

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Gao, B., J. Darling, D. G. Tilley, R. A. Williams, A. Bean, and J. Donahue. "Modelling of a Novel Gas Strut Using Neural Networks." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59119.

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The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal ‘black-box’ models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.
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Kudapa, Vamsi Krishna, Shashank Kumar, Dharmendra Kumar Gupta, Pushpa Sharma, and Rubna KunhAbdulla. "Modelling of Gas Production from Shale Matrix to Fracture Network." In SPE Intelligent Oil and Gas Symposium. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/187473-ms.

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Bhardwaj, Charu, Preeti Godiyal, Mohit Mathur, Amit Ranjan, and Saurabh K. Verma. "Production Optimization of Raageshwari Field Using Network Modelling." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/186921-ms.

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Lamamra, Kheireddine, and Djamil Rechem. "Artificial neural network modelling of a gas sensor for liquefied petroleum gas detection." In 2016 8th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2016. http://dx.doi.org/10.1109/icmic.2016.7804292.

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Lu, Yifei, Thiemo Pesch, and Andrea Benigni. "GasNetSim: An Open-Source Package for Gas Network Simulation with Complex Gas Mixture Compositions." In 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES). IEEE, 2022. http://dx.doi.org/10.1109/osmses54027.2022.9769148.

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Pluta, Adam, Wided Medjroubi, Jan C. Diettrich, Jan Dasenbrock, Hendrik-Pieter Tetens, Javier E. Sandoval, and Ontje Lunsdorf. "SciGRID_gas - Data Model of the European Gas Transport Network." In 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES). IEEE, 2022. http://dx.doi.org/10.1109/osmses54027.2022.9769122.

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Sorensen, Frank, and L. C. Little. "A 3-Dimensional Approach to the Modelling of the Hoadley-Westerose Gas Reservoir/Surface Pipeline Network." In SPE Gas Technology Symposium. Society of Petroleum Engineers, 1993. http://dx.doi.org/10.2118/26143-ms.

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Garrouch, A., and N. H. Smaoui. "An Artificial Neural Network Model for Estimating Tight Gas Sand Permeability." In SPE Asia Pacific Conference on Integrated Modelling for Asset Management. Society of Petroleum Engineers, 1998. http://dx.doi.org/10.2118/39703-ms.

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Reports on the topic "Gas network modelling"

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Kroeger, P. G., R. J. Kennett, J. Colman, and T. Ginsberg. THATCH: A computer code for modelling thermal networks of high- temperature gas-cooled nuclear reactors. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/6239042.

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