Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Estudo Geralarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Estudo Geral
Master thesis . 2025
Data sources: Estudo Geral
addClaim

Numerical analysis of a hydrogen blending and injection in natural gas pipelines

Authors: Reis, Beatriz dos Santos;

Numerical analysis of a hydrogen blending and injection in natural gas pipelines

Abstract

Esta dissertação apresenta um estudo numérico inovador sobre a mistura e injeção de hidrogénio em gasodutos de gás natural, realizado no âmbito do projeto H2NG (Estação de Mistura e Injeção de Hidrogénio em Redes de Gás Natural). O objetivo central deste trabalho foi analisar sistematicamente a influência dos parâmetros de injeção na homogeneidade da mistura hidrogénio–metano, em condições operacionais realistas das redes de transporte de alta e média pressão.Foi aplicada uma metodologia robusta, utilizando ferramentas avançadas de Dinâmica dos Fluidos Computacional através do software ANSYS Fluent. O estudo considerou geometrias de gasodutos representativas das infraestruturas reais, incluindo uma detalhada validação dos modelos utilizados e um rigoroso estudo de independência da malha, garantindo precisão e confiabilidade nos resultados obtidos. Para avaliar a qualidade da mistura, foram adotados indicadores consagrados como o CoV e critérios baseados no número de Reynolds, com o objetivo de identificar as condições ideais que promovem a uniformidade da mistura e minimizam fenómenos indesejados de estratificação.Através de um extenso estudo paramétrico, foram analisadas diversas variáveis, nomeadamente a concentração volumétrica de hidrogénio (10%, 15% e 20%), a velocidade do gás natural, os ângulos de injeção e o diâmetro do injetor. Os resultados obtidos revelaram que condições ótimas de mistura são consistentemente alcançadas com ângulos de injeção perpendiculares, diâmetros de injeção da ordem de 1/7 do tubo principal e razões de velocidade hidrogénio–gás natural acima de 12. De forma inovadora, verificou-se que as redes de média pressão apresentam melhor desempenho em termos de mistura, quando comparadas às redes de alta pressão, especialmente em concentrações mais baixas de hidrogénio, contrariando assim pressupostos habituais no setor.Em suma, este estudo fornece importantes recomendações técnicas e contribui diretamente para a integração segura e eficiente do hidrogénio renovável nas infraestruturas existentes de gás natural, apoiando o processo de transição para um sistema energético mais sustentável e de baixo carbono.

This work presents an innovative numerical analysis addressing the challenge of blending and injecting hydrogen into natural gas pipelines, developed within the context of the H2NG project (Hydrogen Blending and Injection Station for Natural Gas Networks). Its primary aim is to systematically assess how various injection parameters influence the homogeneity of hydrogen–methane mixtures under realistic operational conditions, specifically considering high-pressure and medium-pressure gas transmission networks.A robust methodological framework was employed, using Computational Fluid Dynamics (CFD) simulations via ANSYS Fluent. The study included pipeline geometries that were representative of the actual infrastructure. The CFD models were validated, and a detailed mesh independence study was conducted to ensure the accuracy of the results. The quality of gas mixing was quantified using established indicators, particularly the Coefficient of Variation (CoV) and Reynolds-based criteria, to pinpoint the optimal conditions that promote uniform blending and minimize undesirable stratification phenomena.Through an extensive parametric study, variables such as hydrogen concentrations (10%, 15%, and 20%), natural gas velocities, injection angles, and injector diameters were rigorously analyzed. Results demonstrated that optimal mixing conditions are consistently achieved using perpendicular injection angles, an injector diameter of approximately 1/7 of the main pipeline, and maintaining a velocity ratio between hydrogen and natural gas flows above 12. Notably, medium-pressure pipelines exhibited superior mixing performance compared to high-pressure systems, particularly at lower hydrogen concentration levels—a finding that challenges conventional assumptions within the sector.Ultimately, this research provides significant insights and practical guidelines to support the safe and effective integration of renewable hydrogen into existing natural gas infrastructure, thereby contributing directly to the broader effort of transitioning to a more sustainable and low-carbon energy future.

Universidade de Coimbra - Este trabalho foi desenvolvido no âmbito do projeto H2NG financiado pela Universidade de Coimbra. Foi me atribuida uma bolsa de Licenciada.

Dissertação de Mestrado em Engenharia Mecânica apresentada à Faculdade de Ciências e Tecnologia

Country
Portugal
Related Organizations
Keywords

Hydrogen blending, Natural gas networks, CoV, Transição energética, Redes de gás natural, CFD, Energy transition, Mistura de hidrogénio

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green