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Towards Climate Neutral Thermochemical Energy Conversion

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "I: Energy Fundamentals and Conversion".

Deadline for manuscript submissions: 25 August 2024 | Viewed by 698

Special Issue Editors


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Guest Editor
Department of Aerospace Engineering, Institute for Applied Mathematics and Scientific Computing, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Interests: turbulent combustion; multiphase flow; reactive flow; aerodynamics; supersonic flows; gas explosions; computational fluid dynamics (CFD); numerical methods
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Guest Editor
School of Engineering, Newcastle University, Newcastle-upon-Tyne NE1 7RU, UK
Interests: computational fluid dynamics; turbulent flows; turbulent combustion; heat transfer; non-newtonian fluids; multiphase flows
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Vehicle Power Trains, University of Bundeswehr Munich, 85579 Neubiberg, Germany
Interests: new combustion concepts; alternate fuels (Hydrogen, Ammonia, Methanol); ignition concepts; computational fluid dynamics; turbulent flow; turbulent combustion; ignition modelling; reaction kinetics; thermodynamic and optical experimental methods

Special Issue Information

Dear Colleagues,

The transformation of the transport sector into a climate-neutral economy is particularly challenging, because for some applications, there are only few alternatives to high-energy-density liquid fuels. Therefore, conventional or new concepts for thermochemical energy conversion in combination with e-fuels will play an important role during the transition phase towards a future, carbon-free energy landscape and beyond. The need for new biogenic, synthetic, carbon-free or carbon-reduced fuels, together with the development of new combustion concepts, poses considerable challenges to the research community. The design and development of new combustion systems requires extensive multi-scale and multi-physics experimental and numerical analysis.

In recent years, several contributions from the community have been published that deal with the fundamental aspects of high-fidelity modelling and experimental characterization of internal combustion engines. The urgent need for the use of carbon-free fuels, such as hydrogen or ammonia, or e-fuels, like methanol, DME or ethanol,  require significant research efforts because all of these fuels have largely different thermo-chemical properties, flame speeds, flame temperatures and flammability limits and are sometimes difficult in terms of emission control. In addition, there is the need to make thermochemical energy conversion more efficient using concepts like RCCI or water injection using two injectors or fuel/water emulsions in combination with carbon-free or synthetic energy careers.

This Special Issue aims to contribute to the fundamental physical understanding and high-fidelity modelling of turbulent combustion using alternative fuels and new energy conversion concepts. Contributions are welcome from specialists with analytical, experimental and numerical backgrounds who are able to provide different perspectives regarding this topic.

Prof. Dr. Markus Klein
Prof. Dr. Nilanjan Chakraborty
Prof. Dr. Christian Trapp
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

16 pages, 5933 KiB  
Article
Learning Flame Evolution Operator under Hybrid Darrieus Landau and Diffusive Thermal Instability
by Rixin Yu, Erdzan Hodzic and Karl-Johan Nogenmyr
Energies 2024, 17(13), 3097; https://doi.org/10.3390/en17133097 - 23 Jun 2024
Viewed by 442
Abstract
Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDEs). This paper explores the application of novel operator learning methodologies to [...] Read more.
Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDEs). This paper explores the application of novel operator learning methodologies to unravel the intricate dynamics of flame instability, particularly focusing on hybrid instabilities arising from the coexistence of Darrieus–Landau (DL) and Diffusive–Thermal (DT) mechanisms. Training datasets encompass a wide range of parameter configurations, enabling the learning of parametric solution advancement operators using techniques such as parametric Fourier Neural Operator (pFNO) and parametric convolutional neural networks (pCNNs). Results demonstrate the efficacy of these methods in accurately predicting short-term and long-term flame evolution across diverse parameter regimes, capturing the characteristic behaviors of pure and blended instabilities. Comparative analyses reveal pFNO as the most accurate model for learning short-term solutions, while all models exhibit robust performance in capturing the nuanced dynamics of flame evolution. This research contributes to the development of robust modeling frameworks for understanding and controlling complex physical processes governed by nonlinear PDEs. Full article
(This article belongs to the Special Issue Towards Climate Neutral Thermochemical Energy Conversion)
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