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Gas turbine fuel burn for an aircraft engine can be obtained analytically using thermodynamic cycle analysis. For large-diameter ultra-high bypass ratio turbofans, the impact of nacelle drag and propulsion system integration must be accounted for in order to obtain realistic estimates of the installed specific fuel consumption. However, simplified models cannot fully represent the complexity of installation effects. In this paper, we present a method that combines thermodynamic cycle analysis with detailed Computational Fluid Dynamics (CFD) modelling of the installation aerodynamics to obtain the fuel consumption at a given mission point. The flow field and propulsive forces arising in a transport aircraft powered by an ultra-high bypass ratio turbofan at cruise are first examined to characterise the operating conditions and measure the sensitivity to variations of the incidence at transonic flight. The proposed methodology, in which dynamic balance of the vehicle is achieved at each integration point, is then applied along a cruise segment to calculate the cumulative fuel burn and the change in the specific fuel consumption.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.

This article discusses the problems of exhaust gas emissions in the context of the possibility of their reduction through the use of fuels with hydrogen as an additive or hydrotreatment. These fuels, thanks to their properties, may be a suitable response to more and more demanding restrictions on exhaust emissions. The use of such fuels in reactivity controlled dual fuel engines (RCCI) is currently the most effective way of using them in internal combustion (IC) engines. Low-temperature combustion in this type of engine allows the use of all modern fuels intended for combustion engines with high thermal efficiency. Thermal efficiency higher than in classic engines allows for additional reduction of CO2 emissions. In this work, the research on this subject was compiled, and conclusions were drawn as to further possibilities of popularizing the use of these fuels in a wide spectrum of applications and the prospect of using them on a mass scale.

Global warming might be mitigated if emissions were interrupted through carbon capture technologies, as there is a significant amount of comprehensive studies on them. An outline of the main gaps and trends of a technology is critical for further development. In this context, this study provides an overview of calcium looping carbon capture processes that have proven their potential and commercial viability. A bibliometric analysis is conducted on both Scopus and Web of Science database by seeking the keywords “calcium looping”, “co2 capture”, and “fluidized bed” in titles, abstracts, and keywords. Word selection was based on a list of relevant papers on the topic. These items of data have been processed and analyzed based on the number of publications and citations by emphasizing recent publication evolution, journal influence, the use of specific keywords, and co-citation. Results reveal that the European Union (EU) leads the rankings on the topic, followed by Canada. Keyword choice might have affected the number of citations. Recent studies used limestone as a sorbent and a dual fluidized bed reactor with a calciner or resistance depending on its size. Most studies are focused on technology scale-up. Although scale-up seems to be a priority, multiple studies are designed to assess the effect of steam generation and SO2 on the process.

Graphene is a new generation material, which finds potential and practical applications in a vast range of research areas. It has unrivalled characteristics, chiefly in terms of electronic conductivity, mechanical robustness and large surface area, which allow the attainment of outstanding performances in the material science field. Some unneglectable issues, such as the high cost of production at high quality and corresponding scarce availability in large amounts necessary for mass scale distribution, slow down graphene widespread utilization; however, in the last decade both basic academic and applied industrial materials research have achieved remarkable breakthroughs thanks to the implementation of graphene and related 1D derivatives. In this work, after briefly recalling the main characteristics of graphene, we present an extensive overview of the most recent advances in the development of the Li-ion battery anodes granted by the use of neat and engineered graphene and related 1D materials. Being far from totally exhaustive, due to the immense scientific production in the field yearly, we chiefly focus here on the role of graphene in materials modification for performance enhancement in both half and full lithium-based cells and give some insights on related promising perspectives.

Increasing biogas production in the Three Seas Initiative countries (3SI) is a good way to reduce greenhouse gas emissions and to increase energy self-sufficiency by replacing some of the fossil energy sources. An assessment of the biogas production potential carried out for the 3SI at the NUTS 1 and NUTS 2 level shows that the potential of this energy carrier was stable for the period (from 2010–2021). The results showed that it can cover from approximately 10% (Hungary, Slovakia) to more than 34% (Estonia, Slovenia) of natural gas consumption; moreover, there is strong variation in the value of potential at the regional level (NUTS 2) in most of the countries studied. The biogas production forecast was carried out with the ARIMA model using four regressors, which are GDP, biogas potential utilisation, natural gas consumption and investments in RES (renewable energy sources) infrastructure, including changes in the EU energy policy after 24 February 2022. In the most promising scenario (four regressors), the results obtained for the period from 2022–2030 predict a rapid increase in biogas production in the 3SI countries, from 32.4 ± 11.3% for the Czech Republic to 138.7 ± 27.5% for Estonia (relative to 2021). However, in the case of six countries (Bulgaria, Lithuania, Hungary, Austria, Poland and Romania) the utilisation of 50% of the potential will most likely occur in the fifth decade of the 21st century. The above results differ significantly for those obtained for three regressors, where the highest rise is predicted for Bulgaria at 33.5 ± 16.1% and the lowest for Slovenia, at only 2.8 ± 14.4% (relative to 2021).