Hasil untuk "Fuel"

L. Lynd, J. Cushman, R. Nichols et al.

Recep Altin, Selim Çetinkaya, H. S. Yücesu

Shanfu Lu, Jing Pan, Aibin Huang et al.

Lin Wang, A. Husar, Tianhong Zhou et al.

S. Maaß, F. Finsterwalder, G. Frank et al.

Ertan Alptekin, M. Çanakçı

M. Çanakçı, H. Sanli

Xiao‐Zi Yuan, Haijiang Wang, Jian Colin Sun et al.

Caisheng Wang, M. H. Nehrir, S. R. Shaw

C. Zamfirescu, I. Dincer

Daniil Shirokiy, Andrey Bukaemskiy, Maximilian Henkes et al.

Abstract Cr-doped UO₂ fuels are increasingly adopted for their superior in-reactor performance compared to undoped UO₂, but their spent fuel behaviour, particularly potential Cr speciation and fission product reactivity, remains poorly understood. This investigation has used high energy resolution fluorescence detected X-ray absorption near edge structure (HERFD-XANES) spectroscopy to examine speciation of Cr and Pr/Gd within 200 ppm Cr-doped (U4.4+ 0.7Pr3+ 0.3)O2-x and 200 ppm Cr-doped (U4.4+ 0.7Gd3+ 0.3)O2-x compounds. Despite both being UO2 soluble and undersaturated, analysis indicates that Cr3+ and Pr3+/Gd3+ form perovskite type (Pr3+/Gd3+)Cr3+O3 phases, consistent with classical “grey phases” of spent fuel. The radiation tolerance of these phases was examined via swift heavy ion irradiations of PrCrO3 and GdCrO3 compounds where electron microscopy and grazing incidence synchrotron diffraction indicate significant amorphization but retention of the crystal structure. The investigation highlights the pertinence of considering the chemistry of dopants used for nuclear fuel enhancements regarding their speciation during irradiation and subsequent occurrence within spent fuel.

Zaid Allal, Hassan N. Noura, Flavien Vernier et al.

Accurate prediction of the Remaining Useful Life (RUL) of fuel cell (FC) systems is essential to ensure operational reliability, optimize maintenance strategies, and extend system lifetime in safety-critical hydrogen applications. As FC degradation is governed by complex, nonlinear, and stochastic mechanisms, machine learning (ML) has emerged as a powerful paradigm for data-driven prognostics. This paper presents a structured and comprehensive review of recent ML-based approaches for FC RUL estimation, encompassing supervised, unsupervised, and hybrid methodologies, including regression techniques, support vector machines, ensemble models, neural networks, and advanced deep learning architectures. Despite notable progress, our analysis reveals persistent limitations in the current literature, particularly the widespread neglect of underlying electrochemical and physical degradation laws, as well as the scarcity and ambiguity of explicit RUL and End-of-Life (EoL) labels in publicly available datasets. These challenges significantly constrain model generalization, interpretability, and real-world applicability. To address these gaps, we conduct a comparative analysis of more than 20 recent state-of-the-art studies and propose a unified and generalizable RUL estimation pipeline. This framework integrates data acquisition, preprocessing, feature engineering, model design, and validation, while explicitly accounting for physical consistency and operational constraints. In addition, the paper formulates practical, multi-level recommendations, including first-order guidelines for data modeling and learning strategies, second-order recommendations targeting validation protocols and real-world deployment, and the systematic integration of uncertainty quantification (UQ) techniques to enhance robustness, interpretability, and trustworthiness. By consolidating methodological insights, emerging paradigms, and deployment-oriented considerations, this review provides a comprehensive reference and a forward-looking roadmap for the development of reliable, physics-consistent, and scalable RUL prognostic frameworks for fuel cell systems.

Meng Teng, Gong Zhi, Amr Tolba et al.

Abstract The growing urgency to transition toward clean energy systems has heightened interest in marine renewable energy (MRE) as a sustainable solution for coastal regions facing environmental degradation from fossil fuel use. This study applies the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to evaluate and rank MRE options, offshore wind, tidal, and wave energy, based on four key criteria: Efficiency, Cost, Emissions, and Resource Availability. Expert judgment was used to derive weighted preferences, and a structured decision matrix facilitated performance scoring and ranking. The analysis identified Efficiency as the most influential factor, with offshore wind energy emerging as the top alternative due to its strong performance and scalability. The results offer a practical, adaptable framework for supporting energy planning in coastal zones, enabling decision-makers to balance environmental protection and operational feasibility.

Y. Elabd, M. Hickner

V. Sajith, C. Sobhan, G. Peterson

This paper reports the results of experimental investigations on the influence of the addition of cerium oxide in the nanoparticle form on the major physicochemical properties and the performance of biodiesel. The physicochemical properties of the base fuel and the modified fuel formed by dispersing the catalyst nanoparticles by ultrasonic agitation are measured using ASTM standard test methods. The effects of the additive nanoparticles on the individual fuel properties, the engine performance, and emissions are studied, and the dosing level of the additive is optimized. Comparisons of the performance of the fuel with and without the additive are also presented. The flash point and the viscosity of biodiesel were found to increase with the inclusion of the cerium oxide nanoparticles. The emission levels of hydrocarbon and NOx are appreciably reduced with the addition of cerium oxide nanoparticles.

Cyril Elouard, Sreenath K. Manikandan, Andrew N. Jordan et al.

While quantum measurements have been shown to constitute a resource for operating quantum thermal machines, the nature of the energy exchanges involved in the interaction between system and measurement apparatus is still under debate. In this work, we show that a microscopic model of the apparatus is necessary to unambiguously determine whether quantum measurements provide energy in the form of heat or work. We illustrate this result by considering a measurement-based refrigerator, made of a double quantum dot embedded in a two-terminal device, with the charge of one of the dots being continuously monitored. Tuning the parameters of the measurement device interpolates between a heat- and a work-fueled regimes with very different thermodynamic efficiency. Notably, we demonstrate a trade-off between a maximal thermodynamic efficiency when the measurement-based refrigerator is fueled by heat and a maximal measurement efficiency quantified by the signal-to-noise ratio in the work-fueled regime. Our analysis offers a new perspective on the nature of the energy exchanges occurring during a quantum measurement, paving the way for energy optimization in quantum protocols and quantum machines.

Qi Wang, Qingtao Liu, Jingxiang Lv et al.

With the continuous growth of the number of end-of-life vehicles and the rapid increase in the ownership of pure electric vehicles, the automobile disassembly industry is facing the challenge of transitioning from the traditional fuel vehicles to the mixed disassembly of fuel vehicles and pure electric vehicles. In order to cope with the uncertainty of recycling quantity and the demand of mixed-model disassembly of multiple vehicle types, this paper designs a multi-parallel mixed-model disassembly line (MPMDL), and constructs a corresponding mixed-integer planning model for the equilibrium optimization problem of this disassembly line with the optimization objectives of the minimum number of workstations, the minimum fatigue level of workers and the minimum energy consumption. Combining the differences in disassembly processes between fuel vehicles and pure electric vehicles, an improved non-dominated sorting multi-objective genetic algorithm (INSGA-III) based on the distribution of feasible solutions and dynamic search resource allocation is designed to solve this multi-objective dynamic balance optimization problem, and the two-stage dynamic adjustment strategy is adopted to realize the adaptive adjustment of the disassembly line under the uncertainty of the recycling quantity, and, recently, arithmetic validation is carried out. The results show that the proposed method can effectively improve the resource utilization efficiency, reduce energy consumption, alleviate the workers' load, and provide multiple high-quality disassembly solutions under the multi-objective trade-off. Compared with mainstream multi-objective optimization algorithms, the INSGA-III algorithm shows significant advantages in terms of solution quality, convergence and stability. This study provides a green, efficient and flexible solution for hybrid disassembly of fuel and pure electric vehicles.

Hind Barghash, Zuhoor AlRashdi, Kenneth E. Okedu et al.

The drive to reduce global warming through the mitigation of carbon emissions from fossil fuels is on the rise. Sewage Treatment Plants (STPs) contribute to Greenhouse Gases (GHGs) production. In order to achieve low GHGs emissions, this study presents two strategies for STPs. The first strategy involves generating hydrogen gas through Proton Exchange Membrane (PEM) electrolysis using treated effluent, while the second strategy is based on the adoption of a solar energy system. The study aims to conduct the Life Cycle Assessment (LCA) of the STPs to determine the effects of the source of energy in hydrogen gas production from wastewater. In addition, a LCA for the STPs was carried out using the OpenLCA software for hydrogen gas production via electrolysis and solar energy integration. The findings reveal that climate change impact, fossil fuel depletion, and human toxicity, would reduce by 14,800 kg CO2-Eq. Hydrogen production with solar energy integration exhibits considerable reduction in environmental consequences with considerable improvements in Human Toxicity (550.11 kg 1,4-DCB-Eq), Climate Change (2711.70 kg CO2-Eq), and Fossil Fuel Depletion (1541.11 kg oil-Eq). The solar-powered hydrogen production strategy demonstrates how STPs can help produce hydrogen in a more sustainable and eco-friendly way by lowering greenhouse gas emissions, and reducing dependency on fossil fuels. Based on the findings of this paper, employing solar energy to produce hydrogen from STPs is a viable and effective approach to less environmental hazards and sustainability of energy for major water treatment industries.

O. Doğan

D. Leech, P. Kavanagh, W. Schuhmann