Hasil untuk "Fuel"

V. Dhyani, T. Bhaskar

In the pursuit of renewable sources of energy, biomass is emerging as a promising resource because of its abundance and carbon neutral nature. Pyrolysis is a prevailing technology for biomass conversion into the valuable hydrocarbon and alternative fuels. In this review, pyrolysis of lignocellulosic biomass has been addressed, focusing primarily on the ideal feedstock, technologies, reactors, and properties of the end product. Technical problems in using biofuel from pyrolysis, as transport fuel have also been discussed, along with solutions to address these challenges, and comments on the future scope of the pyrolysis process.

J. Varcoe, P. Atanassov, Dario R. Dekel et al.

This article provides an up-to-date perspective on the use of anion-exchange membranes in fuel cells, electrolysers, redox flow batteries, reverse electrodialysis cells, and bioelectrochemical systems (e.g. microbial fuel cells). The aim is to highlight key concepts, misconceptions, the current state-of-the-art, technological and scientific limitations, and the future challenges (research priorities) related to the use of anion-exchange membranes in these energy technologies. All the references that the authors deemed relevant, and were available on the web by the manuscript submission date (30th April 2014), are included.

P. Mortensen, J. Grunwaldt, P. Jensen et al.

Gang Wu, K. More, C. Johnston et al.

V. Stamenković, B. Fowler, B. Mun et al.

A. Srivastava, R. Prasad

J. Lindl

B. Yurke, A. Turberfield, A. Mills et al.

C. Westbrook, F. Dryer

Ali Akram

The automotive industry is under growing pressure to reduce its environmental impact, requiring accurate predictive modeling to support sustainable engineering design. This study examines the factors that determine vehicle fuel consumption from the seminal Motor Trend dataset, identifying the governing physical factors of efficiency through rigorous quantitative analysis. Methodologically, the research uses data sanitization, statistical outlier elimination, and in-depth Exploratory Data Analysis (EDA) to curb the occurrence of multicollinearity between powertrain features. A comparative analysis of machine learning paradigms including Multiple Linear Regression, Support Vector Machines (SVM), and Logistic Regression was carried out to assess predictive efficacy. Findings indicate that SVM Regression is most accurate on continuous prediction (R-squared = 0.889, RMSE = 0.326), and is effective in capturing the non-linear relationships between vehicle mass and engine displacement. In parallel, Logistic Regression proved superior for classification (Accuracy = 90.8%) and showed exceptional recall (0.957) when identifying low-efficiency vehicles. These results challenge the current trend toward black-box deep learning architectures for static physical datasets, providing validation of robust performance by interpretable and well-tuned classical models. The research finds that intrinsic vehicle efficiency is fundamentally determined by physical design parameters, weight and displacement, offering a data-driven framework for how manufacturers should focus on lightweighting and engine downsizing to achieve stringent global sustainability goals.

Le Zhou, Yanru Yin, Dilshod Nematov et al.

The development of efficient and stable intermediate-temperature solid oxide fuel cells (SOFCs) necessitates high-performance cathode materials that are cobalt-free, cost-effective, and compatible with proton-conducting electrolytes. While Sr2Fe2O6 (SFO)-based ferrites offer a promising cobalt-free alternative, their electrochemical performance requires further enhancement to compete with state-of-the-art cathodes. This study proposes and validates a multi-element doping strategy as a superior approach to tailor the properties of SFO. The specific oxide Sr2Fe1.5Mo0.125Sn0.125Sc0.125Zr0.125O6 (SFO-ZSSM) is designed, synthesized via a solid-state reaction method, and systematically evaluated as a cathode for proton-conducting SOFCs (H-SOFCs). Its performance is benchmarked against a series of SFO cathodes modified with single dopants (Mo, Sn, Sc, Zr). Structural characterization confirms the successful formation of a phase-pure perovskite structure with homogeneous elemental distribution. Electrical conductivity relaxation (ECR) measurements reveal that SFO-ZSSM exhibits dramatically enhanced oxygen and proton transport kinetics compared to all singly-doped counterparts, demonstrating a significant synergistic effect. Consequently, fuel cells employing the SFO-ZSSM cathode deliver exceptional peak power densities of 1580, 1137, and 854 mW cm-2 at 700, 650, and 600 °C, respectively, significantly outperforming cells with single-doped cathodes. Electrochemical impedance spectroscopy further corroborates its superior catalytic activity, showing the lowest polarization resistance. Moreover, the SFO-ZSSM cell demonstrates excellent operational stability over 100 hours, attributed to its robust microstructure and Ba-free composition.

Andrei Kulikovsky, Tatyana Reshetenko

Effective water management is essential for the optimal performance of PEM fuel cells. We have developed an impedance model for liquid water transport through the membrane and coupled it with the two-phase model for cathode side impedance. The complete model was fitted to experimental spectra measured at anode/cathode relative humidities (RH) of 32/32\%, 50/50\% and 100/100\% within a current density range of 100 to 1000 mA cm$^{-2}$ and an air flow stoichiometry of 2. Cathode catalyst layer (CCL) saturation decreases with current density due to a growing liquid pressure gradient. For all RH values, the CCL oxygen diffusivity increases dramatically with cell current due to progressive involvement of larger pores into the proton current conversion. Higher RH leads to higher double layer capacitance, which indicates that liquid water increases the electrochemically active surface area.

Oleksii Havrylenko, Iuliia Myroshnychenko

In the context of escalating geopolitical instability and the global decarbonisation agenda, understanding the strategic role of renewable energy in enhancing national energy security has become increasingly urgent, especially for transition economies with legacy fossil fuel dependencies. This study aims to investigate whether the expansion of renewable energy contributes to measurable improvements in energy security in these countries. The analysis draws on annual panel data from 19 countries spanning the years 2000 to 2023. Renewable energy development is measured using three indicators: the share of renewables in total installed electricity capacity, electricity generation, and gross final energy consumption. Energy security is captured via the World Energy Council’s Trilemma Index, a multidimensional composite score. All renewable indicators were transformed using Box-Cox procedures to correct right-skewed distributions. The panel fixed-effects Granger causality models were estimated to have two-year lags to detect temporal causality while controlling for unobserved heterogeneity. The empirical results consistently show a significant unidirectional causal relationship running from renewable energy to energy security. Specifically, lagged increases in renewable electricity capacity are associated with subsequent improvements in energy security scores (β = 0.36, p < 0.01), as are increases in renewable electricity generation share (β = 0.28, p < 0.01) and in renewable energy's share of final energy consumption (β = 0.38, p < 0.05). Reverse causality tests, examining whether improvements in energy security predict renewable expansion, yield either insignificant or marginal results. These findings underscore that renewable energy acts as a proactive driver of systemic energy resilience, rather than merely a passive beneficiary of a secure energy environment.

Xingxiao Tao, Kai Sun, Rui Chen et al.

Porous metal foam (PMF) flow field is a potential option for proton exchange membrane fuel cells (PEMFCs) due to its excellent capabilities in gas distribution and water drainage. However, the gas-liquid two-phase flow in the PMF flow field on the pore scale is still unclear. In this study, we investigate the gas-liquid two-phase flow in the PMF flow field. Film, plug, and ligament flows are found in the hydrophilic PMF flow field, while slug and droplet flows are found in the hydrophobic PMF flow field. The results suggest that optimizing the pore size, increasing the metal foam surface hydrophobicity, and optimizing the operating condition are helpful for the water management of the PMF flow field. The frequency analysis of the pressure drop also shows that the dominant frequency can be used as an indicator to analyze the transition between different flow patterns.

Padam Prasad Paudel, Sunyong Park, Kwang Cheol Oh et al.

Ginkgo biloba trees are widely planted in urban areas of developed countries for their resilience, longevity and aesthetic appeal. Annual pruning to control tree size, shape and interference with traffic and pedestrians generates large volumes of unutilized Ginkgo biomass. This study aimed to valorize these pruning residues into charcoal by optimizing pyrolysis conditions and evaluating its fuel properties. The pyrolysis experiment was conducted at 400 to 600 degrees Celsius, after oven drying pretreatment. The mass yield of charcoal was found to vary from 27.33 to 32.05 percent and the approximate volume shrinkage was found to be 41.19 to 49.97 percent. The fuel properties of the charcoals were evaluated using the moisture absorption test, proximate and ultimate analysis, thermogravimetry, calorimetry and inductively coupled plasma optical emission spectrometry. The calorific value improved from 20.76 to 34.26 MJ per kg with energy yield up to 46.75 percent. Charcoal exhibited superior thermal stability and better combustion performance. The results revealed satisfactory properties compared with other biomass, coal and biochar standards. The product complied with first grade standards at 550 and 600 degrees Celsius and second grade wood charcoal standards at other temperatures. However, higher concentrations of some heavy metals like Zn indicate the need for pretreatment and further research on copyrolysis for resource optimization. This study highlights the dual benefits of waste management and renewable energy, providing insights for urban planning and policymaking.

Ryan DeBoskey, Parham Oveissi, Venkat Narayanaswamy et al.

Controlling the complex combustion dynamics within solid fuel ramjets (SFRJs) remains a critical challenge limiting deployment at scale. This paper proposes the use of a neural network model to process in-situ measurements for monitoring and regulating SFRJ thrust with a learning-based adaptive controller. A neural network is trained to estimate thrust from synthetic data generated by a feed-forward quasi-one-dimensional SFRJ model with variable inlet control. An online learning controller based on retrospective cost optimization is integrated with the quasi-one-dimensional SFRJ model to regulate the thrust. Sensitivity studies are conducted on both the neural network and adaptive controller to identify optimal hyperparameters. Numerical simulation results indicate that the combined neural network and learning control framework can effectively regulate the thrust produced by the SFRJ model using limited in-situ data.

Hartmut Ruhl, Georg Korn

In "arXiv:2312.13429" Lackner et al. use standard methods to decide if it is possible to ignite mixed fuels. They correctly identify that the increased radiation losses make ignition significantly more challenging than for pure DT fuels, since this leads to higher ignition temperatures. Further, they conclude that at those temperatures the reduced electronic $α$-stopping makes ignition impossible. We show that this conclusion is not correct. The model used for $α$-stopping by Lackner et al. is only approximately correct for low temperatures and hydrogen isotopes. By extending the $α$-stopping model to include ionic $α$-stopping we show in \cite{ruhlkornarXiv5} that the contribution of ionic $α$-particle stopping cannot be neglected. The ionic $α$-stopping together with the neutron stopping, which is also neglected by Lackner et al., lead to elevated ion temperatures implying $kT_i > kT_e$. Those three effects combined lead us to the conclusion, that ignition of mixed fuels is indeed possible with far reaching implications, contrary to the analysis by Lackner.

Lubomír Bureš

Circulating-fuel reactors (CFRs) lose reactivity when delayed-neutron precursors (DNPs) drift out of the core and may regain part of it when the fuel re-enters the core. This paper formulates a physics-based description of both effects by combining DNP transport with residence-time theory. Then, treating the core and ex-core regions as two mixing volumes in series, closed-form expressions for (i) the static reactivity loss due to precursor drift and (ii) the zero-power transfer function that governs linearised dynamics are derived. When the gamma residence-time distributions are used, the new framework is shown to reduce to the plug-flow and Continuous-Stirred-Tank-Reactor limits as special cases, while generalising to intermediate mixing regimes via a single parameter: the degree of mixing. Performed parameter studies show that DNP recirculation has the highest impact when core and ex-core residence times are comparable and the product of the DNP decay constant and the in-core residence time is small. Benchmarks against the Molten-Salt Reactor Experiment are able to reproduce the measured static loss ($k_0 \approx 0.32$ \$) and its frequency response, with $\approx$20% of the steady-state DNP worth arising from recirculation. Additionally, for the EVOL reference Molten-Salt Fast Reactor the model is shown to agree well with the results of high-fidelity Serpent-2 calculations coupled with Computational Fluid Dynamics. Overall, the residence-time approach offers a computationally light yet versatile tool for sensitivity studies and generation of physical intuition for the behaviour of CFRs. Foundation for extensions to importance weighting of DNPs and application of the framework to time-domain analysis is also briefly sketched.

Samuel Mallick, Gianpietro Battocletti, Qizhang Dong et al.

Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear positions may be too computationally intensive for a real-time implementation. This work proposes a learning-based MPC scheme to address this issue. A policy is trained to select and fix the gear positions across the prediction horizon of the MPC controller, leaving a significantly simpler continuous optimization problem to be solved online. In simulation, the proposed approach is shown to have a significantly lower computation burden and a comparable performance, with respect to pure MPC-based co-optimization.

Sabrina Weber, Benedikt Prifling, Martin Juckel et al.

Solid oxide fuel cells (SOFCs) are becoming increasingly important due to their high electrical efficiency, the flexible choice of fuels and relatively low emissions of pollutants. However, the increasingly growing demands for electrochemical devices require further performance improvements. Since it is well known that the 3D morphology of the electrodes, which is significantly influenced by the underlying manufacturing process, has a profound impact on the resulting performance, a deeper understanding for the structural changes caused by modifications of the manufacturing process or degradation phenomena is desirable. In the present paper, we investigate the influence of the annealing time and the operating temperature on the 3D morphology of SOFC anodes using 3D image data obtained by focused-ion beam scanning electron microscopy, which is segmented into gadolinium-doped ceria, nickel and pore space. In addition, structural differences caused by manufacturing the anode via infiltration or powder technology, respectively, are analyzed quantitatively by means of various geometrical descriptors such as specific surface area, length of triple phase boundary per unit volume, mean geodesic tortuosity, and constrictivity. The computation of these descriptors from 3D image data is carried out both globally as well as locally to quantify the heterogeneity of the anode structure.