Hasil untuk "Fuel"

E. Antolini, E. Gonzalez

C. Lamy, A. Lima, Véronique LeRhun et al.

J. Kerres

Xianguo Li, I. Sabir

E. Kunkes, D. Simonetti, R. West et al.

E. Fabbri, D. Pergolesi, E. Traversa

Q. Lu, Wenzhi Li, Xifeng Zhu

B. Logan, J. Regan

Aaron J. Reiter, S. Kong

S. Chan, K. Khor, Z. Xia

C. Chan, A. Bouscayrol, Keyu Chen

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.

G. Knothe, K. Steidley

J. Pausas, Santiago Fernández‐Muñoz

Lin Lin, Zhou Cun-shan, Saritporn Vittayapadung et al.

P. Glarborg, A. Jensen, J. Johnsson

A. Hermann, T. Chaudhuri, P. Spagnol

P. Goodwin, J. Dargay, M. Hanly

G. Walker, Rosie Day

Noah D'Amico, Sandeep Puri, Ian Jones et al.

Graphene hydrogels were created and loaded with uranyl nitrate or thorium nitrate and freeze-dried to produce graphene aerogel nuclear fuels. These aerogels had densities between 0.018-0.035 g/cm3 and consisted of ~7.3 +- 0.5% uranium/thorium by mass. The ultra-low density of the aerogels allows for high energy ions to escape the fuel particles without depositing all their energy as heat, as is typical in nuclear fuels. Their measured alpha activity was ~16 pCi/mg, which could be enhanced up to ~49 pCi/mg by decreasing the thickness of aerogel samples to allow all alpha particles to escape. Additionally, high energy neutrons were used to induce fission to provide a source of fission fragments from the aerogel fuels. This novel form of nuclear fuel has potential applications in space propulsion such as fission fragment rocket engines, as well as in terrestrial applications for modular reactors, direct conversion methods, and in medical radiotherapeutics.

Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis

International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-aware routing policies from physics-calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator. Validated on one full year (2023) of AIS data across seven Gulf of Mexico routes (840 episodes per method), PIER reduces mean CO2 emissions by 10% relative to great-circle routing. However, PIER's primary contribution is eliminating catastrophic fuel waste: great-circle routing incurs extreme fuel consumption (>1.5x median) in 4.8% of voyages; PIER reduces this to 0.5%, a 9-fold reduction. Per-voyage fuel variance is 3.5x lower (p<0.001), with bootstrap 95% CI for mean savings [2.9%, 15.7%]. Partial validation against observed AIS vessel behavior confirms consistency with the fastest real transits while exhibiting 23.1x lower variance. Crucially, PIER is forecast-independent: unlike A* path optimization whose wave protection degrades 4.5x under realistic forecast uncertainty, PIER maintains constant performance using only local observations. The framework combines physics-informed state construction, demonstration-augmented offline data, and a decoupled post-hoc safety shield, an architecture that transfers to wildfire evacuation, aircraft trajectory optimization, and autonomous navigation in unmapped terrain.