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The radiobiological effect on the human health of CANDU spent fuel is assessed using Monte Carlo shielding estimates. The examination of spent fuel occurs after it has been discharged from the reactor. A specific cooling interval is considered, with the radiation dose rates that characterize the used fuel being of interest. Two kinds of fuel were studied in a CANDU standard fuel bundle with 37 fuel components: natural uranium (NU) fuel and slightly enriched uranium (SEU) fuel. The fuel burnup was simulated using the ORIGEN-S algorithm, and the photon sources describing the wasted fuel were retrieved. A generic stainless steel shipping cask type B was used for spent fuel transfer, and radiation doses at the cask wall and in the air up to 8 m away from the shipping cask were computed using the Monte Carlo MORSE-SGC algorithm. To ensure nuclear safety and radiation protection, spent fuel must be maintained in temporary wet cooling storage for six months. The projected dosage rates were modest, allowing for the safe handling of the used fuel shipping cask. The corresponding dosages on human body organs for the two considered spent fuels were estimated without and with shielding. Due to

To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation methods are proposed for predicting fuel consumption, while various optimization methods are proposed to minimize fuel oil consumption. This paper provides a comprehensive review of methods for estimating and optimizing fuel oil consumption in maritime transport. Our novel contributions include categorizing fuel oil consumption \& estimation methods into physics-based, machine-learning, and hybrid models, exploring their strengths and limitations. Furthermore, we highlight the importance of data fusion techniques, which combine AIS, onboard sensors, and meteorological data to enhance accuracy. We make the first attempt to discuss the emerging role of Explainable AI in enhancing model transparency for decision-making. Uniquely, key challenges, including data quality, availability, and the need for real-time optimization, are identified, and future research directions are proposed to addre

Multi-fuel compression-ignition engines offer fuel flexibility but introduce uncertain, time-varying fuel reactivity, represented by cetane number (CN), which complicates cycle-to-cycle combustion-phasing control. This work formulates CA50 regulation under latent CN variation as a partially observable sequential decision problem and systematically evaluates controllers with increasing temporal and representational capacity, including LinUCB, history-augmented contextual bandits, observation-only DDPG, recurrent DDPG, and a proposed GRU-guided RL framework. A Gaussian-process surrogate trained on experimental multi-fuel engine data provides a controlled and reproducible evaluation environment. Results show that myopic and fixed-history bandit methods degrade under CN variation, observation-only RL suffers from latent-state aliasing, and generic recurrence is insufficient when CN evolves rapidly. The proposed framework learns a compact GRU-based representation of fuel reactivity from combustion history and conditions both actor and critic on this estimated signal rather than oracle CN. By training the policy on the same imperfect fuel-reactivity information available at deployment, t

The Fuel Optimizer is an inverse design framework for sustainable aviation fuels that starts from a user-defined merit function and identifies the optimal combination of chemical species or hydrocarbon families that optimize a combination of targets. As a case study, a database of fuel blends meeting selected property standards was simulated in a reactor model to obtain pollutant emissions at cruise conditions. A surrogate model was developed to reduce the computational cost of evaluating candidate blends, taking fuel composition as input and predicting emissions as output. A genetic algorithm was used to optimize fuel formulation according to two merit functions designed to break the nitrogen oxides - CO trade-off, and minimize pollutant emissions over the landing-and-take-off cycle. Constraints included selected property standards (including seal swelling) and composition limits. Fuel candidates from the framework outperformed the training database across all merit functions, and the optimal candidates were validated through reactor simulations.

The nuclear spin state of deuterium-tritium (D-T) fuel sets both the D-T fusion cross section and the emission direction of the fusion-born alphas and neutrons. We show two ways that spin-polarized fuel (SPF) could enhance alpha channeling, the wave-mediated damping of alpha power onto fuel ions rather than electrons, which is predicted to increase fusion power significantly. First, the enhanced SPF cross section produces more alphas, and second, the perpendicular (to the magnetic field) bias of the alphas' kinetic energy couples more efficiently to the perpendicular-resonant channeling waves. The birth anisotropy survives slowing-down and appears as a population inversion of the bulk alpha distribution over a broad region of velocity space, so resonant alphas can drive a suitably tuned channeling wave rather than damp it. Without channeling, SPF roughly doubles the fusion power density through the cross-section boost and its temperature feedback on the reactivity, a well-known result. Our velocity-space calculations find the channeling efficiency about 1.5 times higher for vector-aligned fuel than for unpolarized fuel, and channeling raises the fusion power enhancement to three or

Platooning has emerged as a promising strategy for improving fuel efficiency in automated vehicle systems, with significant implications for reducing emissions and operational costs. While existing literature on vehicle platooning primarily focuses on individual aspects such as aerodynamic drag reduction or specific control strategies, this work takes a more comprehensive approach by bringing together a wide range of factors and components that contribute to fuel savings in platoons. In this literature review, we examine the impact of platooning on fuel consumption, highlighting the key components of platoon systems, the factors and actors influencing fuel savings, methods for estimating fuel use, and the effect of platoon instability on efficiency. Furthermore, we study the role of reduced aerodynamic drag, vehicle coordination, and the challenges posed by instability in real-world conditions. By compiling insights from recent studies, this work provides a comprehensive overview of the latest advancements in platooning technologies and highlights both the challenges and opportunities for future research to maximize fuel savings in real-world scenarios.

Accurate calculation of aircraft fuel consumption plays an irreplaceable role in flight operations, optimization, and pollutant accounting. Calculating aircraft fuel consumption accurately is tricky because it changes based on different flying conditions and physical factors. Utilizing flight surveillance data, this study developed a comprehensive mathematical framework and established a link between flight dynamics and fuel consumption, providing a set of high-precision, high-resolution fuel calculation methods. It also allows other practitioners to select data sources according to specific needs through this framework. The methodology begins by addressing the functional aspects of interval fuel consumption. We apply spectral transformation techniques to mine Automatic Dependent Surveillance-Broadcast (ADS-B) data, identifying key aspects of the flight profile and establishing their theoretical relationships with fuel consumption. Subsequently, a deep neural network with tunable parameters is used to fit this multivariate function, facilitating high-precision calculations of interval fuel consumption. Furthermore, a second-order smooth monotonic interpolation method was constructe

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels, uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potent

While the underlying physics of the ICF approach to nuclear fusion is well understood and a technological implementation of the indirect drive variant of the ICF paradigm has recently been given at NIF commercially viable ICF concepts for energy production and beyond are still under investigation. In the present paper we propose core elements of a novel fast direct drive mixed fuel ICF concept that might be commercially viable. It makes use of ultra-short, ultra-intense laser pulses interacting with nano-structured accelerators embedded into the mixed fuel context. The embedded accelerator technology promises to be highly efficient and capable of fast fuel heating without fuel pre-compression but is not the focus of the paper. It is the predominant purpose of the mixed fuel concept to avoid cryogenic fuels since specific chemical compounds exist that are capable of chemically binding $\text{DT}$. To which extent mixed fuel concepts can work is investigated in the paper. Under the assumption that the proposed direct drive fast heating concept is capable of rapidly heating the fuel uniformly to sufficiently high temperatures it is found with the help of MULTI, an ICF community code,

Electricity demand and generation have become increasingly unpredictable with the growing share of variable renewable energy sources in the power system. Forecasting electricity supply by fuel mix is crucial for market operation, ensuring grid stability, optimizing costs, integrating renewable energy sources, and supporting sustainable energy planning. We introduce two statistical methods, centering on forecast reconciliation and compositional data analysis, to forecast short-term electricity supply by different types of fuel mix. Using data for five electricity markets in Australia, we study the forecast accuracy of these techniques. The bottom-up hierarchical forecasting method consistently outperforms the other approaches. Moreover, fuel mix forecasting is most accurate in power systems with a higher share of stable fossil fuel generation.

Nanostructured solid boron-hydrogen compounds have been suggested as target and fuel for laser fusion, offering improved laser-plasma coupling, avoiding cryogenic fuel handling and fuel pre-compression and ultimately allowing a transit from DT- to aneutronic pB- fusion power production. We describe the scaling of the different energy loss channels (α-particle escape, bremsstrahlung, hydrodynamic expansion work, electron heat conduction) with mixed fuel composition using partial inverse gains (Q's) which allow a simple superposition of losses. This highlights in particular the negative synergy between these loss-channels for such mixed fuels: the dominance of bremsstrahlung over fusion power at low temperatures forces a shift of operation to higher ones, where the plasma gets more transparent to α-particles, and hydrodynamic and heat conduction losses increase strongly. The use of mixed fuels therefore does not eliminate the need for strong precompression of the fuel: in fact, it renders achieving burning plasma conditions much more difficult, if not impossible. A recent suggestion to use tamping of the fuel by cladding with a heavy metal would only reduce hydrodynamic expansion los

High-temperature gas reactors rely on TRIstructural-ISOtropic (TRISO) fuel for enhanced fission product retention. Accurate fuel characterization would improve monitoring of efficient fuel usage and accountability. We developed a new neutron multiplicity counter (NMC) based on boron coated straw (BCS) detectors and used it in coincidence mode for 235U assay in TRISO fuel. In this work, we demonstrate that a high-efficiency version of the NMC encompassing 396 straws is able to estimate the 235U in used TRISO-fueled pebbles or compacts with a relative uncertainty below 2.5% in 100 s. We performed neutronics and fuel depletion calculation of the HTR-10 pebble bed reactor to estimate the neutron and gamma-ray source strengths of used TRISO-fueled pebbles with burnup between 9 and 90 GWd/t. Then, we measured a gamma-ray intrinsic efficiency of 10^-12 at an exposure rate of 340.87 R/h. The low gamma-ray sensitivity and high neutron detection efficiency enable the inspection of used fuel.

This work presents experimental study on opposed flow flame spread over thin hollow cylindrical cellulosic fuel of diameters varying from 10 mm to 49 mm in microgravity environment. To understand the effect of flow and geometry on flame spread, experiments are conducted in low convective opposed flow conditions ranging from 10 cm/s to 30 cm/s for both hollow cylindrical and planar fuels at oxygen concentration of 21% and 1 atm pressure. In the microgravity environment the flame length and the flame spread rate are seen to increase with increase in hollow cylindrical fuel diameter over the flow range studied here. The flame spread rate exhibited a non-monotonic trend with flow speed, for flow of large diameter whereas a monotonic increasing trend is noted for small diameters. The flame spread rate over hollow cylindrical fuel is noted to be higher or at most equal compared to planar fuels over the matrix of experiments conducted in this study. A simplified analysis is carried out to arrive at an expression for flame spread rate over thin hollow cylindrical fuels. The analysis shows that the radiation heat transfer from the hot char to the inner surface of hollow virgin fuel dictates

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e., MIQP-based individual stack control (ISC), with significant fuel cost reductions, FC and battery degradations. The proposed method is compared with classical dynamic programming (DP), with a 2243 times faster computational speed than the DP method while maintaining nearoptimal performance. The case study results show that ISC achieves a 64.68 % total cost reduction compared to CSC in the examined scenario, with substantial reductions across key metrics including battery degradation (4 %), hydrogen fuel consumption (22 %), fuel cell idling loss (99 %), and fuel cell load-change loss (41 %)

Sustainable aviation fuels have the potential for reducing emissions and environmental impact. To help identify viable sustainable aviation fuels and accelerate research, several machine learning models have been developed to predict relevant physiochemical properties. However, many of the models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that have limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid-phase FTIR spectra measurements can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 mL of sample. The method first decomposes FTIR spectra into fundamental building blocks using Non-negative Matrix Factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point,

Large hydrocarbon fuels are used for ground and air transportation and will be for the foreseeable future. Despite their extensive use, turbulent combustion of large hydrocarbon fuels, remains relatively poorly understood and difficult to predict. A key parameter when burning these fuels is the turbulent consumption speed; the velocity at which fuel and air are consumed through a turbulent flame front. Such information can be useful as a model input parameter and for validation of modeled results. In this study, turbulent consumption speeds were measured for three jet-like fuels using a premixed turbulent Bunsen burner. The burner was used to independently control turbulence intensity, unburned temperature, and equivalence ratio. Each fuel had similar heat releases (within 2%), laminar flame speeds (within 5-15 %), and adiabatic flame temperatures. Despite this similarity, for constant Re_D and turbulence intensity, A2 (i.e., jet-A) has the highest turbulent flame speeds and remains stable (i.e., without tip quenching) at lower φ than the other fuels evaluated. In contrast the C1 fuel, which contains no aromatics, has the slowest turbulent flame speeds and exhibits tip quenching at

As the number of adopted alternative fuel vehicles increases, it is crucial for communities (especially those that are susceptible to hazards) to make evacuation plans that account for such vehicles refueling needs. During emergencies that require preemptive evacuation planning, travelers using alternative fuel vehicles are vulnerable when evacuation routes do not provide access to refueling stations on their way to shelters. In this paper, we formulate and solve a novel seamless evacuation route plan problem, by designing $k$-minimum spanning trees with hop constraints that capture the refueling needs of each $k \in K$ vehicle fuel type on their way to reach a shelter. We develop a branch-and-price algorithm based on a matheuristic column generation approach to solve the evacuation problem. We apply the proposed framework to the Sioux Falls transportation network with existing infrastructure deployment and present numerical experiments. Specifically, we discuss the evacuation travel and refueling times under scenarios of various alternative fuel vehicles driving ranges. Our findings show that the characteristics of each vehicle fuel type, like driving range and the refueling infra

The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1%.

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users self-reported fuel economy estimates and government fuel economy ratings are analyzed for more than 7000 garages across the U.S. The empirical analysis, nonetheless, is complicated owing to the presence of important methodological concerns including potential interrelationship between vehicles within the same garage and unobserved heterogeneity. To address these concerns, bivariate seemingly unrelated fixed and random parameter models are presented. With government test cycle ratings tending to over-estimate the actual on-road fuel economy, a significant variation is observed in the fuel economy gaps for the two vehicles across garages. A wide variety of factors such as driving style, fuel economy calculation method, and several vehicle specific characteristics are considered. Drivers who drive for maximum gas mileage or drives with the traffic flow have greater on-road fuel economy relative to the gov