Hasil untuk "Fuel"

Pamela Peralta-Yahya, Fuzhong Zhang, S. D. Cardayré et al.

R. P. John, G. Anisha, K. Nampoothiri et al.

D. J. Durbin, C. Malardier-Jugroot

Somnath C. Roy, O. Varghese, M. Paulose et al.

D. Gust, T. Moore, A. Moore

Eric E Benson, C. Kubiak, Aaron J. Sathrum et al.

Shishir P. S. Chundawat, G. Beckham, M. Himmel et al.

A. Züttel, A. Remhof, A. Borgschulte et al.

S. Lukic, Jian Cao, R. Bansal et al.

J. Dec

Yasmin Aldamen, Dilana Thasleem

The COVID-19 pandemic has led to an unprecedented global crisis, affecting every aspect of life. In the difficult times, the role of the media has become even more crucial. However, it has been noted that some Indian media has spread baseless conspiracy theories, exploiting COVID-19 for certain agendas. The role of Indian media during the COVID-19 pandemic has been questioned due to the biased dissemination of information. This study aims to observe how national news channels, through their prime-time debates, propagated the narrative of a Muslim conspiracy by spreading false information during the initial outbreak of the COVID-19 pandemic in India. It also seeks to understand the role of media narratives and representations in setting the Hindu nationalist agenda and portraying Muslims as anti-national" or the other of the nation. The study sample includes the content of prime-time news programs from six well-known TV news channels in India: Times Now, Republic TV, India TV, Zee News, CNN News 18, and India Today. News media organizations in India tend to favor majoritarian sentiments and ideas while marginalizing and condemning minorities and their beliefs, particularly in relation to religion and religiosity. The overt role of a number of news channels in amplifying the conspiracy against Muslims, particularly in framing them as the ‘other’ or the ‘anti-national’, aligns with the Hindu nationalist agenda. Depending on agenda setting and framing certain issues in a way that demonizes Muslims, the media could perpetuate stereotypes and fuel resentment towards those groups, which are already marginalized or misunderstood.

Rebeca Cabral Gonçalves, Geovana Carvalho Silva, Fernando Lage Araújo Schweizer et al.

This work aims to qualify the use of porous zones for representing fuel assemblies of a proposed SMR reactor in numerical models in other to reduce the computational demand required to study these structures. It employs computational fluid dynamics (CFD) methods to calculate the conservation equations of mass, momentum, and energy within a control volume. Initially, a detailed geometry of the fuel assembly was created and used for isothermal simulations. Based on the results of pressure drop and velocity, equations were used to calculate the coefficients of porosity and pressure drop of the system. These were then utilized to configure a second geometry, consisting of hexahedros divided into thirteen sub-regions according to their cross-sectional area, each having different porosities and pressure drop coefficents. Finally, the results of the two simulations were compared to verify their convergence to allow the use of the porous geometry. The outcomes suggests that, for models with a control volume significantly larger than a single fuel assembly, such as a complete nuclear reactor vessel, the use of porous zones is advantageous, as the variations in average velocity and pressure drop along the length of the structure are small, with the maximum axial velocity variation of -10.99%. However, if the objective is to conduct a more detailed analysis of the entire assembly, this strategy is not recommended, since some specific aspects of fluid behavior are not well capturated, such as radial velocity differences. 

Nick Plewacki, Benjamin Kale, Manu Kamin et al.

Hypersonic flight poses unique propulsion challenges, requiring engines that maintain thrust, efficiency, and stability across a wide range of operating conditions. These engines must transition smoothly between flight regimes and altitudes. Scramjets (supersonic combustion ramjets) play a key role in addressing these challenges. Recent advancements in high-fidelity computational fluid dynamics (CFD) tools allow researchers to explore novel designs and improve the feasibility of hypersonic travel. In this work, we analyze a radical-farming type scramjet engine mounted at the University of Queensland's T4 Wind Tunnel at Mach 10. We use the Improved Delayed Detached Eddy Simulation (IDDES) model, which combines Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) in different flow regions. A novel integrated modeling strategy is introduced, coupling the inlet, fuel injectors, combustor, and nozzle for full-scale engine analysis. Hydrogen combustion is modeled using a Finite Rate Chemistry (FRC) approach with a 12-species, 27-reaction mechanism to capture shock-induced chemical kinetics across equivalence ratios of $φ= 0.5$ to $0.9$. The Takeno flame index analysis reveals multiple combustion regimes, with ignition occurring in the partially premixed regime. This is supported by Chemical Explosive Mode Analysis (CEMA), which identifies regions of high chemical sensitivity, correlating with observed hot pockets and providing insights into autoignition and flame stabilization mechanisms. The combination of IDDES and FRC improves the transport of hydrogen to hot pockets, producing combustion patterns that match experimental results. This work establishes a framework to address critical challenges in future air-breathing propulsion systems.

Morgan Williamson, Aditya Rao, Evan Segura et al.

We investigate the potential of enhancing small (<20 kg) drone endurance by exploiting the high energy density of hydrocarbons using a prototype generator based on commercial-off-the-shelf (COTS) thermoelectric energy conversion technology. A proof-of-concept prototype was developed to vet design and engineering challenges and to bolster validity of resultant conclusions. The combination of the prototype performance and modeling suggests that endurance augmentation remains a difficult technical challenge with no clear immediate remedy despite many expectant alternatives. Across a sample of representative drones including ground- and air-based, multicopter and fixed wing drones, we report the following: from their current maximum values of 12%, thermoelectric (TE) generator module efficiencies must increase by over two times to achieve endurance parity with lithium batteries for VTOL multicopters. On the other hand, current TE efficiencies can compete with lithium batteries for some low power fixed wing and ground-based drones. Technical contributors for these results include weight of non-energy contributing components, low specific power and the associated tradeoff between specific power and specific energy due to fuel mass fraction, and lastly, low efficiencies.

Julian Bedei, Murray McBain, Alexander Winkler et al.

Reinforcement Learning (RL) and Machine Learning Integrated Model Predictive Control (ML-MPC) are promising approaches for optimizing hydrogen-diesel dual-fuel engine control, as they can effectively control multiple-input multiple-output systems and nonlinear processes. ML-MPC is advantageous for providing safe and optimal controls, ensuring the engine operates within predefined safety limits. In contrast, RL is distinguished by its adaptability to changing conditions through its learning-based approach. However, the practical implementation of either method alone poses challenges. RL requires high variance in control inputs during early learning phases, which can pose risks to the system by potentially executing unsafe actions, leading to mechanical damage. Conversely, ML-MPC relies on an accurate system model to generate optimal control inputs and has limited adaptability to system drifts, such as injector aging, which naturally occur in engine applications. To address these limitations, this study proposes a hybrid RL and ML-MPC approach that uses an ML-MPC framework while incorporating an RL agent to dynamically adjust the ML-MPC load tracking reference in response to changes in the environment. At the same time, the ML-MPC ensures that actions stay safe throughout the RL agent's exploration. To evaluate the effectiveness of this approach, fuel pressure is deliberately varied to introduce a model-plant mismatch between the ML-MPC and the engine test bench. The result of this mismatch is a root mean square error (RMSE) in indicated mean effective pressure of 0.57 bar when running the ML-MPC. The experimental results demonstrate that RL successfully adapts to changing boundary conditions by altering the tracking reference while ML-MPC ensures safe control inputs. The quantitative improvement in load tracking by implementing RL is an RSME of 0.44 bar.

Shan Shan

Achieving Sustainable Development Goal 7 (Affordable and Clean Energy) requires not only technological innovation but also a deeper understanding of the socioeconomic factors influencing energy access and carbon emissions. While these factors are gaining attention, critical questions remain, particularly regarding how to quantify their impacts on energy systems, model their cross-domain interactions, and capture feedback dynamics in the broader context of energy transitions. To address these gaps, this study introduces ClimateAgents, an AI-based framework that combines large language models with domain-specialized agents to support hypothesis generation and scenario exploration. Leveraging 20 years of socioeconomic and emissions data from 265 economies, countries and regions, and 98 indicators drawn from the World Bank database, the framework applies a machine learning based causal inference approach to identify key determinants of carbon emissions in an evidence-based, data driven manner. The analysis highlights three primary drivers: access to clean cooking fuels in rural areas, access to clean cooking fuels in urban areas, and the percentage of population living in urban areas. These findings underscore the critical role of clean cooking technologies and urbanization patterns in shaping emission outcomes. In line with growing calls for evidence-based AI policy, ClimateAgents offers a modular and reflexive learning system that supports the generation of credible and actionable insights for policy. By integrating heterogeneous data modalities, including structured indicators, policy documents, and semantic reasoning, the framework contributes to adaptive policymaking infrastructures that can evolve with complex socio-technical challenges. This approach aims to support a shift from siloed modeling to reflexive, modular systems designed for dynamic, context-aware climate action.

Stefano Riva, Carolina Introini, J. Nathan Kutz et al.

The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this task due to the complexity of the strongly coupled physics involved and the extremely harsh and hostile environments, especially for new technologies such as Generation-IV reactors. Data-driven techniques can combine different sources of information, including computational proxy models and local noisy measurements on the system, to robustly estimate the state. This work leverages the novel Shallow Recurrent Decoder architecture to infer the entire state vector (including neutron fluxes, precursors concentrations, temperature, pressure and velocity) of a reactor from three out-of-core time-series neutron flux measurements alone. In particular, this work extends the standard architecture to treat parametric time-series data, ensuring the possibility of investigating different accidental scenarios and showing the capabilities of this approach to provide an accurate state estimation in various operating conditions. This paper considers as a test case the Molten Salt Fast Reactor (MSFR), a Generation-IV reactor concept, characterised by strong coupling between the neutronics and the thermal hydraulics due to the liquid nature of the fuel. The promising results of this work are further strengthened by the possibility of quantifying the uncertainty associated with the state estimation, due to the considerably low training cost. The accurate reconstruction of every characteristic field in real-time makes this approach suitable for monitoring and control purposes in the framework of a reactor digital twin.

Weiyin Xie, Chunxi Huang, Jiyao Wang et al.

Electric vehicles (EVs) are a promising alternative to fuel vehicles (FVs), given some unique characteristics of EVs, for example, the low air pollution and maintenance cost. However, the increasing prevalence of EVs is accompanied by widespread complaints regarding the high likelihood of motion sickness (MS) induction, especially when compared to FVs, which has become one of the major obstacles to the acceptance and popularity of EVs. Despite the prevalence of such complaints online and among EV users, the association between vehicle type (i.e., EV versus FV) and MS prevalence and severity has not been quantified. Thus, this study aims to investigate the existence of EV-induced MS and explore the potential factors leading to it. A survey study was conducted to collect passengers' MS experience in EVs and FVs in the past one year. In total, 639 valid responses were collected from mainland China. The results show that FVs were associated with a higher frequency of MS, while EVs were found to induce more severe MS symptoms. Further, we found that passengers' MS severity was associated with individual differences (i.e., age, gender, sleep habits, susceptibility to motion-induced MS), in-vehicle activities (i.e., chatting with others and watching in-vehicle displays), and road conditions (i.e., congestion and slope), while the MS frequency was associated with the vehicle ownership and riding frequency. The results from this study can guide the directions of future empirical studies that aim to quantify the inducers of MS in EVs and FVs, as well as the optimization of EVs to reduce MS.

N. Melzack, N. Melzack, R. G. A. Wills et al.

Much focus of dual energy-storage systems (DESSs) for electric vehicles (EVs) has been on cost reduction and performance enhancement. While these are important in the development of better systems, the environmental impacts of system and component-level choices should not be overlooked. The current interest in EVs is primarily motivated by environmental reasons such as climate change mitigation and reduction of fossil fuel use, so it is important to develop environmentally sound alternatives at the design stage. Assessing the environmental impact of developmental and mature chemistries provides valuable insights into the technologies that need to be selected now and which to develop for the future. This paper presents a cradle-to-gate (i.e., all raw material and production elements are considered; however, the “use” phase and recycling are not) lifecycle assessment of a DESS with Li-ion and aqueous Al-ion cells and that of one with Li-ion cells and supercapacitors. These are also compared to a full Li-ion EV battery in terms of their environmental impact for both a bus and car case study. Key findings show that the use of a DESS overall reduces the environmental impacts over the vehicle lifetime and provides an argument for further development of aqueous Al-ion cells for this application.