Search Results (38)

The functionality of ionomeric membranes is influenced by small changes of several parameters. Aqueous network formation by phase separation between the hydrophilic and hydrophobic parts of the polymer is one critical factor for water and ion transport. In particular, the transport of highly charged ions like ${\mathrm{V}}^{3+}$ is not well understood. The unsteady diffusion in Nafion, a sulfonic acid based cation exchange polymer, using ${\mathrm{V}}^{3+}$ profiles obtained with micro X-ray fluorescence ($0.5 \mathsf{\mu }$m spot over a $180 \mathsf{\mu }$m scan) yields a diffusion coefficient of $4\times {10}^{-13} {\mathrm{m}}^2{\mathrm{s}}^{-1}$ at ${\lambda }_{H_{2}O/S{O}_3}=12$ and at ca. $20 °$C. It is confirmed that the concentration profile can be described by an error function formalism. The diffusivity, determined from the entire profile, represents mainly the transport into a vanadium free environment with very low ionic strength as the membrane was conditioned in ultra-pure water. The macroscopic ion transport is influenced by local molecular interactions, interconnection of water pockets and long range ionic interactions. The local interactions of ${\mathrm{V}}^{3+}$ were studied using molecular dynamics (MDs) simulations. The MD simulation studies diffusion at a constant ion concentration and short length scale (ca. 30 nm). It gives insights on the effects of dissolved ${\mathrm{V}}^{3+}$ ions on the local structure. Radial distribution functions reveal that at low hydration, the vanadium ions have an ordering effect on water molecules. The diffusion coefficient of ${\mathrm{V}}^{3+}$ is determined on a molecular level from the mean-square displacement yielding $2.5\times {10}^{-10} {\mathrm{m}}^2{\mathrm{s}}^{-1}$ for ${\mathrm{V}}^{3+}$ ions at a membrane water content of ${\lambda }_{H_{2}O/S{O}_3}$ = 6. The phenomenon in which the diffusivity decreases over longer length scales was documented before for water and ${\mathrm{H}}^{+}$ in Nafion; however, this was by only about one order of magnitude. The experimental microscopic approach described by us is universally applicable, e.g., to environments of higher ionic strength, ions with different charges, and different types of ion-exchange membranes. Longer diffusion times allow us to distinguish between different concentration regimes. Full article

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology is required to justify the productization of SiC devices. This work performs a systematic investigation of both technologies at the device and system levels for distinct power module voltage classes (3.3 and 6.5 kV) and circuit topologies. Initially, experimental characterization of state-of-the-art power modules is performed, followed by energy efficiency characterizations at the power converter level. Then, an electrothermal simulation model was built and validated based on experimental results. Accurate system simulations of commercial two- and three-level traction topologies were developed, focusing on efficiency over the entire load range, loadability, potential energy savings under realistic train drive cycles, and a financial comparison of inverter prices per kW. SiC exhibits lower loadability degradation at high switching frequencies (>500 Hz) than Si technology. Energy-saving potentials of 40–70% in the traction inverter with a guaranteed return on investment during the converter’s lifetime are achieved by substituting Si with SiC inverters. In addition, massive energy savings of up to 200 MWh per inverter lifetime can effectively reduce the carbon footprint of railway systems (up to ~76 t CO₂-eq saved during the inverter lifetime). This paper provides essential information for distinct stakeholders to support the decision-making process and design considerations for future railway power conversion technologies. Full article

In this article, we explore the possibility of using reinforcement learning to create convective flow approximation schemes that maintain accuracy and stability at high Courant-Friedrichs-Lewy (CFL) numbers in the finite-volume discretization of advection equations. Unlike most existing data-driven discretization methods, which primarily concentrate on spatial grid refinement, this work emphasizes increasing the allowable time step without compromising solution accuracy. This approach reduces the total number of time integration steps, thereby enabling faster computation. A neural network is used as a surrogate model for reconstructing the convective flow, which takes as input local information about the flow, scalars, and geometry and predicts scalar values at node points. Reinforcement learning is used for training and is formulated as a policy optimization problem, where the long-term reward is defined as the difference between the numerical and reference solutions over the entire simulation period. Both the genetic algorithm and the Deep Deterministic Policy Gradient (DDPG) method are investigated. The effectiveness of the approach is evaluated using a one-dimensional nonlinear advection problem with a constant velocity field. Despite the simplicity of the test case, the results demonstrate that the trained convective flux approximation scheme achieves accuracy comparable to or better than the classical second-order linear upwind (LUD) scheme, while operating at CFL numbers 2–50 times higher than the optimal CFL for LUD, thereby reducing the simulation time by the same factor. This allows for a wider range of stability and accuracy in the finite-volume method and the use of larger time steps without compromising the quality of the solution. The study is intentionally limited to a single spatial dimension and serves as a basic analysis of the method’s applicability. The results demonstrate that reinforcement learning can successfully find more convective flow approximation schemes that improve efficiency at high CFL numbers than conventional explicit second-order schemes, establishing a framework that is subsequently extended in our follow-up work to improve training methods and three-dimensional complex transport problems. The proposed method improves the spatial discretization of convective fluxes, which is independent of the choice of time integration scheme. Therefore, the neural reconstruction can in principle be used in both explicit and implicit finite-volume solvers. Full article

Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carrier consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carrier consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs and HEVs, and BEVs are the least. This study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. Full article

Mangrove ecosystems are crucial coastal habitats that support life and regulate the Earth’s atmosphere. However, these ecosystems face prominent threats due to anthropogenic activities and environmental constraints. For instance, the Saudi Arabian coast is particularly vulnerable to species extinction and biodiversity loss due to the fragility of the ecosystem; this highlights the need to understand the spatial and temporal dynamics of mangrove forests in desert environments. Hence, this is the first national study to quantify mangrove forests and analyze at-risk zone-based land use along Saudi Arabian coasts over 40 years. Thus, the primary contents of this research were (1) to produce a new long-term dataset covering the entire Saudi coastline, (2) to identify the patterns, analyze the trends, and quantify the change of mangrove areas, and (3) to determine vulnerability zoning of mangrove area-based land use and transportation networks. This study used Landsat satellite imagery via Google Earth Engine for national-scale mangrove mapping of Saudi Arabia between 1985 and 2024. Visible and infrared bands and seven spectral indices were employed as input features for the random forest classifier. The two classes used were mangrove and non-mangrove; the latter class included non-mangrove land-use and land-cover areas. Then, the study employed the output mangrove mapping to delineate vulnerable mangrove forest-based land use. The overall results showed a substantial increase in mangrove areas, ranging from 27.74 to 59.31 km² in the Red Sea and from 1.05 to 8.65 km² in the Arabian Gulf between 1985 and 2024, respectively. However, within this decadal trend, there were noticeable periods of decline. The spatial coverage of mangroves was larger on Saudi Arabia’s western coasts, especially the southwestern coasts, than on its eastern coasts. The overall accuracy, conducted annually, ranged between 91.00% and 98.50%. The results also show that expanding land uses and transportation networks within at-risk zones of mangrove forests may have a high potential effect. This study aimed to benefit the government, conservation agencies, coastal planners, and policymakers concerned with the preservation of mangrove habitats. Full article

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development. Full article

Accurate forecasting of city-level large-scale traffic flow is crucial for efficient traffic management and effective transport planning. However, previously proposed traffic flow prediction methods model dynamic spatial correlations across entire traffic networks, leading to high computational complexity, elevated memory usage, and model overfitting. Therefore, a novel grid partition-based dynamic spatial–temporal graph convolutional network was developed in this study to capture correlations within a large-scale traffic network. It includes the following: a dynamic graph convolution module to divide the traffic network into grid regions and thereby effectively capture the local spatial dependencies inherent in large-scale traffic topologies, an attention-based dynamic graph convolutional network to capture the local spatial correlations within each region, a global spatial dependency aggregation module to model inter-regional correlation weights using sequence similarity methods and comprehensively reflect the overall state of the traffic network, and multi-scale gated convolutions to capture both long- and short-term temporal correlations across varying time ranges. The performance of the proposed model was compared with that of different baseline models using two large-scale real-world datasets; the proposed model significantly outperformed the baseline models, demonstrating its potential effectiveness in managing large-scale traffic networks. Full article

Wildfires pose a significant threat to the entire ecosystem. The impacts of these wildfires can potentially disrupt biodiversity and ecological stability on a large scale. Wildfires may alter the physical and chemical properties of burned soil, such as particle size, loss of organic matter and infiltration capacity. These alterations can lead to increased vulnerability to geohazards such as landslides, mudflows and debris flows, where soil erosion and sediment transport play a crucial role. The present study investigates the impact of wildfire on soil erosion by conducting a series of laboratory experiments. The soil samples were burned using two different methods: using firewood for different burning durations and using a muffle furnace at an accurately controlled temperature within 400 °C∼1000 °C. The burned soils were subsequently subjected to surface erosion by utilizing the constant head method to create a steady water flow to induce the erosion. In addition, empirically based theoretical models are explored to further assess the experimental results. The experimental results reveal a loss of organic matter in the burned soils that ranged from approximately 2% to 10% as the burning temperature rose from 400 °C to 1000 °C. The pattern of the grain size distribution shifted to a finer texture in the burned soil. There was also a considerable increase in soil erosion in burned soils, especially at a higher burn severity, where the erosion rate increased by more than five times. The empirical predictions are overall consistent with the experimental results and offer reasonable calibration of relevant soil erosion parameters. These findings demonstrate the importance of post-fire erosion in understanding and mitigating the long-term effects of wildfires on geo-environmental systems. Full article

Thin-film membranes of Pd-Ag and Pd-Cu alloys capable of releasing hydrogen in a wide temperature range have been developed. The surface activation of the membranes with a nanostructured coating made it possible to intensify hydrogen transport through Pd-containing membranes at low temperatures. This effect was achieved by accelerating limiting surface processes by increasing the active area of the membrane. Surface-activated membranes demonstrated the highest values of hydrogen flux over the entire temperature range, which reached up to 49.4 mmol s⁻¹ m⁻² for Pd-Ag membranes and up to 32.9 mmol s⁻¹ m⁻² for Pd-Cu membranes. Membranes modified with filiform nanoparticles demonstrated a hydrogen flux up to 12 times higher than that of membranes with a smooth surface. Based on the results obtained, a theoretical model of hydrogen transport through metal membranes was developed, taking into account the effect of the state of the membrane surface on hydrogen transport at low temperatures. This model makes it possible to predict hydrogen flows in the entire temperature range much more accurately compared to other existing models. The selectivity and stability of the developed membranes over a long period of operation have been confirmed. The study of the effect of the surface activation of Pd-based membranes on the intensification of hydrogen permeability has shown the success of the method developed, which in turn opens up wide opportunities for creating low-temperature, highly efficient membrane hydrogen filters based on palladium and other devices based on them. Full article

Because ammonia is easier to store and transport over long distances than hydrogen, it is a promising research direction as a potential carrier for hydrogen. However, its low ignition and combustion rates pose challenges for running conventional ignition engines solely on ammonia fuel over the entire operational range. In this study, we attempted to identify a stable engine combustion zone using a high-pressure direct injection of ammonia fuel into a 2.5 L spark ignition engine and examined the potential for extending the operational range by adding hydrogen. As it is difficult to secure combustion stability in a low-temperature atmosphere, the experiment was conducted in a sufficiently-warmed atmosphere (90 ± 2.5 °C), and the combustion, emission, and efficiency results under each operating condition were experimentally compared. At 1500 rpm, the addition of 10% hydrogen resulted in a notable 20.26% surge in the maximum torque, reaching 263.5 Nm, in contrast with the case where only ammonia fuel was used. Furthermore, combustion stability was ensured at a torque of 140 Nm by reducing the fuel and air flow rates. Full article

As a novel type of energy storage element, supercapacitors have been extensively used in power systems, transportation and industry due to their high power density, long cycle life and good low-temperature performance. The health status of supercapacitors is of vital importance to the safe operation of the entire energy storage system. In order to improve the prediction accuracy of the remaining useful life (RUL) of supercapacitors, this paper proposes a method based on the Harris hawks optimization (HHO) algorithm and long short-term memory (LSTM) recurrent neural networks (RNNs). The HHO algorithm has the advantages of a wide global search range and a high convergence speed. Therefore, the HHO algorithm is used to optimize the initial learning rate of LSTM RNNs and the number of hidden-layer units, so as to improve the stability and reliability of the system. The root mean square error (RMSE) between the predicted result and the observed result reduced to 0.0207, 0.026 and 0.0341. The prediction results show that the HHO-LSTM has higher accuracy and robustness than traditional LSTM and GRU (gate recurrent unit) models. Full article

A highly polar perovskite SrTiO₃ (STO) layer is considered as one of the promising artificial protective layers for the Zn metal anode of aqueous zinc-ion batteries (AZIBs). Although it has been reported that oxygen vacancies tend to promote Zn(II) ion migration in the STO layer and thereby effectively suppress Zn dendrite growth, there is still a lack of a basic understanding of the quantitative effects of oxygen vacancies on the diffusion characteristics of Zn(II) ions. In this regard, we comprehensively studied the structural features of charge imbalances caused by oxygen vacancies and how these charge imbalances affect the diffusion dynamics of Zn(II) ions by utilizing density functional theory and molecular dynamics simulations. It was found that the charge imbalances are typically localized close to vacancy sites and those Ti atoms that are closest to them, whereas differential charge densities close to Sr atoms are essentially non-existent. We also demonstrated that there is virtually no difference in structural stability between the different locations of oxygen vacancies by analyzing the electronic total energies of STO crystals with the different vacancy locations. As a result, although the structural aspects of charge distribution strongly rely on the relative vacancy locations within the STO crystal, Zn(II) diffusion characteristics stay almost consistent with changing vacancy locations. No preference for vacancy locations causes isotropic Zn(II) ion transport inside the STO layer, which subsequently inhibits the formation of Zn dendrites. Due to the promoted dynamics of Zn(II) ions induced by charge imbalance near the oxygen vacancies, the Zn(II) ion diffusivity in the STO layer monotonously increases with the increasing vacancy concentration ranging from 0% to 16%. However, the growth rate of Zn(II) ion diffusivity tends to slow down at relatively high vacancy concentrations as the imbalance points become saturated across the entire STO domain. The atomic-level understanding of the characteristics of Zn(II) ion diffusion demonstrated in this study is expected to contribute to developing new long-life anode systems for AZIBs. Full article

Some of the most devastating crop diseases and insect pests can be transmitted by wind over extremely long distances. These low-probability but high-impact events can have severe consequences for crop production and food security by causing epidemic outbreaks or devastating insect infestations in previously uninfected geographic areas. Two prominent examples that have recently caused substantial damage to agricultural production are novel strains of wheat rusts and desert locust swarm infestations. Whilst quantitative estimates of long-range atmospheric transport events can be obtained using meteorological transport simulations, the exact characteristics of three-dimensional spatiotemporal dynamics of crop pathogen transport and insect flight on extremely large spatial scales, over entire regions and continents, remain largely unknown. Here, we investigate the feasibility and usefulness of new advanced geospatial data visualization methods for studying extremely long-distance airborne transmission of crop pathogens and insect pests. We combine field surveillance data and a Lagrangian Particle Dispersion Model with novel techniques from computer graphics to obtain, for the first time, detailed three-dimensional visual insights into airborne crop pathogen and insect pest transport on regional and continental scales. Visual insights into long-distance dispersal of pests and pathogens are presented as a series of short 3D movies. We use interactive three-dimensional visual data analysis for explorative examination of long-range atmospheric transport events from a selection of outbreak and infestation sites in East Africa and South East Asia. The practical usefulness of advanced 3D visualization methods for improving risk estimates and early warning is discussed in the context of two operational crop disease and insect pest management systems (for wheat rusts and desert locusts). The tools and methods introduced here can be applied to other pathogens, pests, and geographical areas and can improve understanding of risks posed to agricultural production by crop disease and insect pest transmission caused by meteorological extreme events. Full article

The northeastern part of the cyclonic Rim Current, which encircles the entire basin of the Black Sea, is named as the Northeast Caucasian Current. It periodically approaches the coast, triggering the formation of topographic generated eddies, including long-living isolated anticyclonic eddies and short-living multiple anticyclonic eddies, which group and merge into eddy chain structures. Both types of eddies affect coastal dynamics and interact with multiple river plumes formed in the study area. This interaction determines cross- and along-shelf transport of fluvial water, enhancing the processes of self-cleaning of the coastal zone. In this study, we used a 3D low-dissipation model, DieCAST, coupled with a Lagrangian particle tracking model, and supported by analysis of satellite images, to study the generation and evolution of eddy chains and their interaction with river plumes along the Caucasian coast. Using Fourier and wavelet analyses of kinetic energy time series, we revealed that the occurrence of eddy chains ranges from 10 to 20 days, predominantly in spring-summer season in the area between the Pitsunda and Iskuria capes. During the period of eddy merging, the angular velocities of the orbiting eddies reach maximal values of 7 × 10⁻⁶ rad s⁻¹, while after merging, the angular velocities of the resulting eddies decreased to 5 × 10⁻⁶ rad s⁻¹. Numerical experiments with Lagrangian particle tracking showed that eddy chains effectively capture water from river plumes localized along the coast and then eject it to the open sea. This process provides an effective mechanism of cross-shelf transport of fluvial water, albeit less intense than the influence of isolated anticyclonic eddies, which are typical for autumn-winter season. Full article