To optimize the ignition and combustion performance of nanosecond pulsed surface dielectric barrier discharge (nSDBD) electrodes for NH3/air mixtures, an experimental investigation was conducted by varying the number of radial copper strips (number of conductive areas: CAN = 6, 9, 12) and width (width of conductive areas: CAW = 1 mm, 2 mm, 3 mm) on the powered electrode. Experiments were performed in a constant-volume combustion chamber under an NH3/air mixture with an initial temperature of 363 K, initial pressure of 2 bar, and an excess air ratio (lambda) of 1.0. Diagnostics were implemented using high-speed camera and pressure sensors, with nanosecond pulse parameters set to 100 pulses and a pulse repetition frequency of 20 kHz. The regulatory effects of electrode structural parameters on flame kernel development, flame propagation, and combustion characteristics were systematically analyzed. The results indicate that the CAN and CAW directly govern the flame kernel morphology and evolution by modulating the distribution of discharge energy. Under the optimal configuration of CAN = 6 and CAW = 3 mm, the electric field distribution is uniform with moderate energy density, leading to the stable generation of active free radicals (e.g., O, OH, H). Consequently, the synchronization of flame kernel ignition is maximized, with a consistent, stable number of 6 initial kernels, without obvious fusion or extinction. This configuration significantly compresses the flame development time (FDT = 8.5 ms) and flame rise time (FRT = 26.5 ms). Additionally, the combustion pressure peak occurs earliest (at 54 ms), and the cumulative heat release rate increases the fastest in the early stage. The findings demonstrate that the nSDBD electrode with CAN = 6 and CAW = 3 mm achieves efficient regulation of combustion performance in NH3/air mixtures by leveraging multi-scale synergy among "electric field distribution-energy deposition-active species generation-flame kernel development-combustion timing". This study provides critical technical parameters and theoretical support for the optimal design of ignition systems in ammonia-fueled engines.
To overcome the combustion bottlenecks of ammonia (NH3), such as its long ignition delay and slow flame propagation, this study proposes a “pretreatment-ignition” dual-group discharge strategy based on nanosecond pulsed surface dielectric barrier discharge (nSDBD). Using a self-developed nanosecond pulsed discharge system, combined with a constant volume combustion chamber (CVCC), a high-speed camera, and a combustion pressure test platform, the regulatory mechanism of pretreatment parameters on the discharge characteristics, ignition process, and flame development of NH3/air mixture was systematically investigated. The effects of pretreatment discharge frequency (10.0-12.5 kHz), pretreatment pulse number (PPN = 20-120), and ignition interval (tTI = 1-30 ms) on discharge energy deposition, discharge filament morphology, flame kernel development, and combustion characteristic parameters were analyzed emphatically. The results show that the discharge energy increases with frequency. At the same frequency, the cumulative energy of 50 positive polarity pulses is 11% higher than that of negative polarity pulses. Pretreatment can regulate ignition characteristics through the retention of active particles and local thermal accumulation. With the increase of ignition interval, the length of discharge filaments increases slightly, while the number of discharge filaments decays faster. Pretreatment can increase the initial flame kernel area and the number of stable flame kernels. Excessively high PPN (e.g., 120) will inhibit flame kernel growth due to enhanced gas flow disturbance. At high ignition pulse number (IPN = 120), pretreatment cannot shorten the flame development time (FDT), but it can reduce the flame rise time (FRT). Under low IPN (80), the PPN has a significant impact on the initial combustion rate. There exists an optimal parameter set that balances the promotion effect of active particles and the inhibition effect of the gas flow. The nSDBD dual-group discharge regulatory mechanism revealed in this study provides a new technical path for efficient ignition and combustion optimization of low-reactivity fuels such as NH3.
Modeling dynamic, large-scale urban scenes is challenging due to their highly intricate geometric structures and unconstrained dynamics in both space and time. Prior methods often employ high-level architectural priors, separating static and dynamic elements, resulting in suboptimal capture of their synergistic interactions. To address this challenge, we present a unified representation model, called Periodic Vibration Gaussian (PVG). PVG builds upon the efficient 3D Gaussian splatting technique, originally designed for static scene representation, by introducing periodic vibration-based temporal dynamics. This innovation enables PVG to elegantly and uniformly represent the characteristics of various objects and elements in dynamic urban scenes. To enhance temporally coherent and large scene representation learning with sparse training data, we introduce a novel temporal smoothing mechanism and a position-aware adaptive control strategy respectively. Extensive experiments on Waymo Open Dataset (Sun et al., 2020) and KITTI benchmarks (Geiger et al., 2012) demonstrate that PVG surpasses state-of-the-art alternatives in both reconstruction and novel view synthesis for both dynamic and static scenes. Notably, PVG achieves this without relying on manually labeled object bounding boxes or expensive optical flow estimation. Moreover, PVG exhibits 900-fold acceleration in rendering over the best alternative. The code is available at https://github.com/fudan-zvg/PVG .
During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.
Access to clean energy-here defined as electricity, liquefied petroleum gas, biogas, and ethanol-has increased substantially in low-income and middle-income countries over the past three decades. However, millions still lack reliable and affordable access to electricity and clean cooking fuels. This Series paper explores the drivers of clean energy adoption, assesses tools for tracking progress, and examines persistent barriers-including high costs, unreliable supply, and insufficient availability. Simplistic metrics, such as Sustainable Development Goal 7's binary indicators (eg, whether an individual has an electricity connection or not), risk overstating the health and equity impacts of energy transitions by overlooking fuel stacking, dynamic consumption patterns, and the gendered burden of polluting fuels. Drawing from historical trends and national policies, we show how targeted subsidies, robust supply chains, and coordinated investments have spurred increased clean fuel use. Meaningful gains require moving beyond technical fixes to inclusive, evidence-based strategies that address inequities, ensure affordability and reliability, and deliver lasting health benefits.
