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Quantifying the decay timescale of volcanic sulfur dioxide in the stratosphere
Get PDFThe injection of sulfur dioxide (SO2) into the stratosphere and its subsequent oxidation to form sulfate aerosols after large volcanic eruptions can have profound effects on Earth\u27s climate. The removal of volcanic SO2 in the stratosphere is thought to be driven by its gas-phase oxidation by the hydroxyl radical (OH); once oxidized, it goes on to form sulfate aerosols. However, it has also been suggested that heterogeneous oxidation on ash could also be important or even dominant, which would imply the faster removal of SO2 and thus the faster formation of aerosols, at least in ash-rich plumes. Additionally, recent work uses an assumed exponential fit to determine the total SO2 mass loading following large eruptions; the quality of this fit translates directly to the accuracy of the mass loading estimate. It is therefore of interest to examine how accurately the SO2 decay timescale can be determined from observations and to compare observations to models. Here we evaluate the SO2 decay timescale and its uncertainties following several significant eruptions using three different sets of satellite observations and compare these to the CESM2-WACCM6 model. We show that defining an accurate baseline against which a volcanic injection can be quantified increases the variability and uncertainty in the estimated decay timescale for some satellite datasets. While the typical decay timescale for SO2 is on the order of a few weeks to a month, we find that uncertainties across different altitudes and eruptions result in timescales that can vary by more than a factor of 2. This makes it difficult to attribute variations in the decay timescale to specific SO2-removal processes for the events examined
Turbulence without Walls: Whither the Zeroth Law of Turbulence?
No full textExperimental and numerical studies of incompressible turbulence suggest that the mean dissipation rate of kinetic energy remains constant as the Reynolds number tends to infinity (or the nondimensional viscosity tends to zero). This anomalous behavior is central to many theories of high-Reynolds-number turbulence and has been termed the zeroth law for this reason. Here, we report a sequence of direct numerical simulations of incompressible Navier-Stokes in a box with periodic boundary conditions, which indicate the likelihood that the anomaly vanishes at a rate that agrees with the scaling of third moment of absolute velocity increments. Our results suggest that turbulence without solid boundaries or walls may not develop strong enough singularities to sustain the strict version of the zeroth law
The Impact of TiO Nanoparticles on the Freezing Properties of Droplets
No full textAbstract: To investigate the stationary nanofluid droplets effects of substrate surface temperature and particle concentration on the freezing time, deformation, and droplet contact angle during the freezing process, high-speed CCD image observation was used to study the morphological changes during the freezing process of (TiO–HO) nanodroplets. Nanoparticle droplets were prepared in this study using magnetic stirring and ultrasonic mixing. Three substrate surface temperatures (268, 265, and 263 K)and four concentrations of TiO nanoparticles (5, 10, 30, and50 mg/mL) were considered. The findings demonstrate that the addition of nanoparticles will result in the droplets appearing sanded, a considerable change in the form of the droplet tip, and a decrease in the release of bubbles upon freezing. The increase in supercooling at high concentrations (mg/mL) causes the droplet height to rise, its volume to expand upon freezing, and its shape to shift from ‘‘peach-core’’ to ‘‘cone-like.’’ When TiO nanoparticles were added, the droplets’ longitudinal morphology changed throughout the freezing process, but lateral diffusion was unaffected, even though the contact angle marginally shrank as concentration increased. The droplets with the lowest concentration of TiO particles exhibit the highest longitudinal deformation rate during the droplet freezing process. As the subcooling degree increases, also rises and reaches its maximum at 263 K or 22.26, but as the concentration of nanoparticles grows, drops, and so does the coefficient of segregation,
3D printing of spider web-inspired sound absorbers
No full textSpider webs, with their intricate structures and unique material properties, are among nature’s most elaborate cellular materials. However, replicating their complex designs and performance using conventional manufacturing techniques remains a significant challenge. In this study, we harness our advanced 3D printing capabilities to fabricate realistic spider web structures with enhanced precision for potential noise reduction application. By customizing G-code, we achieve precise control over critical parameters such as thread density, diameter, and geometry, enabling the accurate reproduction of key features found in natural spider webs, including the frame, hub, and spiral threads. To enhance their acoustic functionality, we stack multiple layers of these 3D printed webs to create bulk sound absorbers with a hierarchically structured internal architecture. We test the sound absorption performance using a two-microphone normal-incidence impedance tube setup. This work demonstrates the transformative potential of bio-inspired designs in acoustic engineering, showcasing how nature’s ingenuity, combined with advanced additive manufacturing, can open new pathways for developing innovative, customizable, and sustainable noise control solutions
Nickel Superalloy Composition and Process Optimization for Weldability, Cost, and Strength
No full textAdvanced power generation systems, including advanced ultrasupercritical (A-USC) steam and supercritical carbon dioxide (sCO2) plants operating above 700°C, are crucial for reducing carbon dioxide emissions through improved efficiency. While nickel superalloys meet these extreme operating conditions, their high cost and poor weldability present significant challenges. This study employs integrated computational materials engineering (ICME) strategies, combining computational thermodynamics and kinetics with multi-objective Bayesian optimization (MOBO), to develop improved nickel superalloy compositions. The novel approach focuses on utilizing Ni3Ti (η) phase strengthening instead of conventional Ni3(Ti,Al) (γ’) strengthening to enhance weldability and reduce costs while maintaining high-temperature creep strength. Three optimized compositions were produced and experimentally evaluated through casting, forging, and rolling processes, with their microstructures and mechanical properties compared to industry standards Nimonic 263, Waspaloy, and 740H. Weldability assessment included solidification cracking and stress relaxation cracking tests, while hot hardness measurements provided strength screening. The study evaluates both the effectiveness of the ICME design methodology and the practical potential of these cost-effective η-phase strengthened alloys as replacements for traditional nickel superalloys in advanced energy applications
Determinants of Mangrove Dependence and Implications for Sustainable Livelihoods: Insights from Xuan Thuy National Park’s Buffer Zone
No full textThis study aimed to answer three research questions (1) How do mangroves contribute to household income in the buffer zone of Xuan Thuy National Park, Vietnam? (2) What factors determine households’ dependence on mangroves? and 3) What are the climate-related observations and changes that mangrove-dependent households have noticed in the last five years? Data were collected from 186 households across three rural communes through questionnaire surveys, 10 in-depth interviews, and three focus group discussions. Results indicate that mangroves-related income contributed 52.5% of total annual profits for surveyed households, followed by non-farm activities (40.6%), offshore fishing (6.7%), and agriculture (0.3%). A multivariate regression analysis revealed that experience in mangrove aquaculture, market knowledge, occupation type, and participation in training programs positively influenced household dependence on mangroves. Conversely, higher income from crops, non-farm jobs, and offshore fishing reduced mangrove resource extraction. Surprisingly, factors like land area and number of laborers did not impact mangrove dependence. Policy implications emphasize the need for mangrove conservation, value-addition of aquaculture products, sustainable farming practices, livelihood diversification, and community-based mangrove management approaches. The study contributes to the understanding of socio-ecological dynamics in mangrove-dependent communities and provides insights for balancing conservation efforts with livelihood development
Optimized Trajectory Planning for USVs Under Ocean Currents
No full textThe proposed work focuses on the trajectory planning for unpiloted surface vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. This article uses Gaussian process motion planning (GPMP), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work improves )GPMP by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where optimal path planning for USVs is essential for minimizing costs and optimizing performance
Experimental Warming Alters Free-living Nitrogen Fixation in a Humid Tropical Forest
Get PDFMicrobial nitrogen (N) fixation accounts for c. 97% of natural N inputs to terrestrial ecosystems. These microbes can be free-living in the soil and leaf litter (asymbiotic) or in symbiosis with plants. Warming is expected to increase N-fixation rates because warmer temperatures favor the growth and activity of N-fixing microbes.We investigated the effects of warming on asymbiotic components of N fixation at a field warming experiment in Puerto Rico. We analyzed the function and composition of bacterial communities from surface soil and leaf litter samples.Warming significantly increased asymbiotic N-fixation rates in soil by 55% (to 0.002 kg ha−1 yr−1) and by 525% in leaf litter (to 14.518 kg ha−1 yr−1). This increase in N fixation was associated with changes in the N-fixing bacterial community composition and soil nutrients.Our findings suggest that warming increases the natural N inputs from the atmosphere into this tropical forest due to changes in microbial function and composition, especially in the leaf litter. Given the importance of leaf litter in nutrient cycling, future research should investigate other aspects of N cycles in the leaf litter under warming conditions
Trust-Based Adjustment of Measurement Discrepancies in Distribution Networks
Get PDFThis paper addresses security challenges in modern electricity distribution systems, where supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) networks are increasingly exposed to cyber threats and fraudulent activities, leading to metering discrepancies. As smart grids become more interconnected, identifying compromised meters and devices at scale requires a robust inference framework. While alarm systems provide real-time detection of anomalies such as abrupt energy consumption changes, data losses, and security breaches, the lack of seamless integration between SCADA and AMI alarm data limits their effectiveness. To enhance grid security and resilience, this paper presents a data-driven approach for evaluating metering network trustworthiness by analyzing measurement variations from feeder remote terminal units (FRTUs) and IP-based energy meters (EMs) across primary and secondary distribution networks. The proposed probabilistic trust model leverages historical alarm data and event logs, demonstrating its ability to detect discrepancies in a simulated environment. The inferred trust scores are then used to re-weight and reconcile conflicting measurements, allowing the system to attenuate the influence of untrusted data during anomaly detection and state estimation. By correlating alarm patterns with metering anomalies, this approach strengthens cyber-physical security, enhances operational transparency, and supports the transition to more secure and intelligent distribution networks
Sea state uncertainty-aware monitoring of underwater mooring systems using domain-adapted deep learning techniques
No full textUnderwater mooring systems are essential for marine infrastructure safety but face stiffness reduction and potential failure due to long-term environmental loads like waves and currents, requiring timely and accurate health monitoring. Data-driven deep learning techniques, which identify mooring system health from dynamic responses, offer more efficient and cost-effective solutions compared to traditional methods. However, the high complexity and uncertainty of the sea state pose challenges for effective monitoring tasks utilizing general deep learning models. While ocean wave spectra are often considered invariant over relatively short time scales, the exact time series of free sea surface elevation remains inherently unpredictable, which interferes with the health-related features to degrade the monitoring reliability and accuracy. To address this challenge, this study integrates domain adaptation techniques into deep learning models to mitigate distribution discrepancies and enhance the generalization of these models. Case studies demonstrate that the model exhibits significantly improved generalization ability, showing great potential for managing mooring system monitoring in practical applications