Hasil untuk "Ocean engineering"

Sasan Tavakoli, Danial Khojasteh, Milad Haghani et al.

LU Zhan, WANG Jian, MA Qingyan, XU Changjian, LIANG Xiaofeng

Thrust allocation serves as a critical means for achieving vector propulsion in unmanned surface vessels (USV) equipped with dual waterjet thrusters. However, existing thrust allocation methods employed in vessels featuring azimuth thrusters fail to address the resolution of vector forces for dual waterjet propulsion, due to characteristics such as thrust angle limitations and reverse thrust. To achieve vector motion control of a dual waterjet propelled USV, a hierarchical optimization-based thrust allocation algorithm is proposed. In the first tier, a vector synthesis approach incorporating enhanced angle constraints is utilized to acquire top-tier vector thrust satisfying constraints on the rotating range and rate characteristics of the thrusters. In the second tier, leveraging the top-tier vector thrust values as inputs and considering constraints on thruster power and power change frequency, an optimization method based on seeking minimal distance is proposed. This method facilitates the allocation of reverse thrust angles and nozzle flow velocities for waterjet thrusters, thereby resolving singular issues in dual waterjet thrust allocation. Simulation experiments and the semi-physical simulation experiments validate the effectiveness of the hierarchical optimization-based thrust allocation algorithm for dual waterjet thrusters. The results indicate that this method enables efficient thrust allocation for dual waterjet thrusters, while concurrently limiting fluctuations in thruster power frequency and amplitude during expected thrust variations, thereby reducing shafting wear while achieving target vector thrust.

Chang Liu, Mingxue Geng, Jiaxin Yin et al.

Walnut pellicle is rich in polyphenols that enhance antioxidant capacity and health benefits, including anti-inflammatory and neuroprotective effects. Profiling these compounds has been hindered by their structural diversity, regional variability, and the limitations of traditional metabolomics approaches. This study employed a pseudotargeted metabolomics strategy, integrating ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for untargeted profiling and multiple reaction monitoring (MRM) on a QTRAP system for semi-quantification. We analyzed 21 walnut pellicle samples from Xinjiang, Yunnan and the Taihang Mountains, identifying 406 polyphenols, including flavonoids, hydrolysable tannins, phenolic acids, coumarins, lignans and minor constituents. Multivariate analyses (PCA, PLS-DA, OPLS-DA) revealed region-specific metabolic fingerprints. KEGG pathway enrichment highlighted significant variations in flavonoid and phenylpropanoid biosynthesis across regions, with origin-specific markers like casuarinin, prunin, and ε-viniferin supporting provenance authentication. This research bridges the methodological gap in walnut polyphenol analysis and informs quality assurance, targeted breeding, and functional product development in sustainable food systems.

Ruoxuan Li, Xiangyu Kong, He Li et al.

This paper proposes a cost-based dynamic risk model for offshore wind turbines, leveraging reliability and maintenance data from real offshore wind farms. The model integrates information on wind farm configurations into failure cost modelling and as a basis of that quantifies time-varying failure probabilities and consequences of onsite failures. The model enables dynamic and comparable risk assessment among various offshore wind farms. Results reveal that the auxiliary system represents the highest economic risk, particularly during winter months, and that increasing the distance to the onshore maintenance base leads to a greater rise in system and component risk than simply increasing turbine capacity. The feasibility and advantages of the proposed method are validated through systematic comparative analysis with existing approaches. Overall, this research provides a robust framework for cost-based dynamic risk analysis, supporting economically informed decision-making and season-specific maintenance strategies for offshore wind farms.

Rashina Hoda

Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.

Richard J. Cartwright, Lynnae Quick, Marc Neveu et al.

The instrument payload of the future Habitable Worlds Observatory (HWO) will span a wide range of wavelengths, including the ultraviolet (UV) region that cannot be easily accessed from the ground (< 350 nm). Along with its primary mission to characterize the habitability of candidate exo-Earths, HWO will be well suited for observations of potentially habitable icy ocean worlds in our Solar System, in particular with an integral field spectrograph (IFS). Here, we discuss future HWO observations of ocean worlds including Ceres, Europa, Enceladus, Ariel, and Triton. We explore the observational requirements for capturing ongoing and sporadic geyser activity and for measuring the spectral signatures of astrobiologically-relevant compounds, including water, salts, organics, and other bioessential components. We consider the key observing requirements for an IFS, including wavelength coverage, resolving power (R), angular resolution, and field-of-view (FOV). We also outline some of the potential measurements that would define incremental, substantial, and breakthrough progression for characterizing habitability at ocean worlds, primarily focusing on UV and visible (VIS) wavelengths (90 - 700 nm). Our investigation concludes that a UV/VIS IFS on HWO could make some groundbreaking discoveries, in particular for detection and long-term monitoring of geyser activity and interior-surface exchange of components critical for understanding habitability at ocean worlds.

Shiruo Hu, Wenbo Shan, Yingjia Li et al.

Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.

Angeliki Xenaki, Peter Gerstoft, Ethan Williams et al.

Extensive monitoring of acoustic activities is important for many fields, including biology, security, oceanography, and Earth science. Distributed acoustic sensing (DAS) is an evolving technique for continuous, wide-coverage measurements of mechanical vibrations, which is suited to ocean applications. DAS illuminates an optical fiber with laser pulses and measures the backscattered wave due to small random variations in the refractive index of the material. External stimuli, such as mechanical strain due to acoustic wavefields impinging on the fiber-optic cable, modulate the backscattered wave. Continuous measurement of the backscattered signal provides a distributed sensing modality of the impinging wavefield. Considering the potential use of existing telecommunication fiber-optic cables deployed across the oceans, DAS has emerged as a promising technology for monitoring the underwater soundscape. This review presents advances in DAS in the last decade and details the underlying physics from electromagnetic to mechanical and eventually acoustic quantities. To guide the use of DAS for ocean applications, the effect of DAS acquisition parameters in signal processing is explained. Finally, DAS is demonstrated on data from the Ocean Observatories Initiative Regional Cabled Array for the detection of sound sources, such as whales, ships, and earthquakes.

Pavel Perezhogin, Alistair Adcroft, Laure Zanna

Data-driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly in generalization tasks and may require retuning if the grid resolution or ocean configuration changes. We address the generalization problem by enforcing physics constraints on a neural network parameterization of mesoscale eddy fluxes. We found that the local scaling of input and output features helps to generalize to unseen grid resolutions and depths offline in the global ocean. The scaling is based on dimensional analysis and incorporates grid spacing as a length scale. We formulate our findings as a general algorithm that can be used to enforce data-driven parameterizations with dimensional scaling. The new parameterization improves the representation of kinetic and potential energy in online simulations with idealized and global ocean models. Comparison to baseline parameterizations and impact on global ocean biases are discussed.

Vahidreza Jahanmard, Ali Ramezani-Kebrya, Robinson Hordoir

Neural operators are becoming the default tools to learn solutions to governing partial differential equations (PDEs) in weather and ocean forecasting applications. Despite early promising achievements, significant challenges remain, including long-term prediction stability and adherence to physical laws, particularly for high-frequency processes. In this paper, we take a step toward addressing these challenges in high-resolution ocean prediction by incorporating temporal Fourier modes, demonstrating how this modification enhances physical fidelity. This study compares the standard Fourier Neural Operator (FNO) with its variant, FNOtD, which has been modified to internalize the dispersion relation while learning the solution operator for ocean PDEs. The results demonstrate that entangling space and time in the training of integral kernels enables the model to capture multiscale wave propagation and effectively learn ocean dynamics. FNOtD substantially improves long-term prediction stability and consistency with underlying physical dynamics in challenging high-frequency settings compared to the standard FNO. It also provides competitive predictive skill relative to a state-of-the-art numerical ocean model, while requiring significantly lower computational cost.

Zhiming Chao, Yanqi Liu, Danda Shi et al.

Calcareous sand is widely used as fill material in island construction projects in the South China Sea. The mechanical properties of the interface between calcareous sand and geogrid under high temperatures and complex environmental conditions play a critical role in the long-term stability of such structures. In this study, interfacial pullout tests between calcareous sand and a geogrid are conducted under six temperature conditions (−5 °C, 0 °C, 20 °C, 40 °C, 60 °C, and 80 °C) and various normal stress levels. A database containing 1178 data sets is established from these tests. Based on the test data, four predictive models are developed: support vector machine (SVM), particle swarm optimization SVM (PSO-SVM), genetic algorithm optimization SVM (GA-SVM), and a deep learning long short-term memory network (LSTM). The results indicate that the LSTM model provides significantly higher predictive accuracy and robustness compared to traditional machine learning models, achieving an R2 value of 0.97 on both training and testing datasets and superior performance in RMSE, MAPE, MAE, and MSE. Sensitivity analysis using SHAP values shows that shear displacement has the greatest influence on shear strength, followed by temperature, normal stress, and particle size. Furthermore, based on the LSTM model predictions, an empirical formula for shear strength is proposed, enabling engineers without expertise in deep learning to estimate the shear strength of calcareous sand–geogrid interfaces effectively.

Jing Ni, Lirong Ren, Ying Liang et al.

Selenium nanoparticles (SeNPs) are considered safe selenium supplements. At appropriate concentrations, SeNPs can promote growth, boost immunity, and modulate intestinal microbiota in aquatic animals. However, their effects on the metabolome of aquatic species have not yet been identified. This study evaluated the in vitro effects of our previous synthesized SeNPs, coated with abalone visceral polysaccharide-protein complexes (PSP-SeNPs), on the proliferation of probiotics and common aquatic pathogens, and the in vivo effects on the gut microbiome and metabolome of tilapia. PSP-SeNPs selectively promoted the growth of probiotics Lactobacillus rhamnosus and Lactococcus lactis, while inhibiting the growth of pathogens such as Staphylococcus aureus and Aeromonas hydrophila. In the fish breeding experiment, 360 healthy juvenile tilapias (initial weight 1.5 ± 0.5 g/fish) were divided into four groups (3 replicates/group, 30 fish/replicate): group C was fed with basal diet, groups Y, M and H, were fed with the basal diet supplemented with 0.3 mg/kg of Na2SeO3, 0.3 mg/kg of PSP-SeNPs, and 4.5 mg/kg of PSP-SeNPs, respectively. All fish were tested after 7 weeks of breeding in a recirculating aquaculture system. Results showed that group M significantly increased intestinal microbial diversity and potential probiotics (e.g., Cetobacterium, Fimbriiglobus, and Gemmata), while significantly decreased potential pathogens (Plesiomonas, Citrobacter freundii, and Aeromonas hydrophila). Groups H and Y significantly reduced Citrobacter freundii, but both groups decreased gut microbial diversity to varying degrees. Additionally, the pathogenic bacterial genus Plesiomonas significantly increased in group Y. In group M, the growth-promoting intestinal metabolite alpha-tocotrienol was significantly upregulated, whereas some essential amino acids were significantly downregulated in groups Y and H. In summary, 0.3 mg/kg SeNPs supplementation can regulate the intestinal microbiota, while 4.5 mg/kg SeNPs and 0.3 mg/kg Na2SeO3 can cause amino acid metabolism disorders. SeNPs showed higher safety than Na2SeO3.

Sepand Dyanatkar, Angran Li, Alexander Dungate

Climate change's destruction of marine biodiversity is threatening communities and economies around the world which rely on healthy oceans for their livelihoods. The challenge of applying computer vision to niche, real-world domains such as ocean conservation lies in the dynamic and diverse environments where traditional top-down learning struggle with long-tailed distributions, generalization, and domain transfer. Scalable species identification for ocean monitoring is particularly difficult due to the need to adapt models to new environments and identify rare or unseen species. To overcome these limitations, we propose leveraging bottom-up, open-domain learning frameworks as a resilient, scalable solution for image and video analysis in marine applications. Our preliminary demonstration uses pretrained vision-language models (VLMs) combined with retrieval-augmented generation (RAG) as grounding, leaving the door open for numerous architectural, training and engineering optimizations. We validate this approach through a preliminary application in classifying fish from video onboard fishing vessels, demonstrating impressive emergent retrieval and prediction capabilities without domain-specific training or knowledge of the task itself.

Minghui Yi, Fei Liu, Baoning Chang et al.

Abstract Automated fiber placement (AFP) enables the efficient and precise fabrication of complex-shaped aerospace composite structures with lightweight and high-performance properties. However, due to the excessive compression on the inner edge of the tow placed along the curved trajectory, the resulting defects represented by buckling and wrinkles in spatial tow steering can induce poor manufacturing accuracy and quality degradation of products. In this paper, a theoretical model of tow buckling based on the first-order shear deformation laminate theory, linear elastic adhesion interface and Hertz compaction contact theory is proposed to analyze the formation mechanism of the wrinkles and predict the formation of defects by solving the critical radius of the trajectory, and finite element analysis involving the cohesive zone modeling (CZM) is innovated to simulate the local buckling state of the steered tow in AFP. Additionally, numerical parametric studies and experimental results indicate that mechanical properties and geometric parameters of the prepreg, the curvature of the placement trajectory and critical process parameters have a significant impact on buckling formation, and optimization of process parameters can achieve effective suppression of placement defects. This research proposes a theoretical modeling method for tow buckling, and conducts in-depth research on defect formation and suppression methods based on finite element simulation and placement experiments.

Asuna Gilfoyle, Willow Baird

Ocean acidification, a direct consequence of increased carbon dioxide (CO2) emissions, has emerged as a critical area of concern within the scientific community. The world's oceans absorb approximately one-third of human-caused CO2 emissions, leading to chemical reactions that reduce seawater pH, carbonate ion concentration, and saturation states of biologically important calcium carbonate minerals. This process, known as ocean acidification, has far-reaching implications for marine ecosystems, particularly for marine organisms such as fish, whose migratory patterns are integral to the health and function of these ecosystems.

Ertugrul Basar

Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.

Yong Yang, Chenxu Wan, Shuying Huang et al.

The purpose of pansharpening is to fuse a multispectral (MS) image with a panchromatic (PAN) image to generate a high spatial-resolution multispectral (HRMS) image. However, the traditional pansharpening methods do not adequately take consideration of the information of MS images, resulting in inaccurate detail injection and spectral distortion in the pansharpened results. To solve this problem, a new pansharpening approach based on adaptive high-frequency fusion and injection coefficients optimization is proposed, which can obtain an accurate injected high-frequency component (HFC) and injection coefficients. First, we propose a multi-level sharpening model to enhance the spatial information of the MS image, and then extract the HFCs from the sharpened MS image and PAN image. Next, an adaptive fusion strategy is designed to obtain the accurate injected HFC by calculating the similarity and difference of the extracted HFCs. Regarding the injection coefficients, we propose injection coefficients optimization scheme based on the spatial and spectral relationship between the MS image and PAN image. Finally, the HRMS image is obtained through injecting the fused HFC into the upsampled MS image with the injection coefficients. Experiments with simulated and real data are performed on IKONOS and Pl&#x00E9;iades datasets. Both subjective and objective results indicate that our method has better performance than state-of-the-art pansharpening approaches.

Enlin Wang, Xinghua Lv, Shaoxiao Liu et al.

Positively charged nanofiltration (NF) technology is considered a green and low-cost method for mono/divalent cation separation. Nevertheless, the separation rejection mechanisms of these NF membranes have yet to be extensively investigated. In this work, we fabricated a thin-film composite (TFC) hollow-fiber (HF) NF membrane with a positively charged surface via modification of the nascent interfacial polymerization layer using a branched polyethyleneimine (BPEI)/ethanol solution. Then, we extensively investigated its selective separation mechanism for mono/divalent cations. We proposed and proved that there exists a double-charged layer near the membrane surface, which helps to repel the divalent cations selectively via Donnan exclusion while promoting the fast penetration of monovalent cations. Meanwhile, the membrane skin layer is loose and hydrophilic due to the loose BPEI structure and the abundance of amine groups, as well as the changed fabrication conditions. In this way, we achieved very good mono/divalent cation selectivity and relatively high water permeance for the as-prepared HF NF membrane. We also obtained good anti-fouling, anti-scaling, and acid resistance, and long-term stability as well, which are urgently needed during practical application. Furthermore, we successfully amplified this HF NF membrane and proved that it has broad application prospects in mono/divalent cation separation.

Lizheng Wang, Fanglin Niu, Daxing Qian et al.

In wireless communication wiretap channel, for the eavesdropper to obtain the legitimate receiver decoding rules situation, this paper proposes a LT-SLT fountain code anti-eavesdropping channel coding design. This method targets the Luby Transform (LT) code transmission for some of the original symbols of the Shifted LT (SLT) code and utilizes the fountain code to receive the correct symbols in different noise channels with differential characteristics so that the decoding process of the eavesdropper changes and cannot be decoded in synchronization with the legitimate receivers, and then the partial symbols of the recovered source are different from those of the legitimate receivers. When these symbols continue to participate in SLT decoding, increasing the untranslated rate of the eavesdropper. Experimental results show that although the method proposed in this paper increases the number of decoded symbols by a small amount, the eavesdropper untranslated rate of this scheme gets improved by about 15&#x0025; when the main channel or the wiretap channel is varied individually, compared with LT code and SLT code. When both the main channel and the wiretap channel are varied simultaneously, the untranslated rate of the eavesdropper in this scheme gets approximately 30&#x0025; higher compared to LT code and SLT code, and the untranslated rate of the eavesdropper in this scheme gets approximately 14&#x0025; higher compared to SLT-LT fountain code. When the main channel is worse or slightly better than the wiretap channel, the untranslated rate of eavesdroppers of this scheme is better than that of SLT-LT fountain code, which effectively ensures secure transmission of information.

Andrew G. Sullivan, Lucas M. B. Alves, Georgina O. Spence et al.

Neutron stars in astrophysical binary systems represent exciting sources for multi-messenger astrophysics. A potential source of electromagnetic transients from compact binary systems is the neutron star ocean, the external fluid layer encasing a neutron star. We present a groundwork study into tidal waves in neutron star oceans and their consequences. Specifically, we investigate how oscillation modes in neutron star oceans can be tidally excited during compact binary inspirals and parabolic encounters. We find that neutron star oceans can sustain tidal waves with frequencies between $0.01-20$ Hz. Our results suggest that tidally resonant neutron star ocean waves may serve as a never-before studied source of precursor electromagnetic emission prior to neutron star-black hole and binary neutron star mergers. If accompanied by electromagnetic flares, tidally resonant neutron star ocean waves, whose energy budget can reach $10^{46}$ erg, may serve as early warning signs ($\gtrsim 1$ minute before merger) for compact binary mergers. Similarly, excited ocean tidal waves will coincide with neutron star parabolic encounters. Depending on the neutron star ocean model and a flare emission scenario, tidally resonant ocean flares may be detectable by Fermi and NuSTAR out to $\gtrsim 100$ Mpc with detection rates as high as $\sim 7$ yr$^{-1}$ for binary neutron stars and $\sim0.6$ yr$^{-1}$ for neutron star-black hole binaries. Observations of emission from neutron star ocean tidal waves along with gravitational waves will provide insight into the equation of state at the neutron star surface, the composition of neutron star oceans and crusts, and neutron star geophysics.