Hasil untuk "Ocean engineering"

E. B. Farquhar, E. B. Goldstein, P. J. Bresnahan et al.

Abstract Chronic flooding is an issue for low‐lying coastal communities globally, and it is expected to worsen with rising sea levels. Predicting when and where these floods occur can be difficult as they can be hyper‐local and ephemeral, depending on the flood drivers (e.g., tides, rain). These factors make it difficult to measure the full spatial and temporal extent of chronic floods with in situ sensors. Here, we introduce a low‐cost (<$400 USD), privacy‐preserving camera system that identifies flooding over block‐by‐block spatial extents at high frequencies (20 s–6 min). Our device—a Tiny Camera with machine learning (ML) (TinyCamML)—is a small, solar‐powered, microcontroller‐based camera that uses on‐device ML to classify images of roadways as containing a “flood” or “no flood.” TinyCamMLs transmit only the classifications (a 1 or 0) to a website in real time, providing situation awareness during flood events over the entire image area while keeping data‐transmission costs low and preserving privacy. We demonstrate the TinyCamML's utility during both tidal and compound flood events in North Carolina, USA, which showed differences in flood spatial extents. During this deployment, the TinyCamML detected floods with an 81% accuracy, a 72% precision, and a 90% recall. The utility of the device extends beyond roadway flooding, as the onboard ML model can be easily retrained to capture other rare or ephemeral phenomena.

Renjiong Xu, Zushuai Wei, Shiliang Fu et al.

China&#x2019;s FY-3/4 satellite constellation has significantly advanced global environmental monitoring capabilities. However, the inherent temporal resolution limitations of polar-orbiting satellites result in discontinuous spatiotemporal coverage of current soil moisture products, thereby constraining their application in global hydrological modeling. To overcome this challenge, this study introduces a dedicated spatiotemporal reconstruction model that enhances the spatial continuity of FY-3B satellite soil moisture datasets. The proposed model utilizes a dual-channel input architecture integrating continuous observation data with dynamic mask matrix and leverages partial convolution for effective collaborative spatiotemporal feature extraction. Contextual attention and multilayer transformer encoder are incorporated to generate seamless global daily soil moisture product (2010&#x2013;2019). Validation indicate notable improvements in accuracy: 1) in situ validation increased the mean correlation coefficient from 0.542 to 0.671 and reduce the root mean square error from 0.147 to 0.143 m³/m³; 2) temporal consistency analysis confirms that the reconstructed sequence remains highly synchronized with the original soil moisture products; 3) simulated missing region experiments yielded a coorelation of 0.953 with the original products, with a bias as low as &#x2013;0.004 m³/m³ and an unbiased root mean square error of 0.006 m³/m³. Compared to traditional partial convolution methods, this approach enhances global accuracy by increasing the correlation by 23.8&#x0025;. Notably, this research marks the first implementation of a hybrid attention-partial convolution deep learning model to generate a seamless global daily soil moisture product derived from Fengyun satellites, effectively addressing previous reconstruction limitations.

Bobby Antonio, Kristian Strommen, Hannah M. Christensen

There have recently been many efforts to create machine learnt atmospheric emulators designed to replace physical models. So far these have mainly focused on medium-range weather forecasting, where these `Machine Learnt Weather Prediction' (MLWP) models can outperform leading operational forecasting centres. However, because of this focus on shorter timescales, many of these emulators ignore the effects of the ocean, and take no ocean variables as inputs. We hypothesise that such MLWP models have learnt a best-guess of the evolution of the atmosphere, by implicitly inferring ocean conditions from atmospheric states, with no access to ocean data. Turning this limitation into a strength, we use it as a means to study the role of the oceans on the evolution of the atmosphere. By exploring how model forecast errors relate to properties of the air-sea interface, we infer what ocean information these atmospheric emulators are able to derive from atmospheric data alone, and what they cannot. This highlights the regions and processes through which the ocean independently influences the atmosphere on fast timescales. We perform this analysis for GraphCast, finding clear relationships between air-sea properties and the forecast errors over the ocean, including clear seasonal effects. We then explore what this reveals about GraphCast's internal representation of the ocean. In addition to understanding real-world ocean-atmosphere interactions, this analysis provides guidance for improving forecast skill and physical realism in MLWP models, and for informing how future machine learning models should use ocean information on short timescales.

H. Sinan Bank, Daniel R. Herber

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

Zhenghui Li, Cong Hu, Geetanjali Kishan Lohar et al.

Low–angle submarine landslides pose a greater threat to offshore infrastructure compared to those with steep sliding angles. Understanding the preparation and triggering mechanism of these low–angle submarine landslides remains a significant challenge. This study focuses on a deformed low–angle submarine landslide in the shelf–slope break of the Pearl River Mouth Basin, South China Sea, integrating sedimentology, geophysics, and geotechnology to investigate potential failure mechanisms. The architecture and deformation characteristics of the submarine landslide were elucidated by analyzing multibeam and seismic data. Within the context of the regional geological history and tectonic framework, this study focuses on the factors (e.g., rapid sedimentation, fluid activity, and earthquakes) that potentially contributed to the submarine slope failure. Furthermore, a series of stability evaluations considering the effects of rapid sedimentation and earthquakes was conducted. Our findings indicate that the most probable triggering mechanism involves the combined effects of sedimentation controlled by sea–level fluctuations, high–pressure gas activity, and seismic events. The high–pressure gas, which acts as a long–term preconditioning factor by elevating pore pressures and reducing shear resistance within the sediment, accumulated beneath the upper and middle sections of the low–permeability stratum that was formed during sea–level rise and ultimately evolved into the sliding mass. The overpressure generated by gas accumulation predisposed the submarine slope to instability, and a frequent or moderate earthquake ultimately initiated local failure. This study enhances the mechanistic understanding of low–angle slope failures in the shelf–slope break zone and provides critical insights for assessing marine hazard risks.

Bowen Yang, Kaiwei Xu, Kun Tan et al.

Marine soil–structure interfaces are commonly encountered in marine engineering, where they are inevitably subjected to temperature variations and complex stress conditions, including static, dynamic, and creep loads. However, limited studies have addressed the temperature-dependent mechanical behavior of marine soil–structure interfaces under various loading scenarios. This study introduces a self-developed multifunctional large-scale shear apparatus that enables temperature-controlled testing of marine soil interfaces with various structural materials, including concrete, polymer grids, and polymer layers. The apparatus supports static, dynamic, and creep shear testing under precisely controlled thermal conditions. A series of shear tests were conducted on marine soil–concrete, marine soil–polymer grid, and marine soil–polymer layer interfaces to verify the device’s performance. The test results demonstrate that the apparatus can accurately and reliably capture the mechanical responses of marine soil–structure interfaces under different temperatures and loading modes. Furthermore, the results highlight the significant influence of temperature on the shear behavior of these interfaces, emphasizing the necessity of developing such equipment. The findings offer essential insights for the design, evaluation, and long-term stability of marine engineering structures, supporting the development of practical ocean solutions.

Si-Won Kim, Geon-Woo Kim, Jung-Hyeon Kim et al.

With the growing deployment of multi-USV (unmanned surface vehicle) systems for complex maritime operations, coordinated path planning is critical for safety and efficiency in congested waterways. Classical multi-agent path finding (MAPF) methods, however, often neglect vessel kinematics and collision envelopes, yielding trajectories that are impractical or unsafe at sea. We present a centralized planning framework based on any-angle Safe Interval Path Planning (AA-SIPP) augmented with a vessel-specific maximum yaw-rate constraint. This yields smooth, kinematically feasible trajectories while preserving continuous-time separation. The approach is validated in high-fidelity Gazebo marina simulations involving up to 20 USVs based on the WAM-V platform. Compared with Conflict-Based Search (CBS), a representative grid-based MAPF algorithm, our framework maintained the prescribed safety distances and achieved zero collisions across all scenarios considered, whereas CBS exhibited separation violations in simulation. The method also scales well: mission makespan remained nearly constant as fleet size increased. These results support the applicability of dynamically constrained MAPF to maritime coordination in congested environments.

Meng Zhang, Meng Zhang, Chao Qi et al.

The utilization of synthetic aperture radar (SAR) for depth inversion is crucial for accurate underwater mapping. However, current SAR-based techniques face challenges in segmentation accuracy, which directly affects inversion precision and spatial resolution. Traditional segmentation methods lack efficiency and often result in low-resolution outcomes. To address these issues, we propose a novel SAR water depth inversion method based on variable window sliding segmentation. This method optimizes nearshore image utilization by dynamically adjusting the pixel size and preventing coastline encroachment, leading to more precise swell wavelength measurements. When applied to the eastern sea off Naraha, Japan, our method achieved a minimum mean relative error (MRE) of 9.2% for shallow waters (0 to 20 m depth) and 4.9% for deeper waters (80 to 100 m depth). These results significantly improve upon those of traditional methods, which typically show MREs ranging from 10% to 30%. Additionally, our method achieves a maximum spatial resolution of 5.5 m, a notable advancement in nearshore depth measurement. The study also revealed that different depth ranges and function types, particularly linear and atanh functions, impact measurement performance, demonstrating superior accuracy across multiple metrics.

Rory Basinski-Ferris, Laure Zanna, Ian Eisenman

Antarctic meltwater is expected to increase throughout the coming centuries and impact sea level, ocean circulation, and the coupled climate evolution. This motivates interest in understanding the ocean response to Antarctic freshwater injection, including potential sources of uncertainty. In this study, we use idealized single-basin ocean simulations with meltwater input to examine the dependence of ocean transport and the timescales of the adjustment of regional sea level patterns on: (a) the model resolution and parameter values such as the mesoscale eddy Gent-McWilliams parameterization and vertical diffusivity, thereby partially addressing structural and parametric uncertainty; and (b) the depth of meltwater forcing, which must be prescribed both in our experiments and in most comprehensive climate model simulations, due to a lack of dynamic coupling with an ice sheet model. We find distinct sea level adjustment timescales and changes in the upper and abyssal cells depending on the depth of input, including a near total shutdown of the abyssal cell which only occurs with meltwater injection at the surface. We additionally find correlations between the ocean response to meltwater and the background stratification in each control simulation, which depends on the model resolution and parameter values. These results indicate that, in addition to uncertainty in how ocean models interact with fluxes from ice sheets, the ocean physics and simulated preindustrial state substantially influence the dynamic ocean response to projected ice shelf meltwater fluxes.

Haoming Jia, Yi Han, Xiang Wang et al.

Global ocean forecasting aims to predict key ocean variables such as temperature, salinity, and currents, which is essential for understanding and describing oceanic phenomena. In recent years, data-driven deep learning-based ocean forecast models, such as XiHe, WenHai, LangYa and AI-GOMS, have demonstrated significant potential in capturing complex ocean dynamics and improving forecasting efficiency. Despite these advancements, the absence of open-source, standardized benchmarks has led to inconsistent data usage and evaluation methods. This gap hinders efficient model development, impedes fair performance comparison, and constrains interdisciplinary collaboration. To address this challenge, we propose OceanForecastBench, a benchmark offering three core contributions: (1) A high-quality global ocean reanalysis data over 28 years for model training, including 4 ocean variables across 23 depth levels and 4 sea surface variables. (2) A high-reliability satellite and in-situ observations for model evaluation, covering approximately 100 million locations in the global ocean. (3) An evaluation pipeline and a comprehensive benchmark with 6 typical baseline models, leveraging observations to evaluate model performance from multiple perspectives. OceanForecastBench represents the most comprehensive benchmarking framework currently available for data-driven ocean forecasting, offering an open-source platform for model development, evaluation, and comparison. The dataset and code are publicly available at: https://github.com/Ocean-Intelligent-Forecasting/OceanForecastBench.

Yanhong Lai, Wanying Kang, Jun Yang

On tidally locked lava planets, magma ocean can form on the permanent dayside. The circulation of the magma ocean can be driven by stellar radiation and atmospheric winds. The strength of ocean circulation and the depth of the magma ocean depend on external forcings and the dominant balance of the momentum equation. In this study, we develop scaling laws for the magma ocean depth, oceanic current speed, and ocean heat transport convergence driven by stellar and wind forcings in three different dynamic regimes: non-rotating viscosity-dominant Regime I, non-rotating inviscid limit Regime II, and rotation-dominant Regime III. Scaling laws suggest that magma ocean depth, current speed, and ocean heat transport convergence are controlled by various parameters, including vertical diffusivity/viscosity, substellar temperature, planetary rotation rate, and wind stress. In general, scaling laws predict that magma ocean depth ranges from a few meters to a few hundred meters. For Regime I, results from scaling laws are further confirmed by numerical simulations. Considering the parameters of a typical lava super-Earth, we found that the magma ocean is most likely in the rotation-dominant Regime III.

Huichao Lin, Xiaolong Xu, Muhammad Bilal et al.

Meteorological radar data are essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multichannel meteorological remote sensing satellite data and removing the interference caused by nonprecipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this article, we propose a novel method that combines the advanced encryption standard method to protect the transmission of remote sensing data, a multiscale feature fusion module to extract multiscale features from multichannel meteorological remote sensing satellite data, and an attention technique to reduce the interference of nonprecipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55, and 0.99 on probability of detection, false alarm ratio, critical success index, Heidke skill score, and accuracy scores, respectively.

Luiz Fernando Capretz, Abdul Rehman Gilal

Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyze the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.

Hanchen Yang, Wengen Li, Shuyu Wang et al.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

S. Doyle, G. Aggidis

Abstract Ocean wave energy continues to develop through innovation and a growing number of collaborations around the world. With the vast resource of wave energy on our doorstep it remains a focal point in ocean energy engineering with great potential. In order for wave energy to become more competitive and a serious player in the renewable energy mix, such innovations should not only benefit the wave energy sector but also other technological applications by providing attractive options for synergies in novel projects. This paper concerns the experimental investigation of Oscillating Water Column (OWC) Wave Energy Converter (WEC) technology and its potential as a Multi-Oscillating Water Column (M-OWC). This research investigation utilises a progressive and pragmatic experimental modelling approach, by cross comparing the models of a single standalone OWC, an OWC array and finally a modular M-OWC under the same environment and test conditions. Performance and characteristic responses are analysed while varying the values of OWC spacing, damping and wave conditions. The results indicate that the spacing of OWC chambers significantly affects the performance of an M-OWC. While performance improves with the increase of spacing, the efficiency of the M-OWC is greater than that of a single OWC or the OWC array at reduced spacing values. In addition, the results indicate that an OWC array is less efficient by having individual power take-off systems operating in isolation as opposed to the modular M-OWC.

Zhichao Wang, Y. Ran, Yifan Chen et al.

Abstract Failure mode and effects analysis (FMEA) is a widely applied risk assessment method in engineering and management fields. In a typical FMEA, the ranking orders of failure modes are determined by risk priority number (RPN) which can be determined by multiplication of the scores of risk factors. However, the typical RPN has been criticized for several limitations when employed in practical situations. In this paper, a novel FMEA method using extended matter-element model and analytic hierarchy process (AHP) is developed to overcome the limitations of the typical RPN. The scores of failure modes with respect to risk factors are obtained according to the typical RPN method, and the weights among risk factors are derived from AHP. The extended matter-element model integrating with AHP is developed to calculate the closeness coefficients of failure modes, according to which the ranking orders of failure modes are determined. An illustrative example, which implements FMEA to evaluate an ocean fishing vessel, is presented to demonstrate the application and effectiveness of the proposed method. Finally, a sensitivity analysis is implemented to explore the influences of the weights of risk factors and a comparison analysis is carried out to show the advantages of the proposed method.

Norden E. Huang, Fangli Qiao

a First Institute of Oceanography, Ministry of Natural Resources of China, Qingdao 266061, China Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources of China, Qingdao 266061, China c Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China

Yongsheng Zhou, Feixiang Zhang, Fei Ma et al.

Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized, and the sea&#x2013;land segmentation process to remove land false alarms is time-consuming. Regarding these problems, first, a single low-level path aggregation network is designed specifically for small targets. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Second, adaptive dual-polarimetric feature fusion is proposed to filter the multichannel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Third, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset containing small vessel detection and sea&#x2013;land segmentation labels is created with reference to the LS-SSDDv1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

Yixiang Luomei, Feng Xu, Feng Wang et al.

This article extends the segmental aperture imaging (SAI) algorithm, which tackles the challenges of synthetic aperture radar (SAR) imaging of miniaturized synthetic aperture radar (MiniSAR) onboard multirotor unmanned aerial vehicle (UAV). The SAI algorithm is developed based on an accurate phase error model of both translational and rotational motion of UAV. It does require auxiliary positioning data such as global positioning system&#x002F;inertial navigation system. We further extend it to simultaneously estimate and reconstruct the three-dimensional (3-D) trajectory of UAV from parameters derived from SAI motion compensation. Thus, it in fact accomplishes the simultaneously location and imaging (SLAI) using UAV-borne radar. It is first validated and evaluated via raw signal simulation using realistic trajectory data. For experiment purpose, a multirotor-borne MiniSAR system FUSAR-Ku is developed. Experimental results show that the proposed algorithm can achieve decimeter-resolution imaging performance as measured by various metrics, while simultaneously accomplishing centimeter-to-decimeter 3-D self-positioning capability. It is a first demonstration of SLAI mode with a multirotor-borne MiniSAR.

Di Jiang, Hongzhi Cui, Xiaofeng Zhao et al.

Herein, ceramic-reinforced CoCrNiMoxCB high-entropy alloy coatings were prepared by laser cladding, and the synergistic improvement of wear and corrosion resistance was realized. The effects of molybdenum on the phases, microstructures, and chemical compositions of the coatings were also studied. Wear mechanisms changed from adhesive wear to abrasive plough with the increase of Mo content, leading to excellent improvement in wear resistance. The corrosion resistance was remarkably improved with excellent passive ability and pitting resistance. Coherent interfaces and composition transition at the interfaces provide a nano-scale explanation for the excellent wear and corrosion trade-off of the Mo-8 coating. The results show that the wear and corrosion resistance of the coatings can be improved with the addition of molybdenum.