Hasil untuk "Ocean engineering"

Xiaoyun Wan, Lijun Zhang, Hengyang Guo et al.

Conventional methods for bathymetry inversion based on gravity field data usually adopt gravity anomaly and vertical gravity gradient. Indeed, a gravity gradient tensor (GGT) has six components. Besides the commonly used vertical gravity gradient, the other five components can also contribute to bathymetry inversion, which is investigated in this article. The formulas for the inversion are derived based on the Parker&#x2019;s formula. In order to provide GGT data for the inversion, vertical deflections and gravity anomalies of Scripps Institution of Oceanography, including north_SWOT_02 (1&#x2019;), east_SWOT_02 (1&#x2019;) and grav_SWOT_02 (1&#x2019;) are firstly used to derive GGT in the study region, i.e., Bay of Bengal. And then, six bathymetric grid models are derived using the six components of the gravity gradients, respectively. The results show that within the study area, the <inline-formula><tex-math notation="LaTeX">${{{\bm{T}}}_{{\bm{yz}}}}$</tex-math></inline-formula> component exhibits the best performance among the bathymetric inversion results derived from the single components of GGT. Combining the inversion results of the six GGT components yields a new bathymetric model&#x2014;one with higher accuracy than that derived from the single <inline-formula><tex-math notation="LaTeX">${{{\bm{T}}}_{{\bm{yz}}}}$</tex-math></inline-formula> component. Evaluations using ship-borne depth data indicate that, at the test points, the root mean square error of the combined model is 5.24 meters less than that of the <inline-formula><tex-math notation="LaTeX">${{{\bm{T}}}_{{\bm{yz}}}}$</tex-math></inline-formula>-derived model. When GEBCO_2024 depths are treated as the reference (true values), the RMS error of the combined bathymetric model is 14.31 meters less than that of the <inline-formula><tex-math notation="LaTeX">${{{\bm{T}}}_{{\bm{yz}}}}$</tex-math></inline-formula>-derived model.

Aonan Hao, Jie Han, Xiang Zhou et al.

The infrared scanner (IRS) onboard the HJ-2B satellite is a high-resolution, dual-channel thermal infrared sensor suitable for applications such as land-surface temperature retrieval and smoke detection. Radiometric calibration is a fundamental prerequisite for ensuring the quality and reliability of IRS observation data and is typically performed via two-point (high- and low-temperature) calibration using the instrument&#x2019;s onboard blackbody. However, the effectiveness of this procedure can be constrained by performance degradation of the calibration hardware and by its frequency of usage. In response to these limitations, this article proposes a refined radiometric cross-calibration method for the HJ-2B/IRS sensor over plateau lakes, which adopts the double-difference method as the technical basis and uses the high-accuracy Terra/MODIS as the reference sensor. Implemented through rigorous screening of calibration sites and sampling points, the method explicitly accounts for viewing geometry, as well as contemporaneous surface and atmospheric conditions. First, calibration points between IRS and MODIS are screened by setting a CV threshold and using the RANSAC algorithm. Next, given the surface and atmospheric conditions at the satellite overpass time, radiances at calibration points with large viewing zenith angles were corrected to their nadir-equivalent values, and a temperature-dependent simulated brightness temperature difference function is constructed, from which the fitted calibration coefficients (FCCs) are derived using the double-difference method. Finally, validation with Landsat-8/TIRS indicates that IRS brightness temperatures calculated by the FCCs deviate less from the TIRS observations than those computed with the official calibration coefficients (OCCs) issued by the China Centre for Resources Satellite Data and Application. Furthermore, when compared with the top-of-atmosphere brightness temperatures derived from forward modeling of in situ measurement data, the IRS at-aperture brightness temperature computed using the FCCs shows mean deviations of &#x2212;1.04 K and &#x2212;0.92 K for Band 8 and Band 9, respectively. These values are superior to the corresponding deviations of &#x2212;1.32 K and &#x2212;2.43 K obtained using the OCCs. Following a quantitative analysis of various factors influencing the FCCs, the total uncertainty for IRS Band 8 and Band 9 was determined to be below 0.937&#x0025; and 1.257&#x0025;, respectively.

Ji-Woong Lee, Quang Dao Vuong, Jae-Ung Lee

This study addresses structural distortions in the IMO Carbon Intensity Indicator (<i>CII</i>) for short-voyage training vessels and proposes corrective strategies combining denominator adjustments with controllable pitch propeller (CPP) mode optimization. Using 2024 operational data from a training ship, we computed monthly and annual <i>CII</i> values, identifying significant inflation when time-at-sea fractions are low due to extensive port stays. Two correction methods were evaluated: a hybrid denominator approach converting port-stay <i>CO</i>₂ to equivalent distance, and a Braidotti functional correction. The CPP operating maps for combination and fixed modes revealed a crossover point at approximately 12 kn (~50% engine load), where the combination mode shows superior efficiency at low speeds and the fixed mode at higher speeds. The hybrid correction effectively stabilized <i>CII</i> values across varying operational conditions, while the speed-band CPP optimization provided additional reductions. Results demonstrate that combining optimized CPP mode selection with hybrid <i>CII</i> correction achieves compliance with required standards, attaining a B rating. The integrated framework offers practical solutions for <i>CII</i> management in short-voyage operations, addressing regulatory fairness while improving operational efficiency for training vessels and similar ship types.

Weijie Zhang, Yuming Zeng, Shubo Zhou et al.

Abstract The amount of precipitation directly affects the ecological balance and the economic benefits of the region. However, the highly nonlinear and stochastic nature of precipitation time series data limits the accuracy of predictions. Therefore, improving the prediction accuracy of regional precipitation is crucial for formulating disaster prevention and mitigation measures, as well as for responding to climate change. To achieve a scientific and effective prediction of regional precipitation, this study proposed a precipitation prediction model based on the CEEMDAN-TVMD-IPO-BiLSTM framework. The model first decomposed the original precipitation data using the CEEMDAN decomposition algorithm, output the modal components and residual components, and then used the topology optimization algorithm (TTAO) to optimize the VMD, and decomposed the high-dimensional sequence in the first decomposition result for the second time. An improved parrot optimizer (IPO) algorithm based on chaotic Cat and Cauchy-Gaussian variation was introduced to optimize the bidirectional long short-term memory neural network (BiLSTM). This precisely constructed prediction model was utilized to predict regional precipitation, with historical monthly precipitation data from three representative cities in China—Guangzhou in the east region, Changsha in the central region, and Emeishan in the west region—used to validate the model’s accuracy and robustness. Experimental results indicated that the proposed CEEMDAN-TVMD-IPO-BiLSTM model achieved RMSE values of 32.373, 14.445, and 22.447 for the three cities, respectively, with corresponding R² values of 0.960, 0.972, and 0.977, outperforming other models. This demonstrated its advantages in monthly precipitation prediction, allowing for a better characterization of precipitation fluctuation patterns and providing scientific references for formulating policies to combat droughts and floods.

Pai Zhang, Wangrong Li, Liqiang Xu et al.

Double-arch tunnels in inclined layered jointed rock masses face risks of lining cracking and collapse under bedding-inclined asymmetric stress (BIAS); however, related studies remain limited. Based on a case study of an expressway tunnel case in Zhejiang Province, a three-dimensional discrete element model of a double-arch tunnel was developed using Three-Dimensional Distinct Element Code (3DEC) (version 7.0, Itasca Consulting Group, Inc., Minneapolis, MN, USA). The impacts of joint dip angle (0–90°) and spacing (0.5–6.5 m) on deformation, BIAS evolution, and middle partition wall stability were analyzed. Key findings reveal that joint presence significantly amplifies surrounding rock deformation, with pronounced displacement increases observed on the counter-dip side. The BIAS intensity follows a unimodal distribution with joint dip angles, peaking within the 30–60° range. Increasing joint spacing reduces BIAS effects, with a 57.1% decrease in asymmetric deformation observed when spacing increases from 0.5 m to 6.5 m. The implementation of dip-side pilot excavation with the main tunnel full-face method, combined with an optimized support strategy (installing dip-side bolts perpendicular to joints and extending counter-dip side bolt lengths from 4 m to 6 m), achieved a near-unity stress ratio between tunnel sides under equivalent overburden depths compared to conventional methods. These findings offer theoretical and technical insights for optimizing excavation and reinforcement in similar tunnel engineering contexts.

Dan Xu, Siyuan Zhao, Kaiming Li et al.

The micro-Doppler (&#x03BC;D) characteristics of ballistic targets are crucial for estimating motion and structural parameters, as well as for target recognition. However, existing time-frequency (TF) analysis methods are predominantly nonparametric and suffer from limited resolution, making it challenging to accurately extract &#x03BC;D TF curves. This limitation hampers further applications in this domain. Therefore, under narrowband radar observation conditions, this article proposes a method for &#x03BC;D TF characteristics extraction and scatterer type identification, specifically for smooth-surfaced cone-shaped precession targets. The method first utilizes sliding-window Root-MUSIC to extract the instantaneous &#x03BC;D frequencies of the target. Then, the inverse Radon transform (IRT) and You Only Look Once version 5 algorithm are applied to locate and identify the peaks of cone vertex and cone base after IRT. Based on the peaks, the &#x03BC;D TF trend curves can be reconstructed using the Radon transform (RT). The extracted instantaneous &#x03BC;D frequencies are then associated according to the trend curves, enabling the reconstruction of the &#x03BC;D frequencies for the cone vertex and cone base. Experiments validate the effectiveness and noise robustness of the proposed method. The results demonstrate that the estimation accuracy of the proposed method is independent of the sampling interval and significantly outperforms traditional nonparametric methods.

Kai-Ho Cheng, Chih-Hsun Chang, Yi-Chung Yang et al.

The relationship between wind and waves has been extensively studied over time. However, understanding the local wind and wave relationship remains crucial for advancing renewable energy development and optimizing ocean management strategies. This study used wind and wave data collected by the ten weather buoys in the waters surrounding Taiwan to analyze regional sea states. The relationship between wind speed and significant wave height (SWH) was examined using regression analysis. Additionally, machine learning techniques were employed to assess the relative importance of features contributing to SWH growth. The regression analysis revealed that SWH in the waters surrounding Taiwan was not fully developed, with notable discrepancies observed between the waters east and west of Taiwan. According to the power law formula describing the relationship between wind speed and SWH, the eastern waters exhibited a larger prefactor coupled with a smaller scaling exponent, while the western waters manifested a converse parametric configuration. Through an evaluation of four machine learning algorithms, it was determined that wind speed is the most influential factor driving these regional differences, especially in the waters west of Taiwan. Beyond wind speed, air pressure or temperature emerged as the secondary feature factor governing wind–wave interactions in the waters east of Taiwan.

Lucica Iconaru, Carmen Gasparotti

The design of ships has changed dramatically since the 1970s. We have shifted from manual drafting to digital tools and computers, mostly because computer technology has greatly improved. Nowadays, with the growth of smart digitalization in Industry 4.0, using modern digital software and tools makes ship design more efficient and enhances its quality throughout a ship's entire lifespan. However, this shift has also made operations more complex and requires users of the software to have more specialized training. Today, technologies like automated optimization, simulation-based design, managing the entire product lifecycle, digital twins, and artificial intelligence are commonly used in the shipping industry. These technologies are applied during both the design and construction phases, as well as in preparing and inspecting ships. This paper reviews major advances in these areas and discusses how the industry can address current and future challenges.

Morgan M. Fong, Hilary Egan, Marc Day et al.

Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.

Qingyu Zheng, Qi Shao, Guijun Han et al.

Mesoscale eddies dominate the spatiotemporal multiscale variability of the ocean, and their impact on the energy cascade of the global ocean cannot be ignored. Eddy-resolving ocean forecasting is providing more reliable protection for fisheries and navigational safety, but also presents significant scientific challenges and high computational costs for traditional numerical models. Artificial intelligence (AI)-based weather and ocean forecasting systems are becoming powerful tools that balance forecast performance with computational efficiency. However, the complex multiscale features in the ocean dynamical system make AI models still face many challenges in mesoscale eddy forecasting (especially regional modelling). Here, we develop LanTu, a regional eddy-resolving ocean forecasting system based on dynamics-enhanced deep learning. We incorporate cross-scale interactions into LanTu and construct multiscale physical constraint for optimising LanTu guided by knowledge of eddy dynamics in order to improve the forecasting skill of LanTu for mesoscale evolution. The results show that LanTu outperforms the existing advanced operational numerical ocean forecasting system (NOFS) and AI-based ocean forecasting system (AI-OFS) in temperature, salinity, sea level anomaly and current prediction, with a lead time of more than 10 days. Our study highlights that dynamics-enhanced deep learning (LanTu) can be a powerful paradigm for eddy-resolving ocean forecasting.

Dhanika Mahipala, Trym Tengesdal, Børge Rokseth et al.

Collision avoidance capability is an essential component in an autonomous vessel navigation system. To this end, an accurate prediction of dynamic obstacle trajectories is vital. Traditional approaches to trajectory prediction face limitations in generalizability and often fail to account for the intentions of other vessels. While recent research has considered incorporating the intentions of dynamic obstacles, these efforts are typically based on the own-ship's interpretation of the situation. The current state-of-the-art in this area is a Dynamic Bayesian Network (DBN) model, which infers target vessel intentions by considering multiple underlying causes and allowing for different interpretations of the situation by different vessels. However, since its inception, there have not been any significant structural improvements to this model. In this paper, we propose enhancing the DBN model by incorporating considerations for grounding hazards and vessel waypoint information. The proposed model is validated using real vessel encounters extracted from historical Automatic Identification System (AIS) data.

David Algis, Bérenger Bramas, Emmanuelle Darles et al.

The oceans cover the vast majority of the Earth. Therefore, their simulation has many scientific, industrial and military interests, including computer graphics domain. By fully exploiting the multi-threading power of GPU and CPU, current state-of-the-art tools can achieve real-time ocean simulation, even if it is sometimes needed to reduce the physical realism for large scenes. Although most of the building blocks for implementing an ocean simulator are described in the literature, a clear explanation of how they interconnect is lacking. Hence, this paper proposes to bring all these components together, detailing all their interactions, in a comprehensive and fully described real-time framework that simulates the free ocean surface and the coupling between solids and fluid. This article also presents several improvements to enhance the physical realism of our model. The two main ones are: calculating the real-time velocity of ocean fluids at any depth; computing the input of the solid to fluid coupling algorithm.

Gulshan Saleem, Usama Ijaz Bajwa, Rana Hammad Raza et al.

Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.

Wan-Ling Tseng, Cheng-Wei Lin, Yi-Chi Wang et al.

The transition to renewable energy sources, such as offshore wind farms, is essential in mitigating climate change. Taiwan has set ambitious targets to harness wind energy from the Taiwan Strait, but offshore wind farm installations are highly dependent on weather conditions, particularly wind speeds. This study examines the relationship between the El Niño-Southern Oscillation (ENSO) and offshore wind farm installation by assessing weather windows—periods with wind speeds below 12 m per second at a height of 100 m for at least 12 h. Our analysis shows that during La Niña years, the number of feasible weather windows decreases by up to 40 %, particularly between October and June, compared to neutral and El Niño years. This decrease can be as high as fourfold in December, significantly impacting installation schedules. Seasonal variations are also notable, with wind speeds exceeding 12 m s−1 in winter 66.4 % of the time, compared to 29.4 % in spring, making spring and summer the most favorable periods for installation. However, even during these favorable seasons, La Niña years can bring higher wind speeds, necessitating careful planning. These results underscore the importance of integrating ENSO forecasts into project planning to avoid installation delays and optimize installation timelines. By leveraging seasonal and interannual climate variability predictions, decision-makers can improve the resilience of offshore wind farm projects and ensure efficient energy transition strategies.

Qiuyu Jin, Yuqi Han, Wenzheng Wang et al.

Recently, tracking with unmanned aerial vehicle (UAVs) platforms has played significant roles in Earth observation tasks. However, target occlusion remains a challenging factor during the continuous tracking procedure. In particular, incomplete local appearance features can mislead the tracking network to produce inaccurate size and position estimations when the target is occluded. Furthermore, the tracking network lacks sufficient occlusion supervision information, which may lead to template degradation during template updating. To address these challenges, in this article, we design an occlusion-aware tracker with local-global features modeling, which contains two key components, namely the feature intrinsic association module (FIAM) and the feature verification module (FVM). Specifically, the FIAM divides the local features into blocks and utilizes the transformer network to explore the relative relationships among each subblock, which supplements the damaged local target features and assists the modeling for global target features. In addition, the FVM establishes a correlation measurement network between the target and the template. To precisely evaluate the occlusion status, masked samples with occlusion exceeding 50&#x0025; are selected as negative samples for independent training, which ensures the purity of the target template. Qualitative and quantitative experiments are conducted on publicly available datasets, including UAV20 L, UAV123, and LaSOT. Qualitative and quantitative experiments have demonstrated the effectiveness of the proposed tracking algorithm over the other state-of-the-art trackers in occlusion scenarios.

Yunling Ye, Jin Gan, Huabing Liu et al.

Owing to their excellent physical characteristics of lightweightiness, compactness and rapid deployment, the inflated membrane structures satisfy the demands of maritime salvage and military transportation for long-distance delivery and rapid response. Exploring the failure behaviour of inflated membrane structures can greatly contribute to their widespread applications in ocean engineering. In this research, the main objective is to comprehensively investigate the bending and failure behaviour of inflated membrane structures. Thus, the Surface-Based Fluid Cavity method is employed to set up the finite element model (<i>FEM</i>) which is compared to the experimental results to verify its reliability. In parallel, the effects of internal pressure and wrinkles are discussed. An empirical expression of the ultimate bending loading was fitted by face-centred composite designs of the Response Surface Methodology. The results of experiments and <i>FEM</i> show that the bearing capacity of the inflated membrane structure is positively correlated with the internal pressure but decreased obviously with the occurrence and propagation of wrinkles. The deformation behaviour and the stress distribution are similar to those of the traditional four-point bending beam, and the local instability induced by wrinkles will cause structural failure. In addition, the numerical model and the proposed expression showed deviations below 5% in relation to the experimental measures. Therefore, the <i>FEM</i> and proposed expression are high of reliability and have important engineering guiding significance for the application of inflated membrane structures in ocean engineering.

Wei Xiong, Yanfei Xiang, Hao Wu et al.

Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean, which is the foundation for marine science research and operational oceanography. Modern numerical ocean modeling mainly consists of governing equations and numerical algorithms. Nonlinear instability, computational expense, low reusability efficiency and high coupling costs have gradually become the main bottlenecks for the further development of numerical ocean modeling. Recently, artificial intelligence-based modeling in scientific computing has shown revolutionary potential for digital twins and scientific simulations, but the bottlenecks of numerical ocean modeling have not been further solved. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction. AI-GOMS consists of a backbone model with the Fourier-based Masked Autoencoder structure for basic ocean variable prediction and lightweight fine-tuning models incorporating regional downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has achieved the best performance in 30 days of prediction for the global ocean basic variables with 15 depth layers at 1/4° spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12° spatial resolution and ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new backbone-downstream paradigm for Earth system modeling, which makes the system transferable, scalable and reusable.

Jinyu Li, Ping Hu, Weicheng Cui et al.

Metaverse has attracted great attention from industry and academia in recent years. Metaverse for the ocean (Meta-ocean) is the implementation of the Metaverse technologies in virtual emersion of the ocean which is beneficial for people yearning for the ocean. It has demonstrated great potential for tourism and education with its strong immersion and appealing interactive user experience. However, quite limited endeavors have been spent on exploring the full possibility of Meta-ocean, especially in modeling the movements of marine creatures. In this paper, we first investigate the technology status of Metaverse and virtual reality (VR) and develop a prototype that builds the Meta-ocean in VR devices with strong immersive visual effects. Then, we demonstrate a method to model the undersea scene and marine creatures and propose an optimized path algorithm based on the Catmull-Rom spline to model the movements of marine life. Finally, we conduct a user study to analyze our Meta-ocean prototype. This user study illustrates that our new prototype can give us strong immersion and an appealing interactive user experience.

Pierre Auclair-Desrotour, Mohammad Farhat, Gwenaël Boué et al.

Recent observations and theoretical progress made about the history of the Earth-Moon system suggest that tidal dissipation in oceans primarily drives the long term evolution of orbital systems hosting ocean planets. Particularly, they emphasise the key role played by the geometry of land-ocean distributions in this mechanism. However, the complex way continents affect oceanic tides still remains to be elucidated. In the present study, we investigate the impact of a single supercontinent on the tidal response of an ocean planet and the induced tidally dissipated energy. The adopted approach is based on the linear tidal theory. By simplifying the continent to a spherical cap of given angular radius and position on the globe, we proceed to a harmonic analysis of the whole planet's tidal response including the coupling with the solid part due to ocean loading and self-attraction variations. In this framework, tidal flows are formulated analytically in terms of explicitly defined oceanic eigenmodes, as well as the resulting tidal Love numbers, dissipated power, and torque. The analysis highlights the symmetry breaking effect of the continent, which makes the dependence of tidal quantities on the tidal frequency become highly irregular. The metric introduced to quantify this continentality effect reveals abrupt transitions between polar and non-polar configurations, and between small-sized and medium-sized continents. Additionally, it predicts that a continent similar to South America or smaller (30°-angular radius) does not alter qualitatively the tidal response of a global ocean whatever its position on the planet.