This study reviews the application of wire arc additive manufacturing (WAAM) technology in maritime engineering and investigates an experimentally driven analytical approach for prediction of thermal distributions based on the Rosenthal solution. Two ER70S-6 low-carbon steel WAAM cylinders were fabricated using gas metal arc welding (GMAW) and plasma arc welding (PAW) processes, with interlayer temperatures of 453 °C and 250 °C, respectively. Accurately measuring the temperature field to tailor the microstructure has long been a challenge. The results indicated a significant deviation between the analytical predictions and the experimental data. To address this discrepancy, a hybrid approach combining analytical and experimental results was implemented. Time intervals between layers, extracted from the experimental data, were incorporated into the Rosenthal equation to improve the accuracy of temperature field predictions. The microstructure at the bottom, middle, and top regions of the WAAM components was examined using optical microscopy. Tensile testing and Vickers microhardness measurements were conducted to evaluate mechanical properties. Scanning electron microscopy (SEM) was used to analyze fracture surfaces and identify fracture modes. The results were consistent with those reported for other ER70S-6 cylindrical WAAM components. This work highlights limitations of the Rosenthal solution and emphasizes the need for thermal models in WAAM applications.
Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.
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.
This study aims to investigate the ecological role of sponges as habitat formers on soft-bottom habitats of the mesophotic zone. As habitat formers, sponges significantly enhance benthic habitat complexity and establish associations with a plethora of organisms consequently augmenting local biodiversity. This role becomes particularly critical in areas subjected to intensive bottom trawling, where sponges often comprise a substantial portion of the discarded material. The examination of 114 massive sponge specimens, belonging to 10 sponge species, which were collected as bycatch from bottom trawls in the Aegean and Ionian ecoregions, revealed a total of over 4600 associated individuals of 78 invertebrate taxa, with crustaceans, mollusks, and polychaetes being the dominant groups. The composition of sponge-associated communities showed strong similarities to previously reported cases from shallow water hard substrates of the eastern Mediterranean, while displaying host-specific differences likely influenced by sponge morphology. Although depth did not significantly affect species richness, Shannon diversity, or evenness, a decrease in abundance of associated invertebrates was observed in deeper samples, suggesting a depth-related pattern that deserves further investigation. By forming stable substrate “islands” in otherwise unstable soft substrate environments, sponges play a vital role in structuring benthic communities. Their removal through bottom trawling not only results in the loss of the sponges themselves, but also disrupts the diverse communities they support. We suggest that sponge-associated fauna should be recognized as part of the discarded bycatch and emphasize the need for broader assessments of sponge-mediated biodiversity across similar Mediterranean habitats to support effective management and conservation strategies.
Although offshore wind turbines are essential for renewable energy, their construction and design are quite complex when environmental factors are taken into account. It is quite difficult to examine their behavior under rare but dangerous natural events such as tsunamis, which bring great danger to their structural safety and serviceability. With this motivation, this study investigates the effects of tsunami and wind on an offshore National Renewable Energy Laboratory (NREL) 5 MW wind turbine both hydrodynamically and aerodynamically. First, the NREL 5 MW monopile offshore wind turbine model was parameterized and the aerodynamic properties of the rotor region at different wind speeds were investigated using the blade element momentum (BEM) approach. The tsunami data of the İzmir-Samos (Aegean) tsunami on 30 October 2020 were reconstructed using the data acquired from the UNESCO data portal at Bodrum station. The obtained tsunami wave elevation dataset was imported to the QBlade software to investigate the hydrodynamic and aerodynamic characteristics of the NREL 5 MW monopile offshore under the tsunami effect. It was observed that the hydrodynamics significantly changed as a result of the tsunami effect. The total Morison wave force and the hydrodynamic inertia forces significantly changed due to the tsunami–monopile interaction, showing similar cyclic behavior with amplified forces. An increase in the horizontal force levels to values greater than twofold of the pre-event can be observed due to the İzmir-Samos tsunami with a waveheight of 7 cm at the Bodrum station. However, no significant change was observed on the rated power time series, aerodynamics, and bending moments on the NREL 5 MW monopile offshore wind turbine due to this tsunami.
Shaft alignment is adversely affected by the increasingly severe coupled hull–raft deformation in deep-diving, highly integrated submersibles, thereby compromising operational safety and potentially amplifying vibration noise. To address to this issue, this paper investigates an intelligent alignment control method for the floating raft air spring mounting system (ASMS) applied to marine propulsion unit (MPU) under coupled hull–raft deformation conditions. A multi-objective alignment control algorithm was developed based on the NSGA-II optimization method within an N-step receding horizon optimal control framework, enabling simultaneous achievement of shaft alignment attitude adjustment, hull deformation compensation, raft deformation suppression, and pneumatic energy consumption. Experimental validation was conducted on two distinct ASMS prototypes to evaluate the control algorithm. Tests performed on the ASMS for MPU (MPU-ASMS) prototype demonstrated effective compensation of hull-induced deformations, maintaining shaft alignment offsets within ±0.3 mm and angularities within ±0.5 mm/m. Concurrently, experiments on the floating raft ASMS for the stern compartment (SC-FR-ASMS) achieved precise control of axial offsets within ±0.3 mm, angularities within ±0.5 mm/m, and vertical displacements of critical monitoring points within ±1 mm. The adaptive control strategy additionally proved effective in suppressing raft deformation while simultaneously optimizing pneumatic energy consumption. This research provides robust theoretical and technical foundations for intelligent vibration isolation systems in deep-sea equipment to accommodate extreme-depth-induced hull deformation and large-scale raft deformation.
Underwater gliders, as autonomous underwater vehicles, are integral to oceanographic research, environmental monitoring, and military applications. Given the intricate and ever-changing underwater environment, the precise management of an underwater glider’s dive depth and pitch angle is imperative for optimal functionality.This study introduces a finite-time sliding mode control method for controlling dive depth and pitch angle of underwater gliders. It incorporates a radial basis function neural network in a critic–actor reinforcement learning framework, enhancing navigational performance in difficult conditions. Sea trial data are used to create a dynamic model for the underwater glider, which is then used to design a control law. Sliding mode control is used to align the dive depth and pitch angle with the desired trajectory. Actor and critic neural networks are used to handle disturbances and evaluate error costs. By incorporating standard deviation update technique into actor and critic neural networks, along with weight updates, we improve controller stability and reduce errors in maintaining dive depth and pitch angle. Our approach is proven to be more effective than traditional SMC and reinforcement learning SMC methods in trajectory tracking, even in the presence of disturbances, as shown in the simulation results.
Accurate, rapid, and automatic seafloor sediment classification represents a crucial challenge in marine sediment research. To address this, our study proposes a seafloor sediment classification method integrating convolutional neural networks (CNNs) with small-sample multi-beam backscatter data. We implemented four CNN architectures for classification—LeNet, AlexNet, GoogLeNet, and VGG—all achieving an overall accuracy exceeding 92%. To overcome the scarcity of seafloor sediment acoustic image data, we applied a deep convolutional generative adversarial network (DCGAN) for data augmentation, incorporating a de-normalization and anti-normalization module into the original DCGAN framework. Through comparative analysis of the generated versus original datasets using visual inspection and grayscale co-occurrence matrix methods, we substantially enhanced the similarity between synthetic and authentic images. Subsequent model training using the augmented dataset demonstrated improved classification performance across all architectures: LeNet showed a 1.88% accuracy increase, AlexNet an increase of 1.06%, GoogLeNet an increase of 2.59%, and VGG16 achieved a 2.97% improvement.
Rosiane Andrade da Costa, Maria Wanna Figueiredo, Henrique Fragoso dos Santos
et al.
Corals can be considered holobiont organisms, since they have an important symbiotic relationship with microbial communities such as zooxanthellae, bacteria, Archaea, fungi and viruses. It is important to understand how those microbial communities influence the health of the corals and how environmental conditions could affect them. The present study aimed to describe the bacterial communities associated with three Brazilian coral species, <i>Millepora alcicornis</i>, <i>Mussismilia harttii</i> and <i>Phyllogorgia dilatata</i>, by a culture-independent method, using 16S rRNA gene sequencing. The corals were collected from two distinct coral reefs: Recife de Fora, in Bahia (BA) and Búzios, in Rio de Janeiro (RJ). The phylum Proteobacteria showed the highest relative abundance in most corals and sites. The bacterial compositions of these three corals from the two sample sites were very distinct from each other, not presenting similarities in coral species or related to sampling site. In <i>M. alcicornes</i>/RJ, the most abundant class was Gammaproteobacteria, order Piscirickettsiales, while the same species collected in BA showed unassigned Gammaproteobacteria, and <i>Vibrionaceae</i> was the second most abundant family. <i>M. harttii</i>/BA presented the most distinct bacterial phylum composition with 16 phyla (26% Proteobacteria, 16% Chloroflexi, 12% Acidobacteriota).
Oil spill incidents pose severe threats to marine ecosystems and coastal environments, necessitating rapid detection and monitoring capabilities to mitigate environmental damage. In this paper, we demonstrate how artificial intelligence, despite the inherent high computational and memory requirements, can be efficiently integrated into marine pollution monitoring systems. More precisely, we propose a drone-based smart monitoring system leveraging a compressed deep learning U-Net architecture for oil spill detection and thickness estimation. Compared to the standard U-Net architecture, the number of convolution blocks and channels per block are modified. The new model is then trained on synthetic radar data to accurately predict thick oil slick thickness up to 10 mm. Results show that our optimized Tiny U-Net achieves superior performance with an Intersection over Union (IoU) metric of approximately 79%, while simultaneously reducing the model size by a factor of $\sim$269x compared to the state-of-the-art. This significant model compression enables efficient edge computing deployment on field-programmable gate array (FPGA) hardware integrated directly into the drone platform. Hardware implementation demonstrates near real-time thickness estimation capabilities with a run-time power consumption of approximately 2.2 watts. Our findings highlight the increasing potential of smart monitoring technologies and efficient edge computing for operational characterization in marine environments.
Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.
Nicolas Weeger, Annika Stiehl, Jóakim vom Kistowski
et al.
The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers.
Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.
ZHAO Dongdong, YAN Lei, ZHOU Xingwen, GENG Zongsheng, YAN Shi
This paper proposes a robust state feedback design method based on a new Luenberger observer for uncertain systems with measurement output matrices containing uncertain parameters. First, in view of the problem that it is challenging to measure state variables in practice, an observer is designed through the feedback of the observation state. Considering the situation that the measurement output matrix contains uncertain parameters in the uncertain system, a new Luenberger observer is designed. Then, based on the new Luenberger observer, and combined with the multi-affine representation and the slack variable framework, the convex linear matrix inequality condition related to the Lyapunov function is obtained, and the robust stability analysis based on linear matrix inequalities is conducted for the closed-loop system. Finally, the feasibility of the above condition is tested by an experiment to show the applicability and effectiveness of the proposed method.
Engineering (General). Civil engineering (General), Chemical engineering
With the rapid advancement of underwater communication and unmanned aerial vehicle (UAV) technologies, the potential applications of cross-medium communication in environmental monitoring, maritime Internet of Things (IoTs), and rescue operations, in particular, have attracted great attention. This study explores the feasibility of achieving cross-medium direct acoustic communication through the air–water interface. Specifically, it investigates challenges such as acoustic impedance mismatches and signal attenuation caused by energy loss during interface transmission, aiming to understand their impact on communication performance. Experimental tests employed underwater acoustic transducers as signal transmitters to propagate sound waves directly into the air, attempting to establish communication links with aerial UAV nodes. Preliminary experimental results indicate that even conventional underwater acoustic transducers can achieve information exchange between underwater nodes and UAVs, laying a foundation for further research and application of cross-medium communication systems.
The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogeneous scenes at deployment time. However, training samples are typically acquired continuously in practical applications, making the capability to learn new scenes continually even more crucial. For this purpose, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at inference. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth to learn new scenes continually in both supervised and self-supervised manners. It can maintain high reusability during growth by reusing previous units while obtaining good performance. Additionally, we present a Scene Router module to adaptively select the scene-specific architecture path at inference. Comprehensive experiments on numerous datasets show that our framework performs impressively in various weather, road, and city circumstances and surpasses the state-of-the-art methods in more challenging cross-dataset settings. Further experiments also demonstrate the adaptability of our method to unseen scenes, which can facilitate end-to-end stereo architecture learning and practical deployment.
Yvonne Dittrich, Johan Bolmsten, Catherine Seidelin
Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners. However, as the research moves beyond the observational approach, it requires a different kind of interaction with the software development organisation. This makes action research a challenging endeavour, and it makes it difficult to teach action research through a course that goes beyond explaining the principles. This chapter is intended to support learning and teaching action research, by providing a rich set of examples, and identifying tools that we found helpful in our action research projects. The core of this chapter focusses on our interaction with the participating developers and domain experts, and the organisational setting. This chapter is structured around a set of challenges that reoccurred in the action research projects in which the authors participated. Each section is accompanied by a toolkit that presents related techniques and tools. The exercises are designed to explore the topics, and practise using the tools and techniques presented. We hope the material in this chapter encourages researchers who are new to action research to further explore this promising opportunity.