The presented case study benchmarks a novel design approach for evaluating the survival function of multidimensional dynamic systems subjected to stochastic, nonstationary environmental loading, with particular focus on naval architecture. The proposed design methodology combines a novel log-integral concept of the Integrated Cumulative Distribution Function (ICDF) for accurate modeling of failure probabilities with the Smoothed Particle Hydrodynamics (SPH) Computational Fluid Dynamics (CFD) method to simulate slamming forces on the vessel hull mid-section. The proposed design approach offers a robust tool for reliability and safety assessment of vessels and offshore structures, particularly in complex, nonlinear, adverse marine environments. A traditional four-parameter Weibull parametric fit is used to cross-validate the predicted design values. The combination of ICDF and SPH simulations may provide naval architects with a robust framework for enhancing the reliability analysis of marine structures under dynamic, rapidly changing loading conditions. The major novelty of this study lies in combining an SPH-based CFD approach with a successfully benchmarked novel probabilistic integral ICDF extrapolation scheme, which is particularly suitable for design when the underlying dataset is representative but limited in size. System performance or limit-state function depends on multiple random variables (e.g. load, resistance, and environmental factors). This method enables efficient estimation of extreme impact loads, thereby providing practical support for reliability-based design and operational safety assessment of high-speed craft undergoing underwater/above-water entry and exit processes under nonstationary sea conditions. Engineering relevance: assessing the reliability of high-reliability structures where failure probabilities P Failure are low (e.g. ≤ 10 − 6 ). Fundamental design concepts, such as the Most Probable Maximum (MPM) for non-Gaussian processes with clustering effects, are expressed in terms of a memory-modified mean up-crossing rate in a practical engineering context. The presented ICDF design scheme is shown to provide enhanced accuracy to the design values and P Failure estimates, when the underlying data sample is of limited size.
Even with the state-of-the-art technology of computer-aided design and topology optimization, the present structural design still faces the challenges of high dimensionality, multi-objectivity, and multi-constraints, making it knowledge/experience-demanding, labor-intensive, and difficult to achieve or simply lack of global optimality. Structural designers are still searching for new ways to cost-effectively to achieve a possible global optimality in a given structure design, in particular, we are looking for decreasing design knowledge/experience-requirements and reducing design labor and time. In recent years, Artificial Intelligence (AI) technology, characterized by the large language model (LLM) of Machine Learning (ML), for instance Deep Learning (DL), has developed rapidly, fostering the integration of AI technology in structural engineering design and giving rise to the concept and notion of Artificial Intelligence-Aided Design (AIAD). The emergence of AIAD has greatly alleviated the challenges faced by structural design, showing great promise in extrapolative and innovative design concept generation, enhancing efficiency while simplifying the workflow, reducing the design cycle time and cost, and achieving a truly global optimal design. In this article, we present a state-of-the-art overview of applying AIAD to enhance structural design, summarizing the current applications of AIAD in related fields: marine and naval architecture structures, aerospace structures, automotive structures, civil infrastructure structures, topological optimization structure designs, and composite micro-structure design. In addition to discussing of the AIAD application to structural design, the article discusses its current challenges, current development focus, and future perspectives.
The randomness and complexity of ice loads present major challenges to the safety and stability of offshore platforms. Traditional methods for identifying ice loads often lack accuracy and adaptability under changing environmental conditions. This study proposes a novel inversion method based on Temporal Convolutional Networks (TCNs), integrating finite element simulation with deep learning to effectively identify random ice loads. A random ice load model is first developed, and its dynamic characteristics are validated through finite element analysis. The TCN model is then applied to capture the time-dependent features of ice loads. To improve the model’s generalization ability, its hyperparameters are optimized using particle swarm optimization (PSO). The results show that the TCN model achieves goodness-of-fit (R<sup>2</sup>) values of 0.821 and 0.808 on the training and test sets, respectively, indicating strong predictive performance. Under different ice thickness and velocity conditions, the model achieves R<sup>2</sup> values close to 0.99, demonstrating high robustness. This work represents the first application of TCN to ice load identification. By combining it with simulation data, we offer a high-precision, data-driven approach for dynamic load identification, enhancing the efficiency and reliability of safety assessments for conical offshore platforms.
CHEN Shi, YANG Linsen, LIU Yihong, LUO Huan, ZANG Tianlei, ZHOU Buxiang
Newly built microgrids lack historical operation data, making it challenging to predict renewable power output accurately using conventional data-driven methods, which in turn affects the accuracy of scheduling plans. To address this problem, an optimal scheduling method for newly built microgrids in scenarios with limited sample data is proposed. First, an improved network structure integrating a domain adversarial neural network with a long-short-term memory network is designed. The domain adversarial approach and gradient inversion mechanism are incorporated into transfer learning to improve the generalization ability of the model. This reduces the domain distribution discrepancy in the data, and uses the rich operation data of power stations with similar output characteristics to predict the output of the target station, which overcomes the challenge of poor accuracy under the conditions of small samples. Additionally, the optimal scheduling model is transformed into a Markov decision process and solved using double-delay deep deterministic policy gradient algorithm. Finally, the effectiveness of the proposed method is validated through a case study involving an improved CIGRE 14-node microgrid.
Engineering (General). Civil engineering (General), Chemical engineering
Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R<sup>2</sup> < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R<sup>2</sup> = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation.
Welding is one of the most widely used joining processes for the fabrication of steel parts. Consequently, it is commonly used in the shipbuilding industry for the fabrication of structural T-stiffeners. However, this process introduces inherent imperfections, such as angular deformation and residual stresses, which can affect structural stability and shorten the lifespan of the parts. This study conducts a literature review to replicate numerical analyses from reference studies, validating the proposed simulation methodology by comparing numerical and experimental thermo-mechanical results. A finite element model is created using MSC Patran and the welding process is simulated with Simufact Welding. Once the methodology is validated, a case study is conducted in which the shielded metal arc welding (SMAW) process is simulated using a simultaneously coupled thermo-elasto-plastic analysis, based on the finite element method. The study aims to determine the influence of welding sequences and mechanical boundary conditions on angular deformation and longitudinal residual stresses in the T-joints of narrow and thin plates made of S355J2 structural steel. These plates are used as structural stiffeners in the stern and bow sections of patrol boats. The goal is to propose an optimal welding sequence and boundary condition configuration that mitigates angular distortion and longitudinal residual stresses in the structural members. The proposed welding sequence consists of four weld lines running from the middle of the plate to the end, whilst the mechanical boundary condition supports the plate along the longitudinal ends.
Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt
The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.
Daniel Mendez, Paris Avgeriou, Marcos Kalinowski
et al.
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.
Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer
et al.
Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.
Engineering complex systems (aircraft, buildings, vehicles) requires accounting for geometric and performance couplings across subsystems. As generative models proliferate for specialized domains (wings, structures, engines), a key research gap is how to coordinate frozen, pre-trained submodels to generate full-system designs that are feasible, diverse, and high-performing. We introduce Generative Latent Unification of Expertise-Informed Engineering Models (GLUE), which orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity. We propose and benchmark (i) data-driven GLUE models trained on pre-generated system-level designs and (ii) a data-free GLUE model trained online on a differentiable geometry layer. On a UAV design problem with five coupling constraints, we find that data-driven approaches yield diverse, high-performing designs but require large datasets to satisfy constraints reliably. The data-free approach is competitive with Bayesian optimization and gradient-based optimization in performance and feasibility while training a full generative model in only 10 min on a RTX 4090 GPU, requiring more than two orders of magnitude fewer geometry evaluations and FLOPs than the data-driven method. Ablations focused on data-free training show that subsystem output continuity affects coordination, and equality constraints can trigger mode collapse unless mitigated. By integrating unmodified, domain-informed submodels into a modular generative workflow, this work provides a viable path for scaling generative design to complex, real-world engineering systems.
Romania no longer has a commercial maritime fleet, but it has a large number of active sailors and officers, two naval universities, with faculties of navigation and naval electromechanics, a university with a faculty of shipbuilding and another with a faculty of marine engineering. A small number of Romanian shipowners own maritime vessels, but these are registered under a flag of convenience in other states. This paper analyzes the situation of crewing companies (placement of labor, seafarers, abroad), the problems faced by these independent companies and what are the resistance strategies for this segment of the maritime market. The scientific research is based on the study of official documents, management analyses and other information obtained directly from several sources, but also from several seafarers, about the real situation of maritime seafaring personnel, their relationships with the independent crewing company and with main shipping companies, and the situations they face on board maritime vessels. The work is original, with many new elements and information for the current specialized literature.
Sea-chest sizing is a process that is often based on rules of thumb rather than a set scientific method. This paper aims to create such a method by incorporating real world data and sound engineering principles. Specifically, the use of example species to set a flow velocity that will avoid impingement on fish, comparing engine indicated power to obtain an early estimate for flow rate, and ratio of open area to grating area to size the sea-chest. The main use case of this method is for future Marine Mechanical Design students to size sea-chests on their third-year project vessels. A large part of the Marine Mechanical Design program at the Marine Institute of Memorial University of Newfoundland is creating the third-year design project. This involves taking a previous year’s Naval Architecture third-year project vessel and designing the auxiliary systems for that ship. The sea main and sea chest of the project vessel used in this paper can be seen in figure 1. The students are given structural drawings and minimal information and tasked with making decisions that will turn the bare hull into a working ship. This paper aims to create a preliminary method to size sea-chests for the use of future Marine Mechanical Design students in the creation of their third-year projects. To do this, three key pieces of information are required. The flow velocity through the grating, the flowrate, and the grating open area. A review of current regulations found very little in the way of hard and fast rules about the flow velocity of sea chests. The United States Code of Federal Regulations states: "…each cooling water intake structure at your facility to a maximum through-screen design intake velocity of 0.5 ft/s;". (United States Code of Federal Regulations, 2013, Title 40 Chapter I Subchapter D Part 125.84), however, this regulation is intended for application to cooling water intake structures of civil installations in freshwater bodies rather than for a vessel. Given the limited availability of guidance of velocity through the grating, a suitable metric needed to be determined.
Density of motile microorganisms shows a dynamic character in alleviating and monitoring the momentum, thermal and solutal boundary layers. In sight of this, we examined the flow characteristics on the suspensions of motile microorganisms in the Casson nanofluid due to stretching of a sheet. The stimulus of thermic heat, irregular heat sink or source, thermophorsis and Brownian motion are studied. The flow is laminar and time dependent. The combined influence of heat and mass transfer features are investigated. The velocity slip boundary condition is deemed to investigate the flow features. The modelled equations are highly coupled and nonlinear. So, analytical solution for this model is not possible. Hence, we presented a numerical solution. Suitable similarities are pondered to metamorphose the original PDEs into ODEs and then solved by utilizing R.K. based shooting technique. Influences of varied parameters on the flow fields are discussed in detailed with the aid of graphs and also presented via table. Simultaneous elucidations are bestowed for both Newtonian and non-Newtonian fluids. It is depicted that an enhancement in the thermophoresis parameter results an enhancement in the heat thereby a reduction in concentration. Further, it is characterised that bio convection Lewis number and Peclet number have a reducing behaviour of density of motile microorganisms. Journal of Naval Architecture and Marine Engineering, 21(1), 2024, P: 79- 86
Ahmed Mokhtar Abo El-Ela, Mohamed Mostafa Hussien, Alaaeldeen Mohamed Elhadad
Hydrodynamic optimization is an effective and robust design method that is indispensable in ship hull form performance. Marine engineering and naval architecture have displayed significant interest in integrating bulbous bows on combatant ships. This review aims to gather and assess the existing knowledge regarding the effects of bulbous bow shapes on the hydrodynamic characteristics of combatant ships in terms of historical background, design principles, and impacts of bulbous bow shapes on ship resistance, ship motion in seaways, squat, and seakeeping performance. The review evaluates the effects of bulbous bow shapes on hull hydrodynamics using computational fluid dynamics (CFD), model testing, and full-scale experiments. The text delves into the complexities of improving hull shape and how the bulbous bow design interacts with many operational factors like draft, speed, and sea conditions. This research offers scholars, naval architects, and marine engineers a comprehensive insight into the intricate effects of bulbous bow shapes on combatant ship performance. It aims to consolidate current material to enhance our comprehension of ship design and operation by identifying knowledge gaps and suggesting future research areas.
M. Kumar, Dr. Naga Lakshmi Devi Parasa, Dr Vijaya lakshmi Kunche
In this paper, using interfacial slip on the boundary, the exact solution is obtained for the Stokes flow through a couple stress fluid sphere which is embedded (implanted) in a porous medium. Analytical computations are derived for the stream functions and drag. For the drag force, special conditions are deduced that satisfy the literature's facts. Graphs are created and the numerical results are tabulated. It is noticed that in the external viscous fluid case the porosity parameter and the drag coefficient are directly correlated and for the external couple stress fluid case with raises in slip parameter the coefficient of drag reduces. Journal of Naval Architecture and Marine Engineering, 21 (1), 2024, pp. 41–50