In recent years, Multi-purpose Offshore Platforms (MPOP) have emerged as a novel solution to address the increasing global food and energy demand. Beyond the conceptual and qualitative analysis, this paper develops a hybrid quantitative framework to evaluate the life-cycle performance of co-located Wave Energy Converters (WECs) and offshore aquaculture (AQ) systems. The framework integrates hydrodynamic numerical simulations and probabilistic reliability analysis into a System Dynamics (SD) model to simulate complex subsystem interactions and quantify the system productivity and economic feasibility under environmental and operational uncertainty. A case study in Southern Tasmania demonstrates that the upstream WEC farm effectively reduces the incoming wave heights by up to 23%, mitigating aquaculture mooring tensions of the downstream salmon farm by 18%. This protection effect translates into an economic benefit that the co-located configuration achieves a 30.7% reduction in Life Cycle Costs (LCC) compared to the stand-alone configuration. Furthermore, the MPOP demonstrates the robust power capacity, ensuring a continuous off-grid power supply despite long-term component degradation and fluctuating aquaculture power demand. The results validate the MPOP concept as a commercially viable solution for sustainable blue economy development and provide a comprehensive simulation and decision-making tool for exploring future offshore multi-sector cooperation.

Pitting corrosion-fatigue remains a dominant degradation mechanism in offshore and marine steel structures. Conventional approaches for modelling pitting corrosion-fatigue often fail to capture the stochastic, localised progression of pit-induced fatigue under complex environmental and loading conditions. This study focuses on recent advancements in data-driven methodologies, particularly machine learning (ML) and hybrid models. ML frameworks, ranging from neural networks to ensemble learning, show considerable promise in modelling non-linear, high-dimensional relationships among corrosion, fatigue, and environmental parameters. Hybrid models, which integrate domain knowledge through physics-based features or mechanistic coupling, further enhance predictive accuracy and robustness while offering improved interpretability. This study highlights both opportunities and persistent challenges in the field, including the scarcity of standardised, high-fidelity datasets; difficulties in generalising models across different steel grades and exposure conditions; and the limited interpretability of certain ML algorithms. This study emphasises the development of intelligent, adaptable models to support improved structural health monitoring and asset management in offshore and marine environments.

Austenitic stainless steels such as AISI 316L are widely used in offshore renewable energy structures due to their favourable strength-to-weight ratio and corrosion resistance. However, prolonged exposure to chloride-rich marine environments promotes localised pitting corrosion, which significantly influences structural integrity and fatigue life. The stochastic nature of pit morphology limits the reliability of conventional corrosion severity classification methods based on deterministic thresholds or expert judgment. This study proposes an automated machine learning (AutoML) framework for objective corrosion severity classification of AISI 316L based on pit morphology. Key morphological features, including pit depth, metal loss, and pit diameter obtained from optical profilometry, were used as model inputs. Unsupervised 𝑘-means clustering was first applied to identify intrinsic groupings in the morphology space and to define three physically interpretable severity classes: mild, moderate, and severe. These cluster-derived labels were then used to train a supervised multiclass classifier using the Tree-based Pipeline Optimisation Tool (TPOT) AutoML framework. The optimal AutoML pipeline achieved a classification accuracy of 87.5%, with balanced precision and recall across all severity classes. The results demonstrate that the proposed data-driven methodology reduces subjectivity in corrosion severity assessment and provides a robust and reproducible framework for corrosion evaluation in offshore renewable energy infrastructure.

Offshore renewable energy systems (ORESs) have substantial potential to decarbonize industries and clean energy generation. However, they face challenges, including high costs and risks due to uncertain and hazardous marine environments. To address this challenge, this study presents a multi-objective design and optimization framework for a hybrid standalone ORES powering industrial and domestic green electricity and hydrogen loads simultaneously. The hybrid ORES model integrates offshore wind (OWF) and floating solar (FPV) farms with large-scale battery energy storage systems (BESS) and hydrogen storage systems (HSS) as a firming technology. The framework optimises three conflicting objectives, including minimising cost and potential energy waste and maximising system reliability, subject to various technical and economic constraints. To regulate optimal energy flow and protect ORES components, a simple and efficient power management strategy is presented and utilized. Five recent state-of-the-art multi-objective metaheuristics are applied to solve the hybrid ORES design and obtain Pareto solutions. The comparative analysis utilises widely employed Pareto front hypervolume (HV) metric statistics and Friedman's rank test. A case sensitivity analysis is conducted to evaluate the model's robustness, reliability, and effectiveness. Validation of the methodology is conducted through a real-world case study in Australia's offshore region, showcasing its capacity to supply clean energy to industrial and domestic loads. Results indicate SHAMODE-WO's superior diversity and convergence traits in Pareto-optimal sets, with SHAMODE closely trailing. Furthermore, the average Friedman rank across all three cases designates SHAMODE-WO as the top performer. The proposed framework can facilitate decision-makers in addressing complex multi-objective ORES optimization problems and choosing optimal solutions.

The maritime industry has been actively exploring hydrogen as a sustainable fuel to reduce greenhouse gas (GHG) emissions and mitigate environmental impacts. This paper offers a review of the development of hydrogen-powered vessels from 2000 to 2024, highlighting key technological advancements, regulatory progress, and engineering challenges. The journey began with the launch of the small passenger vessel Hydra in Germany in 2000, marking the inception of hydrogen-powered maritime technology. Over the past two decades, significant strides have been made in hydrogen energy conversion, storage, and bunkering technologies, which have been implemented and tested across various vessels, leading to the growth of a global fleet of hydrogenpowered vessels. As of November 2024, 50 hydrogen-powered vessels, including commercial and noncommercial vessels, have been constructed or retrofitted, each serving as a platform to demonstrate and refine hydrogen technologies and engineering designs. This study synthesises the lessons learned from these vessels, offering insights into the practical applications of hydrogen technologies, fuel bunkering practices, and the design and operational challenges encountered. The novelty of this research lies in its comprehensive analysis of the entire hydrogen-powered fleet, identifying critical gaps and opportunities for further innovation in the maritime sector.

This study investigates the performance and reliability of a Multi-Purpose Offshore Platform (MPOP) system integrating a Wave Energy Converter (WEC) farm and an offshore aquaculture farm. A coupled numerical approach, combining OrcaFlex and SNL-SWAN, was adopted to simulate wave-WEC interactions and wave propagation over large downstream areas. The study highlights the influence of WEC arrangements and wave directions on energy production. The energy requirements of the aquaculture system were modelled under Australian environmental conditions. System reliability was evaluated using failure rate data and scenario simulations via the system dynamics (SD) model. Results show that minor and major failures minimally affect system availability, while critical failures can lead to significant power shortfalls. In an extreme scenario, where all WECs fail, the aquaculture farm would require over 400 MWh of external energy support. This research provides insights into optimizing MPOP configurations and highlights the importance of incorporating reliability and failure scenarios to ensure continuous energy and food production in offshore environments.

Green hydrogen is gaining prominence as a sustainable fuel to decarbonize hard-to-electrify industries and complement renewable energy growth. Among clean hydrogen production technologies, seawater-based PEM electrolysis systems hold substantial promise. However, implementing offshore PEM electrolysis systems faces significant challenges in ensuring long-term availability due to technological infancy and harsh environmental conditions. Ensuring safe and reliable operation is therefore critical to advancing global sustainability goals. While existing research has primarily focused on component-level techno-economic feasibility, limited attention has been given to system-level safety and availability analysis, particularly for offshore renewable-powered seawater-based PEM electrolysis systems. This study addresses this gap by conducting a comprehensive availability analysis of containerized plug-and-play PEM systems in offshore environments. A Bayesian Network model is employed, incorporating Fault Tree Analysis and Reliability Block Diagram approaches, for failure and availability analysis at the system level. A maintenance decision support tool using Influence diagram is developed to analyse different maintenance planning strategies impact on system availability improvement. A case study incorporating industrial modular PEM model is utilized to analyse the developed model effectiveness. The study identifies 81 availability states, with the hydrogen generation subsystem being the most critical to system performance. Comparative analysis shows that applying redundancy across all subsystems improves availability by 18.54 % but reduces Expected Utility by 4.94 %. The optimal strategy involves redundancy for seawater purification, cooling, and monitoring subsystems, with preventive maintenance for hydrogen generation, achieving a maximum EU of 5.29 x 106. This framework supports decision-makers in evaluating system availability under uncertain offshore conditions, optimizing maintenance strategies, and ensuring resilience for large-scale H2 production.

Offshore aquaculture industries face significant challenges in securing reliable and environmentally friendly energy sources. Among the possible solutions, wave energy converters (WECs) show a promising solution to transfer the technology for creating sustainable operations for remotely accessible fish farms. However, ideally integrating them into the aquaculture floating vessels necessitates a careful and thorough design approach. This paper presents a comprehensive framework for evaluating the performance of the AquaPower platform (APP) concept vessel in supporting offshore fish farm operations. Drawing inspiration from established WEC principles, this concept merges a floating platform with a tensioned mooring lines integrated with power take off systems connected to a moored vessel. The framework addresses crucial aspects, including power generation, structural resilience, and mitigation of mooring fatigue-induced deterioration, which are essential for optimizing the APP’s performance. To enhance evaluation the reliability of the structure, the framework introduces a robust surrogate model based on Bayesian data analysis. This enables the assessment of mooring asset reliability and projected lifespan for real-time monitoring. The practical demonstration of this framework investigated through a case study for designing and evaluating the APP, effectively highlighting its potential as a feasible wave energy solution for the progress of offshore aquaculture towards blue economy technology. This paper’s primary aim is to contribute to affirming the feasibility and viability of the APP concept as an effective and sustainable wave energy remedy for offshore aquaculture. The results of this study can be applied to other contexts, demonstrating the framework’s ability to enhance the dependability of various offshore energy structures, including floating wind turbines, and extend their operational lifespan.

The integration of emerging offshore renewable energy systems, transportation, and other facilities through implementing a multipurpose offshore platform (MPOP) presents itself as a potential solution for achieving environmental and socio-economic sustainability in the realm of offshore renewable energy. Currently, development of MPOP layouts is conceptual and mainly relies on the design specifications of other offshore platforms. From sustainability and economic viability perspectives, the safety and reliability of such platforms has significant uncertainty. In this paper, a novel framework for designing MPOP layouts is proposed incorporating inherent safety principles and social and regulatory considerations to address the reliability and safety of such platforms. The applicability of the methodology is demonstrated with a case study. The case study results show that the framework effectively enhances the platform's operational safety reducing potential safety costs with an inherently safer layout. The developed framework can help in developing layout plans for novel and complex offshore facilities at early design stages.

There is growing interest in using hydrogen (H2) as a marine fuel. Fire and explosion risks depend on hydrogen release and dispersion characteristics. Based on a validated Computational Fluid Dynamics (CFD) model, this study performed hydrogen release and dispersion analysis on an under-deck compressed H2 storage system for a Live-Fish Carrier. A realistic, under-deck H2 storage room was modelled based on the ship's main dimensions and operational profile. Det Norske Veritas (DNV) Rules and Regulations for natural gas storage as a marine fuel were employed as base design guidelines. Case studies were developed to study the effect of two ceiling types (flat and slanted) in terms of flammable cloud formation and dissipation. During the leak's duration, it was found that the recommended ventilation rate was insufficient to dilute the average H2 concentration below 25% of the flammable range as required by DNV (1.2% required against 1.3% slanted and 1.4% flat). However, after 35 s of gas extraction, the H2 concentration was reduced to 0.5% and 0.6% in the slanted and flat cases, respectively. The proposed methodology remains valid to improve the ventilation system and assess mitigation alternatives or other leakage scenarios in confined or semi-confined spaces containing compressed hydrogen gas.

About 60% of marine vessels' power is consumed to overcome friction resistance between the hull and water. Air lubrication can effectively reduce this resistance and lower fuel consumption, and consequently emissions. This study aims to analyze the use of a gas-injected liquid lubrication system (GILLS) to reduce friction resistance in a real-world scenario. A 3D computational fluid dynamics model is adopted to analyse how a full-scale ship (the Sea Transport Solutions Designed Catamaran ROPAX ferry) with a length of 44.9 m and a width of 16.5 m is affected by its speed and draught. The computational model is based on a volume of fluid model using the k-omega shear stress transport turbulence model. Results show that at a 1.5 m draught and 20 knots cruising speed, injecting 0.05 kg/s of compressed air into each GILLS unit reduces friction resistance by 10.45%. A hybrid model of natural air suction and force-compressed air shows a friction resistance reduction of 10.41%, which is a promising solution with less required external power. The proposed technique offers improved fuel efficiency and can help to meet environmental regulations without engine modifications.

Corrosion is widely known to be a major cause of the failures in process facilities. Prediction of corrosion damage is therefore essential for industries to manage the availability of their assets. This research aims to investigate the application of supervised machine learning methods for the classification of pitting corrosion damage. Several machine learning classifiers, namely ensemble methods, support vector machine (SVM), K-nearest neighbours, and the decision tree are used to classify the extent of pitting corrosion damage in corroded steel samples. To simulate the corrosion of the steel samples, a series of laboratory experiments were conducted. After processing the results using appropriate statistical methods, the corrosion data was used to train the machine learning models. The trained models can predict the class of corrosion damage with acceptable accuracy using the material and environmental specifications of the samples. Additionally, a discussion on the selection of machine learning techniques which classify corrosion damage using a risk-based approach is provided. With their optimal accuracy and lower risk of misclassification, the SVM and AdaBoost models perform better than the other studied models.

Unlocking the potential of offshore renewables for green hydrogen (GH2) production can be a game-changer, empowering economies with their visionary clean energy policies, amplifying energy security, and promoting economic growth. However, their novelty entails uncertainty and risk, necessitating a robust framework for facility deployment and infrastructure planning. To optimize offshore GH2 infrastructure placement, this work proposes a novel and robust GIS-based multi-criteria decision-making (MCDM) framework. Encompassing thirty-two techno-socio-economic-safety factors and ocean environmental impact analysis this methodology facilitates informed decision-making for sustainable and safe GH2 development. Utilizing the synergies between offshore wind and solar resources, this study investigates the potential of hybrid ocean technologies to enhance space utilization and optimize efficiency. To illustrate the practical application of the proposed framework, a case study examining a GH2 system in Australia's marine region and its potential nexus with nearby offshore industries has been conducted. The performed life cycle assessment (LCA) explored various configurations of GH2 production, storage, and transportation technologies. A Bayesian objective weight integrating technique has been introduced and contrasted statistically with the hybrid CRITIC, Entropy, MEREC and MARCOS-based MCDM approaches. Various locations are ranked based on the net present value of life cycle cost, GH2 production capacity, risk, availability, and environment sustainability factors, illustrating their compatibility. A sensitivity analysis is conducted to confirm that a Bayesian approach improves the decision-making outcomes through identifying optimal criteria weights and alternative ranks more effectively. Empowering strategic GH2 decisions globally, the proposed approach optimizes system performances, cost, sustainability, and safety, excelling in harsh environments.

This paper outlines the challenges and opportunities involved in developing a safe and sustainable hydrogen infrastructure. The growing global energy demand and environmental impacts of fossil fuels have sparked interest in alternative energy sources. Hydrogen, as an environmentally friendly and sustainable energy carrier, offers a promising solution. However, the widespread adoption of hydrogen technologies faces significant safety and data reliability challenges. This paper reviews existing literature on hydrogen safety, encompassing hydrogen leak diffusion, fire and explosion, hydrogen deflagration to detonation transition (DDT), risk assessments, and mitigation techniques associated with different hydrogen facilities. Multiple approaches, including probabilistic risk analysis, computational fluid dynamics (CFD), experimental measurements, and machine learning algorithms (MLAs), to ensure hydrogen safety are also explored. Existing hydrogen-related accidents are also extensively analysed. Despite the progress in hydrogen safety research, challenges and limitations still exist. These include a lack of reliable data, limited AI applications due to data availability issues, the need for safe and economic hydrogen storage, and the importance of providing personnel with adequate safety awareness and knowledge. Moreover, the article identifies future research opportunities in investigating auto-ignition mechanisms, collecting more experimental data, integrating AI and CFD to investigate hydrogen dispersion behaviour, exploring the sensor’s technology, developing inherently safer designs, and studying the integrated impacts of evolving accident scenarios. In conclusion, the paper emphasises the importance of addressing safety challenges to establish a secure and dependable hydrogen infrastructure. It highlights the need for further research to enhance safety protocols, establish robust standards, and support the long-term sustainability goals of the hydrogen industry. The insights provided in this study can contribute to identifying research areas, improving safety measures, and developing future hydrogen infrastructure.

Understanding hydrogen dispersion in a semi-confined space is crucial for the safe operation of hydrogen-related systems, such as hydrogen fuel cell vehicles. Analyzing hydrogen release and its subsequent dispersion can be effectively conducted using computational models, which necessitate experimental measurements for validation. For safety reasons, accidental scenarios are frequently replicated using helium as a hydrogen simulant in experiments, despite some concerns about the comparability of helium and hydrogen dispersions. Hence, this study conducts a comprehensive parametric numerical analysis of the similarities between hydrogen and helium dispersion when released in a semi-confined space. The model is based on the Macquarie Dispersion Chamber and the simulation results are first validated against the measurements. The study focuses on the impact of the leakage rate and ventilation velocity. Three release models were analysed: equal concentration (Method A), equal volumetric flow rate (Method B), and equal buoyancy (Method C). This study extends previous research on Method B, used for gas release in open spaces, to environments where flow-wall interactions become important. It also compares Methods A and C for a few scenarios, examining variations in concentration distribution, purge time, and activation time under different conditions. In the absence of ventilation, Method B effectively compares hydrogen and helium flow rates by accounting for volumetric flow rates, regardless of buoyancy effects. However, under increased ventilation velocities, Method A and Method C exhibit superior agreement across sensors, highlighting the necessity of choosing detection methods according to prevailing environmental conditions.

A dynamic model of the mutual interdependency between humans and machines in offshore wind farms (OWFs) is developed in this paper. The model emphasises the importance of having early indicators to dynamically moderate human behaviour and the need to account for both human and physical subsystems and their mutual interactions for optimal asset management practices. This work simulates three distinct scenarios for: (1) examining the interconnectedness of technical and human dynamics and their implications on error and failure; (2) assessing the impact of production loss on human and organisational behaviour; and (3) evaluating human error probability as an early warning sign of production loss. The findings suggest that adopting an appropriate maintenance culture and considering human error likelihood and production rate in decision-making leads to optimising production and mitigating the risks associated with human error. The research highlights the significance of comprehending the complex interactions between human and machine factors in the operation and maintenance (O&M) of offshore wind turbines (OWTs). The proposed dynamic model helps organisations identify the underlying causes of errors, allowing them to improve their maintenance strategies.

This paper presents a novel methodology for site selection of Offshore Renewable Energy (ORE) systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Focusing on the wave energy, a relatively untapped renewable energy source, this research focuses on optimizing the Wave Energy Converter (WEC) deployment amidst the uncertainties of the offshore environment. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads. Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different environmental conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties into a multi-criteria decision-making (MCDM), enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various sites, leading to the identification of the optimal decision alternative based on maximum expected utility. This work provides a detailed framework for renewable energy stakeholders, helping in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with ORE system installations, promoting efficient and sustainable energy production from ocean resources.

Australia has significant potential for the development of offshore renewable energy systems (ORES), and it can play an essential role in the global energy transition. The planning, design, installation, operation, and end-of-life management of ORES present substantial challenges in terms of the reliability of systems and the safety of operations. This paper focuses on identifying the gaps and challenges related to the structural integrity of ORES, highlighting potential areas for technological and managerial improvements. The paper investigates Australia's existing policies and regulations, identifies their shortcomings, and provides recommendations for their advancement. Key recommendations include implementing robust regulations, enhancing site-specific knowledge, adopting structural health monitoring (SHM) from the design phase, and fostering industry collaboration to accelerate ORES development and sustainability. The findings reveal high failure rates in ORES components, attributed to harsh marine conditions, material degradation, and extreme weather, underscoring the need for standardized protection and preventive measures. Integrating climate change impacts into dynamic risk assessments is crucial for accurate failure and consequent analyses. The study advocates learning from other engineering sectors to bridge existing gaps and align with sustainable offshore development goals. These recommendations aim to assist policymakers, regulators, and technology developers in realising safer and more sustainable ORES for Australia.

This paper aims to enhance the understanding of hydrogen explosions in hydrogen refuelling stations and evaluate associated risk factors using computational fluid dynamics simulations. The model is first validated against the measured data for hydrogen dispersion and explosion. Different scenarios are then modelled to understand the ignition timing and location. The study estimates acceptable distances to minimize asset damage and human injury from explosion incidents. It has been found that higher wind speeds lead to faster and more extensive dispersion of the hydrogen gas released during a leak. In addition, since strong wind can act as a powerful driving force for the shock wave, the impact of the explosion is found to be less. Interestingly, moving the source of ignition to regions with higher hydrogen concentration has a marginal impact on overpressure and temperature; however, the blockage ratio can significantly amplify the overpressure. It is found that cases with high blockage, including storage room, and cases with large volumes of flammable cloud, including leakage from compressor towards the ground, have the highest hazards. The findings will provide valuable insights into fire and explosion prevention in various areas of hydrogen refuelling stations and contribute to safer hydrogen infrastructure construction.

Marine and Offshore renewable structures are widely being developed across the globe, and specifically in Australia, under controlled conditions with little focus on having advanced health monitoring systems, optimization of the structures design and operation to undertake complex operations. Considering a data-driven approach to health management is an essential part of the future offshore renewable energy industries. It allows the detection of critical faults and assists in estimating the remaining useful lifetime (RUL) facilitating more reliable offshore renewable structures. Despite the advantage of machine learning techniques in asset health condition monitoring, few studies have considered the integration of such prognostics into maintenance planning for remotely operated offshore facilities. This paper proposes a model to help the marine and offshore industry in real-time maintenance planning. The model will consider the imperfect RUL prognostics incorporating higher degrees of uncertainty involved with remotely operating systems in offshore environments. A Bayesian Meta-Model is developed for time series change point (CP) detection of multiple anomalies in a random process. The proposed methodology will be useful in incipient failure detection and maintenance planning of complex systems operating in harsh and highly random marine environments.