This study proposes a novel tower damping system to enhance the structural performance of the NREL 5 MW semi-submersible wind turbine under operational and extreme load conditions. Environmental load data from the Norwegian MET center was analyzed to characterize the loading conditions for floating offshore wind turbines (FOWT). The probability density spectrum of sea state data was employed to identify operational load conditions. At the same time, the Inverse First-Order Reliability Method (IFORM) was used to derive the 50-year extreme sea state. Perform a fully coupled Aero-Hydro-Servo-Elastic simulation of the FOWT dynamic model with a damping system using OrcaFlex software. The results reveal that: Under operational sea states, the turbine tower-top displacement was reduced by 60–70%, and acceleration by 30–40%, enhancing tower-top stability. Under extreme loads, tower-top acceleration was reduced by 5–7%, and displacement by 6–8%. Cumulative damage assessments indicate a reduction in fatigue damage of up to 72%, with the effective fatigue life of the tower base extended by 136%. The proposed damping system significantly reduces vibration under fatigue and extreme load conditions.
This paper centers local processes for co-creating transitions towards more sustainable, inclusive, and resilient coastal community development. We have conceptualized a path for change processes with phases of transition including 1. Reasons to convene; 2. Governance and participation "rules"; 3. Building knowledge together; 4. Implementation and experimentation; 5. Post-hoc reflections and assessment; 6. Transfer/reproduction of practices. Here, we focus on the first three stepping stones, which form the foundation of the collaborative process, focusing on the challenges and opportunities encountered as a pilot intervention is planned. We use a framework informed by partnership-, co-creation-, transition-, and justice literatures, to analyze data focused on establishing partnerships for ongoing co-creation of knowledge, empowering actors in the local communities, and selecting options for an intervention pilot. Conclusions relate to (a) trust and preexisting relationships, (b) what inclusion means, (c) internal power differentials, (d) preexisting tensions in the community, (e) challenges to co-creation.
This paper examines the stability of a weak island namely Sumbawa-Lombok of Indonesian grid, interconnected with two infeed HVDC links facilitating 2 x 120 MW power transfer from Sumba and Flores Island. Through power flow, short circuit, small signal stability, resonance stability, and transient stability analyses, it is demonstrated that the existing infrastructure fails to support such transfer due to voltage drops, overloading, and stability limitations. Upgrading to 150 kV and its subsequent component resolves the small-signal and transient stability constraint as its grid strength is increasing. The current findings underscore that the primary limitation lies in the grid's infrastructure, not in dynamic or control constraints. The current result establishes the need for strategic grid reinforcements to support HVDC integration in weak systems and sets the stage for future research on optimizing the extent of such reinforcements.
The expansion of Carbon Capture, Utilization, and Storage (CCUS) highlights the growing need for carbon dioxide (CO2) pipeline transportation. While pure CO2 is non-corrosive, impurities such as H2O and NO2 create a corrosive environment that risks pipeline integrity. This study investigates how H2O and NO2 concentrations, along with temperature, influence corrosion under CO2 pipeline conditions. The investigation was performed in an autoclave setup emulating a linear velocity of 0.96 m/s at 100 bar and temperatures of 5 °C and 25 °C, testing X52 and GR70, and a more corrosion-resistant 9Cr alloy. The results indicated that the presence of NO2 elevated the corrosion rate compared to scenarios without. Low H2O concentration led to a corrosion rate of up to five times higher at 5 °C, compared to at 25 °C, in the presence of NO2. Low to moderate corrosion was observed for the carbon steels without NO2 and with 70 ppmv H2O at both temperatures. Reducing the H2O concentration below 70 ppmv and removing NO2, while SO2 and O2 are present, will only result in low to moderate corrosion in the carbon steel CO2 pipeline. The corrosion rate for X52 and GR70 was 0.065 mm/y and 0.016 mm/y higher or 5 and 3 times greater, respectively, at 5 °C compared to 25 °C. The study concludes that H2O should be maintained below 70 ppmv and NO2 should be eliminated to prevent severe corrosion. Emphasizing the importance of CO2 specification compliance and the need for further research into CO2 compositions that align with the specifications.
This paper proposes an economic and resilient operation architecture for a coupled hydrogen-electricity energy system operating at port. The architecture is a multi-objective optimization problem, which includes the energy system optimal economy as the goal orientation and the optimal resilience as the goal orientation. The optimal resilience orientation looks for the best resilience performance of the port through reasonable energy management including (1) reducing the amount of electricity purchased by the port power grid from the external power grid (2) improving the energy level of electric energy storage (3) improving the energy level of hydrogen energy storage. Taking the actual coupled hydrogen-electricity energy system of Ningbo-Zhoushan Port as an example, four typical scenarios were selected according to renewable generation and load characteristics, and a comparative analysis was carried out during the oriented operation. The results show that although the resilience orientation increases the operating cost compared with the economic orientation, the four scenarios reduce the load shedding by 44.84%, 30.26%, 48.49% and 34.37% respectively when the external power grid is disconnected. The impact of changes in resilience-oriented weight coefficients and hydrogen price on system resilience performance was investigated to provide more references for decision makers.
Hydrogen-electricity integrated multi-energy systems are promising approaches to reduce carbon emissions in ports. However, the stochastic nature of renewable energy and the imbalance between the renewable generation and load demand in ports necessitate the design of an appropriate coupled hydrogen-electricity energy storage systems (CHEESS). This paper proposes a multi-objective optimization model for CHEESS configuration in random unbalanced port integrated multi-energy systems (PIMES), aiming to minimize its life-cycle cost and carbon emissions through co-optimization of sizing and energy management. A hierarchical two-stage framework is proposed to solve the multi-objective model. The proposed optimization framework is applied to a real PIMES at the Ningbo-Zhoushan Port. The results show that the proposed method can save 10.54% of the monetary cost and 19.67% of carbon emissions over the entire life-cycle of the system. The study demonstrates that the proposed framework has the potential to generate significant economic and environmental benefits and provides a feasible solution for port authorities seeking to implement CHEESS, aiming to promote sustainability in port operations.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
Infrastructure-led development is driving geopolitical agendas in the Global South, and Djibouti is a case in point. Strategically situated in the Horn of Africa, Djibouti's seaports and related infrastructure have been modernized through foreign investments to serve international trade and growing African hinterlands. Scholarship often posits foreign logistics investors as hegemons driven by geopolitical interests, and host states as passive recipients. This paper questions such claims. It examines the relationship between "hegemon" and "host state" from the perspective of the latter, in this case the Djibouti government's interactions with Emirati and Chinese infrastructure companies. Methodologically, the analysis is based on interviews, secondary data and document analysis. It applies a conflict-sensitive controversies approach from critical logistics studies to trace contestation over authority in Djibouti's logistics sector. It shows how Djibouti's government applies various strategies (discursive, legal and political) to exercise agency in its logistics sector. The paper concludes that the geopolitics of infrastructure-led development is a reciprocal effort that goes beyond conventional logics of hegemons imposing on host states.
Autonomous unmanned underwater vehicles (UUVs) play a vital role in diverse underwater operations; localization is of great interest for UUVs mirroring the trend seen in self-driving surface and aerial vehicles. Unlike their land and aerial counterparts, underwater environments lack reliable Global Navigation Satellite Systems (GNSS) due to radio wave attenuation in water. Hence, alternative localization methods are imperative for both navigation and operational purposes. This study thoroughly reviews sensor technologies for underwater localization, including sonar, Doppler velocity log, cameras, and more. Different operations necessitate distinct localization accuracies and vehicle and sensor choices. Environmental factors, such as turbidity, waves, and sound disturbances, impact sensor performance. Conclusions are given on the coincidence between operational requirements and sensor specifications, with special attention to the open concerns. These considerations include aspects such as the line of sight for acoustic positioning systems and the requirement for a feature-rich environment for visual sensors. Lastly, a prediction for the future of underwater localization is given, where the tendencies indicate lower costs for sensors, making operation-specific vehicles more attractive, which aligns with an increased demand for cost-efficient autonomous offshore operations.
The completeness and high predictability of hazardous scenarios by hazard identification methods are issues in risk analyses. A way to the improvement is to carry out both an exhaustive - to the extent possible - post-accident and predictive accident analysis. Currently, Natural Language Processing (NLP) allows quick processing of many accident reports. In combination with graphical tools, it is now even possible to automatically output causal diagrammatic models of accidents and visualize them on a multi-scenario accident diagram. A step forward is the application of NLP to support predictive analysis. Predictive accident analysis focuses on identifying deviations from expected or normal conditions, the subsequent events following these deviations, and their interactions leading to an accident. The expected or normal conditions are typically outlined in specifications and procedures. This paper demonstrates how NLP can assist hazard identification and predictive accident analysis during lifting operations on ships and offshore platforms.