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.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
Mooring failures significantly threaten the stability of Floating Offshore Wind Turbines (FOWT) under extreme environmental conditions. This study presents an innovative shared damping mooring system incorporating Seaflex dampers to improve structural stability and operational reliability. Dynamic simulations under 1-year and 50-year return period sea states demonstrate the system’s effectiveness. Under Ultimate Limit State (ULS) conditions, the system reduces surge displacement by 59%, pitch angle by 47%, and mooring line tension by 72%. Under Accidental Limit State (ALS) conditions, it mitigates load spikes, reduces drift displacement by 60%, and improves safety factors by 50%. The comparison shows chain and wire rope configurations have better load reduction performance in the shared damping scheme. Lightweight and adaptable, the Seaflex dampers enhance broad-spectrum damping without affecting platform buoyancy. This study provides a robust solution for improving FOWT safety and durability in harsh marine environments, enabling large-scale offshore wind energy development.
An analytical framework is presented to describe the attenuation of regular and irregular waves propagating over floating seaweed farms. Kelp blades suspended on longlines are modelled, as a first approximation, as rigid bars rotating around their upper ends. Assuming small-amplitude blade motions under low to moderate sea conditions, the frequency-dependent transfer function of the rotations can be obtained, with quadratic drag loads linearized. Subsequently, the hydrodynamic problem with regular waves propagating over suspended seaweed canopies is formulated using the continuity equation and linearized momentum equations with additional source terms in the vegetation region. Analytical solutions are obtained for attenuated regular waves with their heights decaying exponentially as they propagate over the canopy. These solutions are utilized as the basis for predicting wave attenuation of irregular waves while stochastic linearization of the quadratic drag loads is employed. In contrast to energy-conservation-based models, which assume the velocity profile follows linear wave theory, the present solution can predict the reduced velocity inside the canopy. The analytical solutions are validated against experimental data and verified against a numerical flow solver. The model is capable of resolving the wave attenuation, along with velocity profiles and phase lag. Drag and inertial force exhibit cancellation effects on wave decay and both affect phase lag.
Simulation-based analysis estimating both the energy requirement of the entire carbon capture process and the purity of the recovered CO 2 is scarce. The purity of the captured CO 2 is crucial as it must meet a specification before transportation, preventing phase change and damage to the transportation system. This study conducted 31,104 simulations of a monoethanolamine carbon capture plant treating measured flue gas from an existing cement production plant. After capture, the CO 2 is treated through a deoxygenation unit followed by a compression train to fulfill specific quality specifications. Based on the sensitivity analysis, the energy consumption of the post-treatment process decreased with increased purity downstream. Despite this, the total energy consumption was not affected. Moreover, after the two-step purification the CO 2 stream was able to successfully fulfill the specification for NO x, O 2, NH 3, Ar, CO, SO 2. However, failing to meet the H 2O concentration requirements of both considered specifications and the N 2 concentration specified for ship transport. Thus, increasing the post-treatment energy cost or standard adjustments is required for future applications.
This paper models the large periodic plate structure as Kirchhoff-Love plates and introduces a novel implementation of asymptotic homogenization (NIAH) to enable an efficient calculation of the structural stiffness. Compared to full finite element models, applying NIAH to a unit-cell model greatly reduces computational costs. This paper systematically presents the derivation and finite element formulation of asymptotic homogenization (AH), and the development of NIAH. Benchmark cases, including solid, thin-walled, multi-material plates, and a plate with octagonal holes, are used to validate the NIAH implementation. A series of representative fish cage designs are analyzed to investigate the influence of pontoon components, structural layouts, and material distribution on structural stiffness. To ensure the reliability of the calculations, the choice of unit-cell model and the sensitivity of the results to mesh density and unit-cell size are also discussed.
Physical model tests are often conducted during the design process of coastal structures. The wave climate in such tests often includes short-crested nonlinear waves. The structural response is related to the incident waves measured in front of the structure. Existing methods for separation of incident and reflected short-crested waves are based on linear wave theory. For analysis of nonlinear waves, the existing methods are limited to separation of nonlinear long-crested waves. For short-crested waves, the only options so far have been to use estimates without the structure in place. The present paper thus presents a novel method for directional analysis of nonlinear short-crested waves: Non-Linear Single-summation Oblique Reflection Separation (NL-SORS). The method is validated on numerical model data, as for such data, the target is well defined as simulations may be performed with fully absorbing boundaries. Second- and third-order wave theory is used to demonstrate that small errors on the celerity of nonlinear components in the mathematical model of the surface elevation can be obtained if a double narrow-banded directional spectrum is assumed, ie the primary frequency and the directional spreading function must be narrow banded. As the increasing nonlinearity of the waves often arise from waves shoaling on a sloping foreshore, the directional spreading of the waves will decrease due to refraction, and a broad directional spreading function will thus not be experienced in highly nonlinear conditions. The new NL-SORS method is shown to successfully decompose nonlinear short-crested wave fields and estimate the directional spectrum thereof.
This study investigates the critical parameters necessary for evaluating large-scale renewable offshore energy hubs, based on insights from industry experts. Using a Multi-Criteria Decision Analysis methodology, the experts emphasized that, in their view, technical and economic parameters rank higher than environmental and societal considerations when evaluating large-scale renewable offshore projects, including green hydrogen production. Environmental and societal parameters should not be neglected, but they ought to be evaluated outside this assessment framework. This could refer to the environmental impact assessment already in use. These findings provide a foundation for evolving the traditional Triple Bottom Line theory into a Quadruple Bottom Line approach by incorporating technical parameters alongside economic, social, and environmental factors, while addressing the specific challenges of offshore energy hubs. Among all the parameters ranked across the four domains, the top 15 were exclusively techno-economic, with technical and economic scores averaging 4.5 and 4.3 out of 5, respectively. In contrast, societal and environmental scores averaged below 3.0. To ensure the successful deployment of offshore energy hubs, a stepwise approach is recommended to manage complexity, reduce risks, and support scalable solutions. This approach aims to align the needed industrial parameters with the demands of the energy transition.
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 utilized 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.
Background
The transfer of offshore wind farm workers between transport vessels and wind turbines is a hazardous operation with a disproportionately high occurrence of "high potential" incidents. Motion sickness has been reported to affect offshore wind farm worker well-being, and has been identified as a job demand, especially during crew transfer and ladder-climbing operations.
This scoping review sought to determine the extent to which current research defines, describes, and quantifies MS among offshore wind farm workers and to identify relevant research gaps.
Methods
Using terms related to motion sickness and offshore wind farm operations, searches were conducted of the PubMed, Scopus, and Web of Science databases. Studies published in English between 1990 and 2024 were included.
Results
795 articles were retrieved, of which 11 articles met the inclusion criteria. The included articles describe MS as a job demand but do not clearly define it in the research context. Consequently, it remains unclear which symptoms of MS constitute a job demand and how workers are affected. Additionally, indications of motion sickness prevalence are required, using a clear definition which accounts for the wide range of subjective symptoms other than vomiting.
No research appears to have been carried out where motion sickness among wind farm workers has been studied as a broad occupational health issue within the offshore wind energy sector.
Conclusions
This review identifies significant research gaps concerning motion sickness among offshore wind farm workers. Motion sickness-related issues have either been overlooked, studied in isolation, or insufficiently addressed. These issues constitute empirical, methodological, and knowledge gaps, necessitating a need for systematic studies that address these research gaps in the context of the offshore wind energy sector.