Design waves have been used in the past for the probabilistic assessment of wave-induced loads and responses of offshore structures. Various response-conditioning techniques have been employed to determine suitable wave episodes, typically based on linear response transfer functions. Nevertheless, extreme events are not always driven by linear phenomena but can be triggered by near-resonant effects, as in the case of the slow-drift motions of moored floating bodies. Limited research has been devoted to addressing this class of responses using response-conditioned waves (RCW). This paper presents a new approach for deriving RCWs that accounts for combined wave- and low-frequency responses. Both the response amplitude operator (RAO) and the quadratic transfer function (QTF) are employed in an iterative response-conditioning procedure. That permits the identification of appropriate short-duration wave episodes that excite resonant slow-drift motions. These wave episodes are then used in a two-step multi-fidelity design wave methodology for the probabilistic evaluation of the fully nonlinear extreme responses. The proposed approach is validated experimentally for predicting the surge excursions of a moored container ship, and good agreement is found against Monte Carlo results in irregular waves.
In the present paper, the experimental data on wave run-up on slender monopiles from recently published small and large scale tests are reanalyzed using different methods for the wave analysis. The hypothesis is that the post processing has an impact on the results, due to limited depth and highly nonlinear waves in many of the tests. Thus, the identified maximum waves by a zero-down crossing analysis are highly influenced by the reflection analysis method as well as by bandpass filtering. The stagnation head theory with the run-up coefficient is adopted and new coefficients are presented. The hypothesis is verified, and the applied bandpass filter is identified as a large contributor to conservatism in previous studies, as the steep, nonlinear waves that produce the highest run-up can be heavily distorted by the bandpass filter.
The current offshore oil & gas multi-phase production and transportation installations have big challenges related to the slugging flow: An unstable multi-phase flow regime where the flow rates, pressures and temperatures oscillate in the considered processes. Slug can be caused by different operating conditions and installation structures. The most severe slugs are often induced in long vertical risers or production wells, where liquid blocks gas at the riser/well base and correspondingly it causes the pressure to accumulate and hence originates the oscillating performance. There are many severe consequences to the production processes because of the slugging flow. This paper reviews some observed latest status and key challenges about slug detection, dynamical modeling and elimination of slugging flows. Mathematical modeling of slug has been used to investigate the slug mechanism and anti-slug control. Most of available models are based on mass-balance formulations, which often require sufficient data for reliable parameter tuning/identification. Slug elimination and control have been investigated for many years and there exist many solutions to eliminate the slug, but some of these methods can simultaneously reduce the oil & gas production, which is a very big concern as the production rate is the key evaluation parameter for offshore production. We conclude that the slugging flow is a well-defined phenomenon, even though this subject has been extensively investigated in the past decades, the cost-effective and optimal slug modeling and control are still open topics with many related challenges.
There are many uncertainties associated with the estimation of extreme loads acting on a wave energy converter (WEC). In this study we perform a sensitivity analysis of extreme loads acting on the Uppsala University (UU) WEC concept. The UU WEC consists of a bottom-mounted linear generator that is connected to a surface buoy with a taut mooring line. The maximum stroke length of the linear generator is enforced by end-stop springs. Initially, a Variation Mode and Effect Analysis (VMEA) was carried out in order to identify the largest input uncertainties. The system was then modeled in the time-domain solver WEC-SIM coupled to the dynamic mooring solver Moody. A sensitivity analysis was made by generating a surrogate model based on polynomial chaos expansions, which rapidly evaluates the maximum loads on the mooring line and the end-stops. The sensitivities are ranked using the Sobol index method. We investigated two sea states using equivalent regular waves (ERW) and irregular wave (IRW) trains. We found that the ERW approach significantly underestimates the maximum loads. Interestingly, the ERW predicted wave height and period as the most important parameters for the maximum mooring tension, whereas the tension in IRW was most sensitive to the drag coefficient of the surface buoy. The end-stop loads were most sensitive to the PTO damping coefficient.
We present a Spectral Element Fully Nonlinear Potential Flow (FNPF-SEM) model developed for the simulation of wave-body interactions between nonlinear free surface waves and impermeable structures. The solver is accelerated using an iterative p-multigrid algorithm. Two cases are considered: (i) a surface piercing box forced into vertical motion creating radiated waves and (ii) a rectangular box released above its equilibrium resulting in freely decaying heave motion. The FNPF-SEM model is validated by comparing the computed hydrodynamic forces against those obtained by a Navier-Stokes solver. Although not perfect agreement is observed the results are promising, a significant speedup due to the iterative algorithm is however seen.
Increasing developments in the offshore energy sector have led to demand for robotics use in inspection, maintenance, and repair maintenance tasks, particularly for the service life extension of structures. These robots experience slippage due to varying surface conditions caused by environmental factors and marine growth, leading to inconsistent traction forces and potential mission failures in single-drive systems. This paper explores control strategies and mechanical configurations both in simulation and on the physical industrial robot to mitigate slippage in offshore robotic operations, improving reliability and reducing costs. This study examines mechanical and control modifications such as multi-wheel drive (MWD), PID velocity control, and a feedback-linearized slip control system with an individual wheel disturbance observer to detect surface variations. The results indicate that a 3 WD setup with slip control handles the widest range of conditions but suffers from high control effort due to chattering effects. The simulations show potential for slip control; practically, challenges arise from low sampling rates compared to traction changes. In real-world conditions, a PID-controlled MWD system, combined with increased normal force, achieves better traction and stability. The findings highlight the need for further investigation into the mechanical design and sensor feedback, with the refinement of slip control strategies and observer design for the offshore environment.
This paper presents the design and development of a conceptual prototype of an autonomous self-driven inline inspection robot, called Smart-Spider. The primary objective is to use this type of robot for offshore oil and gas pipeline inspection, especially for those pipelines where the conventional intelligent pigging systems could not or be difficult to be deployed. The Smart-Spider, which is real-time controlled by its own on-board MCU core and power supplied by a hugged-up battery, is expected to execute pipeline inspection in an autonomous manner. A flexible mechanism structure is applied to realize the spider's flexibility to adapt to different diameters of pipelines as well as to handle some irregular situations, such as to pass through an obstructed areas or to maneuver at a corner or junction. This adaptation is automatically controlled by the MCU controller based on pressure sensors' feedback. The equipped devices, such as the selected motors and battery package, as well as the human-and-machine interface are also discussed in detail. Some preliminary laboratory testing results illustrated the feasibility and cost-effectiveness of this design and development in a very promising manner.
Due to increased numbers of offshore structures and subsea cables, there is a high demand for underwater maintenance and monitoring. Common options to meet this demand are sonar mapping and imaging. Sonar mapping provides a reliable way for object detection with a high penetration depth, but it is not suitable for tasks that require a detailed insight into the material composition and color of the object. Imaging can provide in-depth, comprehensive information on material properties and external features. This makes it reasonable to investigate its use for object segmentation. Hyperspectral imaging is a subset of imaging which proved to be more effective for airborne object segmentation compared to RGB imaging. This stems from the fact that hyperspectral imaging contains a higher number of spectral bands, justifying the investigation of its applicability in underwater environments. However, underwater imaging faces major challenges such as a variable data quality which is strongly affected by water turbidity, color distortion and a narrow wavelength transmission window. Most of the prior studies conducted on underwater object segmentation relied on RGB images, such as the work carried out by AAU Energy on object segmentation relying on synthetic data [1]. The applicability of hyperspectral reliant object segmentation underwater is yet to be conclusively defined, however, the promising results obtained in airborne conditions are an encouraging prospect. The contribution of this paper is to investigate the applicability of hyperspectral data for underwater object segmentation. In particular, a segmentation algorithm, evaluated in an artificial environment, was researched.
Purpose This study aims to explore how operational resilience can be achieved within supply ecosystems in the delicate yet harsh natural environments of the Arctic. Design/methodology/approach An in-depth, multiple qualitative case study of offshore supply operations in Arctic oil and gas field projects is conducted. Data from semi-structured interviews, personal observations and archival materials are analyzed through institutional work and logics approaches. Findings The findings suggest that achieving social-ecological resilience depends on the interaction between social and natural (irreversible) systems, which are shaped and influenced by various institutional dynamics. Different resilience solutions were detected. Research limitations/implications This study develops a comprehensive understanding of how social-ecological resilience emerges in supply ecosystems through institutional dynamics. The study's empirical basis is limited to offshore oil and gas projects in the Arctic. However, due to anticipated future growth of Arctic economic activities, other types of supply ecosystems may benefit from the study's results.Originality/value This research contributes with empirical knowledge about how social-ecological resilience is created through institutional interaction within supply ecosystems to prevent disruptions of both social and ecological ecosystems under the harsh natural conditions of the Arctic.
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.