The possibility of significantly increasing the degree of regeneration in regenerative gas turbine plants without increasing the size of the recuperator.

The proposed article considers the theoretical prerequisites and proposes a scheme for a regenerative gas turbine installation with an increase in the degree of regeneration at constant recuperator sizes in order to increase the efficiency of the installation. The new scheme excludes the supply of secondary (cooling the heat pipe and combustion products in the combustion chamber) air to the heat exchanger for heating. Reducing the air flow in the recuperator to the values of only the primary (for fuel oxidation) air flow with the recuperator area unchanged leads to an increase in the degree of regeneration and, accordingly, the efficiency of the plant.

Bringing Big Data Technology to Wind Turbine Installation Vessels

Digitalization is a key component of the ongoing Energy Transition. Although the offshore and maritime industries tend to be conservative in the adoption of new technologies, in recent years a digital journey was embraced to stay competitive, safe, and efficient. Data from mobile offshore units can be transformed into something valuable. However, collecting and processing of system’s data requires proper infrastructure, a software platform that handles data delivery and applications that translate the data into valuable information. The challenge is therefore to turn good ideas and intentions into solutions that add real value. With this challenge in mind, in recent years GustoMSC | NOV worked on Big Data technology for wind turbine installation vessels (WTIVs). The purpose of this endeavor is to assist our end users in increasing the safety and efficiency of their operations. This paper addresses some key aspects and components of this digital journey and shares experiences on merging Information Technology (IT) and Operational Technology (OT) environments in an ongoing effort to fulfill the promise that Industrial Internet of Things (IIoT) technology brings. A practical example is presented where Big Data is used to boost the performance of mobile offshore wind installation units.

Super-large-scale flow visualization using natural snowfall for the study of utility-scale wind turbine flows

With the rapid growth of wind turbine installation in recent decades, fundamental physical understanding of the flow around wind turbines and farms is becoming increasingly critical for further efficiency increases. However, the effort to develop this understanding is hindered by the significant challenges involved in modelling such a complex dynamic system with a wide range of relevant scales (blade boundary layer thickness at ∼ 1 mm to atmospheric scales at ∼ 1 km). Additionally, conventional methods used to measure air flow around wind turbines in the field (e.g., lidar) are limited by low spatio-temporal resolutions.

Predicting Wind Loads on the Topside of a Self-Propelled Wind Turbine Installation Jack-Up Using CFD

This paper presents the results of wind load computational fluid dynamics (CFD) calculations performed on the topside structures of a self-propelled wind turbine installation jack-up. The CFD calculations were performed for the jack-up topside structures with and without the deck load. An atmospheric boundary layer profile was applied for the model-scale calculations. The full range of heading angles was considered. The CFD results were validated through comparison with the wind tunnel tests which were carried out at the German-Dutch wind tunnels (DNW) in Marknesse, The Netherlands. Moreover, a comparison is presented between the applied boundary layer profiles throughout the CFD computational domain with those profiles measured in the wind tunnel. The CFD results were found to be in good agreement with the wind tunnel tests for the considered cases, verifying the feasibility of the CFD method as an important design tool for the prediction of wind loads during the design processes of these types of jack-ups.