Offshore Wind Farm (OWF) downtime causes huge financial loss to the stakeholders. One of the major concerns for them is to reduce the downtime of the offshore wind turbine as much as possible. To do this, inventory managers must keep the required number of spare parts in the inventory. It is important to forecast the type and amount of spare parts ahead of time. The maintenance team tries to figure out failure symptoms to predict the approximate time for failure. This prediction helps to purchase and stock spare parts systematically. There is a trade-off between the ordering cost, holding cost, and shortage cost. Proper inventory planning saves a manager from placing expensive emergency orders and also an extended period of holding spare parts. The desired service level should be determined earlier, based on which spare parts planning is done. In this paper, some prominent spare parts models have been studied, findings have been systematically presented, compared against some key determinant factors, critical analysis has been performed and the applicability of the models has been discussed. More than a 100 research articles on spare parts have been reviewed and major contributions from the most relevant articles in OWF have been presented in this paper. One advanced spare parts modeling reported up to 51% cost reduction compared to traditional spare parts planning. Another integrated spare parts planning reported 27% savings. This critical review aims to suggest some guidelines for the managers and other associates of wind farms about the effective and efficient spare parts management technique from the beginning of the turbine installation to the end of its life cycle.

Conventional flood cooling system has some environmental and economic issues. Minimum quantity lubrication (MQL) has been established as a better strategy to control the machining temperature than conventional flood cooling system. Integrated use of carbon nanotube (CNT) with MQL has been drawing attention in recent times. The purpose was to study the effects of hard grinding with key focus on temperature, surface roughness and chips morphology under the application of CNT-mixed MQL and compressed air. The hardness of work material (AISI 1060 steel) was varied at three values (40, 45 and 50 HRC) with the variation of fluid flow rates and infeed rates to observe the machining behaviour. Experimental plan was established using full factorial design of experiment while mathematical models and optimisation were constructed following response surface methodology (RSM). Optimum output responses were flow rate 75 ml/h, infeed (depth of cut) 10 µm and hardness of material is from 40 to 45 HRC. Fluid flow rate of 75 ml/h resulted in best surface finish while work material of 40 HRC hardness generated lowest temperature while machining. A lower hardness values and a moderate flow rate produced favourable machining outcomes, i.e. lower temperature and improved surface finish.

Offshore wind turbines can capture more wind than onshore because of their larger structure and location. This higher yield even fails to reduce the high installation and maintenance cost of an offshore wind farm (OWF). Appropriate turbine parameters and installation site selection may maximize the power generation which is a way to trade off these costs. Knowing the wind thrust force, air density, and power coefficient beforehand can help select an appropriate site for turbine location. Once the site is selected, the optimal value of turbine variables such as height and radius can contribute to higher power yield. In this paper, a MINLP (Mixed Integer Non-Linear Programing) model is formulated with these important variables and the optimal values of these variables are determined to maximize the annual power production ([ ]) from offshore wind farm. The estimated power production, [ ], is calculated using two methods, mathematical programing method and simulation method. Computational result indicates that mathematical programing method is time consuming but more accurate whereas the accuracy of simulation method is proportional to the number of iterations. Although the result of a simulation can be improved to some extent, it cannot be as accurate as mathematical modeling for this study. These study results have great impact on the managerial decision and long range strategic and technical planning for maximizing power generation from an offshore wind farm.

Offshore wind farms are becoming more and more important to sustainable energy strategies, yet their maintenance presents unique logistical challenges. The focus of this research is the Technician Assignment Problem (TAP) which involves a complicated and fluctuating scheduling problem. The key objective is to determine the most effective assignment of technicians to maintenance tasks to maximize operational efficiency and ensure reliable service. This study introduces a mathematical optimization model that processes numerous variables—technician availability, skill sets, and temporal constraints—to minimize unmet maintenance needs and ensure equitable workload distribution. The model is based on assumptions derived from actual operational conditions and human resource practices, guaranteeing that its results are not only theoretically valid but also practically feasible. TAP adheres to labor regulations, employs human resource capabilities, and aims for a smart assignment of workforce. It complies with restrictions that prevent excessive work, require breaks, and ensure that technicians are assigned tasks that match their skills, thus promoting the well-being of the workforce and the efficiency of operations. The computational investigation of the model shows that it has a remarkable ability to improve scheduling decisions which effectively reduce unassigned positions and uniformly distribute work hours. In essence, this research contributes a methodologically robust framework to the field of workforce scheduling, with the potential to inform the maintenance strategies of offshore wind farms and similar complex service systems.