A standard approach to design begins with scaling up state-of-the-art machines to new target dimensions, moving towards larger rotors with lower specific energy to maximize revenue and enable power production in lower wind speed areas. This trend is particularly crucial in floating offshore wind in the Mediterranean Sea, where the high levelized cost of energy poses significant risks to the sustainability of investments in new projects. In this context, the conventional approach of scaling up machines designed for fixed foundations and strong offshore winds may not be optimal. Additionally, modern large-scale wind turbines for offshore applications face challenges in achieving high aerodynamic performance in thick root regions. This study proposes a holistic optimization framework that combines multi-fidelity analyses and tools to address the new challenges in wind turbine rotor design, accounting for the novel demands of this application. The method is based on a modular optimization framework for the aerodynamic design of a new wind turbine rotor, where the cost function block is defined with the aid of a model reduction strategy. The link between the full-order model required to evaluate the target rotor’s performance, the physical aspects of blade aerodynamics, and the optimization algorithm that needs several evaluations of the cost function is provided by the definition of a surrogate model (SM). An intelligent SM definition strategy is adopted to minimize the computational effort required to build a reliable model of the cost function. The strategy is based on the construction of a self-adaptive, automatic refinement of the training space, while the particular SM is defined by the use of stochastic radial basis functions. The goal of this paper is to describe the new aerodynamic design strategy, its performance, and results, presenting a case study of a 15 MW wind turbine blades optimized for specific deepwater sites in the Mediterranean Sea.

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.

A comparative techno-economic analysis has been performed on two innovative pathways for municipal solid waste (100 t/h) thermochemical processing to substitute natural gas. The first pathway is based on updraft gasification with bottom hydrogen oxy-combustion and ashes melting, the second on autothermal chemical looping hydrogen production with Fe2O3/SiC oxygen carrier. Catalytic methanation in a series of adiabatic fixed bed reactors has been implemented and substitute natural gas quality has been evaluated based on the Italian legislation. Although the updraft gasification process shows higher substitute natural gas productivity (16.3 t/h vs 13.7 t/h), better system energy efficiency (42 % vs 35 %) and energy intensity (125 vs 141 GJ/t), the levelized cost of substitute natural gas is more competitive in the chemical looping configuration due to the lower capital expenditure. Product prices of 2.26 /kg and 1.76 /kg have been calculated for updraft gasification and chemical looping, respectively, assuming 8 % discount rate, 80 % capacity factor, and 90 /MWh electricity cost. Sensitivity analyses indicate that, among other parameters, the plant capacity factor and the electric power cost have a relevant impact on the final product cost. Additionally, both pathways are shown to be economically competitive with substitute natural gas production from H2O electrolysis and CO2 capture/purchase. Finally, actions to reach competitivity with fossil natural gas for industrial uses are qualitatively discussed.

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures.

Ammonia combustion is gaining interest as a feasible alternative to traditional fossil fuels because of to the low environmental impact and as hydrogen and energy carrier. This study used Computational Fluid Dynamics (CFD) simulations to compare various turbulence models for premixed ammonia/hydrogen combustion in a swirl-stabilized burner. The primary aim was to identify the best turbulence model for accurately predicting the flow dynamics, combustion behaviour, and emissions profiles of ammonia/hydrogen fuel blends. The turbulence models evaluated were Large Eddy Simulation (LES), Realizable k- ϵ , Renormalization Group (RNG) k- ϵ , k- ω SST, and Reynolds Stress Model (RSM). On the LES side, a further comparison of two subgrid models (Smagorinsky-Lilly and WALE) was investigated. The Flamelet Generated Manifold (FGM) method was utilized with a detailed chemistry scheme taking into consideration all NO_x reactions. To improve the prediction of NO_x emissions, additional scalar transport equations for NO and NO_2 were included. This methodology aimed to be a balance between computational efficiency and the accuracy expected of detailed chemistry models. Validation was done with a swirl burner from Cardiff University’s Gas Turbine Research Centre. Results showed that all turbulence models accurately captured flame characteristics in terms of exhaust temperature and axial velocity with minor differences in the recirculation zones, where only the RSM model can predict the velocity trend as the LES simulation while other RANS models differ by at least 7 m/s. The temperature reached by the LES resulted 100 K higher than the other models in the flame zone. LES simulation can predict the emission value with an error of less than 10 % . Moreover, the error related to emissions derived from the RANS simulations was not negligible, underestimating NO_x emissions by about 35 % . However, RSM model produced results that were closer to those derived from the high-fidelity LES when compared to the others RANS models, particularly in terms of flame thickness and emissions. It was concluded that it is mandatory to perform an unsteady analysis to reach reasonable results.

Offshore wind farms are emerging as a key power plant option for EU's transition to net-zero emissions by 2050. With the growing trend of installing large turbines in multi-gigawatt farms, increasing attention is being given to the visual impact perceived from the coast. This study introduces an optimization method that incorporates visual impact as a social-acceptance indicator and the Levelized Cost of Energy to provide a comprehensive techno-economic sustainability assessment for offshore wind projects. The method resolves a multi-objective and multi-constrained wind farm layout optimization problem in a designated marine area. The number of turbines is one of the independent variables in each studied wind farm and multiple points of observation from the shoreline are contributing to the evaluation on visual impact. The case study is represented by a virtual wind farm located in the Mediterranean Sea, with 15 MW turbines. The results yield a Pareto front, with the trade-off solution represented by a farm with 13 turbines, distributed regularly, and a Levelized Cost of Energy of 110.73 e/MWh. Additionally, four comparative analyses are performed to evaluate the effect of (i) different turbine sizes, (ii) different wake loss models, (iii) different wind data source and (iv) different wind farm areas.

Offshore Wind Turbines (WTs) are characterized by heights that fall within the Atmospheric Boundary Layer (ABL). In ABL the flow is characterized by specific turbulence scales and velocity profile. The motion of the sea surface can influence turbulence in the surface layer, thereby impacting wake dynamics and recovery. In this study, we explore the role of wave modeling in the wake interaction between two wind-oriented IEA 15MW reference WTs, modeled using the Actuator Line Method (ALM). Two hybrid LES-RANS simulations are carried out, one with a flat sea and the other with swell wave motion, imposed by a dynamic computational grid. Turbulence modelling relies on k-omega SST IDDES model, enabling the resolution of the large-scale turbulence associated with wind fluctuations and turbine wakes. The Kaimal velocity spectrum is here applied to generate stochastic velocity fluctuations superimposed as a time-dependent boundary condition to a neutrally stratified ABL velocity profile. Results showed that wind-aligned waves can induce an alternating up- and downwash effect up to 50 meters from the sea surfaces, behaving as a modification (reduction) of the aerodynamic roughness. This effect is more evident in the wake flow. Analysing the performance of the downwind rotor, a 0.5% difference in power production is observed for the case with resolved waves motion.

Abstract Double fluidized bed gasification is based on the circulation of an inert bed between two reaction sections: the gasification reactor, where a solid feedstock, generally biomass. is converted into a syngas by steam or air oxidation; the combustor or riser, where residual char from the thermal decomposition of the feedstock is oxidized by air (in some cases with additional fuel) to provide the energy contribution for the gasification reactions and ensure autothermal operation of the system. In this work, detailed modelling of a dual fluidized bed steam gasification reactor is performed in Aspen Plus by incorporating: (1) gas, char and tar production during thermal decomposition of the feedstock according to experimental correlations developed and taken from the literature, (2) heterogeneous and homogeneous reaction kinetics in the gasifier bed and in the freeboard. The model has been validated by comparison with experimental results on different mixtures containing solid recovered fuels and woody biomass. The model is quite accurate in predicting the gas products composition for different feedstocks mixtures (root mean square error of 12% for CO, CO2, H2 and CH4) and enables coupling with different downstream liquid fuels synthesis processes (synthetic natural gas, methanol, dimethyl-ether, Fischer-Tropsch products etc.).

Offshore wind is nowadays already well developed in the North European countries. Ninety-nine percent of the offshore wind turbines are installed on fixed foundations in shallow waters. For areas with water depth greater than 50–60 m, the floating wind is the cheapest and mostly used technology. This technology is going to reach the commercial phase in a few years, thus disclosing the potential of all marine areas with deep waters close to the coast, including the Mediterranean basin. One of the main challenges for floating offshore wind deployment in this area is the achievement of its economic feasibility. The offshore wind resource in the Mediterranean is generally lower than the one in the North Sea and in Oceans and the cost of offshore wind farms, especially with floating technology, is higher than the present offshore wind farm installations also because this industrial sector has not yet started in this area. However, in the Mediterranean area, the potential of offshore wind to contribute to the decarbonization pathway and reduce the dependence on imported fuel supply is substantial. Numerous studies, examined in this article, have already performed a technical-economic assessment of offshore wind farms in different countries and geographical areas within the basin. A significant number of offshore wind projects are already in different stages of development, confirming the industrial interest and readiness of the Mediterranean offshore wind energy sector. The article provides a comprehensive review of various factors influencing the future deployment of offshore wind in the Mediterranean. It covers a range of topics including technology advancements, resource assessment, wind energy potential, ongoing projects, costs, and economic aspects. Additionally, it discusses environmental sustainability, regulatory frameworks, supply chain logistics, and system integration. The updated review presented in this article could assist decision-makers and stakeholders in gaining a better understanding of the characteristics of this promising sector and accelerating its development. This article is categorized under:

We present an arbitrary Lagrangian–Eulerian variational multiscale (ALE-VMS) computational fluid–structure interaction (FSI) analysis of Wells turbine passive-adaptive blades. We use finite element discretization. The ALE-VMS, as the core method, is complemented with the linear-elasticity mesh moving with mesh-Jacobian-based stiffening and block-iterative FSI coupling. We explore new designs for adaptive (morphing) blades that adapt to changes in flow direction, focusing on small-size reversible turbines. We first conduct a 2D study with a blade made of low-stiffness material. The goal is to achieve a stable passive change in airfoil curvature in response to the aerodynamic forces. With this 2D cascade study, we verify the feasibility of the concept and explore the use of different materials layouts. Then, we conduct a 3D study with time-dependent flow rate, simulating the turbine’s operation in an oscillating water column facility for sea wave energy conversion. The results show that advanced computational FSI analysis provides useful insight into the functioning of these devices.

This chapter presents three applications of computational fluid-structure interaction (CFSI) in the field of turbomachinery. We explore novel designs for morphing blades that adapt to changes in flow direction, focusing on small-size reversible fans and turbines. The model framework is based on the finite element formulations of fluid dynamics, structural mechanics, and mesh moving equations, while a block-iterative approach is used for the FSI coupling. We conduct first a 2D study of a reversible fan cascade made of low-stiffness material. The goal is to achieve a stable passive change in airfoil curvature in response to the aerodynamic forces. A similar design solution is then investigated for a Wells type turbine. A 2D cascade study verifies the feasibility of the concept and explores the use of different material layouts. Then, we test the 3D blade under time-dependent flow rate conditions, simulating operation of the turbine in an oscillating water column facility for sea wave energy conversion. In all cases, the results show that using CFSI provides useful insight into the functioning of these devices.

One of the main reasons of gas turbines performance losses is the deposition of dirt on the compressor blades. Dirt deposit has to be periodically removed to keep the engine performance as high as possible. This is the reason motivating the presence of online water washing systems (WWS) in most of the compressor gas turbines. Such systems aim at cleaning the compressor blades to recover efficiency; thus, the larger the water flow the better it is assumed the compressor is cleaned (fixing all the other conditions). In the present work we simulate the long-term behaviour of a real axial compressor, from the inlet to the first-stage rotor, subject to online water washing with different water flow rates. The frozen rotor approach is adopted to solve the flow field in the rotor region. Simulations are performed by using the unsteady k-ε realizable model coupled with a Lagrangian tracking of the injected liquid phase. Water droplets erosion is handled by using a semi-empirical model developed by the authors. In each simulation 504000 parcels have been tracked, providing statistically reliable predictions. To simulate long-term evolution of the washing process, a discrete mesh morphing technique coupled with the use of specific scale factors is adopted. Each of the tested configuration is composed of three successive erosive steps up to the blade compressor end-of-life. Six different injection configurations are here assessed in terms of long-time average washing efficiency and erosion risk. Results predicted, show the dependence of the considered washing indices on water mass flow rate and set the stage for the development of a washing optimization tool, which helps the design and management processes. In the present simulations the optimal configurations are WAMF* = 0.250 in the case that a small weight is given to the washing indices, and WAMF* = 0.750 for high weights.

Oscillating water column (OWC) plants represent a feasible and well know technology solution to convert the kinetic energy of sea and ocean waves into electric power using a rotating device such as a Wells turbine. Wells turbines operate under alternating direction inflow conditions, provided by the consecutive rise and decline of the oscillating water column. Consequently, the blades are characterized by a symmetrical shape and high thickness, in order to allow the turbine to rotate in the same direction regardless of the flow direction. Such design is however not optimal with respect to the aerodynamic performance, as the flow angle is usually very high even under the design operating conditions. For example, a more pronounced camber of the blade for increasing flow rates and the possibility to invert the concavity of the suction side and pressure side could help improve the performance. A possible solution is to design the blades to accommodate the alternating flow direction by exploiting the passive morphing adaptivity of flexible materials. Flexible blades designed with appropriate structural constraints and materials, inspired by boat sails, could provide the turbine blades a passive adaptive camber line, and consequently a dynamic angle of attack and incidence angle. In this study, we present a fluid-structure interaction computational analysis of a preliminary design of flexible blades for a Wells turbine, from preliminary steady state considerations to unsteady simulations. The simulations are carried out using the Residual Based Variational MultiScale (RBVMS) method to solve the Navier-Stokes equations, the Total Lagrangian formulation (TL) for the structural non-linear elastic problem, and the Solid Extension Mesh Moving Technique (SEMMT) to move the mesh and avoid a continuous remeshing of the computational domain.

In the present paper the effect of turbulence models on cavitation occurrence is evaluated by means of numerical simulations on a NACA 66 (MOD) profile. Here, cavitation will be assessed through the employment of the widely applied Singhal et al. model, imprinted on the use of the Rayleigh-Plesset equation for bubble dynamics description.A practical application of the treated cavitation model is carried out in conjunction with the employment of the Scale Adaptive Simulation (SAS) turbulent model on a 2D NACA test case, focusing the attention on the effect derived from time dependent fluctuations phenomena occurring during cavitation manifestations. Results are then discussed comparing experimental data and simulation-obtained values of the non-dimensional pressure coefficients.

Here we study long-term water droplets erosion and geometry modification caused by water droplets impacts on an axial compressor. Two-phase unsteady numerical simulations were carried out, considering the injection of water droplets and their transport across the fluid flow from the air inflow to the first rotor. Simulations are performed on the whole machine to account for the asymmetric distribution of the spray injectors, the machine struts, IGV and rotor blades. The k-e realizable turbulence model was coupled with the discrete-phase model to track injected droplets motion. Droplets-wall interaction is modelled following the Stanton-Rutland approach to analyse the outcome of the droplet impact (deposit, rebound, splashing). A semi-empirical model was developed and implemented in ANSYS Fluent through a User Defined Function (UDF) to evaluate the droplets erosion. Metal removal was accounted for through nodal mesh displacement implemented in a proper defined UDF. Analysis of the erosion growth was considered to estimate the compressor operating life before maintenance operations. Proper indices were introduced to evaluate the effectiveness of the water washing process. At the end of the simulation workflow, erosion is observed on all the compressor regions, especially in the rotor where erosion peaks are reached at the hub of the leading edge. About 50% of the rotor blades were maintained wet compressor operating life. Erosive phenomena were proved to evolve nonlinearly with time indicating the need to account for the mesh modification for an accurate prediction of the long-time process.

In counteracting fouling phenomenon in gas turbines, which leads to system inefficiencies and performance degradation, water washing technique is very often adopted. Water droplets sprays are injected and, hitting the solid surfaces, remove the dirt deposition. Among the collateral undesirable phenomena related to water washing, blades erosion and liquid film formation are the most remarkable. Despite the former issue was extensively assessed by the authors in previous works, up to the authors’ knowledge the risk of liquid film formation due to water washing was scarcely investigated. Liquid film formation and spreading on a solid surface is a complex phenomenon involving a large number of physical events, such as: droplets impact on a solid surface, splashing phenomena, liquid film dragging under the effect of the carrier phase and droplets separation from the film in proximity of geometry discontinuities. In this paper, an extensively used experimental test case involving all these phenomena was used to test different numerical wall film models available in literature. The test case consists in the injection of a liquid jet in a high velocity crossflow. Some of the liquid jet mass impacts on the opposite solid surface generating a wall film which develops under the dragging effect of the crossflow. A Lagrangian approach was used to track the suspended droplets within the flow field by also considering the turbulent dispersion by means of a Random Walk model. Droplets-wall interaction is considered according to the Stanton-Rutland model, which provides the outcome of a collision (deposit, rebound or splashing), depending on the local impact conditions. If a droplet sticks on a solid boundary, a liquid film generates. Droplets atomization is also accounted for by using the Madabhushi model while Friederich separation model was selected to take into account the detachment of droplets from the film at the geometry edge. Three different numerical simulations have been performed based on different approaches used to solve the liquid film evolution, namely Eulerian one-way coupling, Eulerian two-way coupling and Lagrangian two-way coupling. Numerical results have been compared with the experimental ones from both a qualitative and a quantitative point of view. The wall film shape, its spatial distribution and the variation of the film thickness of the wall centreline have been compared between experimental and numerical simulations proving that the Lagrangian 2-way coupling approach better reproduces the liquid film dynamics observed in the experiments.

Reversible axial fans are widely used in industrial and tunnel ventilation systems, and a lot of research effort is spent in the design process of the blades shape and blades profile. The target is to achieve reasonable performances in both flow directions, but those are still below the levels of the corresponding non-reversible geometries. In this paper, an alternative design solution for reversible axial fan is presented by adopting flexible blades instead of the rigid ones. Such design, inspired by the boat sails, could allow the blade to change its shape by passively adapting to the flow field, from a symmetrical blade profile to a not symmetric one, and thus adapting the curvature to the flow condition. In the paper, a series of alternative materials and material distributions are analysed and compared. The analysis is conducted by performing Fluid-Structure Interaction simulations using stabilized Finite Elements formulations for both the fluid and the structure dynamics. Simulations are performed using the in-house built software FEMpar, which implements the Residual Based Variational MultiScale to model the Navier-Stokes equation, the Total Lagrangian formulation for the non-linear elastic solid and the Solid Extension Moving Mesh Technique to move the fluid mesh.

Numerical simulation is an indispensable tool for the design and optimization of wind farms layout and control strategies for energy loss reduction. Achieving consistent simulation results is strongly related to the definition of reliable weather and sea conditions, as well as the use of accurate computational fluid dynamics (CFD) models for the simulation of the wind turbines and wakes. Thus, we present a case study aiming to evaluate the wake-rotor interaction between offshore multi-MW wind turbines modelled using the Actuator Line Model (ALM) and realistic wind inflow conditions. In particular, the interaction between two DTU10 wind turbines is studied for two orientations of the upstream turbine rotor, simulating the use of a yaw-based wake control strategy. Realistic wind inflow conditions are obtained using a multi-scale approach, where the wind field is firstly computed using mesoscale numerical weather prediction (NWP). Then, the mesoscale vertical wind profile is used to define the wind velocity and turbulence boundary conditions for the high-fidelity CFD simulations. Sea waves motion is also imposed using a dynamic mesh approach to investigate the interaction between sea waves, surface boundary layer, and wind turbine wakes and loads.

Abstract This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2‐D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

Rain erosion of wind turbine blades represents an interesting topic of study due to its non-negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over a representative section of a reference 5MW turbine blade operating in normal turbulence wind conditions.