The Task work plan is structured in four technical work packages (WP2-WP5) supported by a management work package (WP1), delivered during a period of four years 15 March/2025 – 14 March/2029.

Each work package is split into activities, with at least one deliverable per activity. The deliverables are IEA Wind reports, Reports, Metadata with links to critical datasets, plus new models. It is anticipated that the work performed in the task will complement other research efforts by participants, in the context of international or national R&D projects.

The scope of each work package is listed below:

WP1.1 Develop and maintain Task data workspace and website participants

WP1.2 Technical support to task participants to ensure review & quality of deliverables

WP1.3 External communications

WP1.4 Preparation & chair of coordination meetings for all Task participants

WP1.5 Preparation & chair of webinars for dissemination

WP1.6 Reporting to IEA Wind ExCo

WP2.1 Perform a Phenomena Identification and Ranking Table (PIRT) analysis to identify meteorological parameters of critical importance to LEE and to quantitatively assess current measurement and modelling capabilities

The PIRT process is a systematic way of gathering information on a range of specific concepts and ranking their importance to meet some decision-making objective. Our PIRT process will start by identifying a series of atmospheric processes/phenomena of critical parameters of importance to LEE. Then, WP members will rank these processes/phenomena according to how much is known and how much benefit would arise from enhanced knowledge/measurement/modelling capabilities. This will inform activities within other WP actions.

WP2.2 Recommended report for measurement of LEE drivers

New remote sensing and in situ instrumental technologies are rapidly evolving to improve measurements of hydrometeors (number density, phase, fall rates) and wind conditions. This action will collate information about the current state-of-the-art with respect to measurements of meteorological drivers of LEE from space borne remote sensing devices (e.g. radiometers on satellites), ground-based remote sensing devices (e.g. dual-polarization Doppler radar and micro rain radar) and in situ technologies (e.g. disdrometers) and develop best practices for deployment and/or data processing for critical atmospheric process/phenomena identified and prioritised in 2.1. These analyses will specifically address the two different (but complementary) possible “end uses” of meteorological measurements of LEE drivers which have different requirements:

1) Nowcasting (present and near-term future) occurrence of highly erosive events (i.e., occurrence of very heavy rain and/or hail jointly with high wind speeds when the wind turbine blades are rapidly rotating) for erosion-safe mode operation. This application requires rapid decision making and implementation. Accordingly, it has a primary requirement of high instrument availability (i.e., a high likelihood the instrument will be collecting valid data at any time), and actionable data are available in near real time.

2) Long-term assessment of erosion potential on the climate scale to inform decisions regarding implementation of erosion mitigation measures (e.g., leading edge protection). This application requires long records to capture multiple scales of temporal variability (e.g., extreme hail events are infrequent but disproportionately contribute to material stress), high data quality and careful and detailed data processing. Thus, in contrast to use #1, there is not a requirement for data to be available in near real time.

WP2.3 Assessment of modelling capabilities to represent key atmospheric drivers of LEE

Numerical modelling of precipitation phase/intensity and near-surface wind conditions is rapidly improving but fidelity remains highly dependent on the model physics options and resolution applied in addition to the specific environmental conditions. This action will analyse information about the current state-of-the-art with respect to numerical modelling of meteorological drivers of LEE and develop and apply best practice for Verification and Validation (V&V) of numerical modelling of critical atmospheric processes/phenomena identified and prioritised in action 2.1.

Note that we differentiate between “Verification” i.e. determining if a model is implemented in a manner that represents the physical understanding of the process(es) and “Validation” the process of determining the degree to which the model represents the real world (i.e. agreement with observations). The expected outcome of a model V&V process is a quantitative statement of the agreement between experimental data and model prediction, as well as the predictive accuracy of the model for a given model configuration.

WP2.4 Roadmap for LEE atlas

This action will integrate information from actions 2.1-2.3 to develop a framework for translating measurements and modelling to robust geospatial descriptions of LEE potential. This activity will also seek to build robust methods for quantification of uncertainty (and designation of systemic and random errors) in individual parameter measurements/modelling and propagation of uncertainty from measurements and modelling of meteorological drivers of LEE in quantification of LEE. This action will be integrative with the other WPs in terms of translation of measurement/modelling conditions to LEE.

WP3.1 Updated erosion classification system and collaboration across work packages with recent participant results

• Based on LERCat and WP4 developments;
• Look at correlation between field observations of erosion and erosion test stand results;
• Look at erosion test stand power demand changes as erosion progresses; and
• Also, with WP4 look at correlating erosion categorisation with inboard progression of damage/incubation.

WP3.2 Aerodynamic benchmarking, simulations, and reference models

• Aerodynamic benchmarks of LERCat data;
• Correlate erosion categories to sand grain roughness or other roughness parameterization. Application to canonical erosion progression (Springer model) along with actual observations of erosion;
• Predict how higher Reynolds numbers (2-3 times wind tunnel tests) will impact aerodynamics of roughness and erosion and design experiments to address data gaps; and
• Modelling and benchmarking of aerodynamic effects and loss due to several representative LEP solutions.

WP3.3 Annual energy production (AEP) Loss and Reference Erosion Turbines Models

• Reference turbine models for a range of modern turbine types for the prediction of AEP loss based on blade erosion class or actual observations of erosion;
• Model uncertainty in AEP loss predictions based on ideal erosion classification and realistic uncertainty classification;
• Include a range of roughness, erosion, and LEP in the results; and
• Development and publication of simple AEP loss models for the reference turbines applied to a range of wind sites.

WP3.4 Design of an experiment to assess the accuracy of LEE performance loss models based on field observations

• Model uncertainties of field measurements compared to model predictions; and
• Publication on requirements to measure AEP loss (2%) in uncertainty (3%).

WP3.5 Development of methods for erosion safe-mode operation

• Development of a basic erosion safe mode controller to reduce the rate of erosion damage on a wind turbine;
• Development of a reference turbine model with a nominal erosion safe mode controller for demonstration of erosion safe mode potential implementation;
• Explore the use of different types of input data to drive an erosion safe mode controller; and
• Explore the potential of techno-economic trade-offs for erosion safe mode control for different wind farm sites.

WP3.6 Lifetime erosion modelling and O&M decision making

• Combine material erosion model with aerodynamic loss model to predict repair time and aerodynamic loss time history over a turbine lifetime; and
• Demonstrate impact on decision making models through collaboration with IEA Wind Task 43.

WP3.7 Improved droplet impingement model for use in fatigue analysis

• Investigate methods for modelling droplet impingement for improved integration with materials models while considering approaches for more general hydrometeor impingement analysis, such as hail.

WP3.8 System integration and uncertainty analysis

• Integrate a modelling chain of complete erosion system, including ‘erosive events’ (meteorology), fatigue analyses and their sensitivity to weathering (material science), rotor aero-servo-elasticity (aerodynamics, aeroelasticity and control engineering), maintenance and wind energy costs (economics), via collaboration with all other work packages;
• Determine the most influential physics for LEE (e.g. how impactful are blade aerodynamics and hydrometeor shape and size with respect to all other physics);
• Quantify the scatter of the prediction of LEE resulting from different approaches to generate input data by means of measured and assumed data and using alternative modelling choices for the considered disciplines (meteorology, material science, aerodynamics, aeroelasticity, control engineering, economics); and
• Determine the class of uncertain input data that, in isolated or combined fashion, result in the largest uncertainty levels of the outputs of a probabilistic LEE analysis.

Copyright Video by Nicolai Frost-Jensen Johansen

WP4.1 Large-scale comparison and round robin using same type of specimen in several Rain Erosion Test (RET) facilities

Objective: Evaluate the consistency and reliability of the RET across different testing setups to establish the current coefficient of variation (COV%) of the test method.

• Organise a round-robin testing program involving multiple laboratories to compare RET results.
• Analyse the COV% across different testers to identify variations and potential standardisation issues.

WP4.2 RETs under different climatic conditions and effect of droplet size and impact rate on coating lifetime

Objective: Determine if the current RET operating conditions are representative of various rain climates, including the Asian Monsoon, inland US, South America, and changes due to climate change. Examine how variations in droplet size and impact rate affect coating longevity and explore the feasibility of test acceleration.

• Conduct RET under varied conditions that mimic different global rain patterns;

• Compare the damage profiles to assess the representativeness of current testing conditions;
• Perform RET with variable droplet sizes and impact rates;

Evaluate the possibility of accelerating the test by increasing rain quantity without compromising result relevance.

WP4.3 Impact of microplastics emissions from erosion

Objective: Investigate the quantity and potential toxicity of microplastics emissions due to blade erosion.

• Quantify the microplastics generated during RET; and
• Assess the toxicity of these microplastics through ecological and human health risk assessments.

WP4.4 Incorporation of weathering into RET protocol

Objective: Determine the optimal way to integrate weathering ultra-violet (UV-A/B, xenon light exposure, full or reduced “the Norwegian shelf’s competitive position” (NORSOK) test cycles) into RET protocols.

• Design experiments to compare the effects of different weathering cycles on erosion testing; and

Develop recommendations for integrating weathering into RET protocols, including duration and sequencing.

WP4.5 Design of specimens with predefined defects

Objective: Develop a standard method for introducing predefined defects into RET specimens, informed by fracture mechanics, to study the evolution of damage, including artificial lightning damage.

• Create a methodology for incorporating controlled defects into test specimens; and
• Conduct RET on these specimens to assess the progression of damage and validate the test method.

WP4.6 Bridging the gap between several durability testing and material properties

Objective: Investigate the way to explain the changes in function (as evaluated by durability tests including RET, Micro Slurry-jet Erosion test (MSE), pulse jet test, and rubber ball test) in terms of the changes or thresholds of various material properties (as evaluated by strength tests and Dynamic Mechanical Analysis (DMA).

• Conduct literature survey and case studies from participants’ experience.

WP4.7 Analysis on failure modes and mechanisms of various soft materials to integrate into modelling

Objective: Explore sophisticated material property testing and new computer-aided engineering (CAE) analysis technique in soft materials (glassy, crystalline, and rubbery) dealing with various damage modes (delamination, and crack propagation)

• Investigate evaluation tests of UV effects, introduction tests of artificial defects, and V&V of CAE models of hyper-elasticity as specific cases of the above.

WP4.8 Material failure in the real blade erosion

Objective: Analyse actual damage pattern in field in terms of the material properties and damage mechanisms described in 4.6 and 4.7.

• Conduct literature survey and case studies from participants’ experience to identify remaining issues from a materials science perspective.