Johan Meyers

The Science of Making Torque from Wind, commonly known as the Torque conference, is one of the two flagship conferences of the European Academy of Wind Energy, organized biannually, alternating with the Wind Energy Science Conference. The first ever Torque conference was organized in 2004 at TU Delft, making the 2026 event the 11 ^th Torque conference in the series. Torque2026 is organized June 3–5 in Bruges, Belgium, and is taking place at the renowned concert hall. This year’s conference is jointly organized by the University of Leuven, Vrije Universiteit Brussel, and Ghent University. Historically, the Torque conference started as an aero-mechanical conference with focus on the turbine – the choice of the moniker Torque over the more logically sounding “making power from wind” originally tried to reflect that sentiment. Nowadays, the Torque conference has grown in size but also in scope, reflecting the range of disciplines and scales that are of importance, while keeping the core focus on the more technical wind-energy disciplines. To reflect this scientific diversity, the conference is organized into eight scientific themes: Wind resource, metocean & extreme conditions (Theme I); Wind farms and wakes (Theme II); Aerodynamics, aeroelasticity, and aeroacoustics (Theme III); Turbine technology, energy conversion, drive train (Theme IV); Reliability, monitoring and sensing, O&M (Theme V); Structures, structural integrity, materials (Theme VI); Floating Wind (Theme VII); and Emerging technologies, VAWTs, small wind turbine, airborne wind energy (Theme VIII). For the Torque2026 edition, a call for 4-page abstracts was launched in Summer 2025. In total, 627 abstracts were submitted and reviewed by at least two anonymous reviewers. Based on this review process, oral and poster presentations were selected, and authors were invited to submit a full 10-page proceedings paper, taking into account the prior review feedback. These papers were reviewed again by at least two anonymous reviewers with an additional round for major or minor changes, with the option to extend the paper up to a maximum of 11 pages in total to be able to take into account comments and suggestions from the reviewers. Finally 494 papers were accepted for publication in the current conference proceedings, of which 67 in Theme I, 142 in Theme II, 69 in Theme III, 40 in Theme IV, 76 in Theme V, 23 in Theme VI, 47 in Theme VII, and 30 in Theme VIII.

Accurate modeling of wind farm–atmosphere interactions is critical for reliable energy yield assessments and flow control strategies. However, formal model comparison methodologies that quantify model form uncertainty by also accounting for parameter uncertainty are still lacking. This study presents an enhanced Bayesian uncertainty quantification framework for the calibration and validation of engineering wind farm flow models. Building on previous work, the framework explicitly incorporates model inadequacy through a parametrized model error distribution, enabling the separation of model and measurement uncertainties. The improved framework is demonstrated using a large-eddy simulation dataset for wind farm blockage and atmospheric gravity waves in conventionally neutral boundary layers. Two models of differing fidelity – a standard Gaussian wake model and an atmospheric perturbation model (APM) – are calibrated and compared. The posterior distribution of the model parameters reveals insights into model behavior and highlights areas for further improvement, for instance, when estimated parameter values are inconsistent across the model chain. In addition, it is shown that not explicitly incorporating model inadequacy results in an overly confident posterior distribution and renders derived stochastic flow models incapable of representing model uncertainty. A comparison of the quantified model uncertainty shows that the APM has significantly lower uncertainty than a standard wake model for this dataset, as the wake model is unable to represent wind farm blockage effects. This demonstrates the utility of the framework for objective model comparison with quantified parameter and model uncertainty given a reference dataset. Both the framework and the parallelized sequential Monte Carlo algorithm for accelerated posterior sampling are made available through the open-source Python package UMBRA.

This study investigates hydrostatic blockage and excitation of gravity waves in large offshore wind farms within convective boundary layers (CBLs), a regime currently underexplored compared to stable and neutral conditions. Using large-eddy simulations, the performance of a 1.5 GW wind farm is analysed across eight scenarios representative of cold-air outbreaks. These cases vary in capping-inversion height (H = {480,980} m) and surface temperature difference (Δθ = {0,3,6,9} K), producing various convective structures, including rolls and cells. The results demonstrate that intensified convective mixing and boundary layer growth, driven by higher Δθ, significantly alter flow physics. Enhanced buoyancy-driven turbulence improves wake recovery, leading to more uniform power distribution. Simultaneously, the growth of the boundary layer slightly reduces the magnitude of both unfavourable and favourable pressure gradients, an effect particularly pronounced in shallow boundary layers. Efficiency analysis reveals that while wake efficiency (η_w) increases with convective intensity, whereas non-local efficiency (η_nl) remains largely unaffected. Consequently, overall farm efficiency (η_f) improves under stronger convective forcing, though sensitivity diminishes at higher Δθ. Despite increased mixing, hydrostatic blockage and gravity waves persist; even in deep boundary layers, a non-local efficiency of approximately 90 % was recorded. Decreasing the boundary layer height, amplifies the influence of both unfavourable and favourable pressure gradients on wind farm efficiency, whereas the relative contribution of convective mixing becomes less significant.

The height of the atmospheric boundary layer (ABL) exerts a significant influence on flow behavior within wind farms and directly impacts their performance. This study investigates how variations in ABL height and capping inversion layer thickness affect the efficiency and power output of a gigawatt-scale wind farm. Five advanced numerical approaches, ranging from high-fidelity large-eddy simulations (LESs) to Reynolds-averaged Navier–Stokes (RANS), are used to model farm-scale flow dynamics under shallow (∼150 m) and deep (∼500 m) ABL conditions. The results consistently show that shallow ABLs increase flow blockage and turbine wake interactions, leading to reduced power production. In contrast, deeper ABLs promote enhanced wake recovery and increased overall energy yield. These trends were observed across all solvers, demonstrating the robustness of the findings. Notably, while some quantitative differences emerged depending on modeling fidelity and computational domain size, the overarching trends remained consistent among the participating research institutions and industry partners. The simulation cases performed are complex, and the results of the different methods show a variation of up to 10 %, and further research is needed to limit this gap. Based on these results, it is not clear to what extent the variation depends on the fidelity level of the models used. The study concludes that ABL height and stability are critical parameters to consider in wind energy siting and turbine layout design to optimize performance across varying atmospheric conditions.

The impact of spanwise surface temperature heterogeneity on steady stably stratified Ekman layers is systematically studied using large-eddy simulation (LES). Spanwise varying strips of high and low surface temperature are imposed in idealised LES of stable boundary layers (SBLs), in which a steady state results from a balance between cooling at the ground and heating due to imposed synoptic subsidence. Consistent with previous studies on channel flows with streamwise-aligned surface heterogeneity (e.g. Bon & Meyers, J. Fluid Mech . 2022, pp. 1–38), large-scale secondary circulations develop and extend deep into the stable Ekman layer. Coriolis effects enhance counterclockwise circulations while reducing clockwise ones, thereby tilting the mean secondary flow structures towards the left (in the northern hemisphere). Nevertheless, for the considered surface temperature contrasts of 1.5–12 K and spanwise wavelengths of 100–800 m, the impact on mean SBL structure is substantial. As the surface temperature difference or strip width increases, secondary flows and dispersive fluxes strengthen, eventually reaching the top of the SBL. This augmentation further enhances near-surface gradients, elevates SBL depths and low-level jets, and reduces mean surface heat fluxes. Novel correlations between characteristics of the surface heterogeneity and their impact on the mean SBL structure are proposed. Moreover, the local surface fluxes are shown to significantly deviate from the mean, highlighting that horizontally averaged SBL properties do not capture all important physical processes in a heterogeneous flow. Overall, this work affirms that thermal surface heterogeneity is a crucial factor in governing transport processes within the atmospheric stable boundary layer.

Large offshore wind farms can trigger atmospheric gravity waves, with the associated hydrostatic blockage effect impacting their energy yield. Unfortunately, to date no tools exist that can model this wind-farm gravity-wave interaction and blockage at a computational cost that is not drastically higher than conventional engineering wake models. To address this, this paper applies insights from two-scale momentum (2SM) theory to an atmospheric perturbation model (APM), thereby significantly speeding up the latter. This leads to two different models, with one using pre-computed farm-level coefficients to compute the turbine forces and power output, while the other relies on repeated wake model evaluations. The core 2SM hypothesis and both developed models are validated using a prior LES dataset of a large wind farm. Both fast 2SM—APMs perform well, predicting blockage-corrected farm power at a computational cost that is only a factor 40 slower than a standalone wake model.

Accurate modeling of wind-farm atmosphere interactions is critical for reliable energy yield assessments and flow control strategies. However, formal model comparison methodologies that quantify model form uncertainty by also accounting for parameter uncertainty are still lacking. This study presents an enhanced Bayesian uncertainty quantification framework for the calibration and validation of engineering wind farm flow models. Building on previous work, the framework explicitly incorporates model inadequacy through a parameterized model error distribution, enabling the separation of model and measurement uncertainties. The improved framework is demonstrated using a large-eddy simulation dataset for wind-farm blockage and atmospheric gravity waves in conventionally neutral boundary layers. Two models of differing fidelity – a standard Gaussian wake model and an atmospheric perturbation model (APM) – are calibrated and compared. The posterior distribution of the model parameters reveals insights into model behavior and highlights areas for further improvement, for instance, when estimated parameter values are inconsistent across the model chain. In addition, it is shown that not explicitly incorporating model inadequacy results in an overly confident posterior distribution, and renders derived stochastic flow models incapable of representing model uncertainty. A comparison of the quantified model uncertainty shows that the APM has significantly lower uncertainty than a standard wake model for this dataset, as the wake model is unable to represent wind-farm blockage effects. This demonstrates the utility of the framework for objective model comparison with quantified parameter and model uncertainty given a reference dataset. Both the framework and the parallelized sequential Monte Carlo algorithm for accelerated posterior sampling are made available through the open-source Python package UMBRA.

Recent work by Lanzilao and Meyers (2024) has shown that wind-farm blockage introduces an unfavourable pressure gradient in front of the farm and a favourable pressure gradient in the farm, which are strongly correlated with the nonlocal efficiency and wake efficiency, respectively. In particular, the favourable pressure gradient in the farm increases the farm wake efficiency, defined as the average farm power normalized by the average front-row power. Here, we investigate the impact of blockage on wake development and the power of wind turbines using an idealized large-eddy simulation setup in which blockage conditions are artificially introduced using a rigid lid, in addition to using neutral stratification and no wind veer. We simulate both infinite and finite single turbine rows, as well as a setup with two staggered rows. Blockage strength is adjusted by varying the boundary layer height (H) and turbine spacing (S). We find that blockage strongly affects near-wake behaviour, altering Froude momentum theory, by introducing a favourable pressure difference (Δp_NW) across the turbine row. The same setup also leads to an unfavourable pressure difference (Δp_FW) in the far wake, which simply follows from the rigid-lid conditions and the change in momentum flux due to wake recovery. A strong positive correlation of −Δp_NW with both the power coefficient (C_P) and thrust coefficient (C_T) is observed. Specifically, as S and H decrease, −Δp_NW, C_P, and C_T increase. At the same time, a lower induction is observed at the rotor disc, and a lower wake deficit, in the near wake. The reduction of near-wake velocity deficit as a result of blockage also translates into lower deficits and wake widths in the far wake. When scaling the far-wake development with the initial far-wake deficit and width, we do not see a direct effect of the adverse pressure gradient on the wake recovery. However, we do see a profound effect of H on the wake spreading, with higher boundary layers leading to faster spreading. This relates to the fact that the wake can more freely expand vertically in high-boundary layer cases into a larger region of high-speed flow than for shallow boundary layers. Finally, we introduce a simplified Froude momentum balance to parameterize the relation between blockage, pressure drop, and near-wake properties and compare it to the large-eddy simulation results.