The interactions between turbulence and flames in premixed reacting flows are studied for a broad range of turbulence intensities by analyzing scalar (reactant mass-fraction) gradient, vorticity, and strain rate fields. The analysis is based on fully compressible, three-dimensional numerical simulations of H₂-air combustion in an unconfined domain. For low turbulence intensities, a flame reconstruction method based on the scalar gradient shows that the internal flame structure is similar to that of a laminar flame, while the magnitudes of the vorticity and strain rate are suppressed by heat release and there is substantial anisotropy in the orientation of intense vortical structures. As the turbulence intensity increases, the local flame orientation becomes increasingly isotropic, and the flame preheat zone is substantially broadened. There is, however, relatively little broadening of the reaction zone, even for high intensities. At high turbulence intensities, the vorticity and strain rate are only weakly affected by the flame, and their interactions with the scalar gradient are similar to those found in nonreacting turbulence. A decomposition of the total strain rate into components due to turbulence and the flame shows that vorticity suppression depends on the relative alignment between vorticity and the flame surface normal. This effect is used to explain the anisotropy of intense vortices at low intensities. The decomposition also reveals the separate effects of turbulent and dilatational straining on the flame width.

Spectral kinetic energy transfer by advective processes in turbulent premixed reacting flows is examined using data from a direct numerical simulation of a statistically planar turbulent premixed flame. Two-dimensional turbulence kinetic-energy spectra conditioned on the planar-averaged reactant mass fraction are computed through the flame brush and variations in the spectra are connected to terms in the spectral kinetic energy transport equation. Conditional kinetic energy spectra show that turbulent small-scale motions are suppressed in the burnt combustion products, while the energy content of the mean flow increases. An analysis of spectral kinetic energy transfer further indicates that, contrary to the net down-scale transfer of energy found in the unburnt reactants, advective processes transfer energy from small to large scales in the flame brush close to the products. Triadic interactions calculated through the flame brush show that this net up-scale transfer of energy occurs primarily at spatial scales near the laminar flame thermal width. The present results thus indicate that advective processes in premixed reacting flows contribute to energy backscatter near the scale of the flame.

Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.

Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertainties in many aspects of the wake steering problem. For example, infield sensors do not give perfect information, and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of independent uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and it is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. When maximizing expected power production, the OUU strategy also tends to favor smaller yaw angles, which have been shown in previous work to reduce turbine loading. Ultimately, the plant-level wake steering strategy formulated using an OUU approach yields 0.48 % more expected annual energy production for the 11-turbine wind plant than a strategy that neglects uncertainty when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, but it also reduces risk by prescribing strategies that call for less extreme yaw angles.

Natural climate variations in the United States wind resource are assessed by using cyclostationary empirical orthogonal functions (CSEOFs) to decompose wind reanalysis data. Compared to approaches that average climate signals or assume stationarity of the wind resource on interannual time scales, the CSEOF analysis isolates variability associated with specific climate oscillations, as well as their modulation from year to year. Contributions to wind speed variability from the modulated annual cycle (MAC) and the El Niño-Southern Oscillation (ENSO) are quantified, and information provided by the CSEOF analysis further allows the spatial variability of these effects to be determined. The impacts of the MAC and ENSO on the wind resource are calculated at existing wind turbine locations in the United States, revealing variations in the wind speed of up to 30% at individual sites. The results presented here have important implications for predictions of wind plant power output and siting.