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Abstract Condensation trail (contrail) formation in an airplane’s wake requires thermodynamics supersaturation and ice nucleation to form visible ice crystals. Here, using a thermodynamic analysis, we evaluate the potential for forming contrails in a carbon-free, ammonia-powered propulsion system compared to conventional planes powered by jet fuel. The analysis calculates the moisture released by fuel into the atmosphere for each one-degree increase in air temperature due to exhaust gas. It then determines if this moisture can saturate the initially undersaturated atmosphere, maintain saturation as temperature rises, and result in supersaturation with respect to ice while leaving enough moisture for a visible cloud to form. With ammonia increases the critical temperature required for supersaturation. Although ammonia does not generate soot particles in the exhaust gas, various aerosols exist in the atmosphere through other sources that can facilitate heterogeneous ice nucleation. Hence, while ammonia’s contrails might not be as dense, they can form at lower altitudes where the air is warmer and endure longer due to the increased water content, which preserves supersaturation for longer as fresh air dilutes the contrail.

A wind turbine’s tip speed ratio (TSR) is the linear speed of the blade’s tip, normalized by the incoming wind speed. For a given blade profile, there is a TSR that maximizes the turbine’s efficiency. The industry’s current practice is to impose the same TSR that maximizes the efficiency of a single, isolated wind turbine on every turbine of a wind farm. This article proves that this strategy is wrong. The article demonstrates that in every wind direction, there is always a subset of turbines that needs to operate at non-efficient conditions to provide more energy to some of their downstream counterparts to boost the farm’s overall production. The aerodynamic interactions between the turbines cause this. The authors employed the well-known Jensen wake model in concert with Particle Swarm Optimization to demonstrate the effectiveness of this strategy at Lillgrund, a wind farm in Sweden. The model’s formulation and implementation were validated using large-eddy simulation results. The AEP of Lillgrund increased by approximately 4% by optimizing and actively controlling the TSR. This strategy also decreased the farm’s overall TSR, defined as the average TSR of the turbines, by 8%, leading to several structural and environmental benefits. Note that both these values are farm-dependent and change from one farm to another; hence, this research serves as a proof of concept.

Abstract Wind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.