Decades of research have shown that biosensors using photonic circuits fabricated using CMOS processes can be highly sensitive, selective, and quantitative. Unfortunately, the cost of these sensors combined with the...

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (∼-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (∼-0.5ms-1) offshore and a slight positive bias (∼+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.

No abstract

This study considers optimizing the planform of wind turbine blades to ultimately enhance wind plant controls, namely, wake steering strategies. Adjoint-enabled unsteady actuator line simulations are carried out to obtain gradients for optimization of several different performance objectives with respect to blade chord length at 10 locations along blade span. We demonstrate different blade design optimizations that can maximize time-averaged lateral wake deflection, entrainment of kinetic energy, or total power of multiple turbines. Our optimized designs can produce a 4+% increase in wake deflection, a 4Â increase in vertical kinetic energy entrainment, or a 3.6% increase in power when compared with the baseline case. While lateral wake deflection is only modestly sensitive to chord changes, we find that increasing the outboard chord length can dramatically increase kinetic energy entrainment, resulting in faster wake recovery and gains in net power. While this work develops only a few case studies emphasizing relative performance improvements and general trends, these results show the promise of a framework that combines mid-fidelity computation with adjoint-based optimization for control and design problems.adjoint optimization, wind plant controls, wake steering, blade design | INTRODUCTIONModern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy, the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. Plant-level control strategies, such as wake steering or axial induction control, have emerged as a viable solution to some of wind energy's grand challenges 1 by offering a tool for plant operators to exert more control authority over an existing wind plant and also increase power production by reducing plant wake losses. However, these operating regimes are often not prioritized during the design phase, which results in turbines and plants that fail to maximize all of the available power capture at the plant level. Shifting to plant-focused design is further challenged because traditional turbine rotor design tools are based on blade element momentum theory. Models that can trace the impact of blade design choices through the complex turbulent flow physics of wake interactions to understand their effect on control strategies are typically too computationally intensive to be used in the core rotor design iteration loop. Therefore, turbine blades are designed in a greedy approach, where single turbine power production is the