Marcus Hultmark

Wind turbines operate in turbulent atmospheric flow that constantly changes. In field tests, the full impacts of turbine design and operation are difficult to measure because confounding effects from atmospheric flow cannot be controlled. We address the gap in controlled experiments of wind turbines across control settings at dynamic similarity using a unique experimental framework that enables representative study of full-scale wind turbines in a laboratory-sized wind tunnel by pressurizing up to 220 atmospheres. This pressurization enables high-throughput experiments of a scaled wind turbine, at high Reynolds numbers and across yaw-misalignments and tip speed ratios relevant to commercial wind turbines. The results confirm that control strategies currently in use that were designed for turbines operating in ideal conditions without yaw misalignment should be modified, as the tip speed ratio that maximizes power depends on the yaw angle. Critically, the changes in forces and power with misalignment angle strongly depend on the tip speed ratio. We present a Unified Wind Turbine (UWT) model, which accurately predicts the behavior observed in these controlled experiments. The reliability of the UWT model to predict the synergistic effects of yaw misalignment and tip speed ratio enables its use in wind turbine design and wind farm flow control optimization to maximize the energy production and value of wind power systems. The cost-efficient experimental testbed in a pressurized wind tunnel presented herein offers a unique opportunity to study aerodynamics at dynamic similarity with large-scale, commercial turbines without many confounding effects and challenges faced in performing field tests.

Field measurements in the atmospheric surface layer (ASL) are key to understanding turbulent exchanges in the atmosphere, such as fluxes of mass, water vapor, and momentum. However, current field measurement techniques are limited to single-point time series or large-scale flow field scans. Extending image-based laboratory measurement techniques to field-relevant scales is a promising route to more detailed atmospheric flow measurements, but this requires significant increases in the attainable measurement volume while keeping the spatiotemporal resolution high. Here, we present an adaptable particle tracking system using helium-filled soap bubbles, mirrorless cameras, and high-power LEDs enabling volumetric ASL field measurements. We conduct analyses pertinent to image-based field measurement systems and develop general guidelines for their design. We validate the particle tracking system in a field experiment. Single-point Eulerian velocity statistics are presented and compared to data from concurrently operated sonic anemometers. Lagrangian displacement statistics are also presented with a comparison to Taylor’s theory of dispersion. The system improves the state-of-the-art in field measurements in the lower atmosphere and enables unprecedented insights into flow in the ASL.

Lubricated textured surfaces immersed in liquid flows offer tremendous potential for reducing fluid drag, enhancing heat and mass transfer, and preventing fouling. According to current design rules, the lubricant must chemically match the surface to remain robustly trapped within the texture. However, achieving such chemical compatibility poses a significant challenge for large-scale flow systems, as it demands advanced surface treatments or severely limits the range of viable lubricants. In addition, chemically tuned surfaces often degrade over time in harsh environments. Here, we demonstrate that a lubricant-infused surface (LIS) can resist drainage in the presence of external shear flow without requiring chemical compatibility. Surfaces featuring longitudinal grooves can retain up to 50% of partially wetting lubricants in fully developed turbulent flows. The retention relies on contact-angle hysteresis, where triple-phase contact lines are pinned to substrate heterogeneities, creating capillary resistance that prevents lubricant depletion. We develop an analytical model to predict the maximum length of pinned lubricant droplets in microgrooves. This model, validated through a combination of experiments and numerical simulations, can be used to design chemistry-free LISs for applications where the external environment is continuously flowing. Our findings open up new possibilities for using functional surfaces to control transport processes in large systems. Published by the American Physical Society 2024

Determination of airflow rates is an inevitable part of the energy-efficient control of ventilation systems. To achieve efficient control, the flowmeters used must be suitably accurate and create minimum disturbance to the airflow. In this study, we evaluate the quantitative performance characteristics of an innovative micro-electromechanical systems (MEMS) flowmeter, a so-called Elastic Filament Velocimetry (EFV), in ventilation ducts. Two versions of the EFV-sensor, i.e., an 11-nanoribbon and a 22-nanoribbon variety, were evaluated in laboratory studies. The results indicate that the 11-nanoribbon sensor is more suitable for air velocity measurements in ducts than the 22-nanoribbon sensor. The 11-nanoribbon sensor can measure air velocities from 0.3 m/s. The maximum variation of the sensor-output is 3% for velocities over 0.5 m/s. Calibration models have been developed for the 11-nanoribbon sensor. The error due to model calibration is lower than ±5% for velocities over 0.6 m/s. Moreover, laboratory studies were performed to investigate the airflow disturbance in a duct system due to the EFV sensor. The results were compared with the corresponding disturbance caused by two different types of self-averaging probes. At a bulk velocity of 3 m/s, the self-averaging probes introduced a greater pressure drop by at least 50% compared to the EFV-sensor.

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard an UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector’s direction and magnitude. The MAST’s performance was evaluated in a wind tunnel at speeds up to 5 m s⁻¹ and orientations of 0^∘–360^∘. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m s⁻¹ for speed and 1.6^∘ for direction. Furthermore, 95% of measurements are within 0.36 m s⁻¹ for speed and 5.0^∘ for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as `MAST' (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard a UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector's direction and magnitude. The MAST's performance was evaluated in a wind tunnel at speeds up to 5~m/s and orientations of 0 - 360 degrees. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m/s for speed and 1.6 degrees for direction. Furthermore, 95% of measurements are within 0.36 m/s error for speed and 5.0 degree error for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.