Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Exploring Variability of Visual Accessibility Options in Operating Systems

Digital technologies are an opportunity to overcome disabilities, provided that accessibility is ensured. In this paper, we focus on visual accessibility and the way it is supported in Operating Systems (OS). The significant variability in this support has practical consequences, e.g., the difficulty to recommend or select an OS, or migrate from one OS to another. This suggests building a variability model for OS that would classify them and would serve as a reference. We propose a methodology to build such a variability model with the help of the Formal Concept Analysis (FCA) framework. In addition, as visual accessibility can be divided into several concerns (e.g., zoom, or contrast), we leverage an extension of FCA, namely Relational Concept Analysis. We also build an ontology to dispose of a standardized description of visual accessibility options. We apply our proposal to the analysis of the variability of a few representative operating systems.

Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Handling Quantity in Variability Models for System-of-Systems

Problem: Modern systems contain parts that are themselves systems. Such complex systems thus have sets of subsystems that have their own variability. These subsystems contribute to the functionality of a whole system-of-systems (SoS). Such systems have a very high degree of variability. Therefore, a modeling technique for the variability of an entire SoS is required to express two different levels of variability: variability of the SoS as a whole and variability of subsystems. If these levels are described together, the model becomes hard to understand. When the variability model of the SoS is described separately, each variability model is represented by a tree structure and these models are combined in a further tree structure. For each node in a variability model, a quantity is assigned to express the multiplicity of its instances per one instance of its parent node. Quantities of the whole system may refer to the number of subsystem instances in the system. From the viewpoint of the entire system, constraints and requirements written in natural language are often ambiguous regarding the quantities of subsystems. Such ambiguous constraints and requirements may lead to misunderstandings or conflicts in an SoS configuration. Approach: A separate notion is proposed for variability of an SoS; one model considers the SoS as an undivided entity, while the other considers it as a combination of subsystems. Moreover, a domain-specific notation is proposed to express relationships among the variability properties of systems, to solve the ambiguity of quantities and establish the total validity. This notation adapts an approach, named Pincer Movement, which can then be used to automatically deduce the quantities for the constraints and requirements. Validation: The descriptive capability of the proposed notation was validated with four examples of cloud providers. In addition, the proposed method and description tool were validated through a simple experiment on describing variability models with real practitioners.

Effects of BMI and LDL-cholesterol change pattern on cardiovascular disease in normal adults and diabetics

IntroductionTo examine how the risk of cardiovascular disease changes according to degree of change in body mass index (BMI) and low-density lipoprotein (LDL)-cholesterol in patients with diabetes using the health medical examination cohort database of the National Health Insurance Service in Korea. In comparison, the pattern in a non-diabetic control group was also examined.Research design and methodsThe study samples were 13 800 patients with type 2 diabetes and 185 898 non-diabetic controls, and their baseline characteristics and repeatedly measured BMI and LDL-cholesterol until occurrence of cardiovascular disease were collected in longitudinal data. We used the variability model that is joint of mixed effects and regression model, then estimated parameters about variability by Bayesian methods.ResultsThe risk of cardiovascular disease was increased significantly with high average real variability (ARV) of BMI in the patients with diabetes, but the risk of cardiovascular disease was not increased according to degree of ARV in non-diabetic controls. The Bayesian variability model was used to analyze the effects of BMI and LDL-cholesterol change pattern on development of cardiovascular disease in diabetics, showing that variability did not have a statistically significant effect on cardiovascular disease. This shows the danger of the former simple method when interpreting only the mean of the absolute value of the variation.ConclusionsThe approach of simple SD in previous studies for estimation of individual variability does not consider the order of observation. However, the Bayesian method used in this study allows for flexible modeling by superimposing volatility assessments on multistage models.

Exploring neurodegenerative disorders using a novel integrated model of cerebral transport: Initial results

The neurovascular unit (NVU) underlines the complex and symbiotic relationship between brain cells and the cerebral vasculature, and dictates the need to consider both neurodegenerative and cerebrovascular diseases under the same mechanistic umbrella. Importantly, unlike peripheral organs, the brain was thought not to contain a dedicated lymphatics system. The glymphatic system concept (a portmanteau of glia and lymphatic) has further emphasized the importance of cerebrospinal fluid transport and emphasized its role as a mechanism for waste removal from the central nervous system. In this work, we outline a novel multiporoelastic solver which is embedded within a high precision, subject specific workflow that allows for the co-existence of a multitude of interconnected compartments with varying properties (multiple-network poroelastic theory, or MPET), that allow for the physiologically accurate representation of perfused brain tissue. This novel numerical template is based on a six-compartment MPET system (6-MPET) and is implemented through an in-house finite element code. The latter utilises the specificity of a high throughput imaging pipeline (which has been extended to incorporate the regional variation of mechanical properties) and blood flow variability model developed as part of the VPH-DARE@IT research platform. To exemplify the capability of this large-scale consolidated pipeline, a cognitively healthy subject is used to acquire novel, biomechanistically inspired biomarkers relating to primary and derivative variables of the 6-MPET system. These biomarkers are shown to capture the sophisticated nature of the NVU and the glymphatic system, paving the way for a potential route in deconvoluting the complexity associated with the likely interdependence of neurodegenerative and cerebrovascular diseases. The present study is the first, to the best of our knowledge, that casts and implements the 6-MPET equations in a 3D anatomically accurate brain geometry.