High-Fidelity Static Aeroelastic Simulations of the Common Research Model

Biological mimicry can be considered as having a double-layered structure: there is a layer of ecological relations between species and there is a layer of semiotic relations of the sign. The present article demonstrates the limitations of triadic models and typologies of mimicry, as well as their lack of correspondence to mimicry as it actually occurs in nature. It is argued that more dynamical semiotic tools are needed to describe mimicry in a theoretically coherent way that would at the same time allow comparative approach to various mimicry cases. For this a five-stage model of analysis is proposed, which incorporates classical mimicry theory, Jakob von Uexküll’s Umwelt-theory, as well as semiotic and communication analysis. This research model can be expressed in the form of five questions: 1) What is the formal structure of mimicry system? 2) What are the perceptual and effectual correspondences between the participants of mimicry? 3) What are the characteristics of resemblances? 4) How is the mimicry system regulated in ontogenetic and evolutionary processes? 5) How is the mimicry system related to human cultural processes? As a practical example of this semiotic methodology, brood parasitism between the common cuckoo Cuculus canorus and his frequent host species is examined.

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Optimizing Wind Farm Control through Wake Steering using Surrogate Models based on High Fidelity Simulations

10.5194/wes-2019-46 ◽

2019 ◽

Cited By ~ 1

Author(s):

Paul Hulsman ◽

Søren Juhl Andersen ◽

Tuhfe Göçmen

Keyword(s):

Wind Farm ◽

Control Strategies ◽

Atmospheric Condition ◽

Surrogate Models ◽

Normal Operation ◽

Total Power ◽

High Fidelity ◽

Power Gain ◽

Model Errors ◽

Aeroelastic Simulations

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

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Strategies for vaccination agains SARS-COV-2 to efficiently bring R<1

10.1101/2021.01.30.21250828 ◽

2021 ◽

Author(s):

André Voigt ◽

Stig W. Omholt ◽

Eivind Almaas

Keyword(s):

Computer Simulations ◽

Herd Immunity ◽

Random Selection ◽

Reproductive Number ◽

High Fidelity ◽

Wide Spread ◽

Limited Availability ◽

Population Immunity ◽

The Common ◽

Limited Supply

ABSTRACTWith limited availability of vaccines, an efficient use of the limited supply of vaccines in order to achieve herd immunity will be an important tool to combat the wide-spread prevalence of COVID-19. Here, we propose a targeted vaccination approach (EHR) that provides a significant reduction in the necessary number of doses needed achieve herd immunity compared to age-prioritized and random selection vaccination schemes. Using high-fidelity individual-based computer simulations with Oslo, Norway as an example, we find that for a community reproductive number in a setting where R=1.4 without population immunity, the EHR method reaches herd immunity at only 15% of the population vaccinated, whereas the common age-prioritized approach needs 40%. With R=1.9 in the absence of immunity, EHR needs 30% and age-prioritized needs 52%.

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