45 research outputs found
A short note on turbulence characteristics in wind-turbine wakes
Analytical wake models need formulations to mimic the impact of wind turbines
on turbulence level in the wake region. Several correlations can be found in
the literature for this purpose, one of which is the formula proposed in A.
Crespo, J. Hernandez, Turbulence characteristics in wind-turbine wakes, Journal
of Wind Engineering and Industrial Aerodynamics 61 (1) (1996) 71 - 85, which
relates the added turbulence to the induction factor of the turbine, ambient
turbulence intensity, and normalized distance from the rotor through an
equation with one coefficient and three exponents for the effective parameters.
Misuse of this formula with an incorrect exponent for the ambient turbulence
intensity is propagating in the literature. In this note, we implement the
original and the incorrect formulation of turbine-induced added turbulence in a
Gaussian wake model to quantify its impact by studying the Horns Rev 1 wind
farm. The results reveal that the turbulence intensity and the normalized power
of the waked turbines predicted by the wake model with the correct and the
incorrect implementation of turbine-induced added turbulence correlation have a
difference equal to 1.94% and 3.53%, respectively, for an ambient turbulence
intensity of 7.7%. For an ambient turbulence intensity of 4%, these
discrepancies grow to 2.7% and 4.95%.Comment: 4 pages, 2 figure
Frozen propagation of Reynolds force vector from high-fidelity data into Reynolds-averaged simulations of secondary flows
Successful propagation of information from high-fidelity sources (i.e.,
direct numerical simulations and large-eddy simulations) into Reynolds-averaged
Navier-Stokes (RANS) equations plays an important role in the emerging field of
data-driven RANS modeling. Small errors carried in high-fidelity data can
propagate amplified errors into the mean flow field, and higher Reynolds
numbers worsen the error propagation. In this study, we compare a series of
propagation methods for two cases of Prandtl's secondary flows of the second
kind: square-duct flow at a low Reynolds number and roughness-induced secondary
flow at a very high Reynolds number. We show that frozen treatments result in
less error propagation than the implicit treatment of Reynolds stress tensor
(RST), and for cases with very high Reynolds numbers, explicit and implicit
treatments are not recommended. Inspired by the obtained results, we introduce
the frozen treatment to the propagation of Reynolds force vector (RFV), which
leads to less error propagation. Specifically, for both cases at low and high
Reynolds numbers, propagation of RFV results in one order of magnitude lower
error compared to RST propagation. In the frozen treatment method, three
different eddy-viscosity models are used to evaluate the effect of turbulent
diffusion on error propagation. We show that, regardless of the baseline model,
the frozen treatment of RFV results in less error propagation. We combined one
extra correction term for turbulent kinetic energy with the frozen treatment of
RFV, which makes our propagation technique capable of reproducing both velocity
and turbulent kinetic energy fields similar to high-fidelity data
Physics-guided machine learning for wind-farm power prediction: Toward interpretability and generalizability
With the increasing amount of available data from simulations and
experiments, research for the development of data-driven models for wind-farm
power prediction has increased significantly. While the data-driven models can
successfully predict the power of a wind farm with similar characteristics as
those in the training ensemble, they generally do not have a high degree of
flexibility for extrapolation to unseen cases in contrast to the physics-based
models. In this paper, we focus on data-driven models with improved
interpretability and generalizability levels that can predict the performance
of turbines in wind farms. To prepare the datasets, several cases are defined
based on the layouts of operational wind farms, and massive computational fluid
dynamics simulations are performed. The extreme gradient boosting algorithm is
used afterward to build models, which have turbine-level geometric inputs in
combination with the efficiency from physics-based models as the features.
After training, to analyze the models' capability in generalization, their
predictions for the unseen cases with different operating conditions, inflow
turbulence levels, and wind-farm layouts are compared to the Park model and an
empirical-analytical Gaussian wake model. Results show that the physics-guided
machine-learning models outperform both physics-based models showing a high
degree of generalizability, and the machine is not sensitive to the choice of
the physics-based guide model
An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes
In this study, an analytical wake model for predicting the mean velocity field downstream of a wind turbine under veering incoming wind is systematically derived and validated. The new model, which is an extended version of the one introduced by Bastankhah and Porté-Agel, is based upon the application of mass conservation and momentum theorem and considering a skewed Gaussian distribution for the wake velocity deficit. Particularly, using a skewed (instead of axisymmetric) Gaussian shape allows accounting for the lateral shear in the incoming wind induced by the Coriolis force. This analytical wake model requires only the wake expansion rate as an input parameter to predict the mean wake flow downstream. The performance of the proposed model is assessed using the large-eddy simulation (LES) data of a full-scale wind turbine wake under the stably stratified condition. The results show that the proposed model is capable of predicting the skewed structure of the wake downwind of the turbine, and its prediction for the wake velocity deficit is in good agreement with the high-fidelity simulation data
A survey of machine learning wall models for large eddy simulation
This survey investigates wall modeling in large eddy simulations (LES) using
data-driven machine learning (ML) techniques. To this end, we implement three
ML wall models in an open-source code and compare their performances with the
equilibrium wall model in LES of half-channel flow at eleven friction Reynolds
numbers between and . The three models have ''seen'' flows at
only a few Reynolds numbers. We test if these ML wall models can extrapolate to
unseen Reynolds numbers. Among the three models, two are supervised ML models,
and one is a reinforcement learning ML model. The two supervised ML models are
trained against direct numerical simulation (DNS) data, whereas the
reinforcement learning ML model is trained in the context of a wall-modeled LES
with no access to high-fidelity data. The two supervised ML models capture the
law of the wall at both seen and unseen Reynolds numbers--although one model
requires re-training and predicts a smaller von K\'arm\'an constant. The
reinforcement learning model captures the law of the wall reasonably well but
has errors at both low () and high Reynolds numbers
(). In addition to documenting the results, we try to
''understand'' why the ML models behave the way they behave. Analysis shows
that the errors of the supervised ML model is a result of the network design
and the errors in the reinforcement learning model arise due to the present
choice of the ''states'' and the mismatch between the neutral line and the line
separating the action map. In all, we see promises in data-driven machine
learning models
Log-law recovery through reinforcement-learning wall model for large-eddy simulation
This paper focuses on the use of reinforcement learning (RL) as a
machine-learning (ML) modeling tool for near-wall turbulence. RL has
demonstrated its effectiveness in solving high-dimensional problems, especially
in domains such as games. Despite its potential, RL is still not widely used
for turbulence modeling and is primarily used for flow control and optimization
purposes. A new RL wall model (WM) called VYBA23 is developed in this work,
which uses agents dispersed in the flow near the wall. The model is trained on
a single Reynolds number () and does not rely on high-fidelity
data, as the back-propagation process is based on a reward rather than output
error. The states of the RLWM, which are the representation of the environment
by the agents, are normalized to remove dependence on the Reynolds number. The
model is tested and compared to another RLWM (BK22) and to an equilibrium wall
model, in a half-channel flow at eleven different Reynolds numbers (). The effects of varying agents' parameters such as actions
range, time-step, and spacing are also studied. The results are promising,
showing little effect on the average flow field but some effect on wall-shear
stress fluctuations and velocity fluctuations. This work offers positive
prospects for developing RLWMs that can recover physical laws, and for
extending this type of ML models to more complex flows in the future.Comment: arXiv admin note: text overlap with arXiv:2211.0361