# Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Algorithm

- The wind speeds are calculated and archived for a specific computational field, for a number of distinct incoming wind directions (in the present test, 8 incoming wind directions have been considered to cover the entire 360° range).
- The user provides as input data the actual incoming wind direction.
- If the incoming wind direction coincides with one of the directions for which the wind speeds have been calculated, the algorithm selects the corresponding wind result file from the database. If not, then the two wind direction results files on both sides of the actual direction are retrieved and linear interpolation is applied to calculate the appropriate wind results file. It should be noted that linear interpolation is the simplest possible way to construct the intermediate wind field between two successive wind directions. Importantly, linear interpolation should work ideally when the distance between two successive wind directions is too small. More advanced methods based on, e.g., big data science and machine learning, will increase the efficiency of the present idea. These are concepts that will be examined by the authors in the future.

## 3. Governing Equations

_{i}are the velocity components, x

_{i}is the distance, P is the pressure, $K$ is the eddy viscosity, $\nu $ is the kinematic viscosity of the fluid, ${g}_{i}$ are the components of gravity acceleration, r is the gas constant, and T is the absolute temperature. The bar represents time-averaged values.

## 4. The Pilot Test Case

## 5. The Database

## 6. Validation of the Algorithm

## 7. Validation of the Computer Code

#### 7.1. The MUST Wind Tunnel Experiment

#### 7.2. The Numerical Simulations

_{max}where H

_{max}= 3.51 m is the height of the tallest building) and the distance of the eastern boundary from the last obstacle is equal to 52.65 m (15H

_{max}). The height of the field corresponds to 6 times the maximum height of the buildings. These dimensions agree with the proposals of COST Action 732 [14].

_{0}equal to 10

^{−5}m. At the inlet and top plane, a zero value was used as the boundary condition for the velocity components v and w while the Langevin type boundary condition was used for the velocity component u. Finally, as initial conditions, the vertical profile of the velocity of the mean flow imposed on the inlet was used throughout the field. For the RANS simulations, the same boundary conditions were used as in the work [11].

## 8. Validation of the Hydrodynamic Problem

## 9. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Seshaiah, C.V.; Sukkiramathi, K. A Mathematical model to estimate the wind power using three parameter Weibull distribution. Wind. Struct.
**2016**, 22, 393–408. [Google Scholar] [CrossRef] - Chiodo, E.; De Falco, P. Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation. Electr. Power Syst. Res.
**2016**, 141, 549–561. [Google Scholar] [CrossRef] - Tolias, I.C.; Koutsourakis, N.; Hertwig, D.; Efthimiou, G.C.; Venetsanos, A.G.; Bartzis, J.G. Large Eddy Simulation study on structure of turbulent flow in a complex city. J. Wind Eng. Ind. Aerodyn.
**2018**, 177, 101–116. [Google Scholar] [CrossRef] [Green Version] - Coceal, O.; Goulart, E.V.; Branford, S.; Thomas, T.G.; Belcher, S.E. Flow structure and near-field dispersion in arrays of building-like obstacles. J. Wind Eng. Ind. Aerodyn.
**2014**, 125, 52–68. [Google Scholar] [CrossRef] [Green Version] - Koutsourakis, N.; Bartzis, J.G.; Markatos, N.C. Evaluation of Reynolds stress, k-ε and RNG k-ε turbulence models in street canyon flows using various experimental datasets. Environ. Fluid Mech.
**2012**, 12, 379–403. [Google Scholar] [CrossRef] - Berchet, A.; Zink, K.; Muller, C.; Oettl, D.; Brunner, J.; Emmenegger, L.; Brunner, D. A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale. Atmos. Environ.
**2017**, 158, 181–196. [Google Scholar] [CrossRef] - U.S. Naval Research Laboratory. CT-Analyst. Available online: https://www.nrl.navy.mil/Our-Work/Areas-of-Research/Computational-Physics-Fluid-Dynamics/CT-Analyst/ (accessed on 7 December 2021).
- Leitl, B.; Hertwig, D.; Harms, F.; Peeck, C.; Schatzmann, M.; Patnaik, G.; Boris, J.; Obenschain, K.; Fischer, S.; Rechenbach, P. Emergency Response Tool for Accidental Releases; Short Course on Urban and Technical Meteorology (Lecture Notes), Universidad National de Colombia: Bogota, Colombia, 2012. [Google Scholar]
- Launder, B.E.; Reece, G.J.; Rodi, W. Progress in the development of a Reynolds-stress turbulence closure. J. Fluid Mech.
**1975**, 68, 313–348. [Google Scholar] [CrossRef] [Green Version] - Bartzis, J.G. ADREA-HF: A Three Dimensional Finite Volume Code for Vapour Cloud Dispersion in Complex Terrain; CEC JRC Ispra Report EUR 13580 EN; EC Joint Research Centre: Ispra, Italy, 1991. [Google Scholar]
- Efthimiou, G.C. Prediction of four concentration moments of an airborne material released from a point source in an urban environment. J. Wind Eng. Ind. Aerodyn.
**2019**, 184, 247–255. [Google Scholar] [CrossRef] - Bezpalcova, K.; Harms, F. EWTL Data Report/Part I: Summarized Test Description Mock Urban Setting Test; Technical Report; Environmental Wind Tunnel Lab. Center for Marine and Atmospheric Research, University of Hamburg: Hamburg, Germany, 2005. [Google Scholar]
- Yee, E.; Biltoft, C. Concentration fluctuation measurements in a plume dispersing through a regular array of obstacles. Bound.-Layer Meteorol.
**2004**, 111, 363–415. [Google Scholar] [CrossRef] - Schatzmann, M.; Olesen, H.; Franke, J. Model Evaluation Case Studies: Approaches and Results. COST Action 732 Quality Assurance and Improvement of Microscale Meteorological Models; University of Hamburg: Hamburg, Germany, 2010; ISBN 3-00-018312-4. [Google Scholar]
- Tominaga, Y.; Mochida, A.; Yoshie, R.; Kataoka, H.; Nozu, T.; Yoshikawa, M.; Shirasawa, T. AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J. Wind Eng. Ind. Aerod.
**2008**, 96, 1749–1761. [Google Scholar] [CrossRef] - Tominaga, Y.; Iizuka, S.; Imano, M.; Kataoka, H.; Mochida, A.; Nozu, T.; Ono, Y.; Shirasawa, T.; Tsuchiya, N.; Yoshie, R. Cross comparisons of CFD results of wind and dispersion fields for MUST experiment: Evaluation exercises by AIJ. J. Asian Architect. Build Eng.
**2013**, 12, 117–124. [Google Scholar] [CrossRef] [Green Version]

**Figure 2.**The computational field that includes part of the atmospheric surface layer. The 3568 sensors are presented (yellow circles).

**Figure 4.**The wind speed sensors (yellow circles) of the MUST wind tunnel experiment: (

**a**) coarse network, (

**b**) dense network, (

**c**) vertical profiles, (

**d**) uw levels. The 120 obstacles are also presented.

Dimensions of the Field x/y/z (m) | Total Number of Cells | Number of Cells on Each Axis | Minimum/Maximum Cell Sizes (m) | ||||
---|---|---|---|---|---|---|---|

x | y | z | x | y | z | ||

277.85/303.43/21.06 | 22,365,000 | 750 | 852 | 35 | 0.25/5.14 | 0.25/5.14 | 0.25/1.84 |

RANS | LES | |
---|---|---|

U-coarse network (900) | 0.91 | 0.95 |

U-dense network (279) | 0.82 | 0.88 |

U-profiles (566) | 0.94 | 0.97 |

U-uw levels (39) | 1 | 1 |

V-coarse network (900) | 0.8 | 0.83 |

V-dense network (279) | 0.86 | 0.85 |

W-profiles (566) | 0.59 | 0.41 |

W-uw levels (39) | 0.64 | 0.54 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Efthimiou, G.; Barmpas, F.; Tsegas, G.; Moussiopoulos, N.
Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments. *Fluids* **2021**, *6*, 461.
https://doi.org/10.3390/fluids6120461

**AMA Style**

Efthimiou G, Barmpas F, Tsegas G, Moussiopoulos N.
Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments. *Fluids*. 2021; 6(12):461.
https://doi.org/10.3390/fluids6120461

**Chicago/Turabian Style**

Efthimiou, George, Fotios Barmpas, George Tsegas, and Nicolas Moussiopoulos.
2021. "Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments" *Fluids* 6, no. 12: 461.
https://doi.org/10.3390/fluids6120461