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Energies
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Advances in Numerical Modeling and Applications in Energy and Environment
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Advances in Numerical Modeling and Applications in Energy and Environment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 3881

Special Issue Editors

Dr. Esmaeel Eftekharian

E-Mail Website
Guest Editor
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Interests: computational fluid dynamics; wind and fire engineering; turbulent flow; heat transfer; wind energy; computational hydroacoustics/aeroacoustics
Dr. Robert H. Ong

E-Mail
Guest Editor
School of Civil Engineering, University of Sydney, Darlington, New South Wales 2006, Australia
Interests: computational fluid dynamics; environmental fluid mechanics; heat transfer; multiphase flow; turbulent flow; wind engineering; hydrodynamics; aerodynamics

Special Issue Information

Dear Colleagues,

Understanding the physics of environmental fluids associated with fluid motion, mass and heat transfer, and flow transport characteristics is timely due to the increasing focus of climate change. Innovation challenges are continuously required for improved clean and efficient energy captures or generators and optimization techniques in evaluating the dynamic impact of turbulence complexity in technical practice. The development of computational techniques offers the possibility to solve the ever-increasing complexity of the multidisciplinary nature of large and complex simulations.  

To name a few topics, we draw attention to studies focusing on environmental fluid mechanics involving some combination of numerical simulations, experiments, and theoretical analysis. We welcome topics that include but are not limited to the following: 

  • Numerical modeling and computational fluid dynamics simulation in environmental fluid mechanics;
  • Optimization of parameters in the problems in the fields of environmental science, for instance: air, surface, and subsurface degradation or pollutions, atmospheric environment, buildings, urban and industrial environments, etc.;
  • Application of latest developments in renewable energy converters (such as wind, solar, wave) using computational simulations;
  • Visualization of complex flow in turbulent regimes;
  • Fundamental understanding of thermo-dynamics/chemical/physical in environmental fluid flows;
  • Unique numerical and experimental techniques in buoyancy-driven turbulent flows (bushfire enhanced wind, fire whirl/tornado, columnar or convection vortices, etc.);
  • Current challenges in environmental fluid mechanics.

Dr. Esmaeel Eftekharian
Dr. Robert H. Ong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational fluid dynamics
  • environmental fluid flow
  • turbulent flow
  • buoyancy-driven flow
  • renewable energy
  • air pollution
  • energy conversion
  • atmospheric flow

Published Papers (2 papers)

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Research

26 pages, 13033 KiB  
Article
Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation
by Dongqin Zhang, Yang Liang, Chao Li, Yiqing Xiao and Gang Hu
Energies 2022, 15(19), 7431; https://doi.org/10.3390/en15197431 - 10 Oct 2022
Cited by 3 | Viewed by 1656
Abstract
Turbine-induced velocity deficit is the main reason to reduce wind farm power generation and increase the fatigue loadings. It is meaningful to investigate turbine-induced wake structures by a simple and accurate method. In this study, a series of single turbine wake models are [...] Read more.
Turbine-induced velocity deficit is the main reason to reduce wind farm power generation and increase the fatigue loadings. It is meaningful to investigate turbine-induced wake structures by a simple and accurate method. In this study, a series of single turbine wake models are proposed by combining different spanwise distributions and wake boundary expansion models. It is found that several combined wake models with high hit rates are more accurate and universal. Subsequently, the wake models for multiple wind turbines are also investigated by considering the combined wake models for single turbine and proper superposition approaches. Several excellent plans are provided where the velocity, turbulence intensity, and wind power generation for multiple wind turbines can be accurately evaluated. Finally, effects of thrust coefficient and ambient turbulence intensity are studied. In summary, the combined wake models for both single and multiple wind turbines are proposed and validated, enhancing the precision of wind farm layout optimization will be helped by using these wake models. Full article
(This article belongs to the Special Issue Advances in Numerical Modeling and Applications in Energy and Environment)
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23 pages, 7536 KiB  
Article
Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland
by Justyna Kujawska, Monika Kulisz, Piotr Oleszczuk and Wojciech Cel
Energies 2022, 15(17), 6428; https://doi.org/10.3390/en15176428 - 2 Sep 2022
Cited by 20 | Viewed by 1809
Abstract
Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to [...] Read more.
Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM10. The data used to build the models did not take into account the seasons. The algorithms achieved the following R2 values: 0.8 for LR, 0.79 for KNNR, 0.82 for SVM, 0.77 for RT, 0.89, 0.90 for ANN and 0.81 for LSTM. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. In this research, the ANN model performed slightly better than other models. Then, an ANN was used to train a network with five output neurons to predict the approximate level of PM10 at different time points (PM level at a given time, after 1 h, after 6 h, after 12 h and after 24 h). The results showed that the developed and tuned ANN model is appropriate (R = 0.89). The model created in this way can be used to determine the risk of exceeding the PM10 alert level and to inform about the air quality in the region. Full article
(This article belongs to the Special Issue Advances in Numerical Modeling and Applications in Energy and Environment)
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