Machine Learning Techniques in Indoor Environmental Quality Assessment

This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment

Improvement of bem analysis to incorporate stall delay effect and the study of atmospheric boundary layer effect on the wake characteristics of NREL phase VI turbine

Steadily increasing energy consumption, fluctuating fuel costs and concerns about global climate changes have led to the research and evaluation of alternative renewable energies. The wind turbine is among the promising alternative energy sources that have recently gained more attention. In this work, the challenging aspects involved in modelling the rotor aerodynamics and wakes behind the wind turbines are studied. This report covers the literature review of Blade Element Momentum (BEM) analysis of wind turbine, limitations of BEM method, effect of stall delay, wake aerodynamics, atmospheric boundary layer and its effects on the wake characteristics and previous different wake models of near and far wake regions. In this project, experimental data of NREL Phase VI Turbine (sequence S) were used to corroborate the results. There are two main objectives of this project. The first objective is to improve the BEM analysis to account for the three-dimensional (3D) effects due to the rotation of the turbine. The second objective is to contemplate on the effects of atmospheric boundary layers (ABL) on the wake characteristics. These two objectives act as a roadway to enhance coupled BEM-CFD analysis, which is left for the future works. In the coupled BEM-CFD analysis, the BEM method will be applied to calculate the aerodynamic forces of the aerofoil sections along the blade span. The main drawback of BEM analysis is the use of the twodimensional (2D) aerofoil characteristics (CL and CD ) which considers only the axial flow but not radial or spanwise flow along the blade span. This leads to a considerable difference in the lift coefficients between the rotating and non rotating blades, especially at inboard sections of the blade. This 3D phenomenon is called stall delay. The current study includes different proposals for the extrapolation of 2D aerofoil characteristics of the S809 aerofoil and their implementation in BEM analysis and comparison of power predicted with experimental results. Also, four existing stall delay correction models in BEM analysis are examined. In general, these models result in over-prediction of power, especially at high inflow wind speeds. An improved inverse BEM method is developed to compute 3D aerofoil characteristics at different radial locations along the blade span. In addition to five radial locations as described in the NREL/NASA Ames test analysis, 13 additional radial locations are considered for a better understanding of stall delay. A new BEM model with the local radius effect of aerofoil characteristics (other than as a function of Reynolds number and angle of attack only) is proposed. Implementation of the new model showed a good agreement with aerofoil characteristics distribution along the blade span with the 3D aerofoil characteristics computed from the CFD analyses using inverse BEM method. MATLAB code was developed for both BEM and Inverse BEM analyses. Most important in the wind farm analysis is the effect of the atmospheric boundary layer since the turbulence properties of the atmosphere affect the wake characteristics. In this work, the NREL Phase VI Turbine is virtually placed in different atmospheric boundary layers from open sea to city/forest. Simulations are performed with direct rotor modelling using sliding mesh analysis. Since the experiment results of wake characteristics of the NREL Phase VI Turbine was not available, different empirical models are used for comparison. It was evident that wake recovers at a faster rate as the roughness length of the ground increases. Also, it was noted that the turbulence intensity of the wake varies both laterally and vertically, but existing analytical wake turbulence intensity models provide only an averaged value The possible methods for indirect rotor modelling like Actuator Disk, Actuator Line and Actuator Surface methods that lead to coupled BEM-CFD analysis of wind turbine is discussed in brief. The possible ways of improving these indirect rotor models by using the aerodynamic forces computed from improved BEM analysis and wake aerodynamics are provided for the future works.Doctor of Philosophy (MAE

Performance evaluation of air flow and thermal comfort in the room with wind-catcher using different CFD techniques under neutral atmospheric boundary layer

Energy usage in buildings using conventional Heat Ventilation Air Conditioning (HVAC) systems can be considerably reduced on using natural ventilation systems like wind catcher. In addition to high energy consumptions in conventional HVAC, other disadvantages include ozone layer depletion due to CFCs emitted and also cost involved in installation and maintenance. Purpose of this study was to investigate the airflow and thermal comfort analyses of existing common wind catcher design in the city of Yazd using computational fluid dynamics (CFD) technique. In order to have more realistic analysis, neutral homogenous atmospheric boundary layer (ABL) was created at the inlet. Standard k-e turbulence model was used since equations for creating ABL were more related to this model. 3D CFD analysis was performed using both ANSYS FLUENT and OPENFOAM. Thermal comfort analysis was evaluated based on ASHRAE standard 55.Published versio

Effect of different atmospheric boundary layers on the wake characteristics of NREL Phase VI wind turbine

In this study, the interaction of horizontal axis wind turbine (HAWT) with neutrally stratified atmospheric boundary layer (ABL) and its wake characteristics are investigated. Important wake characteristics of wind turbine such as velocity deficit and turbulence level are analyzed. For this purpose, Unsteady Reynolds-Averaged Navier-Stokes (URANS) using k-ε turbulence closure models are performed using commercial Computational Fluid Dynamics (CFD) software called ANSYS FLUENT. Full rotor CFD simulations of the NREL Phase VI wind turbine by virtually placing on a flat surface with different aerodynamic roughness lengths are performed. Discussions on effective modelling of horizontal homogeneity for the undisturbed ABL is included. The influence of inflow ABL's turbulence level in the wake velocity recovery and the ground effect on the wake turbulence intensity (TI) is analyzed. In addition, comparison of rotor aerodynamics of wind turbine in different terrains is performed using pressure coefficient distributions. Finally, the necessity of inclusion of TI recovery in addition to velocity recovery in the wake for the wind farm alignment is discussed

Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake

In this paper, three machine learning (ML) algorithms, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost), are validated to estimate the velocity and turbulence intensity of a wind turbine's wake at distinct downstream distances. To this end, a series of high-fidelity numerical simulations for the NREL Phase VI wind turbine is carried out to generate training and test datasets for the three machine learning algorithms. The predicted wake velocity and turbulence intensity from the ML models are also contrasted with significant existing analytical wake models. Machine learning algorithms estimate velocity and turbulence intensity in the wake in a way commensurate to the Computational Fluid Dynamics (CFD) simulations while running at a similar pace as low-fidelity wake models. The results demonstrate that machine learning-based algorithms can predict velocity and turbulence intensity better with higher precision than the traditional analytical wake models

On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III

This work presents a comparison study of the CFD modeling with two different turbulence modeling approaches viz. unsteady RANS and LES, on a full-scale model of the (New) MEXICO rotor wind turbine. The main emphasis of the paper is on the rotor and wake aerodynamics. Simulations are carried out for the three wind speeds considered in the MEXICO experiment (10, 15, and 24 ms−1). The results of uRANS and LES are compared against the (New) MEXICO experimental measurements of pressure distributions, axial, radial, and azimuth traverse of three velocity components. The near wake characteristics and vorticity are also analyzed. The pressure distribution results show that the LES can predict the onset of flow separation more accurately than uRANS when the turbine operates in the stall condition. The LES can compute the flow structures in wake significantly better than the uRANS for the stall condition of the blade. For the design condition, the mean absolute error in axial and radial velocity components along radial traverse is less than 10% for both the modeling approaches, whereas tangential component error is less than 2% from the LES approach. The results also reveal that wake recovers faster in the uRANS approach, requiring further research of the far wake region using both CFD modeling approaches

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction