Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations

Natural-gas fueled spark-ignition (SI) and compression-ignition (CI) engine performance and emissions

50 years of Computational Wind Engineering: Past, present and future

Wind energy is the fastest growing alternate source of energy in the world since its purely economic potential is complemented by its great positive environmental impact. The wind turbine, whether it may be a Horizontal-Axis Wind Turbine (HAWT) or a Vertical-Axis Wind Turbine (VAWT), offers a practical way to convert the wind energy into electrical or mechanical energy. Although this book focuses on the aerodynamic design and performance of VAWTs based on the Darrieus concept, it also discusses the comparison between HAWTs and VAWTs, future trends in design and the inherent socio-economic and environmental friendly aspects of wind energy as an alternate source of energy.

Wind turbine design with emphasis on Darrieus concept Ion Paraschivoiu

In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

Applications of fuzzy logic in renewable energy systems – A review


 Fossil fuels being the primary source of energy to industrial and power sectors are being consumed at an alarming rate. There is a dire need to search for alternative fuels and optimise the Internal Combustion (IC) engine performance parameters. Traditional methods of testing and optimising the performances of IC engine are complex, time consuming and expensive. This has led the researchers to shift their focus to faster and computationally feasible techniques like soft computing (SC) and machine learning (ML) algorithms, which predict the optimum performance with a substantial accuracy. The present study focuses on the implementation of ANN and ensembling methods (Random Forest Regression and Extreme Gradient Boosting Algorithm) modelling of a CI diesel engine run on waste cooking oil (WCO). A single-cylinder, four-stroke, variable compression ratio diesel engine's performance, combustion, and emission parameters have been predicted using ANN and ML approaches. These models have been developed to predict the brake power, brake thermal efficiency, brake specific fuel consumption, ignition delay, combustion duration, carbon monoxide, carbon dioxide and oxides of nitrogen. All the models have been trained by tuning and optimising different number of hyper-parameters and training algorithms (Levenberg-Marquardt, Scaled Conjugate Gradient and Broyden-Fletcher-Goldfarb-Shanno). Further the most optimum parameters have been selected using hyper-parameter optimization. The mathematical models were assessed for their generalization capability by subjecting them to a set of new testing data.

Evaluating the Operating Parameters of a Compression Ignition Engine Fueled with Waste Cooking Oil - Diesel Blends Using Artificial Neural Network and Ensemble Methods


 This paper explores the function approximation characteristics of Artificial Neural Network (ANN) by implementing it on the vertical-axis Savonius wind rotor technology. In this regard, a suitable experimental dataset documented in literature is exploited to train the ANN comprising the rotor performance as output and 11 different design and operating parameters as input with the help of MATLAB R2020b software. Multiple ANN models are trained by varying the number of hidden neurons which are then evaluated based on their estimation error and correlation coefficient (R) as decision criteria. The optimum ANN architecture demonstrates R ≈ 98 0.96 and 0.98 for the testing and training datasets, respectively. Further, in the quest of finding the optimum performance from the entire power curve of the rotor, the Golden Section Method (GSM) is linked with the trained ANN model. Using these soft computing techniques, a parametric study is carried out to understand the dependency of rotor performance on their design and operating parameters. At the end, a graphical interface is developed as a product so as to allow the user to predict the performance of the new rotor designs intuitively.

Predicting the Optimum Performance of a Vertical-Axis Savonius Wind Rotor With Parametric Modeling Using Artificial Neural Network and Golden Section Method

In the present study, an unsteady computational investigation has been carried out in order to analyze the performance of a single-stage vertical axis wind turbine using ANSYS Fluent 6.3 software package and the results obtained are validated with the experimental data. A Realizable k-e turbulence model with enhanced wall functions has been used to analyze a three dimensional model of this turbine. The data and boundary conditions for this analysis have been taken from the experimental observations. The experiments have been carried out under various load conditions to find the power and torque coefficients. The outcomes of numerical simulation and experiments are compared, analyzed and are found analogous with the data available in open literature.

Ujjwal K. Saha

Papers

Evaluating the Operating Parameters of a Compression Ignition Engine Fueled with Waste Cooking Oil - Diesel Blends Using Artificial Neural Network and Ensemble Methods

Predicting the Optimum Performance of a Vertical-Axis Savonius Wind Rotor With Parametric Modeling Using Artificial Neural Network and Golden Section Method

Unsteady Simulation of a Vertical Axis Wind Turbine