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Savonius turbine is an alternative wind turbine design that has the ability for self-starting from static condition at different blade angle. There are concerns in the structural behavior of the materials used for the Savonius turbine blades under aerodynamic loads. This study aims to optimize the structural and material properties of Savonius wind turbine blades by conducting a comparative analysis of maximum stress and deformation across different materials. This research involves creating detailed numerical models of the turbine blades using Finite Element Analysis to simulate the mechanical behavior under operational loads. Wood, Aluminum, and Steel are investigated to examine its effect of structural performance in the static conditions. In addition to solid isotropic materials, anisotropic materials such as Carbon Fiber and Glass Fiber composites are investigated. Ansys Composite Prepost is used to access the mechanical properties of composites. Initial analysis shows that the deformation is highly dependent on the Young's modulus of each material. Due to the dependency of composite on the fiber properties, a multi-objective optimization is defined to optimize these parameters on a five-layer composite structure to minimize stress, deformation, and weight. It was found that Carbon-Fiber composites exhibit better performance compared to other materials with improvement up to 22% in maximum deformation. These findings highlight the potential for substantial improvements in blade efficiency and durability through careful material selection and optimization. These findings can be utilized for a preliminary design of Savonius wind turbines and other renewable energy sector, where optimizing wind turbine performance is crucial for sustainable energy production to find optimum composite configuration without compromising its aerodynamic performance. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to evaluate the performances of different flow direction methods (FDM) in affecting the runoff aggregation structures within a watershed under the framework of power law distribution. To this end, SFD (single flow direction method), MFD (multiple flow direction method) and IFD (Infinite flow direction method) were applied for determination of flow direction for water particles in Susu river basin, and consequently assessed with respect to the variation of flow accumulation. The results indicate that different patterns of flow accumulation were observed from each flow direction method. Effect of flow dispersion on DEM is strongest with ascending order of SFD, IFD, MFD. However, contribution of individual pixels into outlet follows descending order of SFD, IFD, MFD. Notably MFD and IFD tend to make additional hydrologic abstraction from rainfall excess due to the flow dispersion within the flow paths generated on DEM. This study also investigated the size distribution of flow accumulation which is equivalent to the drainage area generated from the selected FDM. They were fitted to the power law distribution and flow accumulation was recognized as a scale invariance factor based on the parameter estimation for power law distribution by maximum likelihood. It was also noticed that FDM affects the parameter estimation of power distribution where highest exponent was estimated for MFD following by IFD and SFD. [ABSTRACT FROM AUTHOR]

Rapid urbanization in the recent decades has quickly fuelled up the process of global warming. Many mitigation measures have been formulated and implemented in order to tackle the effect of global warming. Green roofs (roof with vegetated cover) have been proposed and implemented by various countries as one of the new environmentally friendly innovation. This paper evaluates the qualitative and quantitative performances of an extensive green roof system under tropical climate. Simulations showed that the peak discharge of stormwater run-off was reduced up to 26% in relation to concrete tile roof. Its reduction ability was decreased for storms with intense rainfall. Increment of pH was observed for the green roof run-off, and the run-off quality ranged between class I and II under Malaysia National Water Quality Index (WQI). High concentrations of phosphate were observed in the run-off samples, which showed that substrates (fertilized planting soil) might be the potential contributor. Findings indicated that there was a reduction up to around 5% for indoor temperature of the building after installation of the extensive green roof system. [ABSTRACT FROM PUBLISHER]

The main purpose of this study is to propose a methodology for identifying vulnerable regions in the Cameron Highlands that are susceptible to soil loss, based on runoff aggregation structure and the energy expenditure pattern of the natural river basin, within the framework of power law distribution. To this end, three geomorphologic factors, namely shear stress and stream power, as well as the drainage area of every point in the basin of interest, have been extracted using GIS, and then their complementary cumulative distributions are graphically analyzed by fitting them to power law distribution, with the purpose of identifying the sensitive points within the basin that are susceptible to soil loss with respect to scaling regimes of shear stress and stream power. It is observed that the range of vulnerable regions by the scaling regime of shear stress is much narrower than by the scaling regime of stream power. This result seems to suggest that shear stress is a scale-dependent factor, which does not follow power law distribution and does not adequately reflect the energy expenditure pattern of a river basin. Therefore, stream power is preferred as a more reasonable factor for the evaluation of soil loss. The methodology proposed in this study can be validated by visualizing the path of soil loss, which is generated from the hillslope process (characterized by the local slope) to the valley through a fluvial process (characterized by the drainage area as well as the local slope). [ABSTRACT FROM AUTHOR]