The reliance on Computational Fluid Dynamics (CFD) simulations has drastically increased over time to evaluate the aerodynamic performance of small-scale wind turbines. With the rapid variability in customer demand, industrial requirements, economic constraints, and time limitations associated with the design and development of small-scale wind turbines, the trade-off between computational resources and the simulation’s numerical accuracy may vary significantly. In the context of wind turbine design and analysis, high fidelity simulation under full geometric and numerical complexity is more accurate but pose significant demands from a computational standpoint. There is a need to understand and quantify performance deterioration of high fidelity simulations under reduced geometric or numerical approximation on a single small scale turbine model. In the present work, the flow past a small-scale Horizontal Axis Wind Turbine (HAWT) was simulated under various geometric and numerical configurations. The geometric complexity was varied based on stationary and rotating turbine conditions. In the stationary case, simple 2D airfoil, 2.5D blade, 3D blade sections are evaluated, while rotational effects are introduced for the configuration 3D blade, rotor only, and the full-scale wind turbine with and without the inclusion of a nacelle and tower. In terms of numerical complexity, the Single Reference Frame (SRF), Multiple Reference Frames (MRF), and the Sliding Meshing Interface (SMI) is analyzed over Tip Speed Ratios (TSR) of 3, 6, 10. The quantification of aerodynamic coefficients of the blade ($C_l$, $C_d$) and turbine ($C_p$, $C_t$) was conducted along with the discussion on wake patterns in comparison with experimental data.

Aspects of the influence of an oscillating mini-flap upon the near wake of an airfoil NACA 4412

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.

The role of a pre-cooler is critical to the sCO₂-BC as it not only acts as a sink but also controls the conditions at the main compressor’s inlet that are vital to the cycle’s overall performance. Despite their prime importance, studies on the pre-cooler’s design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number ($Nu$) and friction factor ($f$). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle’s performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler’s operating regime. Cycle simulation results suggest that the cycle’s performance calculation can be misleading without considering the pre-cooler’s pumping power. Moreover, the optimization study indicates that the compressor’s inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel’s Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle’s efficiency and the pre-cooler’s size.

Vertical Axis Wind Turbines (VAWTs) are omni-directional, low-cost, low-efficiency wind power extractors. A conventional drag-based VAWT consists of multiple thin rotor blades with a typical peak Tip Speed Ratio (λ) of < 1. Their lower cut-in speed and maintenance cost make them ideal for power generation in urban environments. Numerous studies have been carried out analysing steady operation of VAWTs and quantifying their performance characteristics, however, minimal attention has been paid to their start-up dynamics. There are a few recent studies in which start-up dynamics of lift-based VAWTs have been analysed but such studies for drag-based VAWTs are severely limited. In this study, start-up dynamics of a conventional multi-blade drag-based VAWT have been numerically investigated using a time-dependant Computational Fluid Dynamics (CFD) solver. In order to enhance the start-up characteristics of the drag-based VAWT, a stator has been integrated in the design assembly. The numerical results obtained in this study indicate that an appropriately designed stator can significantly enhance the start-up of a VAWT by directing the flow towards the rotor blades, leading to higher rotational velocity (ω) and λ. With the addition of a stator, the flow fields downstream the VAWT becomes more uniform.

This work pertains to the transient modeling and comparative study of active solar thermal space and water heating systems using liquid and air-type solar thermal collectors as the main energy source. The study utilizes TRNSYS to simulate the two systems in the context of Taxila's weather data (located at 33.74°N, 72.83°E), with the goal of meeting peak space and domestic water heating demands of 20 kW and 200 lit/day, respectively. The liquid water-based system (S-1) is primarily composed of a liquid solar collector, thermal storage, an auxiliary heater, connections to the hot water supply, and the space heating load through a water–air heat exchanger. In contrast, the air-based system (S-2), employs a pebble bed storage to store heat extracted from the solar thermal air collector. The heated air is subsequently used directly for space heating and passed through an air–water heat exchanger for water heating. Dynamic simulations of both systems span the entire winter season, and various performance metrics, including solar fraction, primary energy savings, and solar collector thermal efficiency, are computed. The results revealed that at the same collector area, the liquid water-based system (S-1) shows a higher solar fraction than the air-based systems (S-2) while the primary energy savings of the S-1 resulted in lower values than S-2 at smaller collector areas (< ∼30 m2) but surpasses the S-2 with increasing collector size. The optimal collector tilt for both systems is determined to be 50°, while specific storage volumes corresponding to maximum primary energy savings are estimated to be 100 and 40 L/m2 for S-1 and S-2, respectively.