Purpose: Early-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.Methods: ERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.Results: Random forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.Conclusions: The present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.

The present study aims to assess the effects of two different underlying RANS models on overall behavior of the IDDES methodology when applied to different flow configurations ranging from fully attached (plane channel flow) to separated flows (periodic hill flow). This includes investigating prediction accuracy of first and second order statistics, response to grid refinement, grey area dynamics and triggering mechanism. Further, several criteria have been investigated to assess reliability and quality of the methodology when operating in scale resolving mode. It turns out that irrespective of the near wall modeling strategy, the IDDES methodology does not satisfy all criteria required to make this methodology reliable when applied to various flow configurations at different Reynolds numbers with different grid resolutions. Further, it is found that using more advanced underlying RANS model to improve prediction accuracy of the near wall dynamics results in extension of the grey area, which may delay the transition to scale resolving mode. This systematic study for attached and separated flows suggests that the shortcomings of IDDES methodology mostly lie in inaccurate prediction of the dynamics inside the grey area and demands further investigation in this direction to make this methodology capable of dealing with different flow situations reliably.

Wind turbine blades experience excessive load due to inaccuracies in the prediction of aerodynamic loads by conventional methods during design, leading to structural failure. The blade element momentum (BEM) method is possibly the oldest and best-known design tool for evaluating the aerodynamic performance of wind turbine blades due to its simplicity and short processing time. As the turbine rotates, the aerofoil lift coefficient enhances, notably in the rotor’s inboard section, relative to the value predicted by 2D experimentation or computational fluid dynamics (CFD) for the identical angle of attack; this is induced by centrifugal pumping action and the Coriolis force, thus delaying the occurrence of stall. This rotational effect is regarded as having a significant influence on the rotor blade’s aerodynamic performance, which the BEM method does not capture, as it depends on 2D aerofoil characteristics. Correction models derived from the traditional hard computing mathematical method are used in the BEM predictions to take into account stall delay. Unfortunately, it has been observed from the earlier literature that these models either utterly fail or inaccurately predict the enhancement in lift coefficient due to stall delay. Consequently, this paper proposes a novel stall delay correction model based on the soft computing technique known as symbolic regression for high-level precise aerodynamic performance prediction by the BEM process. In complement to the correction model for the lift coefficient, a preliminary correction model for the drag coefficient is also suggested. The model is engendered from the disparity in 3D and 2D aerofoil coefficients over the blade length for different wind speeds for the NREL Phase VI turbine. The proposed model’s accuracy is evaluated by validating the 3D aerofoil coefficients computed from the experimental results of a second wind turbine known as the MEXICO rotor.

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 10⁶ were performed using the Discrete Phase Model (DPM) and k–$\omega$ SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m³. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an $R^2$ value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m³ could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation.

Heat transfer modeling plays a major role in design and optimization of modern and efficient thermal-fluid systems. Further, turbulent flows are thermodynamic processes, and thus, the second law of thermodynamics can be used for critical evaluations of such heat transfer models. However, currently available heat transfer models suffer from a fundamental shortcoming: their development is based on the general notion that accurate prediction of the flow field will guarantee an appropriate prediction of the thermal field, known as the . In this work, an assessment of the capability of the in predicting turbulent heat transfer when applied to shear flows of fluids of different Prandtl numbers will be given. Towards this, a detailed analysis of the predictive capabilities of the concerning entropy generation is presented for steady and unsteady state simulations. It turns out that the provides acceptable results only for mean entropy generation, while fails to predict entropy generation at small/sub-grid scales.

This study investigates the transport and deposition of small particles (500 nm≤dp≤10 μm) in a fully developed turbulent channel flow, focusing on two fluid friction Reynolds numbers: Reτ=180 and Reτ=1000. Using the point particle–direct numerical simulation method under the assumption of one-way coupling, we study how fluid flow (carrier phase) characteristics influence particle deposition. Our findings suggest that changes in flow conditions can significantly alter the deposition behavior of particles with the same size and properties. Furthermore, we show for the first time that gravity has minimal impact on deposition dynamics only at high Reynolds numbers. This research enhances our understanding of small particle deposition and transport in turbulent flows at high Reynolds numbers, which is crucial for various industrial applications.