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Obtaining reliable flow data is essential for the fluid mechanics analysis and control, and various measurement techniques have been proposed to achieve this goal. However, imperfect data can occur in experimental scenarios, particularly in the particle image velocimetry technique, resulting in insufficient flow data for accurate analysis. To address this issue, a novel machine learning-based multi-scale autoencoder (MS-AE) framework is proposed to reconstruct missing flow fields from imperfect turbulent flows. The framework includes two missing flow reconstruction strategies: complementary flow reconstruction and non-complementary flow reconstruction. The former requires two independent measurements of complementary paired flow fields, posing challenges for real-world implementation, whereas the latter requires only a single measurement, offering greater flexibility. A benchmark case study of channel flow with ordinary missing configuration is used to assess the performance of the MS-AE framework. The results demonstrate that the MS-AE framework outperforms the traditional fused proper orthogonal decomposition method in reconstructing missing turbulent flow, irrespective of the availability of complementary paired faulty flow fields. Furthermore, the robustness of the proposed MS-AE approach is assessed by exploring its sensitivity to various factors, such as latent size, overlap proportion, reconstruction efficiency, and suitability for multiscale turbulent flow structures. The new method has the potential to contribute to more effective flow control in the future, thanks to its characteristic that eliminates the requirement for complementary flow fields. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of wind turbine wake characteristics is vital, particularly in the context of expanding large offshore wind farms. Existing wake measurement techniques provide only spatially sparse wake measurement data, limiting their utility in precise wind turbine design and control. This paper introduces a data-driven approach that combines proper orthogonal decomposition (POD) with machine learning (ML) techniques, designing a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. This framework establishes a nonlinear mapping between sensor measurements and low-dimensional POD coefficients. Two distinct sensor placements, wall-mounted and wake-mounted, are investigated for real measurement scenarios. The results highlight the effectiveness of the proposed wake flow estimation method in reconstructing a complete flow field from exceptionally sparse sensor data, with both wall-mounted and wake-mounted strategies, exhibiting promising results with maximum relative errors of 6.37% and 4.51%, respectively. From the reliability assessments considering various configurations of POD modes and sensor numbers, the ROM-WFE framework demonstrates its capability to estimate wake flow effectively, offering a cost-effective tool for practical applications. Furthermore, the framework maintains accuracy even with high-noise and low-frequency data, demonstrating robustness and generalization. This method significantly contributes to wind turbine wake prediction controller design, promising accurate and robust wake flow field estimation, potentially revolutionizing active wake control and enhancing wind farm operational efficiency. • Combining POD and ML techniques to establish a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. • Exploring the effectiveness of ROM-WFE in wall- and wake-mounted sensor placement strategies. • Utilizing fewer than three sensors enhances practicality for real-world applications and improves wind farm efficiency. • Demonstrating resilience and adaptability, maintaining accuracy even with high-noise, low-frequency data. [ABSTRACT FROM AUTHOR]

Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for ocean environments, reducing lift-induced drag and contributing to improved overall performance. To optimize hydrofoil design, a profound comprehension of the complex fluid flows around the hydrofoil structure is essential. In fluid mechanics, a precise and continuous representation of flow dynamics is essential for analysis and control purposes. Thus, obtaining a complete flow field, either through computational fluid dynamics (CFD) or experimental testing is of great significance. Nevertheless, the rigorous requirements of flow field tests render it impractical to directly measure complete flows around the airfoil using current instrumentation, especially for those with complex physical geometries. To tackle this issue, a novel deep learning framework is proposed to reconstruct the complete flow field by leveraging incomplete complementary flow data. As the representative benchmark problems, the flows around the Clark-Y hydrofoil at Re = 7 × 105 and the experimental NACA0012 2D hydrofoil at Re = 1800 have been investigated under different missing-flow scenarios of varying proportions, locations and orientations. Results demonstrate a remarkable agreement between the reconstructed flow field and the ground truth data, indicating the excellent performance of the proposed deep-learning model for missing flow reconstruction. A sensitivity analysis assesses the impact of the snapshot number and the latent space, revealing the method's robustness in selecting these parameters and simplifying its implementation in practical applications. This deep learning method offers the advantage of being implemented using paired incomplete flow fields, without the need for pre-known ground truth results as labels, holding the potential for addressing more complex full-field reconstruction problems in the future. [Display omitted] • Develop novel MS-AE framework for missing flow field reconstruction, surpassing fused POD and AE methods. • Conduct sensitivity analysis of AE-SE method to assess crucial parameters, ensuring reliability. • Apply unsupervised machine learning and multi-scale approach for innovative fluid mechanics reconstruction. [ABSTRACT FROM AUTHOR]

Aims: Temporal lobe epilepsy (TLE) is the most common focal epilepsy syndrome in adults and frequently develops drug resistance. Studies have investigated the value of peripheral DNA methylation signature as molecular biomarker for diagnosis or prognosis. We aimed to explore methylation biomarkers for TLE diagnosis and pharmacoresistance prediction. Methods: We initially conducted genome‐wide DNA methylation profiling in TLE patients, and then selected candidate CpGs in training cohort and validated in another independent cohort by employing machine learning algorithms. Furthermore, nomogram comprising DNA methylation and clinicopathological data was generated to predict the drug response in the entire patient cohort. Lastly, bioinformatics analysis for CpG‐associated genes was performed using Ingenuity Pathway Analysis. Results: After screening and validation, eight CpGs were identified for diagnostic biomarker with an area under the curve (AUC) of 0.81 and six CpGs for drug‐resistant prediction biomarker with an AUC of 0.79. The nomogram for drug‐resistant prediction comprised methylation risk score, disease course, seizure frequency, and hippocampal sclerosis, with AUC as high as 0.96. Bioinformatics analysis indicated drug response–related CpGs corresponding genes closely related to DNA methylation. Conclusions: This study demonstrates the ability to use peripheral DNA methylation signature as molecular biomarker for epilepsy diagnosis and drug‐resistant prediction. [ABSTRACT FROM AUTHOR]

The existing decentralized wind farm control incorporating the wind turbine wake interaction has almost all concentrated on the condition of fixed wind speed/direction without considering the variability of wind in nature. Meanwhile, most of the current wake models cannot meet the requirement for wind turbine control optimization research with the high demand of wake calculation speed and accuracy. In this paper, a novel decentralized control strategy, named the wind-interval-based (WIB) control which adopts the uniform operation among the same interval of variable wind speeds/directions, is proposed for the control optimization study of in-line two scaling wind turbines for demonstration. To prove the effectiveness of the new control mechanism, the ideal instantaneous control is introduced for the comparative study. At the same time, a wake model incorporating the two directly controllable parameters (i.e., the tip speed ratio λ and pitch angle γ) is established based on artificial neural network (ANN). The comparative results show that the total power output applying the instantaneous control and the new WIB control mechanisms are increased by 0.1%–4.1% and 0.45%–3.9% depending on the tested wind scenarios, respectively. By comparing the optimized power output with the two controls, it is found that the error between them is generally lower than 3%, while the proposed WIB control reduces the operational difficulty to a large extent facilitating its application to the real wind turbine operation in reality. In summary, this paper shows that the new proposed WIB control not only reduces the difficulties of the wind turbine control mechanism, but maintains the control effectiveness by achieving a comparable total power output with respect to the traditional instantaneous control, which is of great significance to the wind farm developer. [ABSTRACT FROM AUTHOR]