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1. Wind Farm Prediction of Icing Based on SCADA Data

   https://doi.org/10.3390/en17184629  

   Zhang, Yujie; Rotea, Mario; Kehtarnavaz, Nasser (September 2024, Energies)

   In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine. 

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2. Entropic Pressure Between Fluctuating Membranes With Surface Tension

   https://doi.org/10.1115/1.4068468  

   Hassan, Rubayet; Farokhirad, Samaneh; Ahmadpoor, Fatemeh (September 2025, Journal of Applied Mechanics)

   Entropic pressure, a longstanding topic of interest in biophysics and biomechanics, has been studied for over four decades. Similar to an ideal gas, fluctuating surfaces can generate entropic pressure through thermally driven motions. These thermal fluctuations impact a wide range of biological activities, including but not limited to vesicle fusion, cell adhesion, exocytocis, and endocytocis among many others. It has been proposed (and validated) by many researchers that the entropic pressure near a fluctuating confined fluid membrane without surface tension scales as p∝1/d3, where d is the confining distance, and this power law is size independent. In this article, we show that entropic pressure near a fluctuating fluid membrane could be strongly affected by the membrane’s size and surface tension. We show that while for membranes of size L=1μm and larger, the pressure is size independent, for smaller membranes, the pressure does indeed depend on the membrane’s size. Our findings also shows that the surface tension changes this scaling law and at larger distance makes the pressure decay exponentially. Our work provides insights into how surface tension enhances biological vesicles fusion by suppressing membrane fluctuations, and consequently, the repulsive entropic force, and impacts biomembranes interactions with external objects at the early stage of approaching. 

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3. Microfluidic investigation of the impacts of flow fluctuations on the development of Pseudomonas putida biofilms

   https://doi.org/10.1038/s41522-023-00442-z  

   Wei, Guanju; Yang, Judy Q. (October 2023, npj Biofilms and Microbiomes)

   Abstract Biofilms play critical roles in wastewater treatment, bioremediation, and medical-device-related infections. Understanding the dynamics of biofilm formation and growth is essential for controlling and exploiting their properties. However, the majority of current studies have focused on the impact of steady flows on biofilm growth, while flow fluctuations are common in natural and engineered systems such as water pipes and blood vessels. Here, we reveal the effects of flow fluctuations on the development ofPseudomonas putidabiofilms through systematic microfluidic experiments and the development of a theoretical model. Our experimental results showed that biofilm growth under fluctuating flow conditions followed three phases: lag, exponential, and fluctuation phases. In contrast, biofilm growth under steady-flow conditions followed four phases: lag, exponential, stationary, and decline phases. Furthermore, we demonstrated that low-frequency flow fluctuations promoted biofilm growth, while high-frequency fluctuations inhibited its development. We attributed the contradictory impacts of flow fluctuations on biofilm growth to the adjustment time (T₀) needed for biofilm to grow after the shear stress changed from high to low. Furthermore, we developed a theoretical model that explains the observed biofilm growth under fluctuating flow conditions. Our insights into the mechanisms underlying biofilm development under fluctuating flows can inform the design of strategies to control biofilm formation in diverse natural and engineered systems. 

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4. Magnetoconvection in a horizontal duct flow at very high Hartmann and Grashof numbers

   https://doi.org/10.1017/jfm.2021.987  

   Akhmedagaev, R.; Zikanov, O.; Listratov, Y. (January 2022, Journal of Fluid Mechanics)

   Direct numerical simulations and linear stability analysis are carried out to study mixed convection in a horizontal duct with constant-rate heating applied at the bottom and an imposed transverse horizontal magnetic field. A two-dimensional approximation corresponding to the asymptotic limit of a very strong magnetic field effect is validated and applied, together with full three-dimensional analysis, to investigate the flow's behaviour in the previously unexplored range of control parameters corresponding to typical conditions of a liquid metal blanket of a nuclear fusion reactor (Hartmann numbers up to $10^4$ and Grashof numbers up to $$10^{10}$$ ). It is found that the instability to quasi-two-dimensional rolls parallel to the magnetic field discovered at smaller Hartmann and Grashof numbers in earlier studies also occurs in this parameter range. Transport of the rolls by the mean flow leads to magnetoconvective temperature fluctuations of exceptionally high amplitudes. It is also demonstrated that quasi-two-dimensional structure of flows at very high Hartmann numbers does not guarantee accuracy of the classical two-dimensional approximation. The accuracy deteriorates at the highest Grashof numbers considered in the study. 

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5. Surface Pressure Prediction for Bio-Inspired Unidirectional Canopies in Wall-jet Boundary Layers

   N. Hari, M. Szoke (August 2021, AIAA Aviation 2021)

   null (Ed.)

   An analytical approach has been developed to model the rapid term contribution to the unsteady surface pressure fluctuations in wall jet turbulent boundary layer flows. The formulation is based on solving Poisson’s equation for the turbulent wall pressure by integrating the source terms (Kraichnan, 1956). The inputs for the model are obtained from 2D time-resolved Particle Image Velocimetry measurements performed in a wall jet flow. The wall normal turbulence wavenumber two-point cross-spectra is determined using an extension of the von Kármán homogeneous turbulence spectrum. The model is applied to compare and understand the baseline flow in the wall jet and to study the attenuation in surface pressure fluctuations by unidirectional canopies (Gonzales et al, 2019). Different lengthscale formulations are tested and we observe that the wall jet flow boundary layer contributes to the surface pressure fluctuations from two distinct regions. The high frequency spectrum is captured well. However, the low frequency range of the spectrum is not entirely captured. This is because we have used PIV data only up to a height of 2.3𝜹, whereas the largest turbulent lengthscales in the wall jet are on the order of 𝒚𝟏/𝟐≈𝟔𝜹. Using the flow data obtained from PIV and Pitot probe measurements, the model predicts a reduction in the surface pressure due to canopy at low frequencies. 

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