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This paper implements a spatiotemporal neural network architecture based on the U-Net prototype with four branches, UBranch, to perform both spatial reconstruction and temporal forecasting of flow fields. A high-speed turbulent flow featuring shock-wave turbulent boundary layer interaction is utilized to demonstrate the forecasting in two-dimensional flow frames. The main elements of UBranch consist of convolutional neural networks, which are fast and lightweight for such functions, in a form that bypasses the use of complex and time-consuming long-short-term memory networks. The proposed model can provide the following four future time frames when fed with a sequence of two-dimensional flow images with reasonable accuracy and low root mean square error, and, in parallel, it can indicate the maximum pressure points, which is of primary importance for shock-wave turbulent boundary layer interaction. Apart from the temporal operation, UBranch can also perform spatial super-resolution tasks, reconstructing a low-resolution image to a finer field with increased accuracy. Calculated peak signal-to-noise ratios reach 29.0 for spatiotemporal and 35.0 for spatial-only tasks. [ABSTRACT FROM AUTHOR]

This paper examines the temperature distribution in a closed, rectangular room equipped with an air conditioning system, employing a computational fluid dynamics model to simulate a virtual thermal camera that captures detailed temperature snapshots. A super-resolution framework enhances the postprocessing of these results. Specifically, convolutional neural networks, trained on simulation data, are used to accurately model temperature fields' high-resolution spatial and temporal evolution. The model demonstrates strong performance by accurately reconstructing temperature profiles from low-resolution inputs obtained from filtering data obtained using high-resolution numerical simulations, with quantitative metrics indicating acceptable accuracy for resolutions reduced by up to 50 times. This effectively aligns with ground truth profiles under various conditions. These results underscore the super-resolution model's potential to transform environmental monitoring in smart buildings and complex structures by generating high-resolution thermal maps from low-resolution cameras or limited sensor input. This approach offers a fast, cost-effective, and reliable method for accurately modeling thermal dynamics within the turbulent flow environments of interior spaces. [ABSTRACT FROM AUTHOR]

This study presents a novel methodology for optimizing probe placement in indoor air-conditioned environments by integrating computational fluid dynamics simulations with artificial intelligence techniques in an unsupervised learning framework. The "Reduce Uncertainty and Increase Confidence" algorithm identified spatially distinct thermal and velocity clusters based on temperature and velocity magnitude distributions. Optimization of probe positions within these clusters, guided by sequential least squares programing, resulted in an effective strategy to minimize probe redundancy while maximizing spatial coverage. The methodology highlights the interplay between temperature, relative humidity, velocity, and turbulence intensity, revealing critical insights into airflow behavior and its implications for occupant comfort. The findings of the presented study underscore the potential for targeted probe placement to provide a robust framework for advanced indoor climate control. [ABSTRACT FROM AUTHOR]

Long-sequence time-series forecasting requires deep learning models with high predictive capacity to capture long-range dependencies between inputs and outputs effectively. This study presents a methodology for forecasting pressure time series in shock-wave, turbulent boundary layer interaction flows. Pressure signals were extracted below the λ-shock foot for six deformed rigid panel surface cases, where the low-frequency unsteadiness of the shock–boundary layer interaction is most prominent. The Informer model demonstrated superior performance in accurately predicting the pressure signals. Comparative numerical experiments revealed that the Informer model generally outperformed the Transformer, as indicated by lower root mean square errors and a more accurate power spectrum. The Informer effectively resolved the low-frequency unsteadiness of the λ-shock foot and better matched the ground truth's low- to mid-frequency power content. The forecasted pressure signals accuracy remained robust across all six rigid surface deformation cases, though subtle yet noticeable discrepancies still manifested. The accuracy of the Informer forecasted pressure time series was heavily dependent on the forecasting time step size. A step size of four provided a closer match to the ground truth in a deterministic manner, while a step size of eight achieved a better agreement in a stochastic sense. Larger time step sizes resulted in a gradual decline in accuracy. [ABSTRACT FROM AUTHOR]

The effect of hyperparameter selection in deep learning (DL) models for fluid dynamics remains an open question in the current scientific literature. Many authors report results using deep learning models. However, better insight is required to assess deep learning models' behavior, particularly for complex datasets such as turbulent signals. This study presents a meticulous investigation of the long short-term memory (LSTM) hyperparameters, focusing specifically on applications involving predicting signals in shock turbulent boundary layer interaction. Unlike conventional methodologies that utilize automated optimization techniques, this research explores the intricacies and impact of manual adjustments to the deep learning model. The investigation includes the number of layers, neurons per layer, learning rate, dropout rate, and batch size to investigate their impact on the model's predictive accuracy and computational efficiency. The paper details the iterative tuning process through a series of experimental setups, highlighting how each parameter adjustment contributes to a deeper understanding of complex, time-series data. The findings emphasize the effectiveness of precise manual tuning in achieving superior model performance, providing valuable insights to researchers and practitioners who seek to leverage long short-term memory networks for intricate temporal data analysis. The optimization not only refines the predictability of the long short-term memory in specific contexts but also serves as a guide for similar manual tuning in other specialized domains, thereby informing the development of more effective deep learning models. [ABSTRACT FROM AUTHOR]

The document is a correction notice for a scientific article titled "Darcy–Brinkman Model for Ternary Dusty Nanofluid Flow across Stretching/Shrinking Surface with Suction/Injection." It includes corrections to figures, text, equations, and nomenclature used in the original publication. The corrections aim to clarify and improve the accuracy of the scientific content presented in the article. The corrections were approved by the Academic Editor, and the original publication has been updated accordingly. [Extracted from the article]

Understanding the dispersion of particles in enclosed spaces is crucial for controlling the spread of infectious diseases. This study introduces an innovative approach that combines an unsupervised learning algorithm with a Gaussian mixture model to analyze the behavior of saliva droplets emitted from a coughing individual. The algorithm effectively clusters data, while the Gaussian mixture model captures the distribution of these clusters, revealing underlying sub-populations and variations in particle dispersion. Using computational fluid dynamics simulation data, this integrated method offers a robust, data-driven perspective on particle dynamics, unveiling intricate patterns and probabilistic distributions previously unattainable. The combined approach significantly enhances the accuracy and interpretability of predictions, providing valuable insights for public health strategies to prevent virus transmission in indoor environments. The practical implications of this study are profound, as it demonstrates the potential of advanced unsupervised learning techniques in addressing complex biomedical and engineering challenges and underscores the importance of coupling sophisticated algorithms with statistical models for comprehensive data analysis. The potential impact of these findings on public health strategies is significant, highlighting the relevance of this research to real-world applications. [ABSTRACT FROM AUTHOR]

Developing deep learning models for predicting environmental data is a powerful tool that can significantly enhance equipment design, optimize the implementation of engineering systems, and deepen our understanding of the limitations imposed by flow physics. This study unequivocally demonstrates the accuracy of forecasting models based on popular deep learning algorithms, such as the long-short-term memory model, in turbulent mixing regions associated with flow physics arising from ventilation. This accuracy is contingent on two essential conditions. First, the sparsity of the sampling data is consistent with the model's accuracy overall. Second, the data sparsity ensures reasonable accuracy in the turbulent mixing regions. The investigation combines high-resolution flow simulation data with deep learning predictions of velocity, temperature, and relative humidity in a ventilated confined space. The results of this study, with their high accuracy, not only help to understand the mixing arising from flow circulation but also pave the way for developing predictive capabilities for environmental data. [ABSTRACT FROM AUTHOR]

This paper concerns the interaction of an impinging shock wave with a supersonic turbulent boundary layer over several distinct and permanently deformed surfaces, resulting in differences in the shock-boundary-layer interaction and the surface acoustic loading. High-order numerical simulations featuring two-dimensional surface deformations typically encountered in experiments are performed. The deformation amplitudes are up to half the incoming turbulent boundary-layer thickness. The results show that the high-pressure region about the shock impingement is significantly altered and can become narrower or wider depending on the local surface inclination of the deformed panel mode. The surface curvature is found to not significantly affect the separation and reattachment locations of the recirculation bubble. The power spectrum analysis of the pressure fluctuations along the panel's midspan, where the surface attains the largest deformation amplitude, exhibits a rich and varied response. The pressure power spectrum is amplified in all of the surface deformation modes examined, with the magnitude of the amplification varying in the frequency domain, depending on the location and mode. [ABSTRACT FROM AUTHOR]

A deep learning super-resolution scheme is proposed to reconstruct a coarse, turbulent temperature field into a detailed, continuous field. The fluid mechanics application here refers to an airflow ventilation process in an indoor setting. Large eddy simulations are performed from a dense simulation grid and provide temperature data in two-dimensional images. The images are fed to a deep learning flow reconstruction model after being scaled down to 100 times. Training and testing are performed on these images, and the model learns to map such highly coarse fields to their high-resolution counterparts. This computational, super-resolution approach mimics the process of employing sparse sensor measurements and trying to upscale to a dense field. Notably, the model achieves high performance when the input images are scaled down by 5 – 20 times their original dimension, acceptable performance when 30, and poor performance at higher scales. The peak signal-to-noise ratio, the structure similarity index, and the relative error between the original and the reconstructed output are given and compared to common image processing techniques, such as linear and bicubic interpolation. The proposed super-resolution pipeline suggests a high-performance platform that calculates spatial temperature values from sparse measurements and can bypass the installation of a wide sensor array, making it a cost-effective solution for relevant applications. [ABSTRACT FROM AUTHOR]

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability. To this end, this work proposes a novel short-term knowledge-based method for RA of disease transmission based on fuzzy rules. These rules are constructed using knowledge elicited from domain experts. In contrast to previous approaches, the proposed method considers data captured by several sensors and the ship information system, according to a recently proposed smart ship design. Evaluation with agent-based simulations confirms the effectiveness of the proposed method across various cases. [ABSTRACT FROM AUTHOR]

Understanding of dusty fluids for different Brinkman numbers in porous media is limited. This study examines the Darcy–Brinkman model for two-dimensional magneto-hydrodynamic fluid flow across permeable stretching/shrinking surfaces with heat transfer. Water was considered as a conventional base fluid in which the copper (Cu), silver (Ag), and titanium dioxide ( T i O 2 ) nanoparticles were submerged in a preparation of a ternary dusty nanofluid. The governing nonlinear partial differential equations are converted to ordinary differential equations through suitable similarity conversions. Under radiation and mass transpiration, analytical solutions for stretching sheets/shrinking sheets are obtained. Several parameters are investigated, including the magnetic field, Darcy–Brinkman model, solution domain, and inverse Darcy number. The outcomes of the present article reveal that increasing the Brinkman number and inverse Darcy number decreases the velocity of the fluid and dusty phase. Increasing the magnetic field decreases the momentum of the boundary layer. Ternary dusty nanofluids have significantly improved the heat transmission process for manufacturing with applications in engineering, and biological and physical sciences. The findings of this study demonstrate that the ternary nanofluid phase's heat and mass transpiration performance is better than the dusty phase's performance. [ABSTRACT FROM AUTHOR]

Upscaling flow features from coarse-grained data is paramount for extensively utilizing computational physics methods across complex flow, acoustics, and aeroelastic environments where direct numerical simulations are computationally expensive. This study presents a deep learning flow image model for upscaling turbulent flow images from coarse-grained simulation data of supersonic shock wave–turbulent boundary layer interaction. It is shown for the first time that super-resolution can be achieved using only the coarsest-grained data as long as the deep learning training is performed using hundreds of fine-grained data. The unsteady pressure data are used in training due to their importance in aeroelasticity and acoustic fatigue occurring on aerospace structures. The effect on the number of images and their resolution features used in training, validation, and prediction is investigated regarding the model accuracy obtained. It is shown that the deep learning super-resolution model provides accurate spectra results, thus confirming the approach's effectiveness. [ABSTRACT FROM AUTHOR]

This paper concerns the application of a long short-term memory model (LSTM) for high-resolution reconstruction of turbulent pressure fluctuation signals from sparse (reduced) data. The model's training was performed using data from high-resolution computational fluid dynamics (CFD) simulations of high-speed turbulent boundary layers over a flat panel. During the preprocessing stage, we employed cubic spline functions to increase the fidelity of the sparse signals and subsequently fed them to the LSTM model for a precise reconstruction. We evaluated our reconstruction method with the root mean squared error (RMSE) metric and via inspection of power spectrum plots. Our study reveals that the model achieved a precise high-resolution reconstruction of the training signal and could be transferred to new unseen signals of a similar nature with extremely high success. The numerical simulations show promising results for complex turbulent signals, which may be experimentally or computationally produced. [ABSTRACT FROM AUTHOR]