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The accurate quantification of concentration fluctuations is crucial when evaluating the exposure to toxic, infectious, reactive, flammable, or explosive substances, as well as for the estimation of odor nuisance. However, in the field of Computational Fluid Dynamics (CFD), the industry currently relies predominantly on steady-state RANS turbulence models for simulating near-field pollutant dispersion, which are only capable of producing the time-averaged concentration field. This paper presents a regression relationship for calculating the standard deviation of the local concentration based on the mean concentration and the downstream distance from a point source, over a city-like surface, in the case of the wind direction perpendicular to the streets. The desired peak values and other statistical characteristics can be predicted by assuming a gamma distribution which is fitted based on the average and standard deviation. To obtain the regression function, a deep neural network model was used. The model was trained using timeresolved concentration data obtained from wind tunnel experiments. The validation results show that the concentration fluctuations predicted by the DNN-based model are in satisfactory agreement with the measurement data in terms of the skewness, the kurtosis, the median, and the peak concentrations. Furthermore, the present paper suggests a workflow for estimating the concentration fluctuations based on RANS CFD results, as well as recommendations for generating further training data for specific applications. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning has significant implications for the advancement of computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in predicting boundary layer flows and poor adaptability to geometric shapes. Although graph neural network models for unstructured grid-based unsteady flow prediction have better geometric adaptability, these models suffer from error accumulation in long-term predictions of unsteady flows. More importantly, fully data-driven models often require extensive training time, greatly limiting the rapid update and iteration speed of deep learning models when facing more complex unsteady flows. Therefore, this paper aims to balance the demands for training overhead and prediction accuracy by integrating physical constraints based on the finite volume method into the loss function of the graph neural network. Additionally, it incorporates a twice-message aggregation mechanism inspired by the extended stencil method to enhance the unsteady flow prediction accuracy and geometric shape generalization ability of the graph neural network model on unstructured grids. We focus particularly on the model's predictive accuracy within the boundary layer. Compared to fully data-driven methods, our model achieves better predictive accuracy and geometric shape generalization ability in a shorter training time. [ABSTRACT FROM AUTHOR]

Airwakes are shed behind a ship's superstructure, and they represent a highly turbulent and rapidly distorting flow field. Because of this complexity, they require a baseline: A database of flow velocity points, preferably through experiments and also through high-fidelity computational fluid dynamics simulations. However the database is prohibitively costly, and in situ measurements, at a constant wind angle, are often affected by the weather. A previously developed perturbation scheme helps to generate the autospectrum in closed form from a given wind database, specifically, from the numerically extracted autospectrum for this database. The primary objective of this paper is to demonstrate how deep neural networks can be fruitfully exploited to reduce the dependence on a database of flow velocity points in generating the autospectra, by reducing the amount of in situ data required to achieve the same accuracy as established methods. Two machine learning approaches are developed to predict the autospectrum for a data set when provided with a subset of the wind data set at the input and are assessed using cross-validation. In the first, a neural network is applied to predict the perturbation scheme. Though this method did not predict the autospectrum better than the previously developed perturbation scheme, it laid the foundation for the second method. In the second, a neural network is applied to the autospectra which can be numerically extracted from a database. An improvement is found in the average error computed over all data sets for the model-predicted autospectrum compared to the average error in the autospectrum predicted using the conventional method. The model provided better results for 51.4% of the data sets compared to the conventional method, with many data sets showing marked improvement. The database measured from the Naval Academy YP676 training vessel is used for training and testing the neural network. [ABSTRACT FROM AUTHOR]

Understanding the aerodynamic hysteresis loop phenomenon is essential when assessing aerodynamic performance and designing aircraft control systems. This phenomenon is a result of time delay effects in unsteady flow. Traditional methods of predicting unsteady aerodynamic forces using computational fluid dynamics have drawbacks, such as long cycles and low efficiency. In this paper, we focus on predicting the aerodynamic hysteresis loop of the NACA (National Advisory Committee for Aeronautics) 0012 airfoil in transonic flow using a new model called LIDNN (Latin hypercube sample input deep neural network). This model integrates input signals and optimization methods to improve upon traditional neural network models. Based on the example validation, the LIDNN model is authenticated as an accurate and efficient method in predicting the unsteady aerodynamic hysteresis loop of the NACA 0012 airfoil in transonic flow, and another significant advantage of the proposed model is its ability to solve multivariable problems effectively, even under varying Mach numbers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD). • Innovative Fusion: Introduces a pioneering Computational Fluid Dynamics-Artificial Neural Network (CFD-ANN) approach for optimizing plunger valves, achieving a unique synergy between hydraulic flow and geometric configuration. • Optimized ANN Architecture: Develops a refined ANN architecture with an impressive R2 value of 0.987, demonstrating accuracy in predicting plunger valve performance. Key parameters are modified, leading to an 88% reduction in time expenditure compared to traditional CFD studies. • Geometric Parameter Impact: Analyzes the effects of valve slot number, diameter, and length on plunger valve performance, providing valuable insights into the interplay between geometric factors and hydraulic flow. • Efficient Optimization Process: Validates the enhanced CFD method through experimental work, ensuring a minimal 8.9% error. The study showcases cost-efficiency and superior performance in optimizing plunger valves through the ANN approach. [ABSTRACT FROM AUTHOR]

As the "respiratory tract" of the air breathing engine, the hypersonic inlet plays the role of compressing, decelerating, and pressurizing flow. However, research on the traditional inlet design is mainly based on gas dynamics theory and experience, which not only leads to a long design period but also fails to ensure the performance of the inlet at non-design points under broad working conditions. In recent years, with the further development of flow physics, a variety of advanced inlet design methods have been proposed, and a large number of valuable data have been accumulated by applying high-fidelity computational fluid dynamics numerical simulation and ground wind tunnel tests. A new generation of machine learning technologies, typically represented by deep learning, is developing vigorously. By building a deep neural network structure and using data-driven methods to carry out model training, it can realize typical feature extraction automatically, efficiently, and accurately, which is helpful to deeply explore the hidden flow mechanism between data and establish a fast prediction model of inlet performance. The optimal inlet design scheme can be obtained by applying the performance intelligent prediction model and the multi-objective intelligent evolution algorithm. This paper provides a detailed overview of the latest progress in inlet design by applying traditional ideas, expounds the basic principles and typical applications of some representative and prospective machine learning methods, and especially reports the current research status of the combination of machine learning and inlet. Finally, the future development trends and potential applications of several research directions are summarized. [ABSTRACT FROM AUTHOR]