Simulated Flow Data Research Articles

Modeling the flow of Oued Cherraa Basin (northeastern Morocco) using the SWAT model and evaluating model performance

River flow modeling is essential for water resource management, particularly in poorly gauged basins such as the Oued Cherraa in eastern Morocco. In these areas, modeling serves as an alternative to direct monitoring, helping to understand hydrological responses to water challenges such as floods, droughts and irrigation needs. The study used the SWAT model (ArcSWAT 2012 version) to simulate river flow in the Oued Cherraa basin. A sensitivity analysis, calibration and validation process were carried out using the sequential uncertainty adjustment method (SUFI-2), which optimized model parameters and ensured accurate reproduction of flows. The model was refined to better capture the basin’s complex hydrological processes, providing critical data for water management and planning. Key parameters analyzed include the deep aquifer percolation fraction (RCHRG_DP), the calibration coefficient for normal flow, and the curve number (CN2). The results showed a strong correlation between observed and simulated flow data. The mean monthly flow at Tazarhine (on Oued Zegzel) was 0.99 m³/s, with a simulated value of 1.076 m³/s during calibration and validation.

Self-supervised learning for effective denoising of flow fields

In this study, we proposed an efficient approach based on a deep learning (DL) denoising autoencoder (DAE) model for denoising noisy flow fields. The DAE operates on a self-learning principle and does not require clean data as training labels. Furthermore, investigations into the denoising mechanism of the DAE revealed that its bottleneck structure with a compact latent space enhances denoising efficacy. Meanwhile, we also developed a deep multiscale DAE for denoising turbulent flow fields. Furthermore, we used conventional noise filters to denoise the flow fields and performed a comparative analysis with the results from the DL method. The effectiveness of the proposed DL models was evaluated using direct numerical simulation data of laminar flow around a square cylinder and turbulent channel flow data at various Reynolds numbers. For every case, synthetic noise was augmented in the data. A separate experiment used particle-image velocimetry data of laminar flow around a square cylinder containing real noise to test DAE denoising performance. Instantaneous contours and flow statistical results were used to verify the alignment between the denoised data and ground truth. The findings confirmed that the proposed method could effectively denoise noisy flow data, including turbulent flow scenarios. Furthermore, the proposed method exhibited excellent generalization, efficiently denoising noise with various types and intensities.

Rapid prediction of transient particle transport under periodic ventilation using a non-uniform state Markov chain model

Predicting transient particle transport is crucial to address the risks posed to human health by indoor contaminants and to improve the design and control of ventilation systems. The Markov chain model with a coarse matrix has proven to be efficient in this regard, demonstrating faster speeds than traditional methods and higher computational accuracy when based on non-uniform states. However, its application under unsteady airflow conditions is limited, and discussions of how to determine non-uniform Markov states and their corresponding transient transition probabilities when the flow field changes are limited. Therefore, this study developed an application method of Markov chain model based on fixed non-uniform states in unsteady airflow fields. First, this model rapidly provides various Markov state schemes based on airflow field velocity and clustering methods. It then selects the optimal scheme through frequency and finally obtains the transition matrix through Lagrangian tracking for particle transport prediction. This model reduces the computational cost of transient particle transport when the wind speed changes while ensuring high computational accuracy. The proposed method was validated using published flow field experiments and simulation data. The computational speed of the proposed method was compared with computational fluid dynamics (CFD), and in the case studies considered in this research, the normalized root-mean-square deviation of the model was less than 10% and the total computation time was more than 75% faster than that of CFD.

Sparsity and mixing effects in deep learning predictions of temperature and humidity

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.

Radar Reconnaissance Pulse-Splitting Modeling and Detection Method

When radar receivers adopt digital channelization, it is prone to generating a cross-channel split signal, the rabbit ear effect, and a transition band repeated signal, leading to errors in radar signal sorting or identification. The pulse-splitting model and detection method proposed in this paper can model split pulses and identify them in radar pulse streams, facilitating the merging of split pulses to enhance sorting and identification performance. Firstly, the mechanism of splitting pulse generation is deeply analyzed, and the splitting site theory is proposed. Then, the split pulse signal model and the split pulse number statistical model based on geometric distribution are constructed, which are used to guide the construction of simulation data of split pulse flow with different characteristics. Furthermore, a time-domain convergence degree (TCD) index is proposed to characterize the pulse split phenomenon. At the same time, in order to avoid a large number of threshold searching problems in pulse-splitting detection, an empirical formula for the pulse-splitting detection threshold based on the TCD is given to quickly determine whether there is a pulse train split problem. The selected measured radar pulse stream is verified to follow a geometric distribution at a significance level of 0.05. The proposed method achieved a detection accuracy of at least 99.55% on the simulation dataset and at least 95.68% on the experimental dataset, validating the rationality of pulse-splitting modeling and the effectiveness of the detection method.

Hydrodynamics of polydisperse gas-solid flows: Kinetic theory and multifluid simulation

Polydisperse gas-solid flows, which is notoriously difficult to model due to the complex gas-particle and particle-particle interactions, are widely encountered in industry. In this article, a refined kinetic theory for polydisperse flow is developed, which features single-parameter Chapman-Enskog expansion (the Knudsen number) and exact calculation of the integrations related to pair distribution function of particle velocity without any mathematical approximations. The Navier-Stokes order constitutive relations for multifluid modeling of polydisperse gas-solid flow are then obtained analytically, including the solid stress tensor, the solid-solid drag force, the granular heat flux and the energy dissipation rate. Finally, the model is preliminarily validated by comparing to the discrete element simulation data of one-dimensional granular shear flow and by showing that the hydrodynamic characteristics of gas-solid flows in a bubbling fluidized bed containing bidisperse particles can be successfully predicted.

Simulation of heavy particles fluidized with non-Newtonian fluids

Aqueous solutions of very fine particles behave as non-Newtonian fluids and are used to fluidize heavy spherical particles creating large bed expansions and inhomogeneities similar to those observed in traditional fluidization of particles with Newtonian fluids. This study attempts to extend the validity of traditional computational fluid dynamics (CFD) models that are routinely used for gas-solids fluidization to non-Newtonian fluids. The modified model incorporates a shear-thinning power law viscosity for the non-Newtonian fluid and a recently published drag correlation derived from direct numerical simulation data of flow around randomly positioned spheres. This validation study compares numerical results obtained in 3D fluidized beds with two independent experimental bed porosity data sets for heavy steel and glass spherical particles. The current CFD results show a better agreement with experimental data obtained over a wide range of porosity and Reynolds number than a previously published semi-empirical correlation.

Vorticity cascade and turbulent drag in wall-bounded flows: plane Poiseuille flow

Drag for wall-bounded flows is directly related to the spatial flux of spanwise vorticity outward from the wall. In turbulent flows a key contribution to this wall-normal flux arises from nonlinear advection and stretching of vorticity, interpretable as a cascade. We study this process using numerical simulation data of turbulent channel flow at friction Reynolds number $Re_\tau =1000$ . The net transfer from the wall of spanwise vorticity created by downstream pressure drop is due to two large opposing fluxes, one which is ‘down-gradient’ or outward from the wall, where most vorticity concentrates, and the other which is ‘up-gradient’ or toward the wall and acting against strong viscous diffusion in the near-wall region. We present evidence that the up-gradient/down-gradient transport occurs by a mechanism of correlated inflow/outflow and spanwise vortex stretching/contraction that was proposed by Lighthill. This mechanism is essentially Lagrangian, but we explicate its relation to the Eulerian anti-symmetric vorticity flux tensor. As evidence for the mechanism, we study (i) statistical correlations of the wall-normal velocity and of wall-normal flux of spanwise vorticity, (ii) vorticity flux cospectra identifying eddies involved in nonlinear vorticity transport in the two opposing directions and (iii) visualizations of coherent vortex structures which contribute to the transport. The ‘D-type’ vortices contributing to down-gradient transport in the log layer are found to be attached, hairpin-type vortices. However, the ‘U-type’ vortices contributing to up-gradient transport are detached, wall-parallel, pancake-shaped vortices with strong spanwise vorticity, as expected by Lighthill's mechanism. We discuss modifications to the attached eddy model and implications for turbulent drag reduction.

Contribution of jet flow noise sources to the far-field sound power

Jet noise is the dominant source of noise during aircraft takeoff. This study uses direct numerical simulation (DNS) data of subsonic jet flow obtained from computational fluid dynamics (CFD) simulations to perform acoustic analyses. The non-negative contribution technique (NNC) is employed to identify aeroacoustic sources of jet noise with the greatest contribution to far-field sound power. The NNC technique uses the Lighthill source distribution with an acoustic impedance matrix constructed from radiation kernels of the free-field Green’s function. The fast multipole method (FMM) is integrated with the NNC technique to reduce computational cost arising from decomposition of the acoustic impedance matrix as well as from the high number of CFD grids associated with the DNS data. The results showed that the dominant aeroacoustic sources are associated with the first component of the Lighthill tensor (T11) and are concentrated along the jet centerline, downstream of the nozzle exit.

An explicit representation for mean profiles and fluxes in forced passive scalar convection

We derive explicit formulae for the mean profiles of passive scalars (either temperature or concentration of a diffusing substance), and their respective wall fluxes (either heat or mass fluxes), in forced turbulent convection, as a function of the Reynolds and Prandtl numbers. Direct numerical simulation data for turbulent flow within a smooth straight pipe of circular cross-section, at friction Reynolds number ${{Re}}_{\tau }=1140$ , in the range of Prandtl numbers from ${{Pr}}=0.00625$ to ${{Pr}}=16$ , are used to infer the proper analytical form of the eddy diffusivity. This is leveraged to derive accurate predictive formulae for the mean passive scalar profiles, and for the corresponding logarithmic offset function. Asymptotic scaling laws result for the thickness of the conductive (diffusive) layer, and for the Nusselt number, which significantly extend the predictive envelope of classical formulae.

The impact of system dynamic employee recruitment process on organizational effectiveness

This study aims to examine how employee recruitment influences organizational effectiveness, specifically in financial terms and other factors, using a system dynamics approach. The study utilizes a modeling technique of the system dynamics approach, which includes causal loop diagrams, stock and flow maps, simulation, and on-field data gathering, to understand the nonlinear behavior of the complex system across time. The research method employed in this study involves the collection and analysis of on-field data using the system dynamics approach to model the recruitment process and its impact on the organization. The study investigates the effects of recruitment on organizational outcomes, including financial performance and other related factors. The approach taken in this study is the system dynamics approach, which allows for a comprehensive analysis of the complex and nonlinear relationship between recruitment and organizational effectiveness.

Wavelet analysis of supersonic shock-boundary-layer interaction

We present a wavelet analysis of supersonic shock-boundary-layer interaction. We have used direct numerical simulation data for supersonic flow over a compression ramp and performed orthogonal anisotropic wavelets. The wavelet-based method of extracting coherent structures is applied to the flow vorticity field, decomposed into coherent and incoherent contributions using thresholding of the wavelet coefficients. The statistics of the coherent part of vorticity are close to the statistics of the total field. The study aims to improve our understanding of the shock-boundary-layer interaction, the role of vorticity, and the relationship between the flow's coherent and incoherent vorticity components with the near-wall sound. Our analysis shows a substantial correlation between the incoherent part of vorticity components and wall-pressure fluctuations.

Assessment of the Damage Incurred by biodiversity and ecological degradation via HEC-HMS simulations and of Stream Flow Assessment and Analysis (SAAS).A case study.

Findings on the quality of River Ikpoba samplings is an essential matter because water resources management coupled with hydro-chemical interfaces is of the essence, especially for monitoring and discovering of surface-water contaminants origins. Thus, River Ikpoba samplings were scrutinized using Stream Flow Simulation Model (SFSM) and Multivariate Statistical techniques. Runoff occasion due to land use change shows increase in curve number at peak runoff of 11.60m3/s, while the maximum rainfall is greater in the circumstances up to 141.0 mm. All though the year, not only that envisaged medians always greater than the mean monthly scrutinized, but the anticipated minimum temperature is also bigger, i.e. even more unadventurous prognosis of temperature result in warmer climate scenario in the study region. As the computed runoff occasion p-value (0.998) is greater than the significance level alpha=0.05, one cannot reject the null hypothesis H0. Hence, it was concluded that the simulated flow data are closely related to the observed flow data. Likewise, an increase in curve number (from 74.58 to 79.99) resulted in a corresponding increase in outflow (from 8.00cms to 10.350cms) was observed. Indicating, with increasing curve number, the peak discharge and direct runoff volume also increases proportionately.

Mold sampling during flow simulation

A molding project carried out in Europe emphasizes the importance of real time, shared flow simulation data.

Профилирование торцевых окон трохоидной машины

There is a proposal of a method for the profiling the end windows of trochoid rotary machines according to the given geometric parameters for constructing the trochoid and the degree of gas pressure increase. Its area of application is rotary piston engines and trochoid compressors and pumps. Verification of the mathematical model was carried out on the basis of gas flow simulation data in a three-dimensional setting in the AVL Fire software package. The model of the working process of a trochoid machine is based on the fundamental equations of momentum, energy, diffusion and continuity, written in the Reynolds form and supplemented by the k–?–f turbulence model. The developed method makes it possible to estimate quickly the throughput of the windows of a trochoid rotary machine with less computing resources than in a three-dimensional calculation.

Polymer-turbulence interactions in a complex flow and implications for the drag reduction phenomenon

We present direct numerical simulation data for turbulent duct flow of a finite-extensibility non-linear elastic dumbbell model with the Peterlin approximation (FENE-P) fluid in the high drag reduction regime. While the secondary flow pattern is qualitatively similar to that in a Newtonian fluid, its magnitude is significantly reduced, resulting in a less uniformly distributed velocity profile and hence smaller gradients at the wall. The Reynolds stress tensor in the polymer-laden flow was found to be increasingly anisotropic with most of the turbulent kinetic energy retained in the streamwise component, u′u′¯. We introduce a novel approach for investigating polymer stretching using the anisotropy invariant map of the polymer stress tensor and observe the persistence of both uniaxial and biaxial extension. Analysis of the transport equation for the mean kinetic energy indicates that polymer stretching and relaxation is a highly dissipative process; hence, the introduction of an additional channel for dissipation in a flow is key to drag reduction.

Intelligent well killing control method driven by coupling multiphase flow simulation and real-time data

The exploration and development of offshore oil and gas has greatly alleviated the tension of international energy consumption, however, the complex geological and engineering conditions of offshore drilling lead to complex downhole accidents such as overflow and kick when the wellbore pressure regulation accuracy is poor in offshore drilling. Well killing technology is the primary well control means after oil and gas well overflow and blowout. And the accuracy, real-time and intelligence of wellbore pressure calculation will directly affect the construction period and even the success or failure of well killing. The existing wellbore multiphase flow model still has uncertain parameters depending on the empirical equation determined based on laboratory experiments, and generally lacks real-time interaction with the physical parameters of the drilled formation and the physical parameters of the intrusive fluid under wellbore conditions, which makes the multiphase flow model have the defect of poor pertinence to the formation characteristics and wellbore fluid information at the drilling well formation. In this paper, the differences between the simulation data of multiphase flow model under overflow and circulating well killing conditions and the trend of real-time data at drilling platform, seabed position and bottomhole monitoring points are analyzed. The key parameters of multiphase flow model relying on Laboratory experiment are corrected by real-time inversion. Based on the original universality, the annulus multiphase flow model further highlights the pertinence to the formation characteristics and intrusive fluid characteristics of the drilled section, which is of important guiding significance for the safe and effective conduct of offshore drilling.

Evaluation of machine learning algorithms for predictive Reynolds stress transport modeling

The application of machine learning (ML) algorithms to turbulence modeling has shown promise over the last few years, but their application has been restricted to eddy viscosity based closure approaches. In this article, we discuss the rationale for the application of machine learning with high-fidelity turbulence data to develop models at the level of Reynolds stress transport modeling. Based on these rationales, we compare different machine learning algorithms to determine their efficacy and robustness at modeling the different transport processes in the Reynolds stress transport equations. Those data-driven algorithms include Random forests, gradient boosted trees, and neural networks. The direct numerical simulation (DNS) data for flow in channels are used both as training and testing of the ML models. The optimal hyper-parameters of the ML algorithms are determined using Bayesian optimization. The efficacy of the above-mentioned algorithms is assessed in the modeling and prediction of the terms in the Reynolds stress transport equations. It was observed that all three algorithms predict the turbulence parameters with an acceptable level of accuracy. These ML models are then applied for the prediction of the pressure strain correlation of flow cases that are different from the flows used for training, to assess their robustness and generalizability. This explores the assertion that ML-based data-driven turbulence models can overcome the modeling limitations associated with the traditional turbulence models and ML models trained with large amounts of data with different classes of flows can predict flow field with reasonable accuracy for unknown flows with similar flow physics. In addition to this verification, we carry out validation for the final ML models by assessing the importance of different input features for prediction.

Physics-guided deep learning for generating turbulent inflow conditions

In this paper, we propose an efficient method for generating turbulent inflow conditions based on deep neural networks. We utilise the combination of a multiscale convolutional auto-encoder with a subpixel convolution layer ( ${\rm MSC}_{\rm {SP}}$ -AE) and a long short-term memory (LSTM) model. Physical constraints represented by the flow gradient, Reynolds stress tensor and spectral content of the flow are embedded in the loss function of the ${\rm MSC}_{\rm {SP}}$ -AE to enable the model to generate realistic turbulent inflow conditions with accurate statistics and spectra, as compared with the ground truth data. Direct numerical simulation (DNS) data of turbulent channel flow at two friction Reynolds numbers $Re_{\tau } = 180$ and 550 are used to assess the performance of the model obtained from the combination of the ${\rm MSC}_{\rm {SP}}$ -AE and the LSTM model. The model exhibits a commendable ability to predict instantaneous flow fields with detailed fluctuations and produces turbulence statistics and spectral content similar to those obtained from the DNS. The effects of changing various salient components in the model are thoroughly investigated. Furthermore, the impact of performing transfer learning (TL) using different amounts of training data on the training process and the model performance is examined by using the weights of the model trained on data of the flow at $Re_{\tau } = 180$ to initialise the weights for training the model with data of the flow at $Re_{\tau } = 550$ . The results show that by using only 25% of the full training data, the time that is required for successful training can be reduced by a factor of approximately 80% without affecting the performance of the model for the spanwise velocity, wall-normal velocity and pressure, and with an improvement of the model performance for the streamwise velocity. The results also indicate that using physics-guided deep-learning-based models can be efficient in terms of predicting the dynamics of turbulent flows with relatively low computational cost.

Wind Tunnel Investigation and Numerical Analysis of Fume Behavior in the Vicinity of Rectangular Building Under Moderate Velocity Wind

Abstract A research was conducted to examine the dispersion of pollutants ejected from a chimney around three-dimensional rectangular building. Regarding the experimental study, the wind tunnel experiment comprises data acquired through the dispersion of continuous source tracer discharges from a punctual source situated in a regular network of building-like obstacle, and these data include measurements of mean velocity and turbulence parameters. The relevant data are followed using Particle Image Velocimetry to track various instantaneous and mean dynamic characteristics. Concerning the numerical study, the suggested model simulates both the dynamics and the heat transfer flow field using the overall mean three-dimensional Navier-Stokes equations with an RSM turbulence closure model. The findings of a deep comparison of turbulent flow and dispersion between a full wind tunnel experiment and the model predictions are reported. A high degree of concordance was obtained with the experimental flow and numerical simulation data. The detailed investigation, in which numerical and wind tunnel studies, was performed to evaluate the impact of wind velocity on the pollutant dispersion issued from a chimney around the building in their vicinity. the results clearly showed how wind velocity influenced the environmental air flows and pollutant dispersal pathways. The results of this study show that the shape of the building and the resulting interaction between the wind structure play a determining factor in the distribution of pollutants around a building, thereby affecting the air quality in the various parts of the building.