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Explosion flow fields are characterized by shock waves with varying intensity and position (i.e., explosive loads), which are the primary causes of structural damage. Accurate and rapid prediction of explosive loads is crucial for structural blast-resistant design and daily security management. While existing empirical models and numerical simulation methods can capture the propagation characteristics of explosive shock waves, high-precision simulation requires a massive computational workload, which is insufficient to meet the fast computational demands of various explosive scenarios. To address this contradiction, this study constructed a sparse reconstruction model for two-dimensional explosion fields based on machine learning algorithms. The model utilizes sparse observational data to establish a mapping relationship to the distribution of the entire flow field. The model is built by a physics-informed graph neural network (PIGN). The graph neural network is employed to associate node features, while the physical network is utilized to control model convergence, aiming to enhance model performance. Using the constructed dataset, the PIGN model was tested. Performance and generalization capabilities of the model were assessed by comparing its results with numerical simulation. This evaluation analyzed the relative error distribution and error statistical results of the reconstructed flow field. The results indicate that the PIGN model can effectively reconstruct explosion fields, with an average error in the reconstructed flow field below 4%. Furthermore, when the number of probe points reaches 10, the average error of the flow field reconstructed by the model is close to 6%. This model not only provides a highly reliable distribution of explosion overpressure and pressure-time variations but also, with a well-trained model, accomplishes flow field reconstruction within 1 ms. It offers a novel approach for achieving rapid and reasonable prediction of explosion fields or two-dimensional compressible flow fields. [ABSTRACT FROM AUTHOR]

Large-eddy simulations (LES) and implicit LES (ILES) of three-dimensional compressible Rayleigh–Taylor turbulence with miscible fluids are performed and compared with direct numerical simulation (DNS) at the Atwood number A t = 0.5 and stratification parameters Sr = 1.0 and 4.0. Three sub-grid-scale (SGS) models including constant-coefficient spatial gradient model (CSGM), dynamic Smagorinsky model (DSM), and dynamic mixed model (DMM) are considered. The CSGM model achieves a high accuracy by using the velocity gradients at the neighboring LES grids. The a priori tests show that the CSGM model has significantly higher correlation coefficients and lower relative errors than traditional SGS models. In the a posteriori tests, the probability density functions of SGS terms predicted by the CSGM model are consistent with the filtered DNS results. The CSGM model can accurately predict the small bubble and spike structures, resulting in good predictions of mixing heights and concentration fields. The instantaneous structures, spectra, and statistics of velocity and vorticity fields are also examined, showing the excellent performance of the CSGM model compared to the ILES, DSM, and DMM models. Moreover, the predictions of the temperature and pressure fields by the CSGM model are significantly better than the traditional SGS models and ILES. [ABSTRACT FROM AUTHOR]

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state thermal convection fields based on deep learning technology. The proposed model aims to overcome limitations in traditional frameworks caused by network types, such as the requirement for extensive training data, accuracy loss due to pixelated preprocessing of original data, and inability to predict information near the boundaries with precision. We propose a new framework that consists primarily of a physical-informed neural network (PINN) and a graph convolutional neural network (GCN). The GCN serves as the prediction module and predicts thermal convection in the two-dimensional computational domain by considering the mutual influence between unstructured nodes and their neighbors. On the other hand, the PINN acts as the physical constraint module of the framework by embedding the control equation of thermal convection into the loss function of the neural network, ensuring that the inference and prediction results of the GCN comply with the constraints of the control equation. The advantages of this framework lie in two aspects. First, the computation mechanism of the GCN is more in line with the actual evolution of temperature fields. Second, the PINN enhances the cognitive ability of the surrogate model toward the convection field information. It accurately describes the changes of temperature gradient information at the boundary position and reduces the model's demand for training data. To validate the advantages of the proposed model, we gradually analyzed the model's geometric adaptability and predictive accuracy from the single cylinder case to the double cylinder case. We also investigated the impact of the number of sampling points on model training and compared the model's prediction results with those of a purely data-driven model. The results show that the proposed model exhibits good geometric adaptability and stability. With only 20 training data, the mean error of the proposed model in predicting the velocity and temperature field is less than 1% and 0.6% for the single cylinder, and less than 2% and 1% for the double cylinder case, while the mean error of the purely data-driven GCN model in predicting the velocity and temperature field is 9.4% and 6.4% for the double cylinder case. These findings demonstrate the effectiveness of the proposed physics-informed graph convolutional neural network, allowing for more accurate prediction of fluid flow and heat convection using surrogate model. [ABSTRACT FROM AUTHOR]

This paper presents a novel reduced-order model for internal and external flow field estimations based on a sparse convolution neural network. Since traditional convolution neural network requires "rectangular" matrixes as input, the convolutional operation is computationally inefficient when applied to these problems with input matrix having sparse information. In our approach, we innovatively introduce a sparse convolution neural network (SCNN) to collect spatial information on geometries that are inherently sparse, e.g., the flow in thin pipelines in a much larger domain or the pipelines with random branches. Different from the traditional convolution neural network (CNN) model, the SCNN only collects features from areas with flow information for both the input matrix and each convolutional layer, which not only reduces the consumption of computational resources but also significantly increases network attention to flow area. The model learns the mapping relationship between geometries and the physical field of fluid flow, and the spatial positions of geometry are represented using the nearest wall signed distance function. The proposed SCNN model has the promising adaptability to arbitrary geometry and less computational resource cost compared to the traditional CNN model: the mean error of the SCNN is less than 5.4% (while the CNN is 7.1%) for the internal flow and less than 6.5% (while the CNN is 8.1%) for the external flow. Moreover, the SCNN has 72% less GPU resource usage and 52% less random access memory cost than the CNN for internal flow. For the first time, our framework introduces the sparse convolution network to flow field prediction, and the SCNN shows outstanding performance on prediction accuracy and computational resource saving for the flow problems with a sparse input information. [ABSTRACT FROM AUTHOR]

In the interdisciplinary field of data-driven models and computational fluid mechanics, the reduced-order model for flow field prediction is mainly constructed by a convolutional neural network (CNN) in recent years. However, the standard CNN is only applicable to data with Euclidean spatial structure, while data with non-Euclidean properties can only be convolved after pixelization, which usually leads to decreased data accuracy. In this work, a novel data-driven framework based on graph convolution network (GCN) is proposed to allow the convolution operator to predict fluid dynamics on non-uniform structured or unstructured mesh data. This is achieved by the fact that the graph data inherit the spatial characteristics of the mesh and by the message passing mechanism of GCN. The conversion method from the form of mesh data to graph data and the operation mechanism of GCN are clarified. Moreover, additional relevance features and weight loss function of the dataset are also investigated to improve the model performance. The model learns an end-to-end mapping between the mesh spatial features and the physical flow field. Through our studies of various cases of internal flow, it is shown that the proposed GCN-based model offers excellent adaptability to non-uniformly distributed mesh data, while also achieving a high accuracy and three-order speedup compared with numerical simulation. Our framework generalizes the graph convolution network to flow field prediction and opens the door to further extending GCN to most existing data-driven architectures of fluid dynamics in the future. [ABSTRACT FROM AUTHOR]

The compressibility effect when an oblique shock wave impinges on a turbulent boundary layer was analyzed with a direct numerical simulation using Helmholtz decomposition. The turbulent intensity near the impinging region is significantly enhanced by the interaction of the shock wave and boundary layer. In particular, the interaction behavior can enhance the dilatational components of the Reynolds stress and velocity fluctuations. This also enhances the negative correlation between the dilatational components of streamwise and normal velocity fluctuations. The dilatation fluctuation in the impinging region increases significantly. Moreover, in the impinging region, the dilatational components of the production term and transport term contribute to the production and transport terms of the kinetic energy equation mainly in the vicinity of the interface. This simulation shows that the interaction behavior of an oblique shock wave and turbulent boundary layer can enhance flow compressibility significantly. In the interaction region, the turbulent intensity of the flow field is stronger than those upstream and downstream. This study provides a theoretical basis for the improvement of other simulation methods and turbulence modeling for the interaction of an oblique shock wave and turbulent boundary layer. [ABSTRACT FROM AUTHOR]

A spatially developing supersonic flat plate boundary layer flow at M ∞ = 2.25 is analyzed using the constrained large-eddy simulation (CLES) method. The Reynolds number based on the momentum thickness of the inlet boundary layer is R e θ = 3429. The mean and statistical quantities, including mean velocity, mean temperature, and total temperature, are obtained and compared among the present approach, large eddy simulation (LES) with the dynamic Smagorinsky model, detached eddy simulation (DES), and naturally developed direct numerical simulation (ND-DNS). As a result, CLES can predict these mean quantities and statistics more accurately than LES and DES, and the results are in good agreement with the ND-DNS data. This demonstrates that CLES is an effective method for spatially developing supersonic flat-plate boundary layer flows. [ABSTRACT FROM AUTHOR]

We develop a deep neural network-based reduced-order model (ROM) for rapid prediction of the steady-state velocity field with arbitrary geometry and various boundary conditions. The input matrix of the network is composed of the nearest wall signed distance function (NWSDF), which contains more physical information than the signed distance function (SDF) and binary map; the boundary conditions are represented by specifically designed values and fused with NWSDF. The network architecture comprises convolutional and transpose-convolutional layers, and convolutional layers are employed to encode and extract the physical information from NWSDF. The highly encoded information is decoded by transpose-convolutional layers to estimate the velocity fields. Furthermore, we introduce a pooling layer to innovatively emphasize/preserve information of boundary conditions, which are gradually flooded by other features during the convolutional operation. The network model is trained using several simple geometries and tested with more complex cases. The proposed network model shows excellent adaptability to arbitrary complex geometry and variable boundary conditions. The average prediction error of the network model on the testing dataset is less than 6%, and the prediction speed is two orders faster than that of the numerical simulation. In contrast to the current model, the average error of the network model with the input matrix of the binary map, traditional SDF, and model without pooling layers is around 12%, 11%, and 11%, respectively. The outstanding performance of the proposed network model indicates the potential of the deep neural network-based ROM for real-time control and rapid optimization, while encouraging further investigation to achieve practical application. [ABSTRACT FROM AUTHOR]

The effects of heat sources on the velocity and pressure spectra, Mach number scaling of one-point statistics, and small-scale structures of compressible homogeneous shear turbulence are numerically studied. The dilatational components of flow fields are significantly enhanced by a strong heat source at low turbulent Mach numbers Mt and are dominated by an acoustic mode, leading to a strong acoustic equilibrium between the dilatational velocity and pressure. As the magnitude of the heat source increases, the scaling behaviors of the dilatational components of kinetic energy and dissipation rate change from M t 4 and approach a state that is nearly independent of the turbulent Mach number. Furthermore, a strong heat source has a significant effect on small-scale structures at low turbulent Mach numbers. The conditional probability density functions of the normalized eigenvalues of a strain rate tensor become more dependent on the dilatation owing to the effect of the heat source. For low turbulent Mach numbers with strong heat sources, the ratio of the normalized eigenvalues of the strain rate tensor tends to −1:0:0 and −0.2:0.25:1 in the strong compression and strong expansion regions, respectively, and the dilatational vortex stretching term can significantly enhance the enstrophy production. [ABSTRACT FROM AUTHOR]

The small-scale statistics and local flow topology of compressible homogeneous isotropic turbulence of dense gas are numerically investigated with the turbulent Mach number and Taylor Reynolds number, respectively, nearly equaling 1.0 and 153.0. The initial state of the flow field is in the inversion zone, where the fundamental derivative of gas dynamics is negative. After reaching the stationary state, the flow field includes three different gas regions: a Bethe–Zel'dovich–Thompson (BZT) region, a classical dense gas (CDG) region, and a usual gas region. In the present study, the effects of different gas regions on the statistical properties of the enstrophy production term are investigated. Based on Helmholtz decomposition, it is found that the enstrophy production mainly comes from its solenoidal component. The dense gas effect reduces the production of enstrophy in the compression region and weakens the loss of enstrophy in the expansion region. Furthermore, the properties of flow topology based on the three invariants of the velocity gradient tensor are studied. The expansion region is mainly occupied by the BZT and CDG regions. In the expansion region, the dense gas effect significantly reduces the expansive vortex structure and weakens the contribution of this structure to the enstrophy loss. [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network based reduced-order model for predicting the unsteady flow field over single/multiple cylinders. The neural network model constructs a mapping function between the temporal evolution of the pressure signal on the cylinder surface and the surrounding velocity field, where Convolutional Neural Network (CNN) layers are used as the encoder and deconvolutional neural network layers are used as the decoder. Compared with the network model with a fully connected (FC) decoder, the model with the deconvolution connected (DC) decoder is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism, and symmetry, while the fluid flow, which is described by Navier–Stokes equations containing convection and diffusion terms, displays outstanding features of locality. In this article, the performance of the network models with the FC decoder and the DC decoder is evaluated by studying the problem of flow over a single cylinder first, and then the complexity of the flow structure of the studied problems is enhanced by increasing the number of cylinders and the Reynolds number. The results indicate that both the CNN-FC decoder model and CNN-DC decoder model achieve fast and accurate prediction on the velocity field, and the CNN-DC decoder model gives more robust and precise performance for all studied problems. [ABSTRACT FROM AUTHOR]

In this article, we propose an unsteady data-driven reduced order model (ROM) (surrogate model) for predicting the velocity field around an airfoil. The network model applies a convolutional neural network (CNN) as the encoder and a deconvolutional neural network (DCNN) as the decoder. The model constructs a mapping function between temporal evolution of the pressure signal on the airfoil surface and the surrounding velocity field. For improving the model performance, the input matrix is designed to further incorporate the information of the Reynolds number, the geometry of the airfoil, and the angle of attack. The DCNN works as the decoder for better reconstructing the spatial and temporal information of the features extracted by the CNN encoder. The training and testing datasets of flow fields under different conditions are obtained by solving the Navier–Stokes equations using the computational fluid dynamics method. After model training, the neural network based ROM shows accurate and dramatically fast predictions on the flow field of the testing dataset with extended angles of attack and Reynolds numbers. According to the current study, the neural network-based ROM has exhibited attractive potentials on ROM of the unsteady fluid dynamic problem, and the model can potentially serve on investigating flow control or optimization problems in the future. [ABSTRACT FROM AUTHOR]

As a new class of single-molecule magnets, two-coordinate complexes of open-shell transition metals are comparatively rare and have attracted interest due to their high degree of coordinative unsaturation. However, the dynamic distortion associated with the low coordination number of the metal center hinders the applications of high-density information storage, quantum computing, and spintronics. Here, we propose a series of stable 2D metal–organic frameworks constructed by ideal (1, 3, 5)-benzenetricarbonitrile (TCB) molecules and 5d transition metals (Hf, Ta, W, Re, Os, and Ir) with a highly symmetrical ligand field and rigid π conjugated framework. Among them, TCB-Re exhibits intrinsic ferromagnetic ordering with a considerably large magnetic anisotropic energy (MAE) of 19 meV/atom and high Curie temperature (TC) of 613 K. Under biaxial strain, diverse magnetic states (such as ferromagnetic, paramagnetic, and antiferromagnetic states) can be achieved in TCB-Re by the complicated competition between the in-plane d–px/y–d and out-of-plane d–pz–d superexchange interactions. At a small compressive strain of 0.5%, the MAE for perpendicular magnetization increases substantially to 120 meV/atom; meanwhile, the magnetization and TC above room temperature are well retained. Our results not only extend two-coordinate transition metal complexes to continuous 2D organic magnets but also demonstrate an effective method of strain engineering for manipulating the spin state and MAE. [ABSTRACT FROM AUTHOR]

A fully developed spanwise rotating turbulent channel flow has been numerically investigated utilizing large-eddy simulation. Our focus is to assess the performances of the dynamic variants of eddy viscosity models, including dynamic Vreman’s model (DVM), dynamic wall adapting local eddy viscosity (DWALE) model, dynamic σ (D σ ) model, and the dynamic volumetric strain-stretching (DVSS) model, in this canonical flow. The results with dynamic Smagorinsky model (DSM) and direct numerical simulations (DNS) are used as references. Our results show that the DVM has a wrong asymptotic behavior in the near wall region, while the other three models can correctly predict it. In the high rotation case, the DWALE can get reliable mean velocity profile, but the turbulence intensities in the wall-normal and spanwise directions show clear deviations from DNS data. DVSS exhibits poor predictions on both the mean velocity profile and turbulence intensities. In all three cases, Dσ performs the best. [ABSTRACT FROM AUTHOR]

A constrained large-eddy simulation (CLES) approach is developed for wall-bounded compressible turbulent flows based on its incompressible analogue [Chen et al., 'Reynolds-stress-constrained large-eddy simulation of wall-bounded turbulent flows,' J. Fluid Mech. 703, 1-28 (2012)]. In the new CLES approach, both the subgrid-scale (SGS) stress and the SGS heat flux are decomposed into an averaged part and a fluctuating part in the near-wall region with the mean SGS stress and heat flux constrained by prescribed Reynolds stress model and turbulent heat flux model, respectively. The Smagorinsky SGS models are employed to approximate the SGS stress and heat flux in the remaining region of the flow domain. The present CLES method is validated by simulating the compressible turbulent channel flows at various Reynolds numbers and Mach numbers. The mean velocity profiles, mean temperature profiles, and other statistical quantities and turbulent structures are obtained and well compared among the present approach, direct numerical simulation (DNS), detached eddy simulation (DES) and traditional large eddy simulation (LES). The results demonstrate that the dual constraint of Reynolds stress and turbulent heat flux plays a non-trivial role in enhancing the ability of LES for wall-bounded compressible flows. In addition, the Reynolds and Mach numbers effects on the mean and statistical quantities as well as the turbulence structures are investigated using the proposed CLES method. The main conclusions are in very good agreement with those drawn by other authors using DNS method. All the results manifest that the present CLES technique is an encouraging and useful tool for wall-bounded compressible turbulent flows. [ABSTRACT FROM AUTHOR]

This paper presents a piezo-actuated compliant mechanism with a new boundary constraint to provide concurrent large workspace and high dynamic frequency for precision positioning or other flexible manipulation applications. A two-stage rhombus-type displacement amplifier with the “sliding-sliding” boundary constraint is presented to maximize the dynamic frequency while retaining a large output displacement. The vibration mode is also improved by the designed boundary constraint. A theoretical kinematic model of the compliant mechanism is established to optimize the geometric parameters, and a prototype is fabricated with a compact dimension of 60 mm × 60 mm × 12 mm. The experimental testing shows that the maximum stroke is approximately 0.6 mm and the output stiffness is 1.1 N/μm with the fundamental frequency of larger than 2.2 kHz. Lastly, the excellent performance of the presented compliant mechanism is compared with several mechanisms in the previous literature. As a conclusion, the presented boundary constraint strategy provides a new way to balance the trade-off between the frequency response and the stroke range widely existed in compliant mechanisms. [ABSTRACT FROM AUTHOR]

We designed a high input impedance RF amplifier based on Tower Jazz's 0.18 μm SiGe BiCMOS process for series nanowire detector. The characterization of its gain and input impedance with a vector network analyzer is described in detail for its specificity. The actual 15 dB gain should be the measured value subtracts 6 dB, which is easy to be ignored. Its input impedance can be equivalent to 6.7 kΩ ∥ 3.4 pF though fitting the measurement, whose accuracy is verified. The process of measurement provides a good reference to characterize the similar special amplifier with unmatched impedance.