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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]

In this study, two series of pressurized turbulent jet sooting flames at 1, 3, and 5 bar with either fixed jet velocity or fixed Reynolds number are simulated to study the pressure effects on soot formation and evolution. Through a radiation flamelet progress variable approach with a conditional soot subfilter probability density function (PDF) model to consider the turbulence–chemistry–soot interactions, quantitatively good agreements are achieved for soot volume fraction (SVF) predictions compared with the experimental data, regardless different turbulent intensities and residence times. SVF source terms are then discussed to show the pressure effects on nucleation, condensation, surface growth, and oxidation at different axial positions in these flames. It is found that surface growth and oxidation increase by about three orders of magnitude from 1 to 5 bar, while nucleation and condensation only increase within one order of magnitude. The stronger SVF scaling on pressure than measured data is found to be attributed to the inaccurate surface growth and oxidation scaling on pressure. Further analysis indicates that (i) the uncertainty of C2H2 prediction at elevated pressures is likely a major reason for the too strong surface growth scaling; and (ii) taking account of pressure effects in the conditional subfilter PDF modeling for turbulence–soot–chemistry interactions is likely a key to improve oxidation prediction. The results in this study open up the possibilities for improving future turbulent sooting flame modeling by improving C2H2 chemistry and turbulence–chemistry–soot modeling at elevated pressures. [ABSTRACT FROM AUTHOR]

The evolution of Rayleigh–Taylor instability (RTI) for weakly compressible fluids was numerically simulated using the smooth particle hydrodynamics method. It was found that the speed of spikes and bubbles in most cases will reach a stable value, which is called terminal speed. The calculated terminal speed of the bubble was found to be systematically higher than the theoretical model based on the potential flow hypothesis. This deviation could be modified by including the vortex effect on the terminal speed of the bubble. A significant correlation between the bubble speed and the vorticity in the bubble head was found during the whole evolution of RTI. The analysis of the vortex dynamics in the bubble head region during the terminal speed stage shows that there is a balance between the baroclinic production, viscous dissipation, and convective transport of the vorticity. [ABSTRACT FROM AUTHOR]

In a recent paper, Liu et al. ["Lift and drag in three-dimensional steady viscous and compressible flow", Phys. Fluids 29, 116105 (2017)] obtained a universal theory for the aerodynamic force on a body in three-dimensional steady flow, effective from incompressible all the way to supersonic regimes. In this theory, the total aerodynamic force can be determined solely with the vorticity distribution on a single wake plane locating in the steady linear far field. Despite the vital importance of this result, its validity and performance in practice has not been investigated yet. In this paper, we performed Reynolds-averaged Navier–Stokes simulations of subsonic, transonic, and supersonic flows over a three-dimensional wing. The aerodynamic forces obtained from the universal force theory are compared with those from the standard wall-stress integrals. The agreement between these two formulas confirms for the first time the validity of the theory in three-dimensional steady viscous and compressible flow. The good performance of the universal formula is mainly due to the fact that the turbulent viscosity in the wake is much larger than the molecular viscosity therein, which can reduce significantly the distance of the steady linear far field from the body. To further confirm the correctness of the theory, comparisons are made for the flow structures on the wake plane obtained from the analytical results and numerical simulations. The underlying physics relevant to the universality of the theory is explained by identifying different sources of vorticity in the wake. [ABSTRACT FROM AUTHOR]

The rapid advancement in aeronautics has led to the emergence of intricate dynamic processes and structures, such as vortices, shock waves, flow separation, and turbulence, resulting from the flow around airfoils. Acquiring a profound understanding of these local structures and unraveling the physical mechanisms underlying flow phenomena represents an essential and challenging issue in the field of flight science. In this research, the longitudinal–transverse force theory (L–T force theory), as proposed by previous researchers, is employed to quantitatively assess contributions of local flow structures to aerodynamic forces. Specifically, the research encompasses an analysis of steady and viscous compressible flow over the Royal Aircraft Establishment (RAE)-2822 airfoil, with free-stream Mach number ( M ∞ ) ranging from 0.1 to 2.0. We comprehensively estimate longitudinal forces (L-force) and transverse forces (T-force), along with effects of compressibility on aerodynamic forces. Furthermore, recognizing the necessity for high-precision algorithms in the computation of L–T force theory, this investigation utilizes a sixth-order accuracy algorithm for spatial discretization and differencing. Our analysis reveals that the influence of compressibility and the contributions of L-forces to aerodynamic forces become increasingly significant in high M ∞ regimes as shearing processes weaken. Additionally, a new similarity law is established to characterize aerodynamic forces acting on the RAE-2822 airfoil based on a novel moderating factor, ζmo, reduced from local Mach factors in the L–T force theory. This coefficient, ζmo, elucidates the degree to which transverse processes are modulated by longitudinal processes. Various angles of attack α and airfoils have also been analyzed, including National Advisory Committee for Aeronautics (NACA)0012 and NACA0006, by introducing a parameter denoted as κ to further validate the universality of the new similarity laws. The results demonstrate a high degree of accuracy in fitting the aerodynamic coefficients. [ABSTRACT FROM AUTHOR]

In this work, based on the moving-least-squares immersed boundary method, we proposed a new technique to improve the calculation of the volume force representing the body boundary. For boundary with simple geometry, we theoretically analyze the error between the desired volume force at boundary and the actual force given by the original method. The ratio between the two forces is very close to a constant and exhibits a very narrow distribution. A spatially uniform coefficient is then introduced to correct the force and can be fixed by the least-square method over all boundary markers. Such method is explicit and non-iterative, and is easy to implement into the existing scheme. Several test cases have been simulated with stationary and moving boundaries. Our new method can reduce the residual boundary velocity to the level comparable to that given by the iterative method, but requires much less computing time. Preliminary test suggests that the current method is also effective for nonuniform Eulerian grid. Moreover, the new method can be readily combined with the iterative method and further reduces the residual boundary velocity. • An explicit and non-iterative moving-least-square immersed boundary method. • Applicable to stationary or moving boundary and complex geometry. • Similar level of velocity error at immersed boundary as the iterative method. [ABSTRACT FROM AUTHOR]

Advances in deep learning have enabled physics-informed neural networks to solve partial differential equations. Numerical differentiation using the finite-difference (FD) method is efficient in physics-constrained designs, even in parameterized settings. In traditional computational fluid dynamics (CFD), body-fitted block-structured grids are often employed for complex flow cases when obtaining FD solutions. However, convolution operators in convolutional neural networks for FD are typically limited to single-block grids. To address this issue, graphs and graph networks are used to learn flow representations across multi-block-structured grids. A graph convolution-based FD method (GC-FDM) is proposed to train graph networks in a label-free physics-constrained manner, enabling differentiable FD operations on unstructured graph outputs. To demonstrate model performance from single- to multi-block-structured grids, the parameterized steady incompressible Navier–Stokes equations are solved for a lid-driven cavity flow and the flows around single and double circular cylinder configurations. When compared to a CFD solver under various boundary conditions, the proposed method achieves a relative error in velocity field predictions in the order of 10 − 3 . Furthermore, the proposed method reduces training costs by approximately 20% compared to a physics-informed neural network. To further verify the effectiveness of GC-FDM in multi-block processing, a 30P30N airfoil geometry is considered, and the predicted results are reasonably compared with those given by CFD. Finally, the applicability of GC-FDM to a three-dimensional (3D) case is tested using a 3D cavity geometry. [ABSTRACT FROM AUTHOR]

Accurate prediction of the particle size distribution (PSD) evolution of growing nanometer scale particles is important in designing gas-phase synthesis reactors for the production of nanomaterials. Towards improved predictions of growth, we model the evolution of the PSD of 1-4 nm TiO 2 particles from the decomposition of titanium tetraisoproxide (TTIP) using a two-dimensional (2D) sectional method, uniquely with coagulation rates derived from molecular dynamics (MD) trajectory calculations which account for detailed particle–particle interactions. The PSDs predicted by the 2D sectional method are compared to recent experimental measurements of PSDs in the 1-3 nm range in a flow tube reactor. The nucleation of particles is modeled based on prior mobility measurements of ions attributed to TTIP and their decomposition, with the specific nucleation rate here fitted as a fraction of base nucleation rate (k) derived from these prior measurements. In the 2D sectional model, we examine the influence of the initial (nucleated) particle charge distribution on the PSD, with different coagulation rate coefficients for neutral-charged and charged-charged particle collisions. With the MD-derived coagulation rate coefficients, we find that using nucleation rate coefficients between 0.005 k and 0.03 k leads to strong agreement between modeled PSD and measured PSD for a wide variety of experimental residence times, initial TTIP concentrations and temperatures. Increasing the charge fraction from 0% (uncharged) to 80% (bipolar) does not result in a significant change in the PSD, because the particles rapidly self-neutralize through coagulation based on the simulation results. The results with MD-derived coagulation rate coefficients are compared to those of the sectional method with classical kinetic theory of gases (KTG) rates with a constant enhancement factor to account for potential interactions. Through comparison, we find that the predictions from the classical KTG model consistently exhibit weaker coagulation rates than the MD-derived model during the PSD evolution. • Particle size distribution (PSD) of TiO 2 is studied based on molecular dynamics. • Charging-enhanced coagulation inaccurately predicts PSD of TiO 2. • Importance of nucleation rates is highlighted at low temperatures on PSD growth. [ABSTRACT FROM AUTHOR]

The lack of quantitative risk assessment of airborne transmission of COVID-19 under practical settings leads to large uncertainties and inconsistencies in our preventive measures. Combining in situ measurements and computational fluid dynamics simulations, we quantify the exhaled particles from normal respiratory behaviors and their transport under elevator, small classroom, and supermarket settings to evaluate the risk of inhaling potentially virus-containing particles. Our results show that the design of ventilation is critical for reducing the risk of particle encounters. Inappropriate design can significantly limit the efficiency of particle removal, create local hot spots with orders of magnitude higher risks, and enhance particle deposition causing surface contamination. Additionally, our measurements reveal the presence of a substantial fraction of faceted particles from normal breathing and its strong correlation with breathing depth. • Provide in situ characterization of particle generation through normal breathing. • Reveal presence of droplet and crystalline particles generated through breathing. • Risk assessment of airborne transmission of infectious diseases through virus-containing particles. • Inappropriate ventilation enhances the risk of airborne disease transmission. [ABSTRACT FROM AUTHOR]