Conventional Computational Fluid Dynamics Simulation Research Articles

Parameterized physics-informed neural networks (P-PINNs) solution of uniform flow over an arbitrarily spinning spherical particle

Neural network-based approaches have emerged as alternatives to conventional computational fluid dynamics (CFD) in solving multiphase flow problems. However, most of these approaches are based on data-driven machine learning methods that require training datasets prepared beforehand through CFD simulations or experiments and cannot be used independently. This study presents an unsupervised parameterized physics-informed neural networks (P-PINNs) approach for obtaining the full flow field information over a multi-dimensional parametric space. This approach is then applied to solve the three-dimensional flow around an arbitrarily rotating sphere subjected to a cross-flow. This solution is obtained for a continuous range of three parameters: the Reynolds number based on cross-flow (Re∈[1,400]), non-dimensional streamwise and spanwise angular velocity components (Ωx∗,Ωy∗∈[−3,3]). The predictions from the P-PINNs model are compared against particle-resolved direct numerical simulation (PR-DNS) results, demonstrating that the neural network model predicts all flow variables (velocity, pressure, and their spatial derivatives) accurately with R2≳0.9. The force and torque on the particle obtained from the P-PINNs model are also compared well against the corresponding PR-DNS results with R2>0.94. The key observation is that the computational cost of the unsupervised parameterized neural network model training and deployment is substantially lower than performing numerous conventional CFD simulations to systematically vary the parameters. Furthermore, once trained, the resultant neural network model is substantially more compact than the huge database of discretized numerical solutions. The P-PINNs model is then used to reveal several interesting flow features and phenomena about the rotating sphere, including flow symmetry, secondary drag and lift, critical Reynolds number, flow bifurcation, and flow separation. This study presents P-PINNs as an efficient alternative for solving parameterized flow problems, demonstrating its practical usage in flow physics discovery with the example of flow past an arbitrarily rotating sphere.

Molecular dynamics simulation of argon isochoric transition to supercritical state

AbstractThe effect of the initial atoms distribution on the molecular dynamics (MD) simulation of a model atomic fluid (argon) is investigated for the case of the isochoric phase transition to the supercritical state. In particular, the case of uniformly distributed atoms in the simulation domain is compared with the case of separated liquid and vapor atoms. The sensitivity of simulations to asymmetric nanoscale perturbations in the boundary is also studied. Despite its high computational cost, the MD approach has the potential to successfully address long‐standing problems in computational fluid dynamics (CFD), especially those associated with mathematical singularities, such as contact angles, vortices, phase transitions and so forth. Unlike conventional CFD simulations, where the initial condition is the pressure or velocity distribution in the simulation domain, MD simulations also require the initial position of each molecule. Thus, it is important to understand whether a judicious choice of the initial distribution of molecules can reduce the overall computation time of the simulation. The evolution of the model fluid system during the phase transition was simulated using a Lennard‐Jones interatomic potential, corrected with the Lorentz–Berthelot mixing rule for the interactions with the solid walls. The system was allowed to relax until equilibrium, and then a Heaviside temperature step was applied to the wall to bring the system to supercritical conditions. Results show the initial choice of the atoms distribution can significantly affect the computational time, while the effect of asymmetric perturbations on the boundary is negligible.

Investigation of physics-informed deep learning for the prediction of parametric, three-dimensional flow based on boundary data

The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that these components are assessed for potential safety issues. However, with increasing number of design proposals, risk assessment using Computational Fluid Dynamics (CFD) quickly becomes expensive. We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation. The novelty of our method compared to existing works in the field of PINN lies in the extension of parametric flow prediction to three-dimensional space by applying a mini-batch based Quasi-Newton optimization. We contribute a parametric minibatch training algorithm which enables the utilization of the large datasets necessary for the three-dimensional flow modeling. Further, we introduce a continuous resampling algorithm that allows to represent domain variations, while operating on one static dataset of reduced size. In scope of this work, we could show that our nondimensional, multivariate scheme can be efficiently extended to predict the velocity and pressure distribution in three-dimensional space for different design scenarios and geometric scales. Every feature of our methodology is tested individually and verified against conventional CFD simulations. Finally, we apply our proposed method in context of an exemplary real-world automotive application.

Integrating Pix2Pix and computational fluid dynamics for enhanced indoor airflow prediction: A case study with wing-walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94 % and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

Abstract. In the urban environment, gas and particles impose adverse impacts on the health of pedestrians. The conventional computational fluid dynamics (CFD) methods that regard pollutants as passive scalars cannot reproduce the formation of secondary pollutants and lead to uncertain prediction. In this study, SSH-aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software, OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using the unsteady Reynolds-averaged Navier–Stokes equations (RANS) model. The simulated concentrations of NO2, PM10, and black carbon (BC) are compared with field measurements on a street of Greater Paris. The simulated NO2 and PM10 concentrations based on the coupled model achieved better agreement with measurement data than the conventional CFD simulation. Meanwhile, the black carbon concentration is underestimated, probably partly because of the underestimation of non-exhaust emissions (tire and road wear). Aerosol dynamics lead to a large increase of ammonium nitrate and anthropogenic organic compounds from precursor gas emitted in the street canyon.

Physics and Accuracy of Dual-Solver Simulations of Rotors in Ground Effect

To characterize the interactions between rotorcraft and ground obstacles when tackling modern and emerging problems such as shipboard landings and urban air mobility, first-principles computational models offer the ability to perform design and analytical studies while resolving detailed flow features. However, the cost of Navier–Stokes methods with very high resolution is, for the most part, only tractable for research use cases. Conversely, midfidelity potential-based methods are cost-effective but may not capture important physics that occur near the rotor blades. Hybrid approaches that couple Navier–Stokes and potential solvers have recently been developed, which provide the ability to resolve complex physics with a Navier–Stokes solution while employing a potential solver to resolve far-wake effects for which it is well-suited. This research discusses the impact of new improvements to and best practices for the hybrid Navier–Stokes/free-wake solver OVERFLOW-CHARM applied to rotors in ground effect. Predictions of rotor performance and flow features are compared with experimental data for microscale and subscale rotors. Rotor performance is predicted within 6% of a conventional computational fluid dynamics simulation at approximately 20% of the computational cost and predicted flow features correlate well to experimental flow visualization.

Reduced order model and global sensitivity analysis for return permeability test

Return Permeability Test (RPT) is a laboratory procedure performed on core plugs for estimating the permeability reduction caused by the invasion of drilling and completion fluids into oil and gas reservoirs. Estimating the damage magnitude (i.e., permeability reduction and percentage of the plugged pore throats) from RPT is critical for accurately analyzing and optimizing wells’ production operations. Several parameters such as the extent and permeability of the invaded zone, production rate, and percentage of the open flow area (i.e., the ratio of the pore throats that remain unplugged after the test to total pore throat area of the rock) affect the estimations from RPT by impacting the pressure drop. Therefore, it is necessary to identify the dominant parameters systematically from the production point of view. Also, the numerical solution of the problem is complex and requires a software package, which often has an expensive CPU run time and might not be efficient for engineers all the time. To this end, we present a systematic global sensitivity analysis (GSA) using the Sobol method for two primary purposes. Firstly, to rank the dominant input variables that affect the variation of the inlet pressure of a core plug with a partially open area and a reduced permeability zone at the flow outlet. Secondly, we develop a reduced-order model (ROM) for this complex problem with a controlled error. As a global sensitivity analysis approach, one advantage of the Sobol technique over the local sensitivity analysis is its ability to account for simultaneous changes of multiple input parameters on the variation of the quantity of interest (QI). Another advantage is that the method can be used to create ROMs even in situations where the governing function is not explicitly known. In this study, we selected four variables as the input parameters of our GSA: the thickness and permeability of the invaded zone ( l f and k f ), production velocity v i , and percentage of the open flow area after the test r o . Since the function governing the relationship between the input and output parameters is not known, a Monte-Carlo simulation is used to carry out the required integrations. For this purpose, a total of 8,000 samples from combinations of the selected input variables are generated. The samples are selected from a range of interest for each variable using the Saltelli sample generation algorithm. Then, computational fluid dynamics (CFD) simulations are conducted on the selected input set to generate pressure (QI) at the core inlet. The results show that the internal cake permeability k f has the most significant impact ( ∼ 32%) on total pressure drop. The second and third most dominant first-order variations are the ratio of open flow area to the total core area r o ( ∼ 15%) and injection velocity v i ( ∼ 13%). Also, the combination (simultaneous changes) of r o and k f has the second most dominant effect between all variables ( ∼ 20%). Finally, we presented a ROM for this problem using the dominant input parameters that account for 96% of the changes in QI, and using several examples demonstrates that the error can be controlled. The result of this study is a model that is mathematically simple and runs much faster than the conventional CFD simulations with reasonable accuracy. It is also a tool to identify and rank the dominant input variables and their interactions regarding their impact on QI variations. The proposed methodology will provide useful guidelines for further analysis of RPT results and provide a simple tool for production engineers. • Estimating filtrate properties from RPT is critical in wells production optimization. • RPT numerical solution is complex and requires a software package in efficient. • Sobol technique is used to rank the importance of impacting parameters. • A reduced-order model (ROM) is presented for RPT. • The results show that the internal cake permeability k f has the most significant impact ( ∼ 32%) on the total pressure drop. • The second dominant impact is from the simultaneous changes of r o and k f . • The third dominant variable is the open flow area at the invaded side of the core. • The forth dominant variable is injection velocity v i .

Recurrence CFD—One Step Closer to Simulation Based Process Monitoring

In metallurgy there are many processes involving fluid flows with repeatedly occurring flow patterns. For such flows these recurrent patterns can be described by a series of predefined flow field snapshots. The snapshot database is made in a conventional computational fluid dynamics (CFD) simulation. In this paper, the recurrent simulation (rCFD) of a dephosphorization process is validated against measurements from the literature. Compared with the conventional CFD simulation, rCFD reaches smaller simulation times up to an order of magnitude, thus decreasing computational costs and making numerical simulations of long lasting processes feasible. There is also a good agreement with quantitative experimental results, which makes this method a promising candidate for numerical simulations of many similar metallurgical processes.

Numerical identification of separation bubble in an ultra-high-lift turbine cascade using URANS simulation and proper orthogonal decomposition

The flow-field inside a gas turbine engine, especially in the low-pressure turbine, is very complicated as it is normally accompanied by unsteady flow structures, strong and rapidly changing pressure gradients, intermittent transition of boundary layer, and flow separation and reattachment, especially during off-design performance. In this article, flow separation and reattachment on the suction side of an ultra-high-lift low-pressure turbine blade is studied and characterized using 3D Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations. For turbulence modeling, transitional-SST method (γ-Reθ) is adopted. The simulations are performed at the exit Reynolds numbers of 200,000 and 60,000, and at a constant isentropic exit Mach number of 0.4. The shape and extent of the separation bubble are primarily dependent on large vortical structures due to the Kelvin-Helmholtz instability and spanwise vortex tube shedding. Therefore, a better prediction of these phenomena could result in a more realistic separation bubble identification and consequently more accurate profile loss assessment. In order to better capture the transitional flow characteristics, which are not often readily available from conventional computational fluid dynamics simulations, the method of Proper Orthogonal Decomposition (POD) is used in this study. Non-coherent structures in the main flow, such as separation bubble, are investigated and studied. The POD modes of pressure-field are analyzed to clarify the generation of spanwise vortex tubes after separation point. In the higher Reynolds number, low-energy small-scale structures in the separation zone and downstream of the trailing edge are observed from the POD analysis. In the lower Reynolds number, high-energy large-scale structures shed from the separated shear layer are identified, which are responsible for increasing turbulent kinetic energy as well as increasing profile losses. This study also shows that the combination of URANS and POD can successfully be used to identify the separation bubble.

Reduced-order urban wind interference

A novel approach is demonstrated to approximate the effects of complex urban interference on the wind-induced surface pressure of tall buildings. This is achieved by decomposition of the domain into two components: the obstruction model (OM) of the static large-scale urban context, for which a single computational fluid dynamics (CFD) simulation is run; and the principal model (PM) of the isolated tall building under design, for which repeatable reduced-order model (ROM) predictions can be made. The ROM is generated with an artificial neural network (ANN), using a set of feature vectors comprising an input of local shape descriptors and a range of wind speeds from a training geometry, and an output response of pressure. For testing, the OM CFD simulation provides the flow boundary condition wind speeds to the PM ROM prediction. The result is vertex-resolution surface pressure data for the PM mesh, intended for use within generative design exploration and optimisation. It is found that the mean absolute prediction error is around 5.0% ( σ: 7.8%) with an on-line process time of 390 s, 27 times faster than conventional CFD simulation; considering full process time, only 3.2 design iterations are required for the ROM time to match CFD. Existing work in the literature focuses solely on creating generalised rules relating global configuration parameters and a global interference factor (IF). The work presented here is therefore a significantly alternative approach, with the advantages of increased geometric flexibility, output resolution, speed, and accuracy.

An automated procedure for the optimal positioning of guide plates in a flow manifold using Box complex method

Flow splits and manifolds are encountered in many industrial processes such as heat recovery systems to feed reactant streams and take out product streams. Depending on the application, the flow distribution among the several outlets may be equal or unequal. In this paper we describe a computational method for achieving desired flow distribution in a flow manifold using optimally positioned guide plates. For multiple outlet streams, determining the orientation of the guide plates is a non-trivial problem. The positioning of one guide plate will have an effect on the flow rate through the other outlets. This effect cannot be estimated from simple models but can be calculated using computational fluid dynamics (CFD) simulation of the corresponding flow problem. However, a conventional CFD simulation cannot predict the optimal location of the guide plates. In view of this, we pose the flow distribution problem as a constrained optimization problem and use a well-established algorithm coupled to a computational fluid dynamics (CFD)-based flow solver to develop an automated procedure for the optimal positioning of the guide plates. The robustness of the procedure is illustrated for both equal and unequal flow distributions in a one-inlet, four-outlet manifold with guide plates of fixed and variable lengths.

Nonlinear Dynamic Aalysis of Natural Ventilation in a Two-Zone Building: Part B—CFD Simulations

Computational fluid dynamics (CFD) is a widely accepted design and analysis tool for building ventilation. This paper presents a simple method using commercial CFD software to obtain the solution multiplicity characteristics of laminar and turbulent indoor airflows. The method is known as the two-way continuation simulation, which has been developed for identifying multiple solutions in computational fluid dynamics. We consider airflows in a simple two-zone building with four openings, where one of the zones has a constant heat source, modeling the heat or pollutant generation and spread in buildings governed by natural stack forces. Using the new two-way continuation simulation method, we describe the flow variation and physical mechanisms at different heights in the two zones for different Rayleigh numbers. The results show that two steady solutions with the same boundary conditions and building geometry can be obtained step-by-step in the simulation process. Flow multiplicity can also be found as the height ratio of the two zones increases or decreases. As an important physical parameter, the Rayleigh number can also have some significant influences on the flow bifurcation or variation. Compared with conventional CFD simulation, the two-way continuation simulation method can effectively identify the possible existence of multiple solutions of indoor airflows and pollutant spread with different initial conditions. One shortcoming of the two-way method is that it cannot identify unstable solutions.

Fuel Jet Spread Modeling for Numerical of Aircraft Fuel Dumping Simulation

An analytical jet spread model has been developed for determining the boundary of an axisymmetric jet ejecting into a two-fluid, coflowing stream. When the circumferential velocity of the jet is assumed to be small, the model can be coupled with the conventional computational fluid dynamics (CFD) method for simulation of the fuel dumping from an aircraft. Application of the model is demonstrated by considering the fuel dumping from a V-22 aircraft flying at Mach 0.345 and angles of attack from 7 and 16 deg. The model provides a first-order correction to the CFD simulated results with the jet dispersion boundaries to yield an integrated solution that represents a realistic aircraft fuel dumping environment. UMPING excessive fuel from an aircraft before landing may be necessary due to safety concerns. During fuel dumping, massive fuel can be successfully dumped without any contact be- tween the fuel jet and the aircraft surface. Fuel mist, which is caused by the thickening of the fuel jet boundary, may impinge onto the aircraft even though the main fuel stream does not touch the surface. The thickening of the jet boundary is commonly known as the jet dispersion, the jet spread, or the jet entrainment. It consists of par- ticles of the surrounding medium carried along with it and particles of the jet itself that have been slowed down. To avoid any fuel and/or fuel mist impingement onto any part of the aircraft surface during dumping, the geometry of the fuel jettison probe (FJP) must be properly designed and its location carefully de- termined. This is an important aerodynamic issue requiring resolu- tion by combining the results from wind-tunnel tests, computational fluid dynamics (CFD) simulations, and flight tests. The role of the CFD simulation is to provide an initial design study regarding the FJP geometry configuration, jet orientation, and its location on the aircraft, and also to gain insights into the complex flowfield about a complete aircraft with the FJP for fuel jettison. The problem is complicated not only by the complex geometry, but also the fuel jet characteristics due to jet dispersion. The jet dispersion, or the jet spread, is a subproblem that is embedded in the overall flowfield. It must be accounted for if the simulation truly represents a realistic aircraft fuel dumping environment. Unfortunately, the present capability of the conventional CFD method 1−4 lacks the ability to solve the whole problem in one formulation. The problem would have to be solved separately for the aircraft flowfield and for the embedded jet flow and then coupled together to yield an overall solution that depicts the physical problem. In the present paper, an analytical jet spread model for the fuel dispersion is proposed and described. The model can be coupled with the conventional, Navier-Stokes solution-based CFD method for simulation of the aircraft fuel dumping. The coupling is per- formed by a first-order correction in which the jet boundary due to the jet spread is calculated based on the simulated flowfield, but the flowfield is not updated by the presence of the jet volume. Al- though the work is specially aimed at the V-22 aircraft fuel dumping,

Detailed reaction mechanisms for coal-nitrogen conversion in pulverized fuel flames

The simulation strategy described in this paper provides an alternative to conventional computationalfluid dynamics (CFD) postprocessing to estimate exhaust NO x emissions. The method first analyzes a conventional CFD furnace simulation to specify temperature histories and mixing rates. Then the bulk flow patterns are represented with an equivalent network of idealized reactor elements. Detailed reaction mechanisms are then applied over the reactor network, including the most fully validated reaction mechanisms for coal devolatilization and char oxidation and complete elementary reaction mechanisms for chemistry in the gas phase and on soot. The analysis depicts all the important tendencies among the major intermediates and products from a selection of coals that spanned almost the entire rank spectrum under reaction conditions that spanned the domain of stoichiometric ratio in pulverized fuel flames, albeit in a lab-scale furnace. The main practical benefit of the mechanistic complexity is that simulations based on detailed mechanisms require far fewer parameter adjustments than conventional CFD simulations whenever different fuels are considered.