Search Results (15)

The primary research aim of this manuscript was to present a simplified absorber model and analyse the simulation results of the absorber model created to which, by design, only water was added and the outlet flue gas temperature was optimal. The obtained simulation results of the simplified absorber model were appropriately compared with the operational results of absorbers operating in professional WtE installations. This study focused on the simulation duration. The primary tool used in the paper is OpenFOAM (v2112). Two solvers were used for the calculations: ReactingParcelFoam and LTSReactingParcelFoam. They ran numerical tests on simplified absorber models. We evaluated the results according to the simulation time. We also examined the difference between the measured and calculated flue gas outlet temperatures. The results will guide further research on the absorber. They will speed up and improve the modelling of chemical processes. The only challenge was to define the chemical reactions and add a calcium molecule to the water droplet model. This work shows that we can simplify the absorber’s geometric model. It kept a low relative error and cuts the compute time. Using a local time step instead of a global one in numerical calculations significantly reduced their run time. It did this without increasing the relative error. The research can help develop complex three-phase flow models in the absorber in the future. Full article

An approach based on the OpenFOAM library has been developed to solve a high-speed, multicomponent mixture of a reacting, compressible flow. This work presents comprehensive validation of the newly developed solver, called compressibleCentralReactingFoam, with different supersonic flows, including shocks, expansion waves, and turbulence–combustion interaction. The comparisons of the simulation results with experimental and computational data confirm the fidelity of this solver for problems involving multicomponent high-speed reactive flows. The gas dynamics of turbulence–chemistry interaction are modeled using a partially stirred reactor formulation and provide promising results to better understand the complex physics involved in supersonic combustors. A time-scale analysis based on local Damköhler numbers reveals different regimes of turbulent combustion. In the core of the jet flow, the Damköhler number is relatively high, indicating that the reaction time scale is smaller than the turbulent mixing time scale. This means that the combustion is controlled by turbulent mixing. In the shear layer, where the heat release rate and the scalar dissipation rate have the highest value, the flame is stabilized due to finite rate chemistry with small Damköhler numbers and a limited fraction of fine structure. This solver allows three-dimensional gas dynamic simulation of high-speed multicomponent reactive flows relevant to practical combustion applications. Full article

Direct numerical simulations of non-premixed fuel-rich methane–oxygen flames at 20 bar are conducted to investigate the turbulent mixing burning of gaseous propellants in rocket engines. The reacting flow is simulated by using an EBI-DNS solver within an OpenFOAM frame. The transport of species is resolved with finite-rate chemistry by using a complex skeletal mechanism that entails 21 species. Two different flames at low and high Reynolds numbers are considered to study the sensitivity of the flame dynamics to turbulence. Regime markers are used to measure the probability of the flow to burn in premixed and non-premixed conditions at different regions. The local heat release statistics are studied in order to understand the drivers in the development of the turbulent diffusion flame. Despite the eminent non-premixed configuration, a significant amount of combustion takes place in premixed conditions. Premixed combustion is viable in both lean and fuel-rich regions, relatively far from the stoichiometric line. It has been found that a growing turbulent kinetic energy is detrimental to combustion in fuel-rich premixed conditions. This is motivated by the disruption of the local premixed flame front, which promotes fuel transport into the diffusion flame. In addition, at downstream positions, higher turbulence enables the advection of methane into the lean core of the flame, enhancing the burning rates in these regions. Therefore, the primary effect of turbulence is to increase the fraction of propellants burnt in oxygen-rich and near-stoichiometric conditions. Consequently, the mixture fraction of the products shifts towards lean conditions, influencing combustion completion at downstream positions. Full article

This research employs computational methods to analyze the velocity and mixture fraction distributions of a non-reacting Propane jet flow that is discharged into parallel co-flowing air under iso-thermal conditions. This study includes a comparison between the numerical results and experimental results obtained from the Sandia Laboratory (USA). The objective is to improve the understanding of flow structure and mixing mechanisms in situations where there is no involvement of chemical reactions or heat transfer. In this experiment, the Realizable k-ε eddy viscosity turbulence model with two equations was utilized to simulate turbulent flow on a nearly 2D plane (specifically, a 5-degree partition of the experimental cylinder domain). This was achieved using OpenFOAM open-source software and swak4Foam utility, with the reactingFoam solver being manipulated carefully. The selection of this turbulence model was based on its superior predictive capability for the spreading rate of both planar and round jets, as compared to other variants of the k-ε models. Numerical axial and radial profiles of different parameters were obtained for a mesh that is independent of the grid (mesh B). These profiles were then compared with experimental data to assess the accuracy of the numerical model. The parameters that are being referred to are mean velocities, turbulence kinetic energy, mean mixture fraction, mixture fraction half radius (L_f), and the mass flux diagram. The validity of the assumption that w߰ = v߰ for the determination of turbulence kinetic energy, k, seems to hold true in situations where experimental data is deficient in w߰. The simulations have successfully obtained the mean mixture fraction and its half radius, L_f, which is a measure of the jet’s width. These values were determined from radial profiles taken at specific locations along the X-axis, including x/D = 0, 4, 15, 30, and 50. The accuracy of the mean vertical velocity fields in the X-direction (U_mean) is noticeable, despite being less well-captured. The resolution of mean vertical velocity fields in the Y-direction (V_mean) is comparatively lower. The accuracy of turbulence kinetic energy (k) is moderate when it is within the range of U_mean and V_mean. The absence of empirical data for absolute pressure (p) is compensated by the provision of numerical pressure contours. Full article

Metallurgical processes are characterized by a complex interplay of heat and mass transfer, momentum transfer, and reaction kinetics, and these interactions play a crucial role in reactor performance. Integrating chemistry and transport results in stiff and non-linear equations and longer time and length scales, which ultimately leads to a high computational expense. The current study employs the OpenFOAM solver based on a fictitious domain method to analyze gas-solid reactions in a porous medium using hydrogen as a reducing agent. The reduction of oxides with hydrogen involves the hierarchical phenomena that influence the reaction rates at various temporal and spatial scales; thus, multi-scale models are needed to bridge the length scale from micro-scale to macro-scale accurately. As a first step towards developing such capabilities, the current study analyses OpenFOAM reacting flow methods in cases related to hydrogen reduction of iron and manganese oxides. Since reduction of the oxides of interest with hydrogen requires significant modifications to the current industrial processes, this model can aid in the design and optimization. The model was verified against experimental data and the dynamic features of the porous medium observed as the reaction progresses is well captured by the model. Full article

Even though there is a pressing interest in clean energy sources, compression ignition (CI) engines, also called diesel engines, will remain of great importance for transportation sectors as well as for power generation in stationary applications in the foreseeable future. In order to promote applications dealing with complex diesel alternative fuels by facilitating their integration in numerical simulation, this paper targets three objectives. First, generate novel diesel fuel surrogates with more than one component. Here, five surrogates are generated using an advanced chemistry solver and are compared against three mechanisms from the literature. Second, validate the suggested reaction mechanisms (RMs) with experimental data. For this purpose, an engine configuration, which features a reacting spray flow evolving in a direct-injection (DI), single-cylinder, and four-stroke motor, is used. The RNG k-Epsilon coupled to power-law combustion models is applied to describe the complex in-cylinder turbulent reacting flow, while the hybrid Eulerian-Lagrangian Kelvin Helmholtz-Rayleigh Taylor (KH-RT) spray model is employed to capture the spray breakup. Third, highlight the impact of these surrogate fuels on the combustion properties along with the exergy of the engine. The results include distribution of temperature, pressure, heat release rate (HRR), vapor penetration length, and exergy efficiency. The effect of the surrogates on pollutant formation ($N{O}_X$, $CO$, $C{O}_2$) is also highlighted. The fifth surrogate showed 47% exergy efficiency. The fourth surrogate agreed well with the maximum experimental pressure, which equaled 85 Mpa. The first, second, and third surrogates registered 400, 316, and 276 g/kg fuel, respectively, of the total CO mass fraction at the outlet. These quantities were relatively higher compared to the fourth and fifth RMs. Full article

The generalized multifluid modelling approach (GEMMA) has been developed to handle the multiplicity of flow regimes and the coexistence of interfaces of largely different scales in multiphase flows. The solver, based on the OpenFOAM reactingEulerFoam family of solvers, adds interface resolving-like capabilities to the multifluid solver in the cells occupied by large interfaces. In this paper, GEMMA is further developed to predict stratified and slug flow regimes in horizontal ducts. The suppression of the turbulence and the wall-like behaviour of large interfaces is modelled with an additional dissipation source. This enables an accurate prediction of the velocity and of the turbulence kinetic energy in a stratified channel flow and the capturing of the formation and the travel of liquid slugs in an annulus. Large interfaces are identified and tracked, not only in the smooth and wavy stratified regimes but also in the much more perturbed interfaces of liquid slugs. The present work confirms GEMMA to be a reliable approach to provide all flow regime modelling capabilities. Further development will be focused on large interface momentum-transfer modelling, responsible for the overestimation of the interfacial shear and the limited liquid excursion during slugs, and the extension to interface break-up and the entrainment of bubbles and droplets, to handle the entire range of regimes encountered in horizontal flows. Full article

State-to-state numerical simulations of high-speed reacting flows are the most detailed but also often prohibitively computationally expensive. In this work, we explore the usage of machine learning algorithms to alleviate such a burden. Several tasks have been identified. Firstly, data-driven machine learning regression models were compared for the prediction of the relaxation source terms appearing in the right-hand side of the state-to-state Euler system of equations for a one-dimensional reacting flow of a N₂/N binary mixture behind a plane shock wave. Results show that, by appropriately choosing the regressor and opportunely tuning its hyperparameters, it is possible to achieve accurate predictions compared to the full-scale state-to-state simulation in significantly shorter times. Secondly, several strategies to speed-up our in-house state-to-state solver were investigated by coupling it with the best-performing pre-trained machine learning algorithm. The embedding of machine learning algorithms into ordinary differential equations solvers may offer a speed-up of several orders of magnitude. Nevertheless, performances are found to be strongly dependent on the interfaced codes and the set of variables onto which the coupling is realized. Finally, the solution of the state-to-state Euler system of equations was inferred by means of a deep neural network by-passing the use of the solver while relying only on data. Promising results suggest that deep neural networks appear to be a viable technology also for this task. Full article

In this study, we developed a new reacting flow solver based on OpenFOAM (OF) and Cantera, with the capabilities of (i) dealing with detailed species transport and chemistry, (ii) integration using a well-balanced splitting scheme, and (iii) two advanced computational diagnostic methods. First of all, a flaw of the original OF chemistry model to deal with pressure-dependent reactions is fixed. This solver then couples Cantera with OF so that the robust chemistry reader, chemical reaction rate calculations, ordinary differential equations (ODEs) solver, and species transport properties handled by Cantera can be accessed by OF. In this way, two transport models (mixture-averaged and constant Lewis number models) are implemented in the coupled solver. Finally, both the Strang splitting scheme and a well-balanced splitting scheme are implemented in this solver. The newly added features are then assessed and validated via a series of auto-ignition tests, a perfectly stirred reactor, a 1D unstretched laminar premixed flame, a 2D counter-flow laminar diffusion flame, and a 3D turbulent partially premixed flame (Sandia Flame D). It is shown that the well-balanced property is crucial for splitting schemes to accurately capture the ignition and extinction events. To facilitate the understanding on combustion modes and complex chemistry in large scale simulations, two computational diagnostic methods (conservative chemical explosive mode analysis, CCEMA, and global pathway analysis, GPA) are subsequently implemented in the current framework and used to study Sandia Flame D for the first time. It is shown that these two diagnostic methods can extract the flame structure, combustion modes, and controlling global reaction pathways from the simulation data. Full article

Fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and “outer-loop” techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena involving pyrolyzing solid surfaces. The AMR functionality in the extended solver, called fireDyMFoam, is load balanced, models gas, solid, and liquid phases, and allows us to dynamically track regions of interest, thus avoiding inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability and computational efficiency for fire spread on vertical panels, showing that the AMR solver reproduces results obtained using much larger statically refined meshes, but at a substantially reduced computational cost. We then leverage AMR in an optimization framework for fire suppression based on the open-source Dakota toolkit, which is made more computationally tractable through the use of fireDyMFoam, minimizing a cost function that balances water use and solid-phase mass loss. The extension of fireFoam developed here thus enables the use of higher fidelity simulations in optimization problems for the suppression of fire spread in both built and natural environments. Full article

In this study, a modified Sisko fluid with Buongiorno model effects over a curved surface was considered. The MHD was applied normally to the flow direction, and the effects of chemical reacted and active energy at the curved surface is also discussed. We chose this pertinent non-Newtonian fluid model since it best represents blood composition, and thus helps us venture into complex blood flow problems. Since the flow is discharged over a curved shape, we therefore commissioned curvilinear coordinates to best portray our envisaged problem. We were also required to define various sundry parameters to make our mathematical equations easily solvable. Mathematical modelling was completed by considering traditional assumptions, including boundary layer approximation. Numerical simulation was conducted using MATLAB solver bvp4c. Several numerical tests were conducted to select the best blend of the linked parameters. We noticed thermal flux upsurged when the chemical reaction parameter was increased with the magnetic indicator parameter caused the flow to slow down, while an increasing amount of activation energy enhanced the concentration of the fluid. The numerical results and impacts of assorted parameters on different profiles are elaborated with the help of graphs and a table. Full article

In the present numerical study, implicit large eddy simulations (LES) of non-reacting multi-components mixing processes of cryogenic nitrogen and n-dodecane fuel injections under transcritical and supercritical conditions are carried out, using a modified reacting flow solver, originally available in the open source software OpenFOAM^®. To this end, the Peng-Robinson (PR) cubic equation of state (EOS) is considered and the solver is modified to account for the real-fluid thermodynamics. At high pressure conditions, the variable transport properties such as dynamic viscosity and thermal conductivity are accurately computed using the Chung transport model. To deal with the multicomponent species mixing, molar averaged homogeneous classical mixing rules are used. For the velocity-pressure coupling, a PIMPLE based compressible algorithm is employed. For both cryogenic and non-cryogenic fuel injections, qualitative and quantitative analyses are performed, and the results show significant effects of the chamber pressure on the mixing processes and entrainment rates. The capability of the proposed numerical model to handle multicomponent species mixing with real-fluid thermophysical properties is demonstrated, in both supercritical and transcritical regimes. Full article

Simulations can reduce the time and cost to develop and deploy advanced technologies and enable their rapid scale-up for fossil fuel-based energy systems. However, to ensure their usefulness in practice, the credibility of the simulations needs to be established with uncertainty quantification (UQ) methods. The National Energy Technology Laboratory (NETL) has been applying non-intrusive UQ methodologies to categorize and quantify uncertainties in computational fluid dynamics (CFD) simulations of gas-solid multiphase flows. To reduce the computational cost associated with gas-solid flow simulations required for UQ analysis, techniques commonly used in the area of artificial intelligence (AI) and data mining are used to construct smart proxy models, which can reduce the computational cost of conducting large numbers of multiphase CFD simulations. The feasibility of using AI and machine learning to construct a smart proxy for a gas-solid multiphase flow has been investigated by looking at the flow and particle behavior in a non-reacting rectangular fluidized bed. The NETL’s in house multiphase solver, Multiphase Flow with Interphase eXchanges (MFiX), was used to generate simulation data for the rectangular fluidized bed. The artificial neural network (ANN) was used to construct a CFD smart proxy, which is able to reproduce the CFD results with reasonable error (about 10%). Several blind cases were used to validate this technology. The results show a good agreement with CFD runs while the approach is less computationally expensive. The developed model can be used to generate the time averaged results of any given fluidized bed with the same geometry with different inlet velocity in couple of minutes. Full article

hy2Foam is a newly-coded open-source two-temperature computational fluid dynamics (CFD) solver that has previously been validated for zero-dimensional test cases. It aims at (1) giving open-source access to a state-of-the-art hypersonic CFD solver to students and researchers; and (2) providing a foundation for a future hybrid CFD-DSMC (direct simulation Monte Carlo) code within the OpenFOAM framework. This paper focuses on the multi-dimensional verification of hy2Foam and firstly describes the different models implemented. In conjunction with employing the coupled vibration-dissociation-vibration (CVDV) chemistry–vibration model, novel use is made of the quantum-kinetic (QK) rates in a CFD solver. hy2Foam has been shown to produce results in good agreement with previously published data for a Mach 11 nitrogen flow over a blunted cone and with the dsmcFoam code for a Mach 20 cylinder flow for a binary reacting mixture. This latter case scenario provides a useful basis for other codes to compare against. Full article

A two-temperature CFD (computational fluid dynamics) solver is a prerequisite to any spacecraft re-entry numerical study that aims at producing results with a satisfactory level of accuracy within realistic timescales. In this respect, a new two-temperature CFD solver, hy2Foam, has been developed within the framework of the open-source CFD platform OpenFOAM for the prediction of hypersonic reacting flows. This solver makes the distinct juncture between the trans-rotational and multiple vibrational-electronic temperatures. hy2Foam has the capability to model vibrational-translational and vibrational-vibrational energy exchanges in an eleven-species air mixture. It makes use of either the Park TTv model or the coupled vibration-dissociation-vibration (CVDV) model to handle chemistry-vibration coupling and it can simulate flows with or without electronic energy. Verification of the code for various zero-dimensional adiabatic heat baths of progressive complexity has been carried out. hy2Foam has been shown to produce results in good agreement with those given by the CFD code LeMANS (The Michigan Aerothermodynamic Navier-Stokes solver) and previously published data. A comparison is also performed with the open-source DSMC (direct simulation Monte Carlo) code dsmcFoam. It has been demonstrated that the use of the CVDV model and rates derived from Quantum-Kinetic theory promote a satisfactory consistency between the CFD and DSMC chemistry modules. Full article