Reynolds-averaged Navier-Stokes Model Research Articles

Reconstruction of proper numerical inlet boundary conditions for draft tube flow simulations using machine learning

This paper discusses an innovative strategy to determine appropriate mean and fluctuating inlet boundary conditions for the numerical flow simulations of a hydraulic turbine draft tube. Precise and comprehensive measurements at a draft tube inlet are hard to perform and thus rarely available. Moreover, its complex nature and strong sensitivity to imposed inlet boundary conditions render this flow particularly challenging to predict with numerical methods. The innovative strategy uses machine learning to properly reconstruct the incomplete or unknown inlet boundary conditions from available downstream flow information. Reynolds-Averaged Navier Stokes (RANS) and Large-Eddy Simulations (LES) turbulence modeling methods are used to investigate the draft tube and the numerical results are compared to experimental measurements. Reference simulations are first conducted with standard inlet boundary conditions commonly used in previous numerical works. For RANS, the innovative strategy significantly improves the mean velocity profiles and static pressure evolution inside the draft tube. For LES, its use with an artificial upstream extension allows a proper reconstruction of the upstream turbulent field and a more precise and realistic description of the draft tube head losses.

Investigating roughness effects on ship resistance in shallow waters

Shallow waters influence the behaviour and performance of a vessel by modifying the local pressure distribution, wave making, and boundary layer thickness. The boundary layer thickness is also influenced by surface roughness. No previous studies have investigated the combined effects of shallow water contributions and roughness on ship resistance. This study aims to fill this knowledge gap in the literature by using Unsteady Reynolds Averaged Navier-Stokes modelling. Results show that the total resistance coefficient increases between approximately 22% and 36% in the presence of roughness depending on the speed and depth-to-draft ratio. The numerical model used shows that pressure resistance grows at a faster pace than frictional resistance and increases its relative contribution to the total when roughness is applied, contrary to deep water cases.

Multifidelity validation of digital surrogates using variable-density turbulent mixing models

High-speed experiments studying the Blast Driven Instability are used to validate Reynolds-Averaged Navier-Stokes (RANS) and Large eddy simulation (LES) turbulent mixing models. The work helps to elucidate the three mixing regimes of the instability and shows that the LES can successfully capture, at lease to the first order, the complex physics therein. The work also highlights the limitations of RANS models for transitioning instabilities and how LES results remain highly sensitive to the characterization of initial conditions.

Statistical Analysis on The Near-Wake Region of RANS Turbulence Closure Models for Vertical Axis Tidal Turbine

The flow field in the near wake region (up to six turbine diameters downstream) of a tidal current turbine is strongly driven by the combined wake of the device support structure and the rotor. Accurate characterisation of the near-wake region is important, but it is dominated by highly turbulent, slow-moving fluid. At present, limited number of research has been undertaken into the characterisation of the near-wake region for a Vertical Axis Tidal Turbine (VATT) device using the Reynolds Averaged Navier Stokes (RANS) model in the shallow water environment of Malaysia. This paper presents a comprehensive statistical analysis using the Mean Absolute Error (MEA), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the near-wake region for shallow water application by comparing numerical solutions (i.e., different types of RANS turbulence models using Ansys Fluent) with published experimental data. Seven RANS turbulence models with a single VATT, represented by using a cylindrical object, were employed in the preliminary study. The statistical analysis performed in this study is essential in exploring and giving a detailed understanding on the most suitable RANS turbulence model to be improved, specifically on its near-wake region. In this study, the near wake region is defined as D ≤ 6, where D is the device diameter. The analysis shows that the RANS numerical solutions are unable to accurately replicate the near-wake region based on large statistical errors computed. The average RMSE of near-wake region at z/D = [2, 3, 4, 6] are 0.5864, 0.4127, 0.4344 and 0.3577 while the average RMSE at far-wake region z/D = [8, 12] are 0.2269 and 0.1590, where z is the distance from the cylindrical object along the length of domain. The statistical error values are found to decrease with increasing downstream distance from a cylindrical object. Notably, the standard k–ε and realizable k–ε models are the two best turbulent models representing the near-wake region in RANS modelling, yielding the lowest statistical errors (RMSE at z/D = [2, 3, 4, 6] are 0.5666, 0.4020, 0.4113 and 0.3455) among the tested parameters

Investigating hydraulic loads on a crossbar block ramp using two different computational fluid dynamics models and a physical validation model

Crossbar block ramps provide bed stability and facilitate ecological connectivity in rivers. Two major sources of uncertainty in determining their design loads are their massively turbulent flow and backwater influence. Here, they were addressed using two computational fluid dynamics (CFD) models (created with OpenFOAM) of a complete crossbar block ramp by recording loads (forces and moments) on single crossbars. To model turbulence, the first model used the Reynolds-averaged Navier-Stokes (RANS) approach, and the second model used Large Eddy Simulation (LES). The flow in both models was transient and the free surface was tracked. Their mesh was identical and consisted of about 30.3 million cells distributed on 480 processor cores. The computed sampling interval was 180 s. The CFD models were tested against measurements of water level and pressure from a physical model. All models were in the same scale, 1: 20/3. Three discharges representing three typical flow regimes were studied in each model. Characteristics of the flow regimes were reproduced in all models. The RANS model was up to 5.3 times faster, but produced excessive waves, which likely caused over- and underestimation of crossbar loads. The LES model showed good agreement to the physical model and could be used for load predictions. However, a longer sampling interval as well as a larger variety of discharges would be required to obtain stochastically reliable estimations for the maximum loads. Both models indicated that unsteady waves in the wake-interference flow regime must be considered to find critical loads. The results can support decisions on the methods for future investigations of hydraulic loads on crossbar block ramps.

NUMERICAL SIMULATION OF THREE-DIMENSIONAL CIRCULAR FREE TURBULENT JET FLOW USING DIFFERENT REYNOLDS AVERAGE NAVIER-STOKES TURBULENCE MODELS

The present research work shows the numerical investigation of free turbulent jet behavior as well as thermal characteristics of the jet being issued from the circular nozzle outlet and it is performed by solving the different Reynolds averaged Navier-Stokes (RANS) equation. In this research work, central jet velocity, and turbulent intensity of the jet along with thermal characteristics are calculated along the jet axis to check the coherency, heat-exchanging capability, and widening of the jet. Simulation results of the jet are first validated with the experimental data of previous literature work. The numerical experiment provides a better potential for future improvements in the field of jet flow and exhibits a great deal of resemblance with experimental data with minimum error. Computational fluid dynamics (CFD) is a capable tool for depicting the various flow behaviors and selecting the suitable turbulence model is always required for an accurate prediction of the jet-flow regime. Most of the turbulence models investigated in this research work show good results for turbulence intensity and predicted the near- and far-field region as per the experimental data. From the thermal analysis of the free turbulent jet, it is seen in the present results utilizing the various turbulence RANS models that <i>Nu</i><sub>x</sub> is rather high near the nozzle exit. The standard <i>k-ε</i> model describes the near field and long field of the free jet very well and it is also easy and inexpensive to execute. Furthermore, for the observed free turbulent jet, the most appropriate model is recommended.

QUANTIFICATION AND PROPAGATION OF MODEL-FORM UNCERTAINTIES IN RANS TURBULENCE MODELING VIA INTRUSIVE POLYNOMIAL CHAOS

Undeterred by its inherent limitations, Reynolds-averaged Navier-Stokes (RANS) based modeling is still considered the most recognized approach for several computational fluid dynamics (CFD) applications. Recently, in the turbulence modeling community, quantification of model-form uncertainties in RANS has attracted significant interest. We present a stochastic RANS solver with an efficient implementation of the intrusive polynomial chaos (IPC) method in OpenFOAM. The stochastic solver quantifies and propagates the uncertainties associated with the output of the RANS model (eddy viscosity or Reynolds stress tensor). Two distinct high-dimensional variants of the uncertainties are considered, namely, the random eddy viscosity field (REVF) and the random Reynolds stress tensor field (RRSTF). The randomness is introduced in the approximated eddy viscosity field and the Reynolds stress tensor, while asserting the realizability. The stochastic RANS solver has been tested on various benchmark problems for RANS turbulence modeling. In this study, we discuss two important problems where the stochastic RANS solver shows significantly better performance than the traditional uncertainty quantification (UQ) methods. The first problem analyzed is the flow over periodic hills with a REVF, while the second stochastic problem considered is the flow in a square duct with a RRSTF. Along with the comparison for three different RANS turbulence models, a detailed analysis of the stochastic solver based on various influential model parameters is also presented. The IPC based stochastic solver demonstrated the potential to be used in the UQ analysis of further complex CFD applications, especially when a large number of deterministic simulations is not feasible, e.g., wind farm CFD simulations.

Аеродинамічний аналіз впливу повітряних потоків від вантажівки на рух мотоцикла

The article examines the problem of ensuring the safety of motorcycle movement in the conditions of a turbulent air trail created by a large truck. The development of computer technology, software and methods of computational fluid dynamics (CFD) allows conducting theoretical studies of various aspects of the movement of motor vehicles, which are difficult to reproduce experimentally and which at the same time can have a significant impact on accidents on public roads. A motorcycle is one of the most dangerous vehicles in terms of traffic accidents with human casualties. The analysis of accidents involving motorcycles showed that most of the time their causes are insufficient skill and inability to drive a motorcycle, as well as insufficient attention. At the same time, when driving a motorcycle, movement in conditions of turbulent air flows with a sharp change in their speed and direction is a particular danger. Such conditions arise when overtaking large trucks moving at high speed. An aerodynamic model of the movement of a truck and a motorcycle was built for their different relative positions. From the position of the motorcycle in the turbulent wake behind the truck and after passing the compressed air zone in front of the truck. A study of turbulence models was conducted to assess the accuracy of the most common Reynolds-averaged Navier-Stokes models such as k–ε and k–ω. Zones in which the largest changes in air pressure occur during motorcycle movement have been established, and the magnitude of these changes has been determined. Accordingly, recommendations are provided regarding the characteristics of motorcycle movement when overtaking a large-sized truck and the safe trajectory in which such a maneuver is the safest for drivers with insufficient experience in driving a motorcycle.

Towards the accurate prediction of axial flow and heat transfer in a tightly spaced bare rod bundle configuration

The understanding of flow behaviour and temperature distribution in rod bundles during operation is of great importance for the safety and economical design of existing nuclear reactors and as well as new nuclear technologies. In order to study the thermal-hydraulics phenomena in the fuel assembly geometries, researchers all over the world are increasingly using Computational Fluid Dynamics (CFD) as a reliable research tool. Nevertheless, the prediction capabilities of the most commonly used Reynolds Average Navier-Stokes (RANS) models must be assessed. In this regard, in the present research work, an extensive effort has been put forward and is described in three main steps. As a first step, a wide range of RANS and unsteady RANS computations have been performed to design a numerical experiment for a closely-spaced bare rod bundle in order to perform a direct numerical simulation (DNS), which will serve as a reference for the validation purpose. As a second step, the DNS of this bare rod bundle has been performed for three different Prandtl number (Pr) fluids, i.e. Pr = 2, 1 and 0.025. This DNS has been performed using a higher-order spectral element code and consists of 660 million grid points. Accordingly, an extensive database has been generated for validation purposes. Finally, this database is used to assess the prediction capabilities of different linear and non-linear RANS models. This article will provide a good overview of all three steps and will also highlight the main lessons learned from this research.

Prediction of Self-Sustained Oscillations of an Isothermal Impinging Slot Jet

The present results are focused on the self-sustained oscillations of a confined impinging slot jet and their role in the flow structure and modeling requirements. Unsteady laminar, large-eddy simulation (LES), and Reynolds-averaged Navier–Stokes (RANS) predictions of an isothermal confined impinging jet were validated for several nozzle-to-plate ratios (H/B=4–15) and for laminar (Re=340 and 480) and turbulent (Re=104–2.7×104) conditions. The impinging flow structure was found to be highly influenced by the H/B ratio. For high ratios (H/B>5), the studied steady RANS turbulence models could not satisfactorily predict the high diffusion reported experimentally in the jet-impinging influence zone. The failure of these models has been attributed to the modeling issues of turbulence closures. However, for H/B=8, unsteady laminar 3D and LES calculations were verified, and a sinuous oscillation mode was developed, revealing self-sustained oscillations and the display of periodic flapping of the impinging jet in good agreement with the experiments. The predicted flapping oscillation is one of the reasons for the higher diffusion near the impingement wall, which was verified in several time-averaged experimental studies. The presence of jet flapping matters for clarifying the already long discussion on the RANS model’s validation in predicting impinging jets with high H/B ratios, adding justification to the failure of these turbulence models. This unsteady behavior is correctly computed through LES.

Multi-fidelity Surrogate Modelling of Wall Mounted Cubes

This paper focuses on the application of multi-fidelity surrogate modelling to characteristics of a flow as it changes with a parameter. This provides insight into the potential of combining multi-fidelity modelling approaches with varying fidelities of computational fluid dynamics methods to a parameter space exploration. A limited number of trusted high-fidelity large eddy simulation data points, in combination with an extended study using lower-fidelity Reynolds averaged Navier–Stokes modelling is used as the input for the surrogate model. Multi-fidelity surrogate models are implemented to bridge the low-fidelity and high-fidelity models providing an improved surrogate model over using a single fidelity alone. The flow around tandem wall mounted cubes at varying inlet yaw angle is used as an aerodynamic test case for this methodology. Results presented show that the multi-fidelity surrogate modelling provides a significant improvement over single fidelity modelling for the prediction of global flow properties. This methodology is then extended to combine multiple local flow features into the multi-fidelity model to build up fuller descriptions of the flow at angles not included in the training data for the model. The results of this are presented for both one-dimensional line plots at a range of locations along the center line of the flow and for two-dimensional slices of the velocity field. The multi-fidelity surrogate model produces results at locations in the parameter space away from the high fidelity training data that match closely to large eddy simulation results.

Fluid Dynamics of Turbine Rim Seal Structures: A Physical Interpretation Using URANS

Abstract Unsteady Reynolds-averaged Navier–Stokes modeling (URANS) is a valuable and cost-effective tool for computational fluid dynamics (CFD), including the investigation of mainstream–cavity interaction in turbines. Despite the gap in accuracy with higher order CFD methodologies, URANS is among the few simulation strategies of industrial interest suitable for predicting ingress/egress over a wide range of conditions. This paper presents a numerical study of the flow-field in the upstream double-radial seal of a 1.5 stage turbine. Various configurations are tested, including nonpurged and purged conditions. Rigor of the approach is ensured by a set of sensitivity analyses, allowing the delineation of a best practice on the use of URANS in rim seal simulations: this includes an assessment of the effects of sector size, cavity domain size, and blade count. Time-averaged and time-resolved flow predictions capture coherent structures in the rim gap. An association between the three-dimensional (3D) morphology of these structures and different ingress/egress mechanisms is proposed. Regions of enhanced radial activity are identified to correspond with the blade leading edges. A frequency analysis of unsteady pressure signals probed in the rim gap leads to a calculation of the structure number and speed. The structures are synchronous with the disk rotation for nonpurged cases but rotate at slower speed when purge is introduced. The relative number of blades and vanes directly influences the structure count and velocity. The configuration with no blades is characterized by the slowest structures. The calculations have been conducted at three different flow coefficients for the annulus flow. There is a reduction in radial activity and structure speed at lower flow coefficient, fundamentally related to the reduced pressure asymmetry and gradient of swirl across the rim seal.

Experimental and numerical simulations of air quality in an urban canopy: Effects of roof shapes and atmospheric stability

The roof shapes of buildings and atmospheric stability play vital roles in dispersing and transporting pollutants in urban canopies. This study investigated the influence of roof shapes and atmospheric stability through experimental and numerical simulations. Wind flow and gas diffusion simulations were conducted around buildings in urban canopies using wind tunnel experiments and computational fluid dynamic models based on Reynolds-averaged Navier–Stokes models. Four building roof shapes and three atmospheric stability conditions were used in this study. The results of the experiment and numerical simulations were validated using quantitative computational models based on the validation metrics. The numerical simulation results corresponded with the experimental results for the flow and dispersion. The simulation results showed that the flow velocity of the slanted roof was higher than that of the other roof shapes. The pollutants accumulated near the slanted and pitched roofs' leeward side were higher than those in the flat and hip-shaped roofs. Pollutants in the building increased under stable atmospheric conditions and decreased under unstable atmospheric conditions.

Modeling radial turbine performance under pulsating flow by machine learning method

This work presents the development and application of a machine learning model to predict the unsteady performance of a turbocharger radial turbine subject to on-engine pulsating flow conditions. The model proposed, based on a fully connected neural network, predicts the instantaneous turbine torque and circumferentially-averaged relative inflow angle when given, as only inputs, the total pressure and temperature pulses and the time derivative of the total pressure pulse upstream of the turbine. The training data set for the model is obtained from an experimentally-validated Reynolds-averaged Navier-Stokes model and consists of various operating conditions characterized by different pulse amplitudes and frequencies. Heat transfer effects are neglected by the use of adiabatic boundary conditions while the rotational speed of the rotor is otherwise maintained fixed. Based on the results obtained, the model is shown capable of accurately predicting the turbine torque and local properties of the flow, such as the relative inflow angle, with a high degree of accuracy (coefficient of determination larger than 0.98 ). At first, the model is tested for both interpolation and extrapolation conditions. Given a training data set constituted by only three pulses characterized by different amplitudes, the model accurately predicts the turbine performance both for conditions inside and outside the range of amplitudes of the training data set. Lastly, the model is trained with a larger data set, including both variations of the pulse amplitude and frequency, in order to predict the performance of the turbine subject to a more general pulse shape. The error indicators show an improvement with respect to the extrapolation case due to the larger size of the training data set, showing also a great capability of the model to predict the unsteady performance of the turbine for a more general pulse shape. The model represents a fast and efficient approach for predicting the unsteady turbine performance as compared to more complex experimental set-ups and time-consuming 3D numerical simulations and a valid alternative to the more common 0D-1D models.

Space-filling single square and square fractal grids induced turbulence: Reynolds stress model parameters-optimization

The employment of grid-induced turbulent flow structures as a passive means of augmenting solid-fluid heat transfer is receiving considerable attention, especially in HVAC applications. However, there presently exists a gap in simulation approaches, where industry-accessible CFD packages are unable to accurately express crucial second-order turbulent flow statistics generated by space-filling single square grids (SSGs) and square fractal grids (SFGs). The present study reports a successful application of a revised Reynolds Stress Model (RSM), which accurately replicates the streamwise distributions of centerline mean flow velocity and turbulence intensity in the lee of one SSG (operating under three different flow Reynolds number ReDh) and five geometrically different SFG test cases after undergoing Nelder-Mead downhill simplex optimization of key RSM kernel parameters. The optimized RSM presents a disagreement of, at worst, 4.30% and 9.98%, respectively against experimental hot-wire anemometry measurements of first-order and second-order statistics, and is the first known instance of the RSM being validated for turbulence intensity predictions of SSG- and SFG-induced turbulence. Examination of RSM parameters reveals that the pre-factors for the rates of turbulence dissipation production and destruction (C1,ε and C2,ε) hold greatest effect on simulation accuracy, with additional optimization of the turbulent viscosity pre-factor (Cμ) required for SFG cases. This is attributed to the effect of enhanced turbulent transport due to the cascading and multiscale nature of SFG turbulence, which insofar could not be replicated by Reynolds-Averaged Navier-Stokes (RANS) models. The values of optimized C1,ε, C2,ε, and Cμ range between 1.057 to 1.697, 2.226 to 2.556, and 0.17 to 2.27, respectively. This leads to the largest deviation of −26.6%, 33.1%, and 200% for C1,ε, C2,ε, and Cμ, respectively, when compared to their corresponding default values. With regards to the sensitivity of this parameter set on grid design, it is shown that the grid's fractal iteration number N and thickness ratio tr have greatest influence on the variation of C1,ε, C2,ε, and Cμ, while the effect of ReDh is insignificant. Overall, this study presents an alternative approach to capture the anisotropic and inhomogeneous nature of SSG- and SFG-induced turbulence for industrial heat-transfer applications via an accessible RANS package, which was previously constrained to expensive DNS and LES studies.

Large-eddy simulation of turbulent non-Newtonian flows: A comparison with state-of-the-art RANS closures

In the present research, a comparative study of improved Large-Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS) closures are carried out with the focus on the influence of terms accounting for the effect of Non-Newtonian (NN) power-law fluid rheology. Using different available databases for shear-thinning turbulent pipe flows, a new set of calibrated constants is proposed for the four-equation k-ε-ζ-f RANS model which results in improved overall performance compared to the original constants. Analyzing the results, the accuracy of this model in comparison with the conventional RANS models, to the first degree, is attributed to its intrinsic ability to account for the wall damping effect, which resulted in about 50% improvement in turbulent kinetic energy prediction with respect to the other RANS models, and then, to the extra NN terms leading to the more accurate prediction of the drag-reduction phenomenon. However, the sensitivity of this model results to the model constants is high which can question the validity of the constants for more practical high-Reynolds-number problems. On the other hand, LES models provided more reliable results with the best performance shown by the Dynamic Lagrangian (DL) SGS closure, especially in predicting mean apparent viscosity and second-order statistics. Both NN k-ε-ζ-f RANS and DL LES models demonstrated great potential for non-Newtonian turbulent flows and the choice between them would be based on the level of accuracy required and the limitation of computational costs.

Evaluation of computational fluid dynamics models for predicting pediatric upper airway airflow characteristics.

Computational fluid dynamics (CFD) has the potential for use as a clinical tool to predict the aerodynamics and respiratory function in the upper airway (UA) of children; however, careful selection of validated computational models is necessary. This study constructed a 3D model of the pediatric UA based on cone beam computed tomography (CBCT) imaging. The pediatric UA was 3D printed for pressure and velocity experiments, which were used as reference standards to validate the CFD simulation models. Static wall pressure and velocity distribution inside of the UA under inhale airflow rates from 0 to 266.67mL/s were studied by CFD simulations based on the large eddy simulation (LES) model and four Reynolds-averaged Navier-Stokes (RANS) models. Our results showed that the LES performed best for pressure prediction; however, it was much more time-consuming than the four RANS models. Among the RANS models, the Low Reynolds number (LRN) SST k-ω model had the best overall performance at a series of airflow rates. Central flow velocity determined by particle image velocimetry was 3.617m/s, while velocities predicted by the LES, LRN SST k-ω, and k-ω models were 3.681, 3.532, and 3.439m/s, respectively. All models predicted jet flow in the oropharynx. These results suggest that the above CFD models have acceptable accuracy for predicting pediatric UA aerodynamics and that the LRN SST k-ω model has the most potential for clinical application in pediatric respiratory studies.

Aspect ratio and combined installation in fluidic oscillator-crossflow interactions

This paper describes a computational study of fluidic oscillator-crossflow interactions. Fluidic oscillators are high potential active flow control devices, which offer promise for cooling and flow separation. Previous experimental studies have investigated the internal/external flow fields of fluidic oscillators. This improved the understanding of three-dimensional flow fields; however, this has been limited to single aspect ratio nozzles in single variable installations. Research has not analyzed the effects of varying both oscillator parameters and installation conditions. Therefore, the present aim is to enhance flow field understanding by examining three aspect ratio (AR) nozzles in default perpendicular condition and in a “skewed incline” configuration (β = 60°, α = 75°). An unsteady Reynolds averaged Navier Stokes model was validated with prior experimental data and then used to evaluate time-averaged and time-accurate flow fields at three different blowing ratios. Strouhal number analysis uncovers the effectiveness of parametric changes. Internal flow fields are presented, identifying key differences and similarities. External flow field analysis in regard to spreading angles 55°–65° observes greater wall-normal penetration for lower AR oscillators with improved spanwise penetration for larger AR's. The combined installation produces stronger primary vortices, improving penetration along the wall, while inclination keeps vortices close to the wall. Oscillator effectiveness is dependent on the application.

Bayesian parameter estimation of SST model for shock wave-boundary layer interaction flows with different strengths

The Shock Wave-Boundary Layer Interaction (SWBLI) flow generated by compression corner widely occurs in engineering. As one of the primary methods in engineering, the Reynolds Averaged Navier-Stokes (RANS) methods usually cannot correctly predict strong SWBLI flows. In addition to the defects of the eddy viscosity assumption, the uncertainty of the closure coefficients in RANS models often significantly impacts the simulation results. This study performs parametric sensitivity analysis and Bayesian calibration on the closure coefficients of the Menter k-ω Shear-Stress Transport (SST) model based on the SWBLI with different strengths. Firstly, the parametric sensitivity on prediction results is analyzed using the Sobol index. The results indicate that the Sobol indices of wall pressure and skin friction exhibited opposite fluctuation trends with the increase of SWBLI strength. Then, the Bayesian uncertainty quantification method is adopted to obtain the posterior probability distributions and Maximum A Posteriori (MAP) estimates of the closure coefficients and the posterior uncertainty of the Quantities of Interests (QoIs). The results indicate that the prediction ability for strong SWBLI of the SST model is significantly improved by using the MAP estimates, and the relative errors of QoIs are reduced dramatically.

Modeling particle distribution in a ventilated room with modified discrete random walk methods

The airflow and micro-particle dispersion in a 3-D ventilated scaled room has been simulated numerically. The flow field was studied by the Eulerian method using a Reynolds Averaged Navier-Stokes model, and we used the Lagrangian approach to solve the equations of particle motion. The purpose is to evaluate and compare various discrete random walk methods (DRW) and continuous random walk methods (CRW) to evaluate particle concentration distribution in indoor environments. The isotropic DRW method’s performance has been compared with models in which anisotropy of turbulence is applied, including CRW and modified DRW models based on near-wall direct numerical simulation results, near-wall kinetic energy, and the helicity of the flow. The results reveal that the isotropic DRW method can predict particle concentration in the indoor environment, and using a modified DRW model is not necessary.