Prediction Of Flow Field Research Articles

Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

S-frame discrepancy correction models for data-informed Reynolds stress closure

Despite their well-known limitations, Reynolds averaged Navier-Stokes (RANS) models remain the most commonly employed tool for modeling turbulent flows in engineering practice. RANS models are predicated on the solution of the RANS equations, but the RANS equations involve an unclosed term, the Reynolds stress tensor, which must be modeled. The Reynolds stress tensor is often modeled as an algebraic function of mean flow field variables and turbulence variables. This, however, introduces a discrepancy between the Reynolds stress tensor predicted by the Reynolds stress model and the exact Reynolds stress tensor. This discrepancy can result in inaccurate mean flow field predictions for complex flows of industrial relevance. In this paper, we introduce a data-informed approach for arriving at Reynolds stress models with improved predictive performance. Our approach relies on learning the components of the Reynolds stress discrepancy tensor associated with a given Reynolds stress model in the mean strain-rate tensor eigenframe. These components are typically smooth and hence simple to learn using state-of-the-art machine learning strategies and regression techniques. Our approach automatically yields Reynolds stress models that are symmetric, and it yields Reynolds stress models that are both Galilean and frame invariant provided the inputs are themselves Galilean and frame invariant. To arrive at computable models of the discrepancy tensor, we employ feed-forward neural networks and an input space spanning the integrity basis of the mean strain-rate tensor, the mean rotation-rate tensor, the mean pressure gradient, and the turbulent kinetic energy gradient, and we introduce a framework for dimensional reduction of the input space to further reduce computational cost. Numerical results illustrate the effectiveness of the proposed approach for data-informed Reynolds stress closure for a suite of turbulent flow problems of increasing complexity.

Squirrel-Cage Fan System Optimization and Flow Field Prediction Using Parallel Filling Criterion and Surrogate Model

In this study, the blade shape of the squirrel-cage fan system inside the range hood was optimized using the surrogate model to improve the maximum volume flow rate. The influence of computational fluid dynamics (CFD) noise was concerned. The regression Kriging model (RKM) was used as a surrogate model to reflect the relationship between the design parameters of the blade and the volume flow rate. The parallel filling criterion after re-interpolation was used to improve the optimization efficiency further and ensure global optimization. Through experimental verification, we found that the relative error between the volume flow rate of the optimal sample of RKM and the experiment was only 0.4%. Compared with the prototype, the maximum volume flow rate of the optimal sample of RKM was increased by 2.9%, and the efficiency under the corresponding working conditions was increased by 2%. RKM was used to predict the velocity field of the volute and impeller exit section to explore the feasibility of the RKM in the flow field prediction. Research shows that the RKM cannot accurately predict the velocity of each grid on the cross-section. Still, it can accurately predict the changing trend of the velocity.

Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement

We develop a method for the prediction of flow fields based on local particle image velocimetry (PIV) measurement. High spatial resolution can be achieved by focusing PIV on local flow regions; however, it is difficult for standard dynamic mode decomposition (DMD) to predict the temporally resolved flow field based on limited information in sub-domain. In this regard, the local flow field is embedded using time-delay to augment the spatial dynamics. As such, both high temporally and spatially resolved flow fields can be faithfully obtained from local PIV measurement using the DMD method. Using fabricated patterns, we demonstrate that DMD with time-delay embedding can faithfully predict dynamic patterns over a long time interval, whereas the standard DMD can only match the ground truth briefly following initiation. Using PIV measurement of a wake flow and a highly dynamic sweeping jet flow, the DMD with time-delay embedding can increase the temporal resolution up to 100 times with a prediction error rate of approximately 8%. Compared with wake flow, where unsteady flow patterns are relatively weak, a sweeping jet flow demonstrates that the prediction performance is improved even more significantly using time-delay embedding compared with standard DMD when the flow is highly dynamic. For sweeping jet flow, the prediction error rate can drop from 56% using standard DMD to 8.3% by embedding a time-delay smaller than five steps, for a small cost of calculation time. In addition, the DMD with time-delay embedding shows robustness to small noise. For data with high noise whose signal-to-noise ratio is 15, the method has an error rate of less than 5%.

Numerical Investigation of the Turbulent Flame Propagation in Dual Fuel Engines by Means of Large Eddy Simulation

Dual fuel combustion depicts a possible alternative to reduce emissions from large engines and is characterized by injecting a small amount of diesel fuel into a lean natural gas–air mixture. Thereby, the presence of autoignition, diffusive and premixed combustion determine the high complexity of this process. In this work, an Extended Coherent Flame Model was adapted to consider the effect of natural gas on the ignition delay time. This model was afterward utilized to simulate 25 consecutive engine cycles employing LES. In this framework, the ensemble-average flow field was compared to a RANS solution to assess the advantages of LES in terms of the prediction of the in-cylinder flow field. A detailed investigation of the heat release characteristic showed that natural gas already highly contributes to the heat release at the beginning of combustion. Furthermore, a methodology to investigate the turbulent combustion regimes was utilized. It could be ascertained that the combustion mainly occurs in the regime of thin reaction zones. Possible triggers of cycle-to-cycle variations were determined in the velocity fluctuations in the cylinder axis direction and the flame formation in the gaps between the spray plume. The findings support the understanding of dual fuel combustion and serve as a basis for developing future combustion models.

Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework

Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.

A deep learning based prediction approach for the supercritical airfoil at transonic speeds

In traditional ways, the aerodynamic property of the aircraft is obtained by solving Navier-Stokes equations or performing tunnel experiments. However, these methods are time consuming for the aircraft design and optimization. In comparison, the deep learning technique is capable of handling high dimensional parameters and can describe compressible flow structures clearly and efficiently. For these, an efficient and accurate prediction approach based on the deep neural network is proposed for the compressible flows over the transonic airfoils in this study. By investigating the effects of the input coordinate features of the deep learning method on the prediction accuracy and robustness, the aerodynamic characteristics, such as lift, drag, and pitch coefficients, are obtained from the predicted flow fields. Results indicate that the proposed deep learning prediction method is with a high resolution and efficiency. It is promising to be extended to the optimization and design process of the supercritical airfoil.

Analysis of flow field and turbulence predictions in a lung model applying RANS and implications for particle deposition

Airflow and aerosol deposition in the human airways are important aspects for applications such as pulmonary drug delivery and human exposure to aerosol pollutants. Numerical simulations are widely used nowadays to shed light in airflow features and particle deposition patterns inside the airways. For that purpose, the Euler/Lagrange approach is adopted for predicting flow field and particle deposition through point-particle tracking. Steady-state RANS (Reynolds-averaged Navier-Stokes) computations of flow evolution in an extended lung model applying different turbulence models were conducted and compared to measurements as well as high resolution LES (large-eddy simulations) for several flow rates. In addition, various inlet boundary conditions were considered and their influence on the predicted flow field was analysed. The results showed that the mean velocity field was simulated reasonably well, however, turbulence intensity was completely under-predicted by two-equation turbulence models. Only a Reynolds-stress model (RSM) was able predicting a turbulence level comparable to the measurements and the high resolution LES. Remarkable reductions in wall deposition were observed when wall effects were accounted for in the drag and lift force expressions. Naturally, turbulence is an essential contribution to particle deposition and it is well known that two-equation turbulence models considerably over-predict deposition due to the spurious drift effect. A full correction of this error is only possible in connection with a Reynolds-stress turbulence model whereby the predicted deposition in dependence of particle diameter yielded better agreement to the LES predictions. Specifically, with the RSM larger deposition is predicted for smaller particles and lower deposition fraction for larger particles compared to LES. The local deposition fraction along the lung model was numerically predicted with the same trend as found from the measurements, however the values in the middle region of the lung model were found to be somewhat larger.

Structural Response of a Single-Stage Centrifugal Compressor to Fluid-Induced Excitations at Low-Flow Operating Condition: Experimental and Numerical Study

The article describes an assessment of possible changes in constant fatigue life of a medium flow-coefficient centrifugal compressor impeller subject to operation at close-to-surge point. Some aspects of duct acoustics are additionally analyzed. The experimental measurements at partial load are presented and are primarily used for validation of unidirectionally coupled fluid-structural numerical model. The model is based on unsteady finite-volume fluid-flow simulations and on finite-element transient structural analysis. The validation is followed by the model implementation to replicate the industry-scale loads with reasonably higher rotational speed and suction pressure. The approach demonstrates satisfactory accuracy in prediction of stage performance and unsteady flow field in vaneless diffuser. The latter is deduced from signal analysis relying on continuous wavelet transformations. On the other hand, it is found that the aerodynamic incidence losses at close-to-surge point are underpredicted. The structural simulation generates considerable amounts of numerical noise, which has to be separated prior to evaluation of fluid-induced dynamic strain. The main source of disturbance is defined as a stationary region of static pressure drop caused by flow contraction at volute tongue and leading to first engine-order excitation in rotating frame of reference. Eventually, it is concluded that the amplitude of excitation is too low to lead to any additional fatigue.

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

A comparative study of interaction of random waves with a cluster of structures on a berm with RANS and LES models

The interactions of random waves with a cluster of structures positioned in a gridded array are investigated through a series of experiments and high-fidelity RANS and LES simulations. The performances of two different turbulence models, k−ω SST and LES, are evaluated in terms of the predicted flow field and resulting loading on the structures, both in the time and frequency domains. The comparative analyses show that the k−ω SST model predicts the free surface elevations slightly better than that of the LES model. The k−ω SST model fails to resolve the velocity fluctuations leading to the underestimation of the flow kinetic energy. The k−ω SST model underpredicts the wave runup/rundown, as well as the depth-integrated pressure force, compared to the LES model. The wave runup heights and onshore-directed depth-integrated pressure forces are found to follow the Rayleigh distribution. The comparisons of the wave runup heights and corresponding onshore-directed hydrodynamic forces follow a relatively linear trend that is within the ∓95% confidence intervals.

Numerical prediction of slurry erosion and its influence on prevailing flow conditions using a dynamic mesh method

With the help of Computational Fluid Dynamics (CFD), the amount and location of slurry erosion can be predicted using renowned models. However, all common models do not change the hydraulic contour of the system and can therefore be interpreted as a CFD-post-processing step. Thus, the influence of slurry erosion on the fluid flow is currently not considered and the effects of changing flow conditions on the system performance or perhaps energy efficiency are not predictable. Building upon this state of the art, a numerical method based on the idea of a dynamic mesh procedure is developed. The method uses the predicted erosion to locally deform the mesh during simulation. The method contains the generation of an array-based mesh structure, the precise movement of affected boundary nodes including the tracking of inner nodes as well as a reconstructing procedure to improve mesh quality after deformation. The whole method is implemented in a commercial CFD-code. The functionality of the method is examined using the example of a standardized water pump. The comparison to corresponding experimental results revealed an accurate prediction of the erosion distribution in the pump’s front casing. Furthermore, the evaluation of the pump’s performance curves indicated an accurate prediction of the changing flow field caused by erosive wear.

A COMBINED NEURAL NETWORK AND MULTIPLE MODIFICATION STRATEGY FOR REYNOLDS-AVERAGED NAVIER-STOKES TURBULENCE MODELING

Solving the Reynolds-averaged Navier-Stokes (RANS) equation remains an effective and practical approach in engineering applications, but the uncertainty of Reynolds stress modeling will lead to discrepancies in the prediction accuracy of this approach. With the development of artificial intelligence, the data-driven method of turbulence model combined with machine learning algorithm is more effective than the original RANS model, however, the stability and prediction accuracy of the data-driven method could still be further improved. In the present paper, a fully connected neural network is constructed to predict the eddy viscosity, and this neural network is called as Eddy Viscosity Neural Network (EVNN). Additionally, a tensor-based neural network (TBNN) is also applied to predict the higher-order eddy viscosity relationship between the unclosed quantity and the analytical quantity, and the basis tensors are used to ensure the Galilean invariance. Finally, the closed-loop accuracy of the predicted flow field is realized through multiple modifications. For the method above, the neural network which is combined by EVNN and TBNN, is trained by using the high-fidelity data generated by the large eddy simulation (LES) and the baseline data obtained by the RANS simulation. Compared with the high-fidelity LES results, the results of the modified model exhibit significantly higher accuracy in the posterior velocity field, the mean pressure coefficient, and the mean friction coefficient than the original RANS model. It can be found that the implicit treatment of the linear part of the Reynolds stress can enhance the numerical stability, and the modification of the nonlinear part of the Reynolds stress can better predict the anisotropic characteristics of the flow field. Furthermore, the prediction accuracy is further improved through the multiple modification strategy. Therefore, the combined neural network and multiple modification strategy developed in this paper, have strong potentials in data-driven turbulence modeling and engineering applications in the future.

A Study on Liquid Spray Evaporation into a High Temperature Gas Stream.(Dept.M)

An analytical model has been developed for the process of liquid spray evaporation into a high temperature gas stream. In this model, the calculation scheme utilizes the cellular approach in-which each cell is regarded as a control volume and the gas-droplet interaction is considered. Based on an experimental data, droplets of a liquid spray are classified into several size distribution groups, The conservation laws of mass, momentum and energy are applied to each group considering the evaporation and diffusion of droplets, Predictions of gas-droplet Flow-field, trajectories of the Individual droplets, rate of evaporation and change of size distribution of the spray droplets have been obtained. An experimental verification of the prediction results has been carrid out. An optical arrangement is used for measurements of sire distribution of spray droplets into a high temperature gag stream. The comparison shows a reasonable agreement of the results based on the analytical model with the experimental measurements for each of droplet size distribution and changes of Sauter Dean diameter at different axial distances from the injection nozzle plane.

Computational Analysis of Water-Submerged Jet Erosion

Erosion causes substantial damage in many industrial equipment such as pump components, valves, elbows, and plugged tees. In most cases, erosion is coupled with corrosion, resulting in major financial loss (nearly 3.4% of the global gross domestic product) as evidenced in oil and gas industries. In most cases, the erosion occurs in a submerged water medium. In this paper, erosion characteristics of stainless steel 316 were investigated computationally in a water-submerged jet impingement setup. The erosion profiles and patterns were obtained for various parameters over ranges of inlet velocities (3 to 16 m/s), nozzle diameters (5 to 10 mm), nozzle–target distances (5 to 20 mm), nozzle shapes (circular, elliptical, square, and rectangular), impingement angles (60° to 90°), and particle sizes (50 to 300 µm). The range of Reynolds number studied based on nozzle diameters is 21,000–120,000. The Eulerian–Lagrangian approach was used for flow field prediction and particle tracking considering one-way coupling for the particle–fluid interaction. The Finnie erosion model was implemented in ANSYS-Fluent 19.2 and used for erosion prediction. The computational model was validated against experimental data and the distributions of the erosion depth as well as the locations of the of maximum and minimum erosion points are well matched. As expected, the results indicate an increase in loss of material thickness with increasing jet velocity. Increasing the nozzle diameter caused a reduction in the maximum depth of eroded material due to decreasing the particle impact density. At a fixed fluid inlet velocity, the maximum thickness loss increases as the separation distance between the nozzle outlet and target increases, aspect ratio of nozzle shape decreases, and impingement angle increases. The erosion patterns showed that the region of substantial thickness loss increases as nozzle size/stand-off height increases and as particle size decreases. In addition, increasing the aspect ratio and impingement angle creates skewed erosion patterns.

Modelling Aspects in the Simulation of the Diffusive Flame in A Bluff-Body Geometry

Gas turbines are expected to play a key role in the energy production scenario in the future, and the introduction of carbon-free fuels is fundamental for the development of a sustainable energy mix. The development of a reliable numerical model is thus fundamental in order to support the design changes required for the burners. This paper presents the results of a numerical investigation on a turbulent, diffusive, combustion test case, with the purpose of identifying the best compromise between accuracy and computational cost, in the perspective of the model application in real, more complex, geometries. Referring to a test case has two main advantages. First, a rather simple geometry can be considered, still retaining a few peculiar flow features, such as recirculation vortices and shear layers, which are typical of real applications. Second, the experimental setup is much more detailed than in the case of real turbines, allowing a thorough model validation to be performed. In this paper, the Standard 2-equations k-ε model and the Speziale-Sarkar-Gatski Reynolds Stress Model are considered. Moreover, both the FGM combustion model and the detailed chemistry model are used, coupled with two chemical reaction mechanisms, and their results are compared. Finally, a standard and an enhanced near-wall approach are employed to solve the transport equations close to the walls. The results show a good agreement in the temperature distribution at the axial positions corresponding to the experimental measurements. Overall, the standard wall function approach for describing the near-wall flow proved to be more effective at increasingly higher distances from the jet centre. Such differences are related to the formulations employed by the two near-wall approaches, which led to changes in the predicted flow field around the fuel jet. Finally, the adoption of a reaction mechanism describing in detail the species concentration is mandatory whenever the reliable prediction of the NOx formation is of primary importance. The conclusion reached in this paper can be helpful for the development of reliable and cost-effective CFD models of turbine combustors.

The Impact of Various Modeling Decisions on Flow Field Predictions in a Centrifugal Compressor

Abstract Computational tools have become increasingly important in design and research applications in recent years due to increasing computational resources. In most cases, model geometry and flow physics are simplified to reduce the complexity of the computational model. While this was necessary historically, modern computational tools are capable of including realistic features such as fillets, surface roughness, and heat transfer. This work presents extensive and systematic numerical results from a simulation of a centrifugal compressor stage for an aero-engine application. Numerical results are compared to detailed experimental data to investigate the effect of various modeling decisions, including turbulence models, on the predicted aerodynamics developing through the diffuser passage. Roughness and the inclusion of fillets significantly impact the flow development, especially with the shear-stress transport (SST) turbulence model. This approach leads to the conclusion that the BSL-EARSM model is best able to predict the experimentally determined diffuser flow profiles and overall performance trends with the inclusion of the previously mentioned model features. Additionally, misleading conclusions can be reached if modeling decisions are based on merely matching overall performance values. Finally, frozen rotor simulations are used to roughly approximate the impact of unsteadiness on the flow field. The results show a significant impact and also that the inclusion of approximate unsteady effects tends to further improve the predictive capability of the computational models that were considered.

Experimental Fluid Dynamics Applications in Radial Turbomachines: Inlet Recirculation in Centrifugal Compressor, Rotating Stall and Flow in Vaneless Diffuser, and Improvement in Accuracy of CFD for Predicting Flow Fields in a Radial Turbine Rotor

Performance of a turbomachine strongly depends on its internal flow. CFD (Computational Fluid Dynamics) is now an essential tool obtaining information on such internal flow as well as its performance. However, when CFD is taken into a design process, the accuracy and feature of a CFD code must be understood. CFD requires EFD (Experimental Fluid Dynamics) for validating and ensuring its accuracy. Besides, EFD provides some empirical rules and models to complex phenomena that CFD needs high computational costs and long turnaround time and that are difficult to be integrated in a design work. Since CFD and EFD are complementary to each other, EFD with CFD or CFD with EFD serves as a powerful tool to understand flow phenomena in turbomachines. So, EFD is still important and frequently applied in industries to design and develop radial turbomachines. Examples of EFD conducted during the design of centrifugal compressors and radial turbines in the author’s affiliation are introduced here.

Physical insight into axisymmetric scramjet intake design via multi-objective design optimization using surrogate-assisted evolutionary algorithms

Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfields is of particular importance for high-performance intake design, prior to consideration of viscous effects in the design process. Further, inviscid axisymmetric intakes serve as a base for streamline tracing, one of the most promising design methodologies for scramjet intakes. Multi-objective optimization studies have been conducted via surrogate-assisted evolutionary algorithm to gain physical insights into axisymmetric intake design in this study. The results indicate the existence of global optimum solutions that can simultaneously achieve maximum compression efficiency and minimum drag for any degree of compression in case the outflow is supersonic at the intake exit, which has been verified by theory. In addition, a correlation between compression efficiency and flow uniformity has been found and discussed quantitatively. This assures the optimality of the Busemann intakes in that they simultaneously offer high compression efficiency and uniform flow at the intake exit in the inviscid regime.

Prediction of PbLi fluid flow and temperature field in a thermal convection loop for qualification of fusion materials

Two modeling tools, a thermohydraulics code and a computational model in COMSOL Multiphysics, have been developed, tested and then applied to the analysis of fluid flow and heat transfer in a thermal convection loop (TCL). Such a device has recently been used to experimentally evaluate corrosion compatibility of APMT (Fe–21Cr–5Al–3Mo) steel with high-temperature molten eutectic lead-lithium (PbLi) alloy at Oak Ridge National Lab, TN, USA. The 1-D thermohydraulics code allows for rapid calculations of the loop parameters as a function of the applied heat flux. The supplemental COMSOL finite-element computations provide detailed 3-D velocity and temperature field data but are much more time consuming. Both modelling tools demonstrate an excellent agreement in the computed circulation velocity as well as maximum and minimum temperatures. The performed TCL analysis focuses on flow development in the “hot” and “cold” legs, formation of Dean vortices in corner regions, blocking effect of the immersed samples, and radiative and convective cooling effects under the experiment-relevant conditions for Prandtl number Pr=0.015, Grashof numbers Gr∼107, and Reynolds numbersRe∼104.