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The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation. The authors hypothesise the behaviour of their insight errors (IEs) and proceed to quantify the IEs provided by the crowd participants. They then use the insight framework to model the behaviours of the errors. Using the crowd responses and models from the framework, they test the hypotheses and use the results to validate the framework for the speedup of CFD applications.

The authors use a randomised between-subjects experiment with blocking. CFD grid resolution is the independent variable while IE is the dependent variable. The experiment has one treatment factor with five levels. In case varying timestamps has an effect on insight variance levels, the authors block the responses by timestep. In total, 150 participants are randomly assigned to one of five groups and also randomly assigned to one of five blocks within a treatment. Participants are asked to complete a benchmark and open-ended task.

The authors find that the variances of insight and perception errors have a U-shaped relationship with grid resolution, that similar to the previously studied visualisation applications, the IE framework is valid for insights generated from CFD results and grid resolution can be used to predict the variance of IE resulting from observing CFD post-processing results.

To the best of the authors’ knowledge, no other work has measured IE variance to present it to simulation users so that they can use it as a feedback metric for selecting the ideal grid resolution when using grid resolution to speedup CFD simulation.

The paper aims to capture the rack-level thermal dynamics in data center. It proposes the rack-level response experiments as well as transient Computational Fluid Dynamics (CFD) analysis to characterize the local thermal environment of the system.

A single sever simulator rack and its two neighboring racks with its cold and hot aisle containment have been modeled with known cold air supply temperature and flow rate for transient CFD analysis. The heat load was kept constant initially and varied case-to-case basis, which includes capturing the rack-level response with respect to changes in input. However, the response experiments on simulator rack were performed for 14 h by variation of server heat loads as step and ramp input.

The paper provides the detailed transient CFD analysis of data center racks. The local cold air flow rates and temperature at the vicinity of the racks showed significant effect due to changes in input. It was concluded that the rack-level dynamics impacts the thermal environment of data center and hence cannot be ignored.

The high computing devices and faster internet demands have led to major thermal management concerns for data center operators. To tackle this issue, capturing the system thermal dynamics is imperative. However, the system-level CFD analysis is computationally expensive. Therefore, this paper deals with the rack-level transient CFD study using commercial tool STAR CCM+.

This paper includes the modeling of the servers as a porous media as well as the multigrid method to enhance the computational speed. The successful implementation of this approach validated through experiments. This would help to establish a base for research in any type of data center.

This paper provides the porous media approach to model servers and multigrid method to enhance the computational speed. At the same time, the thought of characterizing the local dynamics at the vicinity of data center racks is unique.

The macroscopic properties of dried sand can be correctly modelled when the accurate determination of the microscopic properties is available. The microscopic properties between the particles such as the coefficients of rolling (µ _r) and sliding (µ _s), are numerically determined in two different ways: with and without considering the fluid effect. In an earlier study, the microscopic properties were determined by discrete element method (DEM) and without considering the air effect on the macroscopic properties such as the Angle of Repose. The purpose of this paper is to recalibrate the microscopic properties through a coupling between the DEM and computational fluid dynamics (CFD).

The first step is dedicated to the calibration of the CFD-DEM model through modelling a single particle sedimentation within air, water, and silicon oil. The voidage and drag models, the grid size ratio (D/dx), the domain size ratio (W/D), and the optimum coupling interval between the CFD and DEM were investigated through comparing the CFD-DEM results with the analytical solution and experimental data. The next step is about modelling an Hourglass with the calibrated CFD-DEM model to recalibrate the µ _r and µ _s of dried sand particles.

It was concluded that the air has a minor effect on the macroscopic properties of the dried sand and the µ _r and µ _s that were obtained with the DEM can be utilized in the CFD-DEM simulation.

Utilizing the granulometry of dried quartz sand in the calibration process of the CFD-DEM method has raised the possibility of using the µ _r and µ _s for other applications in future studies.

This paper aims to present a fast and effective approach to tackle complex fluid structure interaction problems that are relevant for the aeronautical design.

High fidelity computer-aided engineering models (computational fluid dynamics [CFD] and computational structural mechanics) are coupled by embedding modal shapes into the CFD solver using RBF mesh morphing.

The theoretical framework is first explained and its use is then demonstrated with a review of applications including both steady and unsteady cases. Different flow and structural solvers are considered to showcase the portability of the concept.

The method is flexible and can be used for the simulation of complex scenarios, including components vibrations induced by external devices, as in the case of flapping wings.

The computation mesh of the CFD model becomes parametric with respect to the modal shape and, so, capable to self-adapt to the loads exerted by the surrounding fluid both for steady and transient numerical studies.

Computational fluid dynamics (CFD)/computational structural dynamics (CSD) coupling analysis is an important method in the research of helicopter aeroelasticity due to its high precision. However, this method still suffers from some problems, such as wake dissipation and large computational cost. In this study, a new coupling method and a new air load correction method that combine the free wake model with the CFD/CSD method are proposed to maintain computational efficiency whilst solving the wake dissipation problem of the prior coupling methods.

A new coupling method and a new air load correction method that combine the free wake model with the CFD/CSD method are proposed. With the introduction of the free wake model, the CFD solver can adopt two-order accuracy schemes and fewer aerodynamic grids, thus maintaining computational efficiency whilst solving the wake dissipation problem of the prior coupling methods.

Compared with the predictions of the prior methods and flight test data, those of the proposed method are more accurate and closer to the test data. The difference between the two methods in high-speed forward flight is minimal.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed method further.

In this paper, a CFD/CSD/free wake coupling method is proposed to improve the computational accuracy of the traditional CFD/CSD coupled method and ensure the computational efficiency.

The purpose of this paper is to review the applications of the chemical reactor network (CRN) approach for modeling the combustion in gas turbine combustors and classify the CRN construction methods that have been frequently used by researchers.

This paper initiates with introducing the CRN approach as a practical tool for precisely predicting the species concentrations in the combustion process with lower computational costs. The structure of the CRN and its elements as the ideal reactors are reviewed in recent studies. Flow field modeling has been identified as the most important input for constructing the CRNs; thus, the flow field modeling methods have been extensively reviewed in previous studies. Network approach, component modeling approach and computational fluid dynamics (CFD), as the main flow field modeling methods, are investigated with a focus on the CRN applications. Then, the CRN construction approaches are reviewed and categorized based on extracting the flow field required data. Finally, the most used kinetics and CRN solvers are reviewed and reported in this paper.

It is concluded that the CRN approach can be a useful tool in the entire process of combustion chamber design. One-dimensional and quasi-dimensional methods of flow field modeling are used in the construction of the simple CRNs without detailed geometry data. This approach requires fewer requirements and is used in the initial combustor designing process. In recent years, using the CFD approach in the construction of CRNs has been increased. The flow field results of the CFD codes processed to create the homogeneous regions based on construction criteria. Over the past years, several practical algorithms have been proposed to automatically extract reactor networks from CFD results. These algorithms have been developed to identify homogeneous regions with a high resolution based on the splitting criteria.

This paper reviews the various flow modeling methods used in the construction of the CRNs, along with an overview of the studies carried out in this field. Also, the usual approaches for creating a CRN and the most significant achievements in this field are addressed in detail.

The paper aims to develop an efficient data-driven modeling approach for the hydroelastic analysis of a semi-circular pipe conveying fluid with elastic end supports. Besides the structural displacement-dependent unsteady fluid force, the steady one related to structural initial configuration and the variable structural parameters (i.e. the variable support stiffness) are considered in the modeling.

The steady fluid force is treated as a pipe preload, and the elastically supported pipe-fluid model is dealt with as a prestressed hydroelastic system with variable parameters. To avoid repeated numerical simulations caused by parameter variation, structural and hydrodynamic reduced-order models (ROMs) instead of conventional computational structural dynamics (CSD) and computational fluid dynamics (CFD) solvers are utilized to produce data for the update of the structural, hydrodynamic and hydroelastic state-space equations. Radial basis function neural network (RBFNN), autoregressive with exogenous input (ARX) model as well as proper orthogonal decomposition (POD) algorithm are applied to modeling these two ROMs, and a hybrid framework is proposed to incorporate them.

The proposed approach is validated by comparing its predictions with theoretical solutions. When the steady fluid force is absent, the predictions agree well with the “inextensible theory”. The pipe always loses its stability via out-of-plane divergence first, regardless of the support stiffness. However, when steady fluid force is considered, the pipe remains stable throughout as flow speed increases, consistent with the “extensible theory”. These results not only verify the accuracy of the present modeling method but also indicate that the steady fluid force, rather than the extensibility of the pipe, is the leading factor for the differences between the in- and extensible theories.

The steady fluid force and the variable structural parameters are considered in the data-driven modeling of a hydroelastic system. Since there are no special restrictions on structural configuration, steady flow pattern and variable structural parameters, the proposed approach has strong portability and great potential application for other hydroelastic problems.

The purpose of this paper is to advance the multiphysics analysis of helicopter rotors under icing conditions by coupling the iced rotor’s aerodynamics, analyzed by CFD, with the rotor’s structural characteristics, analyzed by CSD.

The current work introduces supercomputer-based computational approaches capable of assessing the impact of ice accretion on the aerodynamics, blade dynamics, vibrations and loading of a rotorcraft. The rigid and elastic motions of the blades are accounted for through a loose coupling of the flow solver to a multibody dynamics solver. The coupling framework allows for comprehensive aeroelastic simulations of iced rotors in hover and in forward flight.

The flow and structural modules were validated on a full helicopter configuration in forward flight using the ROBIN experimental model. The tip structural deflections were in very close agreement with the experimental measurements.

The results of the CFD analyses are limited by the available experimental results they can be compared to. In dry air CFD, three-dimensional (3D) experiments occur first and CFD is then compared to them; in icing, the opposite is true: 3D experiments (if they are ever done, as they are very expensive) chase CFD and sometimes never occur.

This paper presents an outline of how CFD and computational stress dynamics (CSD) analyses can be linked and provides a toolbox for deeper investigation of the complex flows over helicopters operating under difficult in-flight icing conditions.

More and more helicopters are designed to be able to operate in hostile environments such as rescuing and saving lives over the oceans or mountains, conditions under which icing encounters cannot be avoided.

A loosely coupled CFD/CSD framework that accounts for the rotor blades structural response to aerodynamic loading and ice accretion in hover and forward flight has been presented. This versatile and cost-effective framework provides a more accurate estimation of the helicopter rotor performance and its degradation due to icing encounters during the early design stages than traditional CFD tools.

The purpose of this paper is to propose an effective methodology for the industrial design of tangential inlet cyclone separators that is based on the fully three-dimensional (3D) simulation of the flow field within the cyclone coupled with an effective genetic algorithm.

The proposed fully 3D computational fluid dynamics (CFD) model makes use of the Reynold stress model for the accurate prediction of turbulence, while the particle trajectories are simulated using the one-way coupling discrete phase, which is a model particularly effective in case of low concentration of dust. To validate the CFD model, the numerical predictions are compared with experimental data available in the scientific literature. Eight design parameters were chosen, with the two objectives being the minimization of the pressure drop and the maximization of the collection efficiency.

The optimization procedure allows the determination of the Pareto Front, which represents the set of the best geometries and can be instrumental in taking an optimal decision in the presence of such a trade-off between the two conflicting objectives. The comparison among the individuals belonging to the Pareto Front with a more standard cyclone geometry shows that such a CFD global search is very effective.

The proposed procedure is tested for specific values of the operating conditions; however, it has general validity and can be used in place of typical procedures based on empirical models or engineers’ experience for the industrial design of tangential inlet cyclone separators with low solid loading.

Such an optimization process has never been proposed before for the design of cyclone separators; it has been developed with the aim of being both highly accurate and compatible with the industrial design time.

Atherosclerosis tends to occur in the distinctive carotid sinus, leading to vascular stenosis and then causing death. The purpose of this paper is to investigate the effect of sinus sizes, positions and hematocrit on blood flow dynamics and heat transfer by different numerical approaches.

The fluid flow and heat transfer in the carotid artery with three different sinus sizes, three different sinus locations and four different hematocrits are studied by both computational fluid dynamics (CFD) and fluid-structure interaction (FSI) methods. An ideal geometric model and temperature-dependent non-Newtonian viscosity are adopted, while the wall heat flux concerning convection, radiation and evaporation is used.

With increasing sinus size, the average velocity and temperature of the blood fluid decrease, and the area of time average wall shear stress (TAWSS)with small values decreases. As the distances between sinuses and bifurcation points increase, the average temperature and the maximum TAWSS decrease. Atherosclerosis is more likely to develop when the sinuses are enlarged, when the sinuses are far from bifurcation points, or when the hematocrit is relatively large or small. The probability of thrombosis forming and developing becomes larger when the sinus becomes larger and the hematocrit is small enough. The movement of the arterial wall obviously reduces the velocity of blood flow, blood temperature and WSS. This study also suggests that the elastic role of arterial walls cannot be ignored.

The hemodynamics of the internal carotid artery sinus in a carotid artery with a bifurcation structure have been investigated thoroughly, on which the impacts of many factors have been considered, including the non-Newtonian behavior of blood and empirical boundary conditions. The results when the FSI is considered and absent are compared.