Search results

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 purpose of this paper is to perform the computational fluid dynamics (CFD) simulation with experimental validation to investigate the particle segregation effect in abrupt and smooth shapes circulating fluidized bed (CFB) risers.

The experimental investigations were carried out in lab-scale CFB systems and the CFD simulations were performed by using commercial software BARRACUDA. Special attention was paid to investigate the gas-particle flow behavior at the top of the riser with three different superficial velocities, namely, 4, 6 and 7.7 m/s. Here, a CFD-based noble simulation approach called multi-phase particle in cell (MP-PIC) was used to investigate the effect of traditional drag models (Wen-Yu, Ergun, Wen-Yu-Ergun and Richardson-Davidson-Harrison) on particle flow characteristics in CFB riser.

Findings from the experimentations revealed that the increase in gas velocity leads to decrease the mixing index inside the riser. Moreover, the solid holdup found more in abrupt riser than smooth riser at the constant gas velocity. Despite the more experimental investigations, the findings with CFD simulations revealed that the MP-PIC approach, which was combined with different drag models could be more effective for the practical (industrial) design of CFB riser. Well agreement was found between the simulation and experimental outputs. The simulation work was compared with experimental data, which shows the good agreement (<4%).

The experimental and simulation study performed in this research study constitutes an easy-to-use with different drag coefficient. The proposed MP-PIC model is more effective for large particles fluidized bed, which can be helpful for further research on industrial gas-particle fluidized bed reactors. This study is expected to give throughout the analysis of CFB hydrodynamics with further exploration of overall fluidization.

The present work aims to investigate the capabilities of accurately predicting the six-degrees-of-freedom (6DoF) trajectory and the flight behavior of a flare-stabilized projectile using computational fluid dynamics (CFD) and rigid body dynamics (RBD) methods.

Two different approaches are compared for calculating the trajectory. First, the complete matrix of static and dynamic aerodynamic coefficients for the projectile is determined using static and dynamic CFD methods. This discrete database and the data extracted from free-flight experiments are used to simulate flight trajectories with an in-house developed 6DoF solver. Second, the trajectories are simulated solving the 6DoF motion equations directly coupled with time resolved CFD methods.

Virtual fly-out simulations using RBD/CFD coupled simulation methods well reproduce the motion behavior shown by the experimental free-flight data. However, using the discrete database of aerodynamic coefficients derived from CFD simulations shows a slightly different flight behavior.

A discrepancy between CFD 6DoF/RBD simulations and results obtained by the MATLAB 6DoF-solver based on discrete CFD data matrices is shown. It is assumed that not all dynamic effects on the aerodynamics of the projectile are captured by the determination of the force and moment coefficients with CFD simulations based on the classical aerodynamic coefficient decomposition.

Biocontaminants represent higher risks to occupants' health in shared spaces. Natural ventilation is an effective strategy against indoor air biocontamination. However, the relationship between natural ventilation and indoor air contamination requires an in-depth investigation of the behavior of airborne infectious diseases, particularly concerning the contaminant's viral and aerodynamic characteristics. This research investigates the effectiveness of natural ventilation in preventing infection risks for coronavirus disease (COVID-19) through indoor air contamination of a free-running, naturally-ventilated room (where no space conditioning is used) that contains a person having COVID-19 through building-related parameters.

This research adopts a case study strategy involving a simulation-based approach. A simulation pipeline is implemented through a number of design scenarios for an open office. The simulation pipeline performs integrated contamination analysis, coupling a parametric 3D design environment, computational fluid dynamics (CFD) and energy simulations. The results of the implemented pipeline for COVID-19 are evaluated for building and environment-related parameters. Study metrics are identified as indoor air contamination levels, discharge period and the time of infection.

According to the simulation results, higher indoor air temperatures help to reduce the infection risk. Free-running spring and fall seasons can pose higher infection risk as compared to summer. Higher opening-to-wall ratios have higher potential to reduce infection risk. Adjacent window configuration has an advantage over opposite window configuration. As a design strategy, increasing opening-to-wall ratio has a higher impact on reducing the infection risk as compared to changing the opening configuration from opposite to adjacent. However, each building setup is a unique case that requires a systematic investigation to reliably understand the complex airflow and contaminant dispersion behavior. Metrics, strategies and actions to minimize indoor contamination risks should be addressed in future building standards. The simulation pipeline developed in this study has the potential to support decision-making during the adaptation of existing buildings to pandemic conditions and the design of new buildings.

The addressed need of investigation is especially crucial for the COVID-19 that is contagious and hazardous in shared indoors due to its aerodynamic behavior, faster transmission rates and high viral replicability. This research contributes to the current literature by presenting the simulation-based results for COVID-19 as investigated through building-related and environment-related parameters against contaminant concentration levels, the discharge period and the time of infection. Accordingly, this research presents results to provide a basis for a broader understanding of the correlation between the built environment and the aerodynamic behavior of COVID-19.

The purposes of this paper are to review the progress of and conclude the trend for paint thickness simulation for painting robot trajectory planning.

This paper compares the explicit function-based method and computational fluid dynamics (CFD)-based method used for paint thickness simulation. Previous research is considered, and conclusions with the outlook are drawn.

The CFD-based paint deposition simulation is the trend for paint thickness simulation for painting robot trajectory planning. However, the calculation of paint thickness resulting from dynamically painting complex surface remains to be researched, which needs to build an appropriate CFD model, study approaches to dynamic painting simulation and investigate the simulation with continuously changing painting parameters.

This paper illustrates that the CFD-based method is the trend for the paint thickness simulation for painting robot trajectory planning. Current studies have been analyzed, and techniques of CFD modeling have also been summarized, which is vital for future study.

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 development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them.

A three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods.

Although the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively.

The model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs.

The Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

Numerical simulation of icing has become a standard. Once the iced shape is known, however, the analyst needs to update the computational fluid dynamics (CFD) grid. This paper aims to propose a method to update the numerical mesh with ice profiles.

The present paper concerns a novel and fast radial basis functions (RBF) mesh morphing technique to efficiently and accurately perform ice accretion simulations on industrial models in the aviation sector. This method can be linked to CFD analyses to dynamically reproduce the ice growth.

To verify the consistency of the proposed approach, one of the most challenging ice profile selected in the LEWICE manual was replicated and simulated through CFD. To showcase the effectiveness of this technique, predefined ice profiles were automatically applied on two-dimensional (2D) and three-dimensional (3D) cases using both commercial and open-source CFD solvers.

If ice accreted shapes are available, the meshless characteristic of the proposed approach enables its coupling with the CFD solvers currently supported by the RBF4AERO platform including OpenFOAM, SU2 and ANSYS Fluent. The advantages provided by the use of RBF are the high performance and reliability, due to the fast application of mesh smoothing and the accuracy in controlling surface mesh nodes.

As far as authors’ knowledge is concerned, this is the first time in scientific literature that RBF are proposed to handle icing simulations. Due to the meshless characteristic of the RBF mesh morphing, the proposed approach is cross solver and can be used for both 2D and 3D geometries.

The purpose of this paper is to analyse the thermal environment of two engineering testing centres cooled via different means using computational fluid dynamics (CFD), focussing on the indoor temperature and air movement. This computational technique has been used in the analysis of thermal environment in buildings where the profiles of thermal comfort parameters, such as air temperature and velocity, are studied.

A pilot survey was conducted at two engineering testing centres – a passively cooled workshop and an air-conditioned laboratory. Electronic sensors were used in addition to building design documentation to collect the required information for the CFD model–based prediction of air temperature and velocity distribution patterns for the laboratory and workshop. In the models, both laboratory and workshop were presumed to be fully occupied. The predictions were then compared to empirical data that were obtained from field measurements. Operative temperature and predicted mean vote (PMV)–predicted percentage dissatisfied (PPD) indices were calculated in each case in order to predict thermal comfort levels.

The simulated results indicated that the mean air temperatures of 21.5°C and 32.4°C in the laboratory and workshop, respectively, were in excess of the recommended thermal comfort ranges specified in MS1525, a local energy efficiency guideline for non-residential buildings. However, air velocities above 0.3 m/s were predicted in the two testing facilities, which would be acceptable to most occupants. Based on the calculated PMV derived from the CFD predictions, the thermal sensation of users of the air-conditioned laboratory was predicted as −1.7 where a “slightly cool” thermal experience would prevail, but machinery operators in the workshop would find their thermal environment too warm with an overall sensation score of 2.4. A comparison of the simulated and empirical results showed that the air temperatures were in good agreement with a percentage of difference below 2%. However, the level of correlation was not replicated for the air velocity results, owing to uncertainties in the selected boundary conditions, which was due to limitations in the measuring instrumentation used.

Due to the varying designs, the simulated results of this study are only applicable to laboratory and workshop facilities located in the tropics.

The results of this study will enable building services and air-conditioning engineers, especially those who are in charge of the air-conditioning and mechanical ventilation (ACMV) system design and maintenance to have a better understanding of the thermal environment and comfort conditions in the testing facilities, leading to a more effective technical and managerial planning for an optimised thermal comfort management. The method of this work can be extended to the development of CFD models for other testing facilities in educational institutions.

The findings of this work are particularly useful for both industry and academia as the indoor environment of real engineering testing facilities were simulated and analysed. Students and staff in the higher educational institutions would benefit from the improved thermal comfort conditions in these facilities.

For the time being, CFD studies have been carried out to evaluate thermal comfort conditions in various building spaces. However, the information of thermal comfort in the engineering testing centres, of particular those in the hot–humid region are scantily available. The outcomes of this simulation work showed the usefulness of CFD in assisting the management of such facilities not only in the design of efficient ACMV systems but also in enhancing indoor thermal comfort.

This paper aims to present a novel approach for computing particle temperatures in simulations coupling computational fluid dynamics (CFD) and discrete element method (DEM) to predict flow and heat transfer in fluidized beds of thermally thick spherical particles.

An improved lumped formulation based on Hermite-type approximations for integrals to relate surface temperature to average temperature and surface heat flux is used to overcome the limitations of classical lumped models. The model is validated through comparisons with analytical solutions for a convectively cooled sphere and experimental data for a fixed particle bed. The coupled CFD-DEM model is then applied to simulate a Geldart D bubbling fluidized bed, comparing the results to those obtained using the classical lumped model.

The validation cases demonstrate that ignoring internal thermal resistance can significantly impact the temperature in cases where the Biot number is greater than 0.1. The results for the fixed bed case clearly demonstrate that the proposed method yields significantly improved outcomes compared to the classical model. The fluidized bed results show that surface temperature can deviate considerably from the average temperature, underscoring the importance of accurately accounting for surface temperature in convective heat transfer predictions and surface processes.

The proposed approach offers a physically more consistent simulation without imposing a significant increase in computational cost. The improved lumped formulation can be easily and inexpensively integrated into a typical DEM solver workflow to predict heat transfer for spherical particles, with important implications for various industrial applications.