Computational Fluid Dynamics Predictions Research Articles

Back to Bernoulli: a simple formula for trans-stenotic pressure gradients and retrospective estimation of flow rates in cerebral venous disease

BackgroundVenous sinus stenosis can be associated with cerebrovascular disorders. Understanding the role of blood flow disturbances in these disorders is often hampered by the lack of patient-specific flow rates. Our...

Aerodynamic and aeroacoustic design of electric ducted fans

This paper describes the aerodynamic and aeroacoustic design of an electric ducted fan (EDF). A prototype EDF was designed and built with three different rotor-stator sets to operate at design flow coefficients ϕd=[0.60,0.75,0.90]. Computational Fluid Dynamics (CFD) simulations show that changing the design flow coefficient changes the proportions of endwall and blade profile loss, with the ϕd=0.75 design providing the optimum balance. CFD predictions of aerodynamic performance are compared with experimental measurements and show good agreement. Data collected from full annulus Unsteady Reynolds-Averaged Navier Stokes (URANS) CFD simulations is used to predict tonal noise radiation using a numerical solution to Farassat's Formulation 1A, and compare favourably to acoustic measurements in an anechoic chamber. All three EDF iterations show similar levels of broadband noise radiation, although tonal noise radiation decreases with increasing design flow coefficient and results in more favourable Sound Quality Metrics performance. Design flow coefficient is shown to have a first-order impact on both the aerodynamic and aeroacoustic behaviour of the EDF, and it is proposed that an optimal design, encompassing both aerodynamic performance and reduced acoustic impact, can be achieved at design flow coefficients between ϕd=0.75 and ϕd=0.90.

A Computational Analysis of Turbocharger Compressor Flow Field with a Focus on Impeller Stall

Understanding the flow instabilities encountered by the turbocharger compressor is an important step toward improving its overall design for performance and efficiency. While an experimental study using Particle Image Velocimetry was previously conducted to examine the flow field at the inlet of the turbocharger compressor, the present work complements that effort by analyzing the flow structures leading to stall instability within the same impeller. Experimentally validated three-dimensional computational fluid dynamics predictions are carried out at three discrete mass flow rates, including 77 g/s (stable, maximum flow condition), 57 g/s (near peak efficiency), and 30 g/s (with strong reverse flow from the impeller) at a fixed rotational speed of 80,000 rpm. Large stationary stall cells were observed deep within the impeller at 30 g/s, occupying a significant portion of the blade passage near the shroud between the suction surface of the main blades and the pressure surface of the splitter blades. These stall cells are mainly created when a substantial portion of the inlet core flow is unable to follow the impeller’s axial to radial bend against the adverse pressure gradient and becomes entrained by the reverse flow and the tip leakage flow, giving rise to a region of low-momentum fluid in its wake. This phenomenon was observed to a lesser extent at 57 g/s and was completely absent at 77 g/s. On the other hand, the inducer rotating stall was found to be most dominant at 57 g/s. The entrainment of the tip leakage flow by the core flow moving into the impeller, leading to the generation of an unstable, wavy shear layer at the inducer plane, was instrumental in the generation of rotating stall. The present analyses provide a detailed characterization of both stationary and rotating stall cells and demonstrate the physics behind their formation, as well as their effect on compressor efficiency. The study also characterizes the entropy generation within the impeller under different operating conditions. While at 77 g/s, the entropy generation is mostly concentrated near the shroud of the impeller with the core flow being almost isentropic, at 30 g/s, there is a significant increase in the area within the blade passage that shows elevated entropy production. The tip leakage flow, its interaction with the blades and the core forward flow, and the reverse flow within the impeller are found to be the major sources of irreversibilities.

On the determination of the standing oblique detonation wave in an engine combustor using laser absorption spectroscopy of hydroxyl radical

Freejet experiments were conducted in the hypersonic flight-duplicated shock tunnel (JF-12) to understand the detonation combustion mode in the combustor of a standing oblique detonation ramjet (Sodramjet) engine prototype. Besides the traditional schlieren imaging to capture the oblique shock positions in the combustor, we implemented laser absorption spectroscopy of hydroxyl (OH) radical with three laser beams around the shock front. The Sodramjet engine combustor provides an ideal condition to measure OH because its molar fraction within the detonation front is estimated to exceed 0.03, which is much higher than other combustors. However, the harsh environment of freejet shock tunnel test poses a great challenge to the reliability of measurement systems. In this work, near-infrared tunable laser diode sensors centered at 1528 nm were chosen to measure the line-of-sight average of OH partial pressure. Continuous absorption measurements were conducted near the shock front, indicating a thin OH region after shock compression that could only be achieved by detonation combustion. The time-varied OH partial pressure was well predicted by the computational fluid dynamics (CFD) prediction. Our measurement results verified the detonation combustion of Sodramjet engine prototype and showed that in situ, real-time detonation diagnostics based on laser absorption spectroscopy is possible in freejet experiments of shock tunnel.

Assessment of CFD Predictions Using Experiments from a Heated Gas-Cooled Pebble Bed Facility

This paper presents computational fluid dynamics (CFD) simulations of the coolant gas flow in a pebble bed reactor core to assess the suitability of CFD models to accurately predict the temperature distribution and possible occurrence of local hot spots that may affect pebble integrity. This study assessed CFD predictions against temperature distribution measurements from the SANA test facility at the Research Center Jülich in Germany. A realistic pebble bed structure of randomly packed 1584 pebbles was produced using the discrete element method to model the pebble packing in detail. A total of 96 experimental temperature pebble points were used for the assessments, covering a broad range of heating powers (10 kW ≤ Poperation ≤ 35 kW). A good agreement between the CFD predictions and the SANA measurements was obtained for two coolants, nitrogen and helium, along the height of the pebble bed. It is anticipated that a better understanding of the suitability of the existing CFD models gained through this study will aid in the identification of gaps and areas of improvement for CFD to support the design and safety evaluations for pebble bed small modular reactors.

Impact of semi-empirical methods implemented in heat, air, and moisture (HAM) models on predicted wind-driven rain (WDR) loads and hygrothermal responses

The amount, duration, and intensity of wind-driven rain (WDR) are instrumental to the durability and sustainability of building components. Semi-empirical methods implemented in heat, air, and moisture (HAM) models have proved to be a practical and efficient approach to quantify WDR exposure of building components and to examine WDR's influence on hygrothermal responses of building envelopes. However, specific assumptions and simplifications in the implementations can cause discrepancies in WDR calculations and hygrothermal responses. While measured datasets or computational fluid dynamics (CFD) predictions are employed to validate WDR calculations in previous studies, few address the further influence on hygrothermal responses, particularly confronted with full-scale experimental datasets. This paper starts with a general formulation of semi-empirical methods for WDR calculation and elaborates the ASHRAE and ISO standards implemented in two representative HAM models, WUFI and DELPHIN. In the first phase of simulations, two full-scale experimental datasets at Leuven (Belgium) and Eindhoven (the Netherlands) are reproduced by the two models to compare predicted WDR loads. In the second phase, the predicted and measured WDR loads are imposed as boundary conditions of different scenarios of one benchmark case to further analyze the influence on the hygrothermal responses. The results indicate that both models roughly reproduce the semi-empirical methods. Because of disparity in algorithms and implementations and differences in weather and building conditions, the simulated WDR loads show similar trends with deviations to the measured datasets. The predicted hygrothermal responses show deviations to different extents, presumably due to disparity in WDR load predictions during certain periods.

Computational fluid dynamics analysis of scaled Thermo-Compressor Performance in Multi-effect Distillation plants with Thermal Vapor Compression

This paper investigates scaled Thermo-Compressors for Multi-Effect Distillation with Thermal Vapor Compression (MED-TVC) desalination plants using Computational Fluid Dynamics (CFD). Initial validation compared CFD predictions with a real MED-TVC plant. Scaled-down Thermo-Compressors exhibited constant entrainment and pressure ratios, validating design scalability. The study expanded to test with compressed air as a practical substitute for steam under identical conditions. Results showed minimal deviation in entrainment and pressure ratios, affirming the feasibility of air testing. To account for temperature differences, a correction factor was introduced for accurate prediction when testing with air. Crucially, we explored substituting high-temperature steam with compressed air for testing. Lowering the air temperature allowed a reduced pressure of 13.12 bar while maintaining equivalent performance ratios compared to steam at 16 bar and 474.7 K. These findings are pivotal for developing small-scale Thermo-Compressor prototypes, facilitating testing with steam or air, and reducing risks and costs associated with full-scale fabrication. The study contributes practical insights for optimizing Thermo-Compressor designs in MED-TVC desalination, presenting compressed air as a viable alternative for efficient and cost-effective testing.

Fluid-to-fluid similarity and CFD predictions of surrogate fluid as a replacement for R11 refrigerant used in a subcooled flow boiling analysis: Validation against reference experimental data

In this study, we present the possibility of substituting the primary fluid Freon 11 (CCl3F, R11) used in the experimental thermohydraulic test facility which offers the advantage to obtain phase change at relatively low heat fluxes and pressure if compared to water working fluid. Nevertheless, R11 is a CFC (Chlorofluorocarbures) part of the gases that contribute to the degradation of the ozone layer.For this purpose, four working fluids (non-CFC) are judiciously selected to replace the R11, namely the R123, R134a, R143a and CO2, and this through multidimensional Computational Fluid Dynamics (CFD) modeling of steady state flow boiling phenomenon. The calculation model covers a wide range of mass fluxes (435 – 1443 kg/m2s) and heat fluxes (1.2– 36 W/cm2); it predicts the heat transfer rates and transitions points for each flow regime. In this research, the experiment's data were compared to the outcomes of Ansys-CFX computations, using a steady Reynolds-Averaged Navier-Stokes (Steady-RANS) model, to investigate the best replacement refrigerant for R11. The turbulence effects on the mean flow and near wall are taken into consideration using the Shear Stress Transport (SST k-ω) turbulence model. The results from the four fluids were transposed to their water equivalent and compared to those deduced from the experimentation and correlations available in the literature to select the appropriate substitution fluid. The results qualitative and quantitative for vertical and horizontal subcooled boiling flow at the CHF location show that, the most effective surrogate fluid is R123 (HCFC), is still widely used in the refrigeration and air-conditioning industry, then R134a (HFC), then R143a (HFC).

Optimization of Savonius rotor blade performance using Taguchi method: Experimental and 3D-CFD approach

The Savonius hydrokinetic turbine (HKT) is both cost-effective and reliable, providing clean energy with minimum environmental impact. The efficiency of the Savonius rotor can be improved by modifying the blade profile to increase the effective torque. However, past research works reported that modifying the blade profile is quite challenging due to small Cp improvement and design constraint. Therefore, the present study proposes a newly developed blade profile blueprint, akin to parameterizable designs such as the modified Bach and Benesh profiles but offering more shape versatility. This new blueprint is optimized with a systematic design of experiment (DOE) using a 3D computational fluid dynamics (CFD) simulation to maximize the Cp. The best design is determined statistically using the Taguchi method and analysis of variance (ANOVA). The optimized rotor profile enhances Cp by approximately 10.9 % compared to the conventional Savonius rotor at the optimal TSR (best Cp = 0.159) and 16.7 % improvement at a higher TSR value of 0.9 (Cp = 0.158). Moreover, the optimized rotor outperforms the Fibonacci rotor (Fibonacci blade profile) by 27 % in terms of efficiency. A thorough CFD analysis suggests that the optimized blade profile has a greater lift generated at the convex side of the advancing blade due to its streamlined shape, which delays the flow separation further up to the root of the blade. The confirmation test (experiment) is also performed to verify the Cp improvement of the optimized rotor, and the results herein concluded that the present CFD prediction is accurate and consistent with the experimental values.

Prediction method of centrifugal pump running in turbine mode based on losses analysis

Interest of Pump as Turbine (PAT) is growing with diverse applications in engineering. Usually, the data of PATs are not available in the hands of pump manufacturers. Therefore, performance prediction methods appear as an important research area of PATs. The current prediction methods reposed on expensive, inaccurate and time consuming experimental methods. In the scope of this work, a generic and robust prediction method is built up for a centrifugal impeller PAT. The most significant hydraulic losses were derived in PAT mode, these are namely, the shock losses at the impeller inlet, the swirling losses at the impeller outlet and the impeller wall frictional losses. The Euler head, the available total head and the hydraulic efficiency were computed as well. The global efficiency was computed taking into account the machine mechanical and volumetric efficiencies, enabling therefore to perform comparison of the new prediction method with experimental, computational fluid dynamic (CFD), Rossi and Perez performances prediction methods. From where it resulted a good agreement between the given prediction methods for the entire range of operation, confirming the robustness and the applicability of the developed prediction method. The relative difference between the new prediction method and CFD data and between the new prediction method and experimental data remained higher for lower discharge conditions, notably for extreme part load conditions, where a small error could result in very high relative difference.

Analysis of surimi extrusion behavior during 3D printing by modified Computational Fluid Dynamics (CFD) and quick prediction of printability using machine learning based on texture data

The purpose of this study was to develop a modifying Computational Fluid Dynamics (CFD) model for precise analysis of surimi extrusion behavior and establish a machine learning method for quickly prediction of surimi printability. The 2D rotational axisymmetric model used in this study to replace the 3D/4D physical model, which could improve the simulation efficiency. To improve the accuracy of the CFD simulations, the wall “no-slip” boundary condition assumed in the textbook was replaced with a boundary condition of wall slip. This reduced the error between the simulated results and the measured ones to <1%, and thus calculated required extrusion pressure below 17,034 Pa for printability. The CFD calculation efficiency was improved 33 times than the previously reports by the modifying CFD model, and the simulated results for the required extrusion pressure of inks could be obtained within 10 s. Additionally, a machine-learning method (partial least squares regression model) based on texture data was proposed to quickly predict the required extrusion pressure of surimi to assessed printability. The machine learning model showed a good performance with an R2 > 0.95.

Liquid hydrogen tank chill and no-vent fill prediction using computational fluid dynamics

Cryogenic tank chill and fill is an important cryogenic fluid management technology that supports and enables many of NASA’s long-duration space missions. For no-vent fill, the receiver tank pressure remains below the supply tank pressure during the entire duration of the fill so that the tank does not require venting. This is especially advantageous for tank fill operations in low gravity where the position of the liquid is not always known and venting the tank may cause loss of propellant by venting liquid. In lieu of expensive tests conducted on-orbit, accurate computational models capable of predicting receiver tank pressure during cryogenic propellant tank fill may be used to reduce system and propellant mass as well as mission risk. However, these numerical models must be validated or anchored to test data. This study presents computational fluid dynamics (CFD) models with conjugate heat transfer that are used to predict a liquid hydrogen tank chill and no-vent fill ground test conducted at Lewis Research Center (now Glenn Research Center) in 1991. The specific test case chosen for model validation implemented an upward-facing jet near the bottom of a cylindrical 34 liter tank. CFD predictions are compared to experimental measurements of tank pressure, fill level, and wall temperature. The CFD results show reasonable agreement to the test data but overpredict the pressure collapse near the end of the fill despite the liquid jet penetrating the liquid-vapor interface.

A deep learning computational fluid dynamics solver for simulating liquid hydrogen jets

Modeling and simulating the sudden depressurization of liquids inside nozzles is a significant challenge because of the plethora of the associated complex phenomena. This pressure drop together with the rapid phase change of the liquid is important characteristics of flash boiling. Computational fluid dynamics (CFD) multiscale simulations of flashing jets usually deploy additional models for modeling heat and mass transfer with long computational times. Intermediate steps such as volumetric meshing in mesh-based methods can also significantly increase the computational cost. This paper aims at providing academia and industry with a modeling tool to simulate and investigate the complex multi-facet phenomenon of flash-boiling atomization deploying a machine-learning method that could save thousand Central Processing Unit hours offering instantaneous CFD predictions. The presented machine-learning CFD method completely replaces the traditional CFD simulations workflow and requires little simulation expertise from the end-user. Notably, this is a novel model that couples for the first time the thermodynamic non-equilibrium with convolutional neural networks to simulate flashing liquid hydrogen jets thousand times faster than the standalone CFD solver. The accuracy of the novel approach is evaluated, demonstrating adequate accuracy compared to different unseen simulations and experiments. This work offers the groundwork for further accelerating CFD predictions in multiphase flow problems and could significantly improve testing flash-boiling scenarios in various industrial settings.

Numerical Study of Three-Dimensional Models of Single- and Two-Phase Nanofluid Flow through Corrugated Channels

This study delves into computational fluid dynamics (CFDs) predictions for SiO2–water nanofluids, meticulously examining both single-phase and two-phase models. Employing the finite volume approach, we tackled the three-dimensional partial differential equations governing the turbulent mixed convection flow in a horizontally corrugated channel with uniform heat flux. The study encompasses two nanoparticle volume concentrations and five Reynolds numbers (10,000, 15,000, 20,000, 25,000, and 30,000) to unravel these intricate dynamics. Despite previous research on the mixed convection of nanofluids using both single-phase and two-phase models, our work stands out as the inaugural systematic comparison of their predictions for turbulent mixed convection flow through this corrugated channel, considering the influences of temperature-dependent properties and hydrodynamic characteristics. The results reveal distinct variations in thermal fields between the two-phase and single-phase models, with negligible differences in hydrodynamic fields. Notably, the forecasts generated by three two-phase models—Volume of Fluid (VOF), Eulerian Mixture Model (EMM), and Eulerian Eulerian Model (EEM)—demonstrate remarkable similarity in the average Nusselt number, which are 24% higher than the single-phase model (SPM). For low nanoparticle volume fractions, the average Nusselt number predicted by the two-phase models closely aligns with that of the single-phase model. However, as the volume fraction increases, differences emerge, especially at higher Reynolds numbers. In other words, as the volume fraction of the nanoparticles increases, the nanofluid flow becomes a multi-phase problem, as depicted by the findings of this study.

Computational Fluid Dynamics Prediction of External Thermal Loads on Film-Cooled Gas Turbine Vanes: A Validation of Reynolds-Averaged Navier–Stokes Transition Models and Scale-Resolving Simulations for the VKI LS-94 Test Case

Given the increasing role of computational fluid dynamics (CFD) simulations in the aerothermal design of gas turbine vanes and blades, their rigorous validation is becoming more and more important. This article exploits an experimental database obtained by the von Karman Institute (VKI) for Fluid Dynamics for the LS-94 test case. This represents a film-cooled transonic turbine vane, investigated in a five-vane linear cascade configuration under engine-like conditions in terms of the Reynolds number and Mach number. The experimental characterization included inlet freestream turbulence measured with hot-wire anemometry, aerodynamic performance assessed with a three-hole pressure probe in the downstream section, and vane convective heat transfer coefficient distribution determined with thin-film thermometers. The test matrix included cases without any film-cooling injection, pressure-side injection, and suction-side injection. The CFD simulations were carried out in Ansys Fluent, considering the impact of mesh sizing and steady-state Reynolds-Averaged Navier-Stokes (RANS) transition modelling, as well as more accurate transient scale-resolving simulations. This work provides insight into the advantages and drawbacks of such approaches for gas turbine hot-gas path designers.

Validation of computational fluid dynamics models for airway deposition with SPECT data of the same population

This study compared computational fluid dynamic (CFD) model predictions on aerosol deposition in six asthmatic patients to the in-vivo results of the same group. Patient-specific ventilation and internal air distribution were prescribed using inspiratory and expiratory CT scans of each patient, accounting for individual lobar air flow distribution. Moreover, the significant influence of realistic mouth and throat geometries on regional deposition is demonstrated. The in-vivo data were obtained from single photon emission computed tomography (SPECT) in 6 subjects with mild asthma selected from a database of historical clinical trials. The governing flow and particle tracking equations were solved numerically using a commercial CFD tool, and the modeled deposition results were compared to the SPECT data. Good agreement was found between the CFD model, applying k-ω SST turbulence model, and SPECT in terms of aerosol deposition. The average difference for the lobar deposition obtained from CFD model and SPECT/CT data was 2.1%. The high level of agreement is due to applying patient specific airway geometries and inspiratory/expiratory CT images, anatomical upper airways, and realistic airway trees. The results of this study show that CFD is a powerful tool to simulate patient-specific deposition if correct boundary conditions are applied and can generate similar information obtained with functional imaging tools, such as SPECT.

CFD Simulation Models and Diffusion Models for Predicting Carbon Dioxide Plumes following Tank and Pipeline Ruptures—Laboratory Test and a Real-World Case Study

Ruptures of pipelines can result in dangerous fluids spreading toward populated areas. It is critical for designers to have tools that can accurately predict whether populated areas might be within a plume rupture zone. Numerical simulations using computational fluid dynamics (CFD) are compared here with experimental and real-world carbon dioxide ruptures. The experimental data were used to validate the computer model; subsequently, the algorithm was used for a real-world rupture from 2020 that occurred in the USA. From experiments, CFD predictions were superior to diffusion model results based on measurements made downstream of the release (within 1% concentration). Results from the real-world simulation confirm that a nearby town was in a plume pathway. Citizens in the town sought medical attention consistent with the calculated plume concentrations. CFD predictions of the airborne concentration of carbon dioxide in the town approximately 1 mile (1.5 km) downstream of the rupture reveal time-averaged concentrations of ~5%. One person was unconscious for ~45 min at a distance of 0.6 miles from the rupture site; other unconscious persons were in the center of the town (~1 mile from the rupture site) and ~1.2 miles from the rupture. These reports are in excellent agreement with the calculated plume concentrations in the region.

Laser Thomson scattering measurements around magnetized model in rarefied argon arcjet plume

To elucidate the role of the Hall effect in magnetohydrodynamic (MHD) aerobraking in rarefied flows,we measured the radial distributions of electron temperature and density in front of a magnetized model in a rarefied argon arcjet wind tunnel using the laser Thomson scattering method. We also developed a water-cooled magnetized model to prevent thermal demagnetization during the measurement. The measured electron densitydistributions were inexcellent agreement with computational fluid dynamics (CFD) predictions. It was also found that the magnetic field had little effect on the electron density distribution around the model. In the case without the magnetic field, the measured electron temperature almost agreed with the CFD prediction. However, the measured electron temperature increase caused by applying themagnetic field was about 1,000 K less than that of the CFD prediction. This discrepancy indicates that the location of an insulating boundary in the plasma is far from the model.

Experimental and CFD modeling of a progressive cavity pump (PCP) using overset unstructured mesh part 2: Two-phase flow.

In oil well operations, accurate modeling of two-phase liquid-gas flow in Progressive Cavity Pumps (PCPs) is vital. This study aimed to comprehensively analyze such flow in a metal-metal PCP using computational fluid dynamics (CFD) simulations validated by experimental data. The methodology applied to the High-Resolution Interface Capturing (HRIC) scheme is to compute the convective transport of phase fractions, yielding insights into the intricate dynamics of these flows. Validation was achieved using laboratory-acquired data and comparisons with extant publications. The findings confirmed the CFD simulations' alignment with field data for two-phase liquid-gas flows. Gas compression and effective viscosity gradient were crucial for accurate pump performance prediction. An investigation into the “gas seal” phenomenon using the Eulerian Multiphase Model elucidated the distinct separation of gas and liquid phases. The influence of the gas volume fraction on pump metrics like torque, power, and efficiency was explored, with CFD predictions exhibiting less than 8% error. The study underscores the pivotal role of CFD tools in predicting pump behavior under diverse conditions. It advances our understanding of two-phase flows in oil wells, especially regarding the gas's impact on pump components. This research offers the oil industry a refined modeling tool, enhancing the comprehension of oil well pump dynamics.

A modelling workflow for quantification of photobioreactor performance

In this work we have developed a comprehensive modelling workflow for the quantification of photobioreactor performance. Computational Fluid Dynamics (CFD) modelling combined with Lagrangian particle tracking was used to characterise the flow field inside the reactor; this information was combined with a Monte-Carlo model of light attenuation and a kinetic growth model to predict the performance of the system over the duration of the entire batch. The CFD model was validated against measurements of the overall hold-up, local hold-up and mixing time for superficial velocities between 0.6 and 6 cm s−1 in a pilot-scale bubble column photobioreactor, with the CFD predictions agreeing with the experimental data. Comparison was also made between the predicted biomass concentration and experimental measurements using the diatom Phaeodactylum tricornutum, with the model predictions being in good agreement with the experimental results. The model was used to investigate a range of operating conditions and reactor designs, with the most promising predicted to give a 40 % increase in the biomass productivity. Results from this work can be used for the in-silico design and optimisation of photobioreactor systems, thereby enabling their wider use as a sustainable production technology.