CFD modeling and analysis of a two-phase vapor separator

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

Effect of ship motion on separation efficiency in crude oil separator with coalescer

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to ‘losses’ in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

The work described here concentrates on under-expanded, axisym-metric turbulent jets issuing into quiescent conditions. Under-expanded turbulent jets are applicable to most aircraft propulsion applications that use convergent nozzles. Experimental studies used laser doppler velocimetry (LDV) and pitot probe measurements along the jet centreline. These measurements were made for two nozzle pressure ratios (2·5 and 4·0) and at various streamwise positions up to 10 nozzle diameters downstream of the nozzle exit plane. A computational fluid dynamics (CFD) model was developed using the Fluent code and utilised the RNG K-ε two-equation turbulence model. A mesh resolution of approximately one hundredth of nozzle exit diameter was found to be sufficient to establish a mesh independent solution.Comparison of the jet centreline axial velocity (LDV data) and pressure ratio (pitot probe data) showed good agreement with the CFD model. The correct number of shock cells had been predicted and the shock strength agreed well between the experiments and numerical model. The CFD model was, however, found to over-predict the shock cell length resulting in a longer supersonic core. There was some evidence, based on analysis of the LDV measurements that indicates the presence of swirl and jet unsteadiness, which could contribute to a shortening of the shock cell length. These effects were not modelled in the CFD. Correlation between the LDV and pitot probe measurements was generally good, however, there was some evidence that probe interference may have caused the premature decay of the jet. Overall, this work has indicated the good agreement between a CFD simulation using the RNG k-ε turbulence model and experimental data when applied to the prediction of the flowfield generated by under-expanded turbulent jets. The suitability of the LDV technique to jet flows with velocities up to 500ms-1has also been demonstrated.

Auxiliary power units (APUs) are gas turbine engines that provide high-pressure air and electrical power to aircraft systems. They provide primary power while the aircraft is parked on the ramp, starting services for the main engines, and backup power while the aircraft is in flight. Many APUs employ inlet systems which include a “pop-up” door that allow for the capture of freestream ram pressure during flight. This results in increased inlet recovery and a corresponding improvement in the performance of the APU. This APU door, when open, is exposed to airflow instability inherent in the aircraft boundary layer in the aft section of the fuselage, where APUs are typically housed. Additionally, the pop-up nature of the inlet door produces a large region of separated airflow off of the back side of the door. Systematic vortex shedding is frequently a major component of this separated region. As new APU doors are made with less rigid material to save weight, a need to better understand the unsteady aerodynamic excitations of the flow field around the door has arisen, as these new doors may be more susceptible to vibration during flight. Recent advances in Computational Fluid Dynamics (CFD) meshing tools and transient modeling have enabled a CFD study to be performed which will investigate this time-dependent phenomenon. As transient CFD analysis is still a relatively new field for commercial CFD codes, a calibration was needed to verify the accuracy of the CFD predictions and to form any calibration correction terms. Honeywell Aerospace owns a Boeing 757 flight test vehicle which is normally used to flight test propulsion engines. However, this aircraft also includes a pop-up APU inlet door that is similar to most other APU inlet door styles. This APU door was instrumented using high response pressure transducers placed on the forward and aft sides of the inlet door as well as upstream of the door to measure upstream instability. The aircraft was flown at a variety of flight conditions and APU operating points and the transient data was recorded. After the completion of the flight test, a CFD model was constructed of the B757 flight test vehicle. Because aerodynamic instability can be generated anywhere on the aircraft, the entire airframe from nose to tail was modeled. The APU inlet door geometry was also created, meshed and added to the CFD model. This CFD model was run in a transient mode to simulate the exact same flight conditions and APU operating points as were tested during the flight test. Dynamic results in the time and frequency domains predicted by the CFD analyses were compared to flight test data and correlation and calibration factors were derived.

Mechanical Heating Ventilation and Air-Conditioning (HVAC) systems account for 60% of the total energy consumption of buildings. As a sector, buildings contributes about 40% of the total global energy demand. By using passive technology coupled with natural ventilation from wind towers, significant amounts of energy can be saved, reducing the emissions of greenhouse gases. In this study, the development of Computational Fluid Dynamics (CFD) analysis in aiding the development of wind towers was explored. Initial concepts of simple wind tower mechanics to detailed design of wind towers which integrate modifications specifically to improve the efficiency of wind towers were detailed. From this, using CFD analysis, heat transfer devices were integrated into a wind tower to provide cooling for incoming air, thus negating the reliance on mechanical HVAC systems. A commercial CFD code Fluent was used in this study to simulate the airflow inside the wind tower model with the heat transfer devices. Scaled wind tunnel testing was used to validate the computational model. The airflow supply velocity was measured and compared with the numerical results and good correlation was observed. Additionally, the spacing between the heat transfer devices was varied to optimise the performance. The technology presented here is subject to a patent application (PCT/GB2014/052263).

Inferior turbinate reduction (ITR) is a commonly performed procedure for the treatment of nasal obstruction. Which portion of the inferior turbinates should be surgically addressed to improve nasal airflow has yet to be determined. To use computational fluid dynamics (CFD) analysis to evaluate the airflow changes after reduction along different portions of the inferior turbinate. Computed tomographic scans of 5 patients were selected. Seven CFD models were created for each patient: 1 unaltered and 6 various ITRs, including 3 one-third ITRs (anterior, middle, and posterior one-third); 2 two-thirds ITRs (anterior and posterior two-thirds); and 1 full-length ITR model. Total airflow rate and nasal resistance was obtained through CFD analysis, and regression analysis was performed on the increased nasal volume, locations, and nasal resistance for all 5 patients. Total airflow rate and nasal resistance was obtained through CFD analysis, and regression analysis was performed on the increased nasal volume, locations, and nasal resistance for all 5 patients. Full ITR over the whole length was consistently most effective to improve nasal airflow and resistance for all 5 patients (2 men and 3 women), adjusted for the volume. Regression analysis showed a strong linear (R2≥0.79) relationship between nasal volume changes and nasal airflow. However, the most effective location of partial turbinate reduction was not consistent among patients. Surprisingly, for some patients, posterior ITRs were more effective than anterior ITRs. The site of most effective partial ITR differed from 1 side to the other even in the same individual. The effectiveness of partial ITR and target location likely depends on individual patient anatomy. The fact that full ITRs were consistently most effective and the linear regression between flow and nasal volume changes may indicate that the entire length of the IT has a functional impact on nasal airflow and resistance. NA.

Computational fluid dynamics (CFD) models are powerful computational tools to simulate urban-landscape scale atmospheric dispersion events. They are proven to be very useful for security management and emergency response. Essential inputs to CFD models include landscape characteristics, which are often captured by various GIS data layers. While it is logical to couple GIS and CFD models to take advantage of available GIS data and the visualization and cartographic rendering capabilities of GIS, the integration of the two tools have been minimal. In this paper, we took the first step to evaluate the use of GIS data in CFD modeling. Specifically, we explore how efficient is to use GIS data in CFD models and how sensitive the CFD results are to different GIS data formats. Using campus topography and building data, and the FEFLO-URBAN CFD model, we performed atmospheric release simulations using topographic data in contour and raster formats. We found that using raster format was quite efficient and contour data required significant effort. Though the simulation outputs from the two data formats were not identical, their overall outcomes were similar and did not post alarming discrepancies. We concluded that using GIS data have tremendous potential for CFD modeling.

Introduction: We examined the viability of pairing wire-obtained flow pressure data with computational fluid dynamic (CFD) analysis for accurate patient-specific 3D reconstruction of flow through cerebral vessels—an approach that provides comprehensive velocity data and other flow parameters to assist the preventative diagnosis of cerebral stenosis. Methods: Two physical phantom models of a cerebral stenosis—differing in regard to vascular morphology at and around the stenosis—were prepared, filled with physiological saline and connected individually to a flow pump (Vascular Simulations LLC Left Heart Replicator) applying pulsatile flow. Pressure measurements were taken upstream of the stenosis by microcatheter-guided wires to define an inlet boundary condition for CFD analysis. Pressure and velocity at 2 cm proximal to stenosis, the stenosis inlet, the stenosis outlet, and 2 cm distal to stenosis were measured to validate the CFD simulated flow. Results: Excellent agreement was observed between CFD and wire-measured time-dependent flow pressure, with the expected pulsatile behavior observed at each location. Between the two methods, differences in maximum systolic pressures ranged between |0.73±0.72|% and |3.90±0.74|%. Differences in minimum diastolic pressures ranged between |1.79±1.33|% and |9.08±1.42|%. Velocity data from wire measurements lacked pulsatile behavior and showed differences ranging between |2.93±5.20|% and |120.10±2.91|% from CFD-obtained measurements, which did show correct pulsatile behavior. Larger discrepancies were strongly associated with areas of higher CFD predicted velocities (r=0.86). Conclusion: Guidewire flow measurements provided CFD analysis with accurate inlet boundary conditions and validated CFD simulation results. CFD analysis allowed for detailed visualization of key flow parameters that may be difficult to physically measure—such as velocity—that are relevant for understanding risk factors associated with cerebral stenosis on a case-by-case basis.

Hydrodynamic Behaviour of Helical Rings Random Packing Using CFD Simulation

<div class="section abstract"><div class="htmlview paragraph">Designing an efficient vehicle coolant system depends on meeting target coolant flow rate to different components with minimum energy consumption by coolant pump. The flow resistance across different components and hoses dictates the flow supplied to that branch which can affect the effectiveness of the coolant system. Hydraulic tests are conducted to understand the system design for component flow delivery and pressure drops and assess necessary changes to better distribute the coolant flow from the pump. The current study highlights the ability of a complete 3D Computational Fluid Dynamics (CFD) simulation to effectively mimic a hydraulic test. The coolant circuit modeled in this simulation consists of an engine water-jacket, a thermostat valve, bypass valve, a coolant pump, a radiator, and flow path to certain auxiliary components like turbo charger, rear transmission oil cooler etc. A commercial CFD software, Simerics-MP+®, is used to simulate the hydraulic test for two different positions of the poppet valve of the thermostat, viz. a closed position and an 50% opening, at different speeds of the engine. In the CFD model, the complete geometrical details of water-jacket, thermostat, and pump are considered. The remaining components are approximated as pipes with flow resistance models to account for flow and pressure drop at different engine speeds. Firstly, the standalone pump performance is validated in the operating regime of interest, followed by the calibration of the resistance models for the simplified components. At the end, complete system level 3D simulations are conducted and validated for the above mentioned two positions of the poppet valve. The flow distribution and pressure drop across different components show good comparison with the hydraulic test data within 7% error band.</div></div>

In handling Floating LNG storage and transportation, the rollover phenomena, referred to the spontaneous mixing of stratified LNG layers leading to increased LNG boil off rates, is one of the major safety concerns in LNG storage tanks. Stratification occurs when the LNG in the tank forms in different layers with different densities and temperatures. Heat leaks through the tank insulation leads to sudden mixing of these various density layers. This causes a sudden release of large amount of vapor and over pressurization of the tank leading to tank damage and failure. In FLNGs, the motion of the cargo can advance the boil off rates further. A Computational Fluid Dynamics (CFD) model is proposed to capture the rollover phenomenon and its impact on boil off rate under static LNG conditions. The CFD model is first validated against experimental data for density distribution and is subsequently used to conduct a series of CFD studies to evaluate the rollover in different operating pressure conditions inclusive of heat leakage through wall. The study involves a fully thermodynamics and phase change coupled multiphase model accounting for the presence of multi-component system and is used to predict the boil off rate during a rollover occurrence in a static storage tank. CFD analysis provides a comprehensive time dependent mixing behavior, inclusive of flow, density temperature distributions and mass transfer effects, of an LNG system under different conditions of feed rate, thermal gradients and storage pressure. In particular, with rollover event, the sudden change in density variations and boil off rates are captured. There were many accidents reported in the LNG plants due to rollover. Different methods exist to monitor and prevent the rollover such as equipment installation to promote mixing, avoiding long stoppage time etc. The proposed CFD model can model the rollover phenomenon accurately while being able to predict the feasibility and efficiency of various rollover prevention methods.

In underground distribution systems, a CVT (cross-linked polyethylene insulated PVC sheathed triplex) cable is installed in a protective pipe. When a fault arc occurs on the CVT cable, the pressure instantly increases due to the high temperature of the arc, then propagates in the pipe. The pressure rise and its propagation may seriously damage the pipe and connected power equipment. There are many different sizes of pipes and cables depending on the installation situation, and fault arcs occur under various conditions. Therefore, the simulation technique is effective for examining such pressure rise and propagation in addition to short-circuit tests. The objective of this paper is to present a CFD (computational fluid dynamics) modeling that can be used to evaluate the characteristics of the pressure rise and propagation due to a fault arc in the protective pipe. The pressure-rise waveforms obtained by CFD analysis were found to be similar to those from short-circuit test results. In addition, the dependence of the maximum pressure rise on the size of the CVT cable and on the arc current was simulated well. Therefore, CFD modeling is sufficient for study of the pressure rise and its propagation in a protective pipe. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 181(3): 9-18, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22291

A “credible” computational fluid dynamics (CFD) model has the potential to provide a meaningful evaluation of safety in medical devices. One major challenge in establishing “model credibility” is to determine the required degree of similarity between the model and experimental results for the model to be considered sufficiently validated. This study proposes a “threshold-based” validation approach that provides a well-defined acceptance criteria, which is a function of how close the simulation and experimental results are to the safety threshold, for establishing the model validity. The validation criteria developed following the threshold approach is not only a function of Comparison Error, E (which is the difference between experiments and simulations) but also takes in to account the risk to patient safety because of E. The method is applicable for scenarios in which a safety threshold can be clearly defined (e.g., the viscous shear-stress threshold for hemolysis in blood contacting devices). The applicability of the new validation approach was tested on the FDA nozzle geometry. The context of use (COU) was to evaluate if the instantaneous viscous shear stress in the nozzle geometry at Reynolds numbers (Re) of 3500 and 6500 was below the commonly accepted threshold for hemolysis. The CFD results (“S”) of velocity and viscous shear stress were compared with inter-laboratory experimental measurements (“D”). The uncertainties in the CFD and experimental results due to input parameter uncertainties were quantified following the ASME V&V 20 standard. The CFD models for both Re = 3500 and 6500 could not be sufficiently validated by performing a direct comparison between CFD and experimental results using the Student’s t-test. However, following the threshold-based approach, a Student’s t-test comparing |S-D| and |Threshold-S| showed that relative to the threshold, the CFD and experimental datasets for Re = 3500 were statistically similar and the model could be considered sufficiently validated for the COU. However, for Re = 6500, at certain locations where the shear stress is close the hemolysis threshold, the CFD model could not be considered sufficiently validated for the COU. Our analysis showed that the model could be sufficiently validated either by reducing the uncertainties in experiments, simulations, and the threshold or by increasing the sample size for the experiments and simulations. The threshold approach can be applied to all types of computational models and provides an objective way of determining model credibility and for evaluating medical devices.