Computational fluid dynamics (CFD) analysis of mixing in styrene polymerization

Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space. A point-to-point comparison of the results showed absolute differences in product yield as low as 2.4% yield at low residence times and up to 19.1% yield at high residence times with strong correlation between predicted and experimental percent yields. CFD was found to underestimate the product yields at low residence times and overestimate at higher residence times. The correlation in predicted product yield and the agreement in identifying significant factors in reaction performance reveals the utility of CFD as a valuable tool in the design of continuous flow tube reactors with significantly reduced experimentation.

This paper presents the first simulation model of a tilting pad journal bearing (TPJB) using three-dimensional (3D) computational fluid dynamics (CFD), including multiphase flow, thermal-fluid, transitional turbulence, and thermal deformation of the shaft and pads employing two-way fluid–structure interaction (FSI). Part I presents a modeling method for the static performance. The model includes flow between pads BP, which eliminates the use of an uncertain, mixing coefficient (MC) in Reynold's equation approaches. The CFD model is benchmarked with Reynold's model with a 3D thermal-film, when the CFD model boundary conditions are consistent with the Reynolds boundary conditions. The Reynolds model employs an oversimplified MC representation of the three-dimensional mixing effect of the BP flow and heat transfer, and it also employs simplifying assumptions for the flow and heat transfer within the thin film between the journal and bearing. This manufactured comparison shows good agreement between the CFD and Reynold's equation models. The CFD model is generalized by removing these fictitious boundary conditions on pad inlets and outlets and instead models the flow and temperature between pads. The results show that Reynold's model MC approach can lead to significant differences with the CFD model including detailed flow and thermal modeling between pads. Thus, the CFD approach provides increased reliability of predictions. The paper provides an instructive methodology including detailed steps for properly applying CFD to tilt pad bearing modeling. Parts I and II focus on predicting static and dynamic response characteristic responses, respectively.

At present, fossil-fuel power plants supply the majority of the world’s demand of electric energy. The environmental and health threats from anthropogenic emissions of greenhouse gases (GHG) of these power plants have been considered as one of the main reasons for global climate change. In order to make the existing fossil fuel energy sources more environmentally friendly, a new generation of advanced power plant with increased output and environmental performance is required to be built. Oxy-fuel combustion technique can potentially provide significant opportunities for near-zero emissions from the existing and new-build power plants by capturing and storing the carbon dioxide emissions from burning fossil fuels and biomass. To obtain a better understanding of the overall oxy-fuel combustion process, it is important to investigate the effects of oxy-fuel-fired conditions on the combustion characteristics and heat transfer performance as innovations in the energy used. As a first step in developing and validating a computational tool to numerically model combustion of gaseous fuel, a computational fluid dynamics (CFD) modelling study has been carried out involving propane combustion with associated chemical reactions, radiative heat transfer, and turbulence. Three different combustion environments that were adopted experimentally in a 100 kW drop tube firing unit, were examined. One air-fired (reference case) and two oxy-fuel-fired cases (21 vol.% O2 for one combustion case (OF21) and 27 vol.% O2 for the other combustion case (OF27)) were investigated. The irreversible singlestep and reversible multi-step reaction mechanisms were considered. The results obtained with the multi-step chemistry mechanism showed improved agreement, particularly in the flame zone. The unburnt fuel in air-fired and OF27 cases was less than that of the OF21 case due to the low oxygen concentration used in the latter combustion case. As a second step of this research program, a comprehensive CFD modelling study was undertaken by integrating the combustion of pulverized dry lignite in several combustion environments. Four different cases were investigated: an air-fired and three different oxy-fuel combustion environments (25 % vol. O2 concentration (OF25), 27 % vol. O2 concentration (OF27), and 29 % vol. O2 concentration (OF29) were considered. The available experimental results from a lab-scale 100 kW firing lignite unit (Chalmers’ furnace) were selected for the validation of these simulations. The findings showed reasonable agreement with the qualitative and quantitative measurements of temperature distribution profiles and species concentration profiles at the most intense combustion locations inside the furnace. Through the use of Computational Fluid Dynamics (CFD), it is concluded that the resident time, stoichiometry, and recycled flue gas rates are relevant parameters to optimize the design of oxy-fuel furnaces. Under the same oxy-fuel combustion conditions as above, the appropriate mathematical models with the related kinetics parameters were implemented. The purpose was to accurately calculate the temperature distributions, species concentrations (O2, CO2, CO, H2O, and H2), NOx emission concentrations, and the radiation heat transfer. In this step of the modelling investigation, the multi-step chemical reaction mechanisms were conducted on the gas-phase and solid-phase of the coal reaction in one-, two-, and three-step reaction schemes. The predicted results showed a reasonably good agreement against the measured data for all the combustion cases, but in the three-step scheme the results were highly improved, particularly in the flame envelope zone. In the final step of the investigation, a computational fluid dynamics (CFD) modelling study has been developed to investigate Victorian brown coal combustion in a 550 MW utility boiler under air-fired and three oxy-fuel-fired scenarios. User-defined functions (UDFs) were written and incorporated into the CFD code in order to calculate the following mathematical models: the PC devolatilization, char burnout, multi-step chemical reactions, mass and heat transfer, carbon in fly-ash, and NOx formation/destruction. A level of confidence of the CFD model was achieved by validating four different parameters of the conventional combustion case. The numerical results of OF29 combustion condition were considerably similar to the reference firing results in terms of gas temperature levels and radiative heat transfer relative to the OF25 and OF27 combustion cases. In addition, a significant increase in the CO2 concentrations and a noticeable decrease in the NOx formation were observed under all oxy-fuel combustion scenarios. This study of oxy-fuel combustion in a large-scale tangentially-fired boiler is important prior to its implementation in real-life.

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.

Pulverized coal fired combustion is a major contributor to global emissions of nitric oxide (NO). Release of NO into the atmosphere causes toxic NO, reduction of ozone, formation of smog, and reduced visibility. Computational fluid dynamic (CFD) modelling of NO emissions from pulverized coal fired boilers offers several benefits including optimisation of boiler operating conditions to minimize NO emissions and development of low NO technology. Formation of NO within a pulverized coal fired boiler is a complex process, yet simple mechanisms are used to approximate the formation process. CFD modelling of NO formation requires prediction of the 1. nitrogen release rate from coal during devolatilization and char combustion, 2. possibly the type of nitrogen compound released during devolatilization and char combustion, 3. gas phase conversion of nitrogen compounds to NO (including possible NO reduction mechanisms), 4. contributions of thermal and prompt NO, and 5. conversion of char nitrogen to NO. This thesis assessed currently used models for processes 1-5 for accuracy and reliability in simple systems and CFD modelling. Models for processes 1-5 were improved, if required. CFD models currently assume the rate of nitrogen release equals a times the carbon release rate, where a equals 1. Laboratory and utility boiler measurements showed that a varies between 0.8 and 1.2. The sensitivity of a to NO emissions should be tested in CFD models for a variety of cases. If NO emissions are sensitive to a, a value of 1.07 is recommended for utility boilers as estimated from utility boiler measurements. The type of nitrogen compound released during devolatilization and char combustion may affect the reaction kinetics converting nitrogen compounds to NO. Previous laboratory measurements concluded that HCN and NH3 are the major nitrogen compounds released during devolatilization. The most likely compound released during char combustion is NO. The conversion of HCN and NH3 to NO was found to vary in fuel-rich conditions at temperatures between 1200 K and 2000 K. Therefore a NO prediction model should include conversion of HCN and NH3 to NO. Currently used global kinetic mechanisms were found to inadequately predict NO formation. Commonly used thermal NO mechanisms significantly over-predict NO formation at temperatures greater than 2200 K and long residence times. This trend is not critical in coal-fired systems because maximum temperatures are less than 2000 K. The de Soete mechanism is commonly used to predict the conversion of HCN to NO. This mechanism was found to under-predict NO formation at temperatures less than 1700 K. The de Soete mechanism was adjusted to improve predictions at low temperatures. An investigation into the use of reduced reaction mechanisms found it was not theoretically possible to reduce a comprehensive reaction mechanism to the extent it could be incorporated into a CFD model, and solve within the same simulation time as a CFD model using global kinetics. An empirical equation was developed that fits the profile of NO formation and HCN destruction. The equations fit NO and HCN profiles across a wide range of conditions. Values of NO and HCN at conditions other than the simulated ones are calculated by interpolation. This mechanism offers an alternative approach to prediction of NO formation by global kinetic mechanisms. Currently available char nitrogen to NO equations are simple, and do not consider parameters such as char size, temperature and char surface area. Empirical equations were developed to calculate the conversion of char nitrogen to NO given the char size, temperature, O2 concentration, NO concentration, char surface area and intrinsic reactivity. The accuracy and reliability of NO predictions determined by CFD models was assessed by comparison of model predictions and utility boiler measurements. CFD models were assessed by four criteria including how accurately NO emissions were predicted, how accurately NO profiles and profiles of species important to NO formation within the burner region were predicted, how accurately temperature and O2 concentrations were predicted and how accurately the model predicted trends in NO emissions as combustion conditions changed. The CFD model investigated, Furnace, could not satisfy the set criteria and therefore modelling results should be interpreted carefully.n

Experimental and CFD analysis of photocatalytic gas phase vinyl chloride (VC) oxidation

Solid-liquid mixing plays a significant role in crystallization, suspension polymerization, leaching, solid-catalyzed reaction and adsorption. In this study, a computational fluid dynamic (CFD) model was developed for solid-liquid mixing in a cylindrical tank equipped with a top-entering impeller. The multiple reference frame (MRF) technique, k-ε model and Eulerian-Eulerian approach were employed to simulate the impeller rotation, turbulent flow and multiphase flow, respectively. The effects of impeller speed, solid concentration, particle size, solid density and impeller clearance on the mixing performance of four different impellers (A310, marine propeller, pitched blad turbine and A320) were investigated. The CFD results were in good agreement with experimental data measured using electrical resistance tomography (ERT). In order to investigate the mixing quality in this study, the impeller speed required for maximum homogeneity, clouding height, and just-suspended impeller speed were investigated.

Computational fluid dynamics (CFD) model has been widely used for prototyping data centers. Evolving it to high-fidelity {\em digital twin} is desirable for the management and operations of large-scale data centers. Manually calibrating CFD model parameters to achieve twin-class fidelity by specially trained domain expert is tedious and labor-intensive. To reduce manual efforts, existing automatic calibration approaches developed for various computational models apply heuristics to search model configurations within an empirically defined parameter bound. However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity. This paper presents Kalibre, a knowledge-based neural surrogate approach that performs CFD model calibration by iterating four key steps of i) training a neural surrogate model based on CFD-generated data, ii) finding the optimal parameters at the moment through neural surrogate retraining based on sensor-measured data, iii) configuring the found parameters back to the CFD model, and iv) validating the CFD model using sensor-measured data as the ground truth. Thus, the parameter search is offloaded to the neural surrogate which is ultra-faster than CFD model's iterated solving. To speed up the convergence of Kalibre, we integrate prior knowledge of the twinned data center's thermophysics into the neural surrogate design to improve its learning efficiency. With about five hours computation on a 32-core processor, Kalibre achieves mean absolute errors (MAEs) of $0.81^o$C and $0.75^o$C in calibrating two CFD models for two production data halls hosting thousands of servers each while requires fewer CFD solving processes than existing baseline approaches.

A numerical investigation was performed to study the variation in axial and lateral velocity profiles occurring downstream of the inlet nozzle of a typical Westinghouse 17×17 PWR fuel assembly. A Computational Fluid Dynamic (CFD) model was developed with commercial CFD software. The model comprised the lower region of the fuel assembly, including: the Debris Filter Bottom Nozzle (DFBN), P-grid, Bottom Inconel grid, one and half grid span, as well as the lower core plate hole. The purpose of the study was to obtain insight into the flow redistribution resulting from the interaction of the jet arising from the lower core plate hole and the fuel assembly structure. In particular the axial and lateral velocities before and after the nozzle were studied. The results, axial and lateral velocity contours, streamlines and maximum axial and lateral velocity distributions at various elevations are presented and discussed in relation to the potential risk of high turbulent excitation over the rod and the resulting rod-to-grid fretting-wear damage. The CFD model results indicated that the large jet flows from the lower core plate are effectively dissipated by DFBN nozzle and the grids components of the fuel assembly. The breakup of the large jets in the DFBN and the lower grids helps to reduce the steep velocity gradients and thus the rod vibration and fretting-wear risk in the lower part of the fuel assembly. The presented CFD model is one step towards developing advanced tools that can be used to confirm and evaluate the effect of complex PWR structures on flow distribution. In the future the presented model could be integrated in a larger CFD model involving several fuel assemblies for evaluating the lateral velocities generated due to the non-uniform inlet conditions into the various fuel assemblies.

An analysis and development method for augmenting flow and pressure performance of electronic cooling axial fans using a fixed vane stator is established using classical hand calculations, 2-dimensional (2D) Computational Fluid Dynamics (CFD) analysis, data from a design of experiments, and 3-dimensional (3D) CFD modeling. Where the size of electronic enclosures may disallow an increase in diameter of axial fans but allow for an increase in depth; a fixed vane stator is implemented to recapture lost dynamic pressure associated with swirl and radial flow vectors from the axial fan blades thus augmenting the pressure/flow curve of the unit. Stator blade effectiveness is evaluated and optimized first using data associated with National Advisory Committee for Aeronautics (NACA) airfoil shapes and then using 2-dimensional (2D) CFD analyses on both the impeller and stator blades. CFD modeling approaches and solving methods are discussed. A Design of Experiments (DOE) is utilized to verify and optimize the performance of the stator vanes and identifies the effectiveness of the stator vane angle, curvature of the stator leading edge, and number of stator vanes. At a constant back pressure the best performing DOE geometry delivered a 22% improvement in flow at constant electrical power input and a 41% improvement in flow at constant acoustic noise. This result was confirmed using a 3D CFD modeling. This analysis and development method provides a good baseline for evaluating and choosing proper stator vane geometries for flow improvement in axial fans.

Bubbly flow behavior simulation in two-phase chemical reactors such bubble column type reactors is widely employed for chemical industry purposes. The computational fluid dynamics (CFD) approach has been employed by engineers and researchers for modeling these types of chemical reactors. In spite of the CFD robustness for simulating transport phenomena and chemical reactions in these reactors, this approach has been known as expensive for modeling such turbulent complex flows. Artificial intelligence (AI) algorithm of the adaptive network-based fuzzy inference system (ANFIS) are largely understood and utilized for the CFD approach optimization. In this hybrid approach, the CFD findings are learned by AI algorithms like ANFIS to save computational time and expenses. Once the pattern of the CFD results have been captured by the AI model, this hybrid model can be then used for process simulation and optimization. As such, there is no need for further simulations of new conditions. The objective of this paper is to obviate the need for expensive CFD computations for two-phase flows in chemical reactors via coupling CFD data to an AI algorithm, i.e., differential evolution based fuzzy inference system (DEFIS). To do so, air velocity as the output and the values of the x, and y coordinates, water velocity, and time step as the inputs are inputted the AI model for learning the flow pattern. The effects of cross over as the DE parameter and also the number of inputs on the best intelligence are investigated. Indeed, DEFIS correlates the air velocity to the nodes coordinates, time, and liquid velocity and then after the CFD modeling could be replaced with the simple correlation. For the first time, a comparison is made between the ANFIS and the DEFIS performances in terms of the prediction capability of the gas (air) velocity. The results released that both ANFIS and DEFIS could accurately predict the CFD pattern. The prediction times of both methods were obtained to be equal. However, the learning time of the DEFIS was fourfold of ANFIS.

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.

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.

This study describes a methodology combining a recently proposed renormalization inversion technique with a building‐resolving computational fluid dynamics (CFD) approach for source retrieval in the geometrically complex urban regions. It presents the first application of the renormalization inversion approach to estimate an unknown continuous point release in real situations at an urban scale. The renormalization inversion approach is based on an adjoint source‐receptor relationship and is purely deterministic in nature. The source parameters (i.e., source location and release rate) are reconstructed from a finite set of point measurements of concentration acquired from some sensors and the adjoint functions computed from a CFD model fluidyn‐PANACHE that is able to represent the geometric and flow complexity inherent in the urban regions. The inversion procedure is evaluated for a point source reconstruction using measurements from the Mock Urban Setting Test (MUST) field experiment. Source reconstructions are performed for 20 trials of the MUST experiment of a continuous point release in an idealized urban geometry consisting of a regular array of shipping containers. The steady state flow fields are computed by solving the three‐dimensional Reynolds‐averaged Navier‐Stokes equations by using a finite volume scheme. Then, in each MUST trial adjoint functions are obtained and used for the source identification. Inversion results are presented with both synthetic and real measurements in various atmospheric stabilities varying from neutral to stable and very stable conditions. With real concentration measurements, the point source is retrieved within an average Euclidean distance of 14.6 m from the actual source location. The estimated source intensity is overpredicted by an average factor of 1.37 of the true release rate. In a posterior uncertainty analysis with 10% random noise in measurements, it is demonstrated that standard deviation in the location error and release strength, respectively, varies by 5.22 m and ∼21% from their mean value for all 20 trials. A sensitivity analysis shows that the use of nonzero measurements helps in reducing the uncertainties involved in the source reconstruction. The source reconstruction results in various stability conditions exhibit the reliability of the renormalization inversion methodology coupled with the CFD approach in an urban area. The present methodology can be used by emergency regulators as a tool to detect the unknown accidental or deliberated releases in the complex urban environments.

In our study the hydrodynamic behavior of a gasification reactor with two inlets and one outlet was numerically examined by the method of residence time distribution. We developed a Computational Fluid Dynamics (CFD) model of the reactor to solve the momentum and component balance equations of the system. The component balance equation was implemented to support the residence time distribution calculation. The mesh dependency of the CFD solution was checked by error rate and calculation time. In order to characterize the mixing and flow within reactor pulse experiments were performed numerically. A tracer impulse was introduced for 1 s to both inlets at the same time with concentration of 100 mol/m3 approaching the Dirac delta function and tracer concentration at the outlet boundary of system was recorded. Compartmental modelling has a strong advantage over CFD as it has much smaller computational need. Therefore a compartment model in order to model the hydrodynamic structure of the reactor was also developed and compared to the results obtained by CFD model.