A parallel, block-based, three-dimensional, hexahedral finite-volume scheme with adaptive mesh refinement has been developed for the solution of the 10-moment Gaussian closure for the modelling of fully three-dimensional micro-scale, non-equilibrium flows. The Gaussian closure has been shown to be a more effective tool for modelling rarefied flows lying within the transition regime than the Navier-Stokes equations, which encounter mathematical difficulties approaching free-molecular flows, and is computationally less expensive than particle-based methods for flows approaching the continuum limit. The hyperbolic nature of the moment equations is computationally attractive and the generalized transport equations can be solved in an accurate and efficient manner using Godunov-type finite-volume schemes as considered here. Details are given of the Gaussian closure, along with extensions for diatomic gases and slip-flow boundaries. Numerical results for several canonical flows demonstrate the potential of these moment closures and the parallel solution scheme for accurately predicting fully three-dimensional non-equilibrium flow behaviour.

The effects of pressure on various properties of ten different syngas fueled flames were analyzed using one and two dimensional simulations. One-dimensional premixed flames were modeled in CANTERA. Flame speed, adiabatic flame temperature and thermal diffusivity as functions of equivalence ratio and pressure were quantified for the fuels using four chemical kinetic mechanisms. Data from the different mechanisms displayed good agreement with data from previous experimental benchmarks. Two-dimensional axisymmetric co-flow flames were simulated in a state of the art computational framework for modeling laminar flames. Flame structure comparisons were made with past experimental and numerical results as well as with theoretical predictions. Good agreement in stoichiometric flame height was observed with past theoretical and numerical flame height measurements. Visible flame heights had little correlation with the stoichiometric flame heights. The flame radius was also noted to be proportional to p^-0.35 at high pressures instead of p^-0.5 as predicted by theory.

Hollow fiber ultrafiltration is a viable low cost alternative technology for the concentration or separation of protein solutions. However, membrane fouling and solute build up in the vicinity of the membrane surface decrease the performance of the process by lowering the permeate flux. Major efforts have been devoted to study membrane fouling and design more efficient ultrafiltration membrane systems. The complexity of membrane fouling, however, has limited the progress to better understand and predict the occurrence of fouling. This work was motivated by the desire to develop a microscopic Computational Fluid Dynamics (CFD) model to capture the complexity of the membrane fouling during hollow fiber ultrafiltration of protein solutions. A CFD model was developed to investigate the transient permeate flux and protein concentration and the spatial fouling behavior during the concentration of electroacidified (pH 6) and non- electroacidified (pH 9) soy protein extracts by membrane ultrafiltration. Electroacidification of the soy protein to pH 6 was found to decrease the permeate flux during UF which resulted in longer filtration time. Lower electrostatic repulsion forces between the proteins at pH 6 (near the protein isoelectric point) resulted in a tighter protein accumulation on the membrane surface suggested to be responsible for the lower permeate flux observed in the UF of the electroacidified soy protein extract. A new transient two-component fouling resistance model based on the local pressure difference, permeate velocity and protein concentration was implemented in the resistance-in-series flux model to describe the dynamics of the reversible and irreversible fouling during the filtration and the effect of pH on the membrane fouling. Good agreement between the experimental data and the model predictions was observed. Mathematical modeling was performed to estimate the osmotic pressure and diffusion coefficient of the proteins bovine serum albumin (BSA) and soy glycinin, one of the major storage proteins in soy, as a function of protein concentration, pH, and ionic strength. Osmotic pressure and diffusion coefficient of proteins play vital roles in membrane filtration processes because they control the distribution of particles in the vicinity of the membrane surface, often influencing the permeation rate. Therefore, understanding the behavior of these properties is of great importance in addressing questions about membrane fouling. An artificial neural network was developed to analyze the estimated data in order to find a simple relation for osmotic pressure as a function of protein concentration, pH, and ionic strength. For both proteins, the osmotic pressure increased as pH diverged from the protein isoelectric point. Increasing the ionic strength, however, reversed the effect by shielding charges and thereby decreasing the osmotic pressure. Osmotic pressure of glycinin was found lower than that of BSA. Depending on how much pH was far from the isoelectric point of the protein, osmotic pressure of BSA could be up to three times more than the glycinin’s. Two different trends for diffusion coefficient at specified pH and ionic strength were observed for both proteins; diffusion coefficient values that decreased with protein concentration and diffusion coefficient values that passed through a maximum. A rigorous CFD model based on a description of protein interactions was developed to predict membrane fouling during ultrafiltration of BSA. BSA UF was performed in a total recycle operation mode in order to maintain a constant feed concentration. To establish a more comprehensive model and thereby alleviate the shortcomings of previous filtration models in literature, this model considered three major phenomena causing the permeate flux decline during BSA ultrafiltration: osmotic pressure, concentration polarization, and protein adsorption on the membrane surface. A novel mathematical approach was introduced to predict the concentration polarization resistance on the membrane. The resistance was estimated based on the concentration and thickness profile of the polarization layer on the membrane obtained from the solution of the equation of motion and continuity equation at a previous time step. Permeate flux was updated at each time step according to the osmotic pressure, concentration polarization resistance, and protein adsorption resistance. This model had the ability to show how microscopic phenomena such as protein interactions can affect the macroscopic behaviors such as permeate flux and provided detailed information about the local characteristics on the membrane. The model estimation was finally validated against experimental permeate flux data and good agreement was observed.

The goal of this research is to apply modern methods of analysis to the design of a tungsten-cermet Nuclear Thermal Rocket (NTR) core. An NTR is one of the most viable propulsion options for enabling piloted deep-space exploration. Concerns over fuel safety have sparked interest in an NTR core based on tungsten-cermet fuel. This work investigates the capability of modern CFD and neutronics codes to design a cermet NTR, and makes specific recommendations for the configuration of channels in the core. First, the best CFD practices available from the commercial package Star-CCM+ are determined by comparing different modeling options with a hot-hydrogen flow experiment. Next, through grid convergence and sensitivity studies, numerical uncertainty is shown to be a small contributor to overall uncertainty; while fuel thermal conductivity, hydrogen specific heat, and fission energy deposition are found to have a large impact on simulation uncertainty. The model-form error is then estimated by simulation of a NERVA fuel element from an NRX-A6 engine test, where the peak temperature matches measured data to within 2.2%. Using a combination of Star-CCM+ and MCNP for neutronics, typical uncertainties are estimated at 3% for predicting fuel temperature, 2% for hydrogen temperature, and 5% for pressure. The second part uses the aforementioned analysis methods in a parametric study to determine what coolant channel size and distribution is optimum for a 10 klbf-thrust cermet NTR core. By varying the channel diameter and pitch-to-diameter ratio (p/d), it is found that a diameter of 0.12 cm with a p/d of 1.8 results in the lightest core with a peak temperature of 2850 K. The study also shows that element-by-element mass flow rate zoning is the best method for handling radial power peaking. In addition, a detailed simulation of a cermet design developed at the Argonne National Laboratory shows that modifications to the historical fuel element design are required to avoid overheating. The final part investigates the ability of Star-CCM+ to model fuel element failure modes. Through a combination of uncertainty quantification and a parametric analysis, this thesis ultimately lays a groundwork for future detailed design of cermet NTR fuel elements.

The health risks posed by hot weather are growing as increasingly frequent extreme weather is brought about by climate change. People spend upwards of 80% of time indoors and so human health is largely dependent on the internal environment of buildings. In the building industry engineers currently design buildings for high-energy performance by maximising heat retention, and whilst this may be effective in cold winters, it can lead to unbearable indoor conditions in hot summers. Thermal comfort inside buildings is a well-discussed topic both in industry and academia, but absolute peak thresholds, especially for heat stress still require development. In this thesis the outcomes of research into the effects of current and future hot weather on the heat stress of occupants inside buildings are presented. Hot weather data from the current climate and mortality rates are compared and several temperature metrics are analysed with respect to health risk forecasting performance, so that peak threshold limits for human health indoors are established for the building design industry. Reference weather data used in building simulations for health assessment is currently chosen based on air temperature alone. In this thesis new reference weather data is created for near-extreme and extreme weather and for current and future climates, based on the peak threshold metric research and future weather analysis. By 2050 hot weather reference years currently occurring once every seven years could become an annual occurrence, and by 2080 extreme hot weather reference years currently occurring once in twenty-one years could become an annual occurrence. Computational fluid dynamics is then used to simulate the internal heat stress inside a building model, and a surrogate model is created to emulate heat stress levels for full calendar years of future climates for several UK locations. It is envisaged that the results presented in this thesis will help inform the industry development of new reference data and aid better building design.

No description available.

The development work of a race car revolves around numerous goals such as drag reduction, maximizing downforce and side force, and maintaining balance. Commonly, these goals are to be met at the same time thus increasing the level of difficulty to achieve them. The methods for data acquisitions available to a race team during the season is mostly limited to wind tunnel testing and computational fluid dynamics, both of which are being heavily regulated by sanctioning bodies. While these methods enable data collection on a regular basis with repeat-ability they are still only a simulation, and as such they come with some margin of error due to a number of factors. A significant factor for correlation error is the effect of tires on the flow field around the vehicle. This error is a product of a number of deficiencies in the simulations such as inability to capture loaded radius, contact patch deformation in Y direction, sidewall deformation and overall shifts in tire dimensions. These deficiencies are evident in most WT testing yet can be captured in CFD. It is unknown just how much they do affect the aerodynamics performance of the car. That aside, it is very difficult to correlate those findings as most correlation work is done at WT which has been said to be insufficient with regards to tire effect modeling. Some work had been published on the effect of tire deformation on race car aerodynamics, showing a large contribution to performance as the wake from the front tires moves downstream to interact with body components. Yet the work done so far focuses mostly on open wheel race cars where the tire and wheel assembly is completely exposed in all directions, suggesting a large effect on aerodynamics. This study bridges the gap between understanding the effects of tire deformation on race car aerodynamics on open wheel race cars and closed wheel race cars. The vehicle in question is a hybrid of the two, exhibiting flow features that are common to closed wheel race cars due to each tire being fully enclosed from front and top. At the same time the vehicle is presenting the downstream wake effect similar to the one in open wheel race cars as the rear of the wheelhouse is open. This is done by introducing a deformable tire model using FEA commercial code. A methodology for quick and accurate model generation is presented to properly represent true tire dimensions, contact patch size and shape, and deformed dimension, all while maintaining design flexibility as the model allows for different inflation pressures to be simulated. A file system is offered to produce CFD watertight STL files that can easily be imported to a CFD analysis, while the analysis itself presents the forces and flow structures effected by incorporating tire deformation to the model. An inflation pressure sweep is added to the study in order to evaluate the influence of tire stiffness on deformation and how this results in aerodynamic gain or loss. A comparison between wind tunnel correlation domain to a curved domain is done to describe the sensitivity each domain has with regards to tire deformation, as each of them provides a different approach to simulating a cornering condition. The Study suggests introducing tire deformation has a substantial effect on the flow field increasing both drag and downforce.In addition, flow patterns are revealed that can be capitalized by designing for specific cornering condition tire geometry. A deformed tire model offers more stable results under curved and yawed flow. Moreover, the curved domain presents a completely different side force value for both deformed and rigid tires with some downforce distribution sensitivity due to inflation pressure.

Recent technological advances have lead to the development and application of multirotors for commercial package delivery and air taxis. They differ from helicopters because they operate at lower Reynolds numbers, induce more rotor-rotor interactions, and are controlled using variable-speed rather than variable-pitch rotors. The dynamic flow field of a multirotor leads to complex aerodynamics and aeroacoustics that can only be fully captured computationally using high-fidelity methods. A very popular high-fidelity approach used in the traditional rotorcraft simulation field is unsteady Reynolds-averaged Navier-Stokes (URANS). The URANS equations are implemented in this research using the CREATE-AV Helios computational code suite; however, when applying this method to smaller multirotors, three major challenges arise. The first challenge is to understand how the computational methods perform at the lower operational Reynolds numbers relevant to smaller multirotors. Implementation of URANS requires turbulence modeling, and models should account for laminar-turbulent transition at lower Reynolds numbers. This challenge is addressed by evaluating the effect of laminar-turbulent transition models in the Helios suite. This thesis demonstrates that current laminar-turbulent transition models in Helios do not improve either performance or noise predictions for small rotors. Fully-turbulent models provide reasonable accuracy at a lower cost. The second challenge to the high-fidelity multirotor simulation framework is the lack of an appropriate trim algorithm. For a computational prediction to be useful, it is necessary to simulate the rotorcraft in realistic flight conditions. This is accomplished via computation of trim, the set of controls and vehicle state that achieves a desired flight condition. Trim algorithms exist for helicopters that utilize blade pitch control but there has been no validation of high-fidelity computations that include trim for multirotors that control using individual rotor-speeds. This thesis presents the development and validation of a high-fidelity approach for multirotor trim using loose aerodynamic coupling. The newly developed trim algorithm currently solves unconstrained multirotor systems, which are underdetermined because the number of controls is greater than the number of targets, by adding an additional optimization for minimum power. The importance of including trim and performing the aerodynamic calculations with high-fidelity is demonstrated. The third challenge arises when applying high-fidelity methods to predict multirotor aeroacoustics. URANS alone cannot capture the full acoustic spectrum directly because the turbulent fluctuations are modeled and the grid cannot extend to the far field reliably. Therefore, it is necessary to develop models for portions of the spectrum and to use an acoustic analogy that converts sources in the CFD as noise to the far-field. However, there are complex interactional effects, a multitude of types of noise sources, and different methods for utilizing an acoustic analogy. It is also difficult to develop computations and experiments in which the noise sources match. The PSU-WOPWOP code is used to implement the Ffowcs-Williams and Hawkings equation with both impermeable and permeable surface approaches. Additionally, UCD-Quietfly is used to model some broadband noise sources. Through adaptation of existing tools and development of novel computational methods, the ability to predict multirotor noise is explored via computational investigation with comparison to experimental measurements to the extent possible. This thesis offers a new high-fidelity capability for predicting multirotor aerodynamics and aeroacoustics in trimmed flight. The approach enables final design analysis for developers of emerging multirotor systems who need reliable performance and noise predictions before full prototype development and testing. Capabilities developed in this thesis provide a platform upon which new trim optimization approaches can be created and tested, such as one that minimizes noise. / 2023-05-23T00:00:00Z

Indiana University-Purdue University Indianapolis (IUPUI) / Natural gas substitution for diesel can result in significant reduction in pollutant emissions. Based on current fuel price projections, operating costs would be lower. With a high ignition temperature and relatively low reactivity, natural gas can enable promising approaches to combustion engine design. In particular, the combination of low reactivity natural gas and high reactivity diesel may allow for optimal operation as a reactivity-controlled compression ignition (RCCI) engine, which has potential for high efficiency and low emissions. In this computational study, a lean mixture of natural gas is ignited by direct injection of diesel fuel in a model of the heavy-duty CAT3401 diesel engine. Dual-fuel combustion of natural gas-diesel (NGD) may provide a wider range of reactivity control than other dual-fuel combustion strategies such as gasoline-diesel dual fuel. Accurate and efficient combustion modeling can aid NGD dual-fuel engine control and optimization. In this study, multi-dimensional simulation was performed using a nite-volume computational code for fuel spray, combustion and emission processes. Adaptive mesh refinement (AMR) and multi-zone reaction modeling enables simulation in a reasonable time. The latter approach avoids expensive kinetic calculations in every computational cell, with considerable speedup. Two approaches to combustion modeling are used within the Reynolds averaged Navier-Stokes (RANS) framework. The first approach uses direct integration of the detailed chemistry and no turbulence-chemistry interaction modeling. The model produces encouraging agreement between the simulation and experimental data. For reasonable accuracy and computation cost, a minimum cell size of 0.2 millimeters is suggested for NGD dual-fuel engine combustion. In addition, the role of different chemical reaction mechanism on the NGD dual-fuel combustion is considered with this model. This work considers fundamental questions regarding combustion in NGD dual-fuel combustion, particularly about how and where fuels react, and the difference between combustion in the dual fuel mode and conventional diesel mode. The results show that in part-load working condition main part of CH4 cannot burn and it has significant effect in high level of HC emission in NGD dual-fuel engine. The CFD results reveal that homogeneous mixture of CH4 and air is too lean, and it cannot ignite in regions that any species from C7H16 chemical mechanism does not exist. It is shown that multi-injection of diesel fuel with an early main injection can reduce HC emission significantly in the NGD dual-fuel engine. In addition, the results reveal that increasing the air fuel ratio by decreasing the air amount could be a promising idea for HC emission reduction in NGD dual-fuel engine, too.

High performance streaming processors have achieved the distinction of being very efficient and cost-effective in terms of floating-point capacity, thereby making them an attractive option for scientific algorithms that involve large arithmetic effort. Graphics Processing Units (GPUs) are an example of this new initiative to bring vector-processing to desktop computers; and with the advent of 32-bit floating-point capabilities, these architectures provide a versatile platform for the efficient implementation of such algorithms. To exemplify this, the implementation of a Conjugate Gradient iterative solver for PDE solutions on unstructured two- and three-dimensional grids using such hardware is described. This would greatly benefit applications such as fluid-flow solvers which seek efficient methods to solve large sparse systems. The implementation has also been successfully incorporated into an existing object oriented CFD code, thereby enabling the option of using these architectures as efficient math co-processors in the computational framework.