Browsing by Subject "Computational"

Now showing 1 - 11 of 11

CFD evaluation of internal flow effects on turbine blade leading-edge film cooling and overall cooling with shaped hole geometries
(2021-06-18) Easterby, Christopher Conway; Bogard, David G.
In gas turbine engines, the highest heat loads occur at the leading-edge areas of turbine blades and vanes. To protect the blades and vanes, a “showerhead” configuration of film cooling holes is often used for this location, in which several rows of holes are configured closely together to maximize film coverage. Typically, these film cooling holes are fed by impingement cooling jets, helping to cool the leading edge internally, but also changing the internal flow field. The effects of these internal flow fields on film cooling are not well known, and experimental research is very limited in its ability to analyze them. Because of this, computational fluid dynamic (CFD) simulations using RANS were used as a way to analyze these internal flow fields. To isolate the effects of the impingement jet, results were compared to a pseudo-plenum internal feed, and rotation in the hole caused by the impingement was found to be a key factor in performance. Computational results from both coolant feed configurations were compared to experimental results for the same configurations. The CFD RANS results were found to follow the same trends as the experimental results for both the impingement-fed and plenum-fed cases, suggesting that RANS is able to accurately model some of the important physics associated with leading-edge film cooling. Additionally, the effects of the impingement feed on overall cooling effectiveness were analyzed and found to be significant at lower blowing ratios but less significant at higher blowing ratios.
Comprehensive modeling approach for air breathing electric propulsion in VLEO
(2021-12-05) Stromeyer, Roman; Raja, Laxminarayan L.
The development of spacecraft electric propulsion (EP) technologies and the in-situ harnessing and utilization of resources in space are two very relevant and contemporary challenges in spaceflights. Both are sought out to be addressed by the development of air-breathing electric propulsion (ABEP) methods which are purposed to harness the thin upper atmospheres of gaseous planets as propellant while in low orbits. This ambition has particularly been directed toward the drive to expand very low-earth orbits (VLEO) for improve spatial resolution, reduced orbital debris, reduced launch costs and numerous other advantages. The ambition of this paper is to (1) parametrically study the feasible operating conditions for three EP technologies: an ABEP particle collector, a radio-frequency (RF) ionizer and a Hall thruster, (2) develop a comprehensive air collisional cross section reaction mechanism to enable high-fidelity reactive particle simulations, (3) simulate the gas breakdown and plasma discharge within an RF ionizer operating under rarefied gas flow and (4) validate a 1-D hybrid magnetized plasma simulation against experimental data of two Hall thrusters: the FAKEL SPT-100ML and the ECHT, the latter of which is designed to operate on nitrogen. Novel simulation results are presented for the use of nitrogen and an extended discussion is given on the considerations between conventional EP propellants vs. atmospheric species. Further areas of improvement and further investigation are outlined for both the simulations and the reactive chemistry mechanism
A computational model for the diffusion coefficients of DNA with applications
(2010-05) Li, Jun, 1977-; Gonzalez, Oscar, 1968-; Demkowicz, Leszek F.; Makarov, Dmitrii E.; Rodin, Gregory J.; van de Geijn, Robert A.
The sequence-dependent curvature and flexibility of DNA is critical for many biochemically important processes. However, few experimental methods are available for directly probing these properties at the base-pair level. One promising way to predict these properties as a function of sequence is to model DNA with a set of base-pair parameters that describe the local stacking of the different possible base-pair step combinations. In this dissertation research, we develop and study a computational model for predicting the diffusion coefficients of short, relatively rigid DNA fragments from the sequence and the base-pair parameters. We focus on diffusion coefficients because various experimental methods have been developed to measure them. Moreover, these coefficients can also be computed numerically from the Stokes equations based on the three-dimensional shape of the macromolecule. By comparing the predicted diffusion coefficients with experimental measurements, we can potentially obtain refined estimates of various base-pair parameters for DNA. Our proposed model consists of three sub-models. First, we consider the geometric model of DNA, which is sequence-dependent and controlled by a set of base-pair parameters. We introduce a set of new base-pair parameters, which are convenient for computation and lead to a precise geometric interpretation. Initial estimates for these parameters are adapted from crystallographic data. With these parameters, we can translate a DNA sequence into a curved tube of uniform radius with hemispherical end caps, which approximates the effective hydrated surface of the molecule. Second, we consider the solvent model, which captures the hydrodynamic properties of DNA based on its geometric shape. We show that the Stokes equations are the leading-order, time-averaged equations in the particle body frame assuming that the Reynolds number is small. We propose an efficient boundary element method with a priori error estimates for the solution of the exterior Stokes equations. Lastly, we consider the diffusion model, which relates our computed results from the solvent model to relevant measurements from various experimental methods. We study the diffusive dynamics of rigid particles of arbitrary shape which often involves arbitrary cross- and self-coupling between translational and rotational degrees of freedom. We use scaling and perturbation analysis to characterize the dynamics at time scales relevant to different classic experimental methods and identify the corresponding diffusion coefficients. In the end, we give rigorous proofs for the convergence of our numerical scheme and show numerical evidence to support the validity of our proposed models by making comparisons with experimental data.
Computational modeling of tumor cell growth as a function of nutrient dynamics guided by time-resolved microscopy
(2021-12-03) Yang, Jianchen (Ph. D. in biomedical engineering); Yankeelov, Thomas E.; Brock, Amy; Dunn, Andrew K; Virostko, Jack
The varying and extreme nutrient conditions found in the tumor microenvironment force reprogramming of metabolism in tumor cells. This metabolic reprogramming has been identified as a hallmark of cancer. This dissertation focuses on the development and validation of an experimental-mathematical approach that predicts how the dynamics of glucose and lactate influence tumor metabolism and development. Firstly, we developed a baseline model that predicts tumor cell growth as a function of glucose availability. We employed time-resolved microscopy to track the temporal change in the number of live and dead tumor cells under different initial conditions and seeding densities. A family of mathematical models that describes the overall tumor cell growth in response to the initial glucose and confluence was constructed. The most parsimonious model selected from the family using the Akaike Information Criteria was calibrated and validated in two breast cancer cell lines (BT-474 and MDA-MB-231) and demonstrated accuracy in predicting tumor growth. Secondly, we developed noninvasive imaging of nutrient dynamics with a stable transfection of two FRET reporters, one assaying glucose concentration and one assaying lactate concentration, in the MDA-MB-231 breast cancer cell line. The FRET ratio from both reporters was found to increase with increasing concentration of the corresponding ligand and decrease over time for high initial concentration of the ligand. Significant differences in the FRET ratio corresponding to metabolic inhibition were found when cells were treated with glucose/lactate transporter inhibitors. The FRET reporters enabled us to track intracellular glucose and lactate dynamics, providing insight into tumor metabolism and response to therapy over time. Finally, we compared mechanism-based and machine learning models for predicting tumor cells growth when we introduced an inhibitor of glucose uptake as a potential treatment. We extended the baseline model to account for glucose uptake inhibition, considering both the real glucose level in the system and the glucose level accessible to tumor cells. The random forest model provided the best prediction while the mechanism-based model presented a comparable predictive capability.
Computational, theoretical investigation of materials for a sustainable energy future
(2016-08) Stauffer, Shannon Kaylie; Henkelman, Graeme; Mullins, Charles B; Crooks, Richard; Hwang, Gyeong; Milliron, Delia
Over the past several decades there has been significant progress in electronic structure theory, statistical sampling algorithms and computational resources which can be leveraged to calculate fundamental properties of materials and estimate rates of relevant chemical reactions. In the following dissertation, I use computational methods to address the materials problem of a sustainable energy future. Energy storage technologies have played a vital role in the mobile-technology revolution and the transition to utilize more sustainable energy sources; however improvements to the energy density, charge/discharge rate, and safety of rechargeable batteries are needed to realize the ambitious goals of fully electric vehicles and on-grid storage in areas with intermittent, renewable power sources. Li-ion batteries, in general, have a potential to fulfill these demands. In the following work, a new, high energy density electrode material with little capacity loss is considered. Additionally, the complex interaction between an electrode/electrolyte model system is considered in a potential dependent computational framework. Having a sustainable energy future also means utilizing energy-efficient processing in industrial scale applications. Separation processes use roughly 12% of all energy consumed in the United States due to energy-intensive thermal separation techniques. A final study looks at an alloy catalysts for the separation of ethylene from ethane/ethylene mixtures. A unique selectivity property was discovered that may help design catalysts to replace thermal separation of gases.
Diagonal plus low rank approximation of matrices for solving modal frequency response problems
(2010-12) Vargas, David Antonio; Bennighof, Jeffrey Kent, 1960-; Sirohi, Jayant
If a structure is composed mainly of one material but contains a small amount of a second material, and if these two materials have significantly different levels of structural damping, this can increase the cost of solving the modal frequency response problem substantially. Even if the rank of the contribution to the finite element structural damping matrix from the second material is very low, the matrix becomes fully populated when transformed to the modal representation. As a result, the complex-valued modal matrix that represents the structure’s stiffness and structural damping is both full rank, because of the diagonal part contributed by the stiffness, and fully populated, because of off-diagonal imaginary terms contributed by the second material’s structural damping. Solving the modal frequency response problem at many frequencies requires either the factorization of a coefficient matrix at every frequency, or the solution of a complex symmetric eigenvalue problem associated with the modal stiffness/structural damping matrix. The cost of both of these approaches is proportional to the cube of the number of modes included in the analysis. This cost could be reduced greatly if the damping properties of the structure were handled carefully in modeling the structure, but in practical computation of the modal frequency response, the information that could potentially reduce the computational cost is often unavailable. This thesis explores the possibilities of obtaining a representation of the complex modal stiffness/structural damping matrix as a diagonal matrix plus a matrix of minimal rank. An algorithm for computing a “diagonal plus low rank” (DPLR) representation is developed, along with an iterative algorithm for using an inexact DPLR approximation in the solution of the modal frequency response problem. The behavior of these algorithms is investigated on several example problems.
Fatigue behavior of post-installed shear connectors used to strengthen continuous non-composite steel bridge girders
(2016-08) Ghiami Azad, Amir Reza; Engelhardt, Michael D.; Williamson, Eric B., 1968-; Helwig, Todd A; Jirsa, James O; Taleff, Eric M
Many older bridges in Texas are constructed with floor systems consisting of a concrete slab over steel girders. A potentially economical means of strengthening these floor systems is to connect the existing concrete slab and steel girders using post-installed shear connectors to change the behavior of the beam from non-composite to partially-composite. Since fatigue is one of the main concerns in designing bridges, investigating the fatigue properties of these post-installed shear connectors becomes crucial. Results from direct-shear testing show that post-installed shear connectors have a better fatigue life compared to conventional welded shear studs. However, based on currently available data from direct-shear tests, fatigue life of post-installed shear connectors is still inadequate for economical retrofit in some cases. Furthermore, it is unclear if direct-shear tests provide an appropriate means of evaluating fatigue performance. The objective of this dissertation is to develop new and more accurate approaches for evaluating the fatigue characteristics of post-installed shear connectors. This objective is addressed through large-scale beam fatigue tests and computational studies. The focus of the work is on evaluating fatigue life of shear connectors based on both slip and stress demands.
Insights into computational methods for surface science and catalysis
(2021-12-06) Ciufo, Ryan Anthony; Henkelman, Graeme; Humphrey, Simon M; Hwang, Gyeong S.; Webb, Lauren J.
The fundamental understanding of both the reactions at catalytic surfaces and the ways in which these surfaces change throughout a catalytic cycle and lifetime are important for both academic and industrial disciplines. To develop these understandings on complex catalytic systems, ultra-high vacuum techniques such as molecular beam studies, temperature programmed desorption, reflection-absorption infrared spectroscopy and Auger electron spectroscopy can be used to study the simplest interactions between gas molecules and surfaces. These interactions can be studied from a bottom-up approach to learn about the system in question, upon which additional complexities can be added. To parallel these experimental techniques, a number of computational methods can be used to support findings and guide new experiments. Ab-initio electronic structure calculations allow for a better understanding of adsorbate-surface interactions, while long timescale dynamic simulations provide insight into the time evolution and kinetics of catalysts and catalytic surfaces. Empirical and machine-learning guided potentials can be developed to lessen computational cost while retaining accuracies comparable to ab-initio calculations. Fitting such potentials ultimately allows for larger calculations to be performed and longer timescales to be simulated. The above methods will be applied to a number of industrially and academically relevant catalytic systems, including studying the interaction of H₂ and CO with Cobalt based Fischer-Tropsch catalysts and the interaction between hydrogen and palladium surfaces. Additionally, the development of a machine learning package to fit and use interatomic potentials will be discussed.
Predictive modeling of optical enantiomeric excess determination assays for high-throughput asymmetric reaction screening
(2023-04-21) Howard, James Russell; Anslyn, Eric V., 1960-; Dalby, Kevin N; Hull, Kami L; Page, Zachariah A
High-throughput screening (HTS) of asymmetric transformations is vital to the development of modern pharmaceuticals and fine chemicals. Enantiomeric excess (ee) determination of asymmetric transformations is accelerated through the use optical techniques such as circular dichroism (CD)-based ee determination assays. However, the implementation of these assays requires calibration experiments using enantioenriched materials, which ultimately hinder the use of these assays in real-world applications. We report the prediction of calibration curves used in ee determination assays for chiral amines using a data-driven approach. By leveraging density functional theory-based chemical descriptors, we have developed a model that predicts calibration curves without performing prior calibration experiments. This calibration curve prediction method was applied to a multicomponent ortho-iminoboronic acid assembly. The ee values measured with the predicted calibration curves were within 10% of those measured with the experimental calibration curves. The generality of this approach was demonstrated using an octahedral Fe(II) complex for the ee determination of chiral amines. A diverse library of analytes was created to elucidate the electronic and steric factors which influence the CD response of the Fe(II) complex. After assessing the scope and limitations of the assay, we generated a model of calibration curves which could be used to determine the ee of unknown solutions with less than 6% error. This computational approach circumvents the need for chiral resolution to perform calibration experiments, which will ultimately accelerate reaction discovery and optimization.
Quantifying the vasculogenic potential of induced pluripotent stem cell-derived endothelial progenitors in angiogenic hydrogels
(2020-09-03) Crosby, Cody O'Keefe; Zoldan, Janeta; Baker, Aaron B; Parekh, Sapun H; Rosales, Adrianne M; Suggs, Laura J
The circulatory system, composed of an intricate hierarchical network of branching blood vessels, delivers dissolved nutrients and gases to more than 37 trillion cells in the human body. Pathologies that impair these blood vessels, i.e., cardiovascular disease, afflict nearly a million Americans and more than 17 million patients worldwide. To combat the incidence and subsequent progression of cardiovascular disease, researchers have sought to develop in vitro models of vasculature and transplant engineered, vascularized tissue. These efforts have encountered three primary obstacles that have hindered the translation of vascular tissue engineering to the clinic. First, current cell sources are inadequate: cells isolated from other adult patients rapidly undergo senescence and engender an immune response upon implantation. Second, there is a dearth of biomaterials that recapitulate the dynamic signaling environment of the extracellular matrix, which is critical to ensuring a physiological response in vivo. Third, current quantification methods are insufficient: comparing vascular network topologies across different studies remains a difficult endeavor, and many algorithms unnecessarily simplify a complex three-dimensional structure to a planar analysis. To specifically address these three challenges, we quantitatively evaluated the vasculogenic potential, i.e., ability to self-assemble into vessel-like networks, of induced pluripotent stem cell-derived endothelial progenitors (iPSC-EPs) in extracellular matrix-mimicking biomaterials. In this dissertation, we describe the derivation, isolation, and encapsulation of iPSC-EPs in collagen hydrogels. We varied the structural protein density, the concentration of angiogenic growth factor, and proteolytic activity to optimize the resulting microvascular network. We additionally developed a novel computational pipeline, constructed in ImageJ and MATLAB, to quantitatively evaluate vascular network topology while preserving the architecture of the network. However, the collagen hydrogels compacted rapidly and exhibited limited mechanical strength. We, therefore, synthesized a novel hybrid interpenetrating polymer network (IPN) hydrogel composed of collagen and norbornene-modified hyaluronic acid (NorHA). Our results suggested that this IPN hydrogel exhibited highly tunable mechanical properties, resisted compaction, and stimulated angiogenesis. Our two studies underscore the importance of understanding the role of mechano-regulation on vasculogenesis so that ECM-mimicking angiogenic biomaterials can be effectively deployed in the clinic and ultimately improve vascular health
Transport in higher dimensional phase spaces
(2016-12) Curry, Christopher Timothy; Morrison, Philip J.; Horton, Jr., Claude W; Hazeltine, Richard; Matzner, Richard; Gamba, Irene
We use a four dimensional symplectic mapping, the coupled cubic-quadratic map, to provide evidence of Arnol’d Diffusion in phase space. We use the method of frequency analysis for dynamical systems to demonstrate the existence of regular orbits, and show that these orbits enclose weakly chaotic orbits which escape in finite time around the tori. A new collocation method for frequency analysis is employed by adapting it to allow for higher precision results. Arbitrary precision numerics are used to obtain highly accurate orbits for long timescales, and the adapted frequency method is used to obtain highly accurate frequencies of the mapping. We review the method of frequency analysis, demonstrate its effectiveness and accuracy in determining frequencies and finding tori in simple systems and low-dimensional mappings, and extend the results to higher dimensions. In the four dimensional mapping, we find several regular orbits with irrational frequency ratios, indicating the existence of tori in the phase space, as well as interior orbits that escape around these tori.