Browsing by Subject "simulation"

Now showing 1 - 20 of 28
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Advancing Racial Equity in the Minneapolis Park System: How Could Organizations with Divergent Goals Work Together?
    (E-PARCC at Syracuse University, 2020) Yuan (Daniel), Cheng; Brooke, Dirtzu
    "Advancing Racial Equity in the Minneapolis Park System” is a role-play simulation designed to help students understand the challenges in creating a collaborative governance regime when actors involved have different understandings of the core issue. It also helps students understand how complex structural elements underpin systemic inequalities, and then learn strategies to advance racial equity in public service provisions. This simulation is relevant for classes dealing with collaborative governance, public engagement processes, stakeholder involvement, collaborative problem-solving, and increasing diversity and inclusion in public policy making.
  • Thumbnail Image
    Item
    Applications of Genomewide Selection in a New Plant Breeding Program
    (2019-07) Neyhart, Jeffrey
    Newly established breeding programs must undergo population improvement and determine superior germplasm for deployment in diverse growing environments. More rapid progress towards these goals may be made by incorporating genomewide selection, or the use of genomewide molecular markers to predict the merit of unphenotyped individuals. Within the context of a new two-row barley (Hordeum vulgare L.) breeding program, my objectives were to i) investigate various methods of updating training population data and their impact on long-term genomewide recurrent selection, ii) assess genomewide prediction accuracy with informed subsetting of data across diverse environments, and iii) validate genomewide predictions of the mean, genetic variance, and superior progeny mean of potential breeding crossses. My first study relied on simulations to examine the impact on prediction accuracy and response to selection when updating the training population each cycle with lines selected based on predictions (best, worst, both best and worst), model criteria (PEVmean and CDmean), random sampling, or no selections. In the short-term, we found that updating with the best or both best and worst predicted lines resulted in high prediction accuracy and genetic gain; in the long-term, all methods (besides not updating) performed similarly. In an actual breeding program, a breeder may want phenotypic data on lines predicted to be the best and our results suggest that this method may be effective for long-term genomewide selection and practical for a breeder. In my second study, a 183-line training population and 50-line offspring validation population were phenotyped in 29 location-year environments for grain yield, heading date, and plant height. Environmental relationships were measured using phenotypic data, geographic distance, or environmental covariables. When adding data from increasingly distant environments to a training set, we observed diminishing gains in prediction accuracy; in some cases, accuracy declined with additional data. Clustering environments led to a small, but non-significant gain in prediction accuracy compared to simply using data from all environments. Our results suggest that informative environmental subsets may improve genomewide selection within a single population, but not when predicting a new generation under realistic breeding circumstances. Finally, my third study used genomewide marker effects from the same training population above to predict the mean (μ), genetic variance (VG), and superior progeny mean (μSP ; mean of the best 10% of lines) of 330,078 possible crosses for Fusarium head blight (FHB) severity, heading date, and plant height. Twenty-seven of these crosses were developed as validation populations. Predictions of μ and μSP were moderate to high in accuracy (rMP = 0.46 – 0.69), while predictions of VG were less accurate (rMP = 0.01 – 0.48). Predictive ability was likely a function of trait heritability, as rMP estimates for heading date (the most heritable) were highest and rMP estimates for FHB severity (the least heritable) were lowest. Accurate predictions of VG and μ are feasible, but, like any implementation of genomewide selection, reliable phenotypic data is critical.
  • Thumbnail Image
    Item
    Connecting macroscopic properties to microstructure of block copolymer materials through simulation
    (2024-05) Collanton, Ryan
    Due to their molecular topology, block copolymers exhibit rich and unique physical properties that make them of interest for use in a wide variety of applications. In this thesis, we first discuss the methodology and development of software for performing self-consistent field theory calculations. We then investigate the properties of block copolymers and their microscopic origin in two distinct contexts. First, we consider the equilibrium states of materials consisting solely of block copolymers or of majority block copolymer. Block copolymers are well-known to self-assemble into ordered microstructures. These microstructures constitute a balance of entropic and enthalpic contributions to the free energy. The particular microstructure that forms depends on the block copolymer chemistry, the temperature, and if it is a multi-component system, the blending fraction. In addition to the long-established classical phases such as lamellae or hexagonal cylinders, near the turn of the century, theoretical efforts led to predictions of the stability of more complicated phases in block copolymer melts such as the Frank-Kasper A15 phase might be stable in block copolymer melts. Furthermore, Frank-Kasper phases, originally discovered in metals in the 1950s, had been reported to form in other soft matter systems such as lipid-containing micelles. They nonetheless remained elusive in block copolymer materials until 2010 when the Frank-Kasper σ phase was serendipitously discovered. This discovery was followed by the discovery of many more complex ordered microstructures in polymer melts and blends, leading to questions about what drives these phases to be stable. In this work, we calculate structures of diblock copolymer melts using self-consistent theory and perform a novel analysis that connects free energy to crystal structure. We find that the transition from the body-centered cubic (bcc) phase to the σ phase is driven by a decrease in enthalpy, and that this decrease is a result not of a decrease in contact area but instead of a sharpening of the interface. This contradicts previous explanations employing strong-stretching theory. We then transition to investigate the properties of immiscible polymer blends compatibilized with block copolymers as small weight-fraction additives. Due to vanishing entropy of mixing, all commercially relevant polymers are thermodynamically immiscible. In blends, this manifests as large phase-separated domains with weak separating interfaces across which there is a very low degree of entanglement between dissimilar polymers. Thus, blends of immiscible polymers exhibit poor mechanical properties. However, these blends are of interest because of their potential relevance in multi-functional materials or in recycling of highly heterogeneous waste streams. Early efforts attempted to improve blend properties without any additives by refining the structure of the interface to promote entanglement. In the past two decades, however, attention has turned to block copolymers as potential additives to achieve the two goals of compatibilization: reduction of domain size and improvement of mechanical properties. Block copolymers are known to act in a surfactant-like fashion by reducing interfacial tension at homopolymer-homopolymer interfaces. Furthermore, they have been found to improve mechanical strength through anchoring mechanisms such as entanglement and co-crystallization. Diblock copolymers were first investigated for their performance as “compatibilizers”, and recent work has shown other architectures such as linear multiblock copolymers to significantly outperform diblock copolymers. In this work, we investigate the thermodynamic and mechanical properties of polymer blends compatibilized by linear multiblock copolymers using coarse-grained molecular dynamics. We find that the density and uniformity of interfacial crossings determined the reduction of interfacial tension. Furthermore, we identify the existence of an optimal loading of copolymer that maximizes toughness and strain-at-break, and examine the mechanism of failure of a glassy compatibilized blend under uniaxial elongation. We show that failure occurs via a two-step mechanism that involves cavitation at the interface followed by simultaneous re-densification and chain pullout. This mechanism is qualitatively different from failure of a single-component polymer glass, but leads to a nearly identical stress-strain response.
  • Thumbnail Image
    Item
    Data and code in support of: Release of live baitfish by recreational anglers drives fish pathogen introduction risk
    (2022-06-06) McEachran, Margaret C.; Phelps, Nicholas B. D.; Drake, D. Andrew R.; Mladonicky, Janice M.; Picasso, Catalina; thom4412@umn.edu; McEachran, Margaret; University of Minnesota Department of Fisheries, Wildlife and Conservation Biology; University of Minnesota Department of Veterinary Population Medicine; University of Minnesota Gabbert Raptor Center; Fisheries and Oceans Canada Great Lakes Research Laboratory
    This repository contains supplementary information, simulation data, and R computer code to accompany the manuscript titled "Release of live baitfish by recreaional anglers drives fish pathogen introduction risk." The purpose of this project was to quantify the risk of fish pathogen introduction, conceptualized as the number of fish infected with a priority pathogen released in a given year of fishing, under a range of conditions.
  • Thumbnail Image
    Item
    Data-Driven Analysis and Insight of Human Motion
    (2020-01) Sohre, Nicholas
    Motion is a central element of the human experience. Artificial Intelligence (AI) and robotics technologies continue to transform society, but work is needed to enable solutions that engage with our motion-driven reality. Critical to an understanding human motion is the ability to model and accurately simulate virtual humans. To that end, my thesis provides data-driven analysis and insight for human motion. I identify two key aspects of realistic human motion simulations: being both \textit{natural} in appearance while covering the rich \textit{variety} of motions exhibited by humans. I describe how motion data can be leveraged to both simulate realistic motion, as well as validate simulation realism through a combination of data-driven analysis and user study approaches. Computational methods for human motion are largely studied in the context of computer graphics and virtual character animation. Drawing from and expanding on work in this field, my work applies data-driven methods for simulating humans in several settings: that of facial motion, local crowd simulation, and global navigation. The methods and analysis in this dissertation present contributions to the fields of AI, robotics, and computer graphics in supporting my thesis that data-driven methods can be used to create and validate realistic simulations of human motion. In the first part of my thesis, I study the simulation of realistic human smiles by conducting a large user study to connect observer reactions to computer animated faces. The result is a rich dataset providing value beyond that of this thesis to interdisciplinary research. I use the data to train a generative model with a new machine learning heuristic (PVL) that I develop, which tunes the trade-offs in creating a variety of happy smiles. I validate the realism of the PVL results with a follow up user study. The second part of my thesis studies the simulation of realistic human navigation. I perform a data-driven evaluation of the impact of collision avoidance on user experiences in virtual reality (VR), validating its importance for enabling the feeling of presence. I leverage motion data of shoppers to drive new insights for human navigation decisions, discovering an entropy law governing item retrieval patterns. Finally, I present a deep-learning technique (SPNets) for simulating realistic human navigation behaviors in indoor settings trained on optimal paths. The resulting agents exhibit several human-like behaviors, such as intelligent backtracking, narrowing down goal locations, and environment familiarity. I validate the realism of SPNet simulations using paths from a user study on the same navigation tasks.
  • Thumbnail Image
    Item
    Developing genomic tools to breed for climate-adapted plant varieties
    (2023-03) Della Coletta, Rafael
    Climate change is a major threat to global food security, as current plant varieties used by farmers may not adapt to new growing environments. To mitigate this problem, plant breeders must use all available tools to speed up the development and release of new climate-adapted varieties. In this dissertation, I discuss how the recent advances in crop genomics due to improvements in sequencing technology, genome assembly methods, and computational resources are revolutionizing plant breeding. Particularly, I argue that the analysis of the complete catalog of genetic variation of a crop can provide useful information for plant breeders. I demonstrate that modeling this pan-genome information can increase the accuracy of multi- environment genomic prediction models, a tool widely used by breeders to develop new plant varieties. I also show how utilizing prior information on genetic variants associated with certain phenotypes can help simulate traits that are more realistic and relevant for breeders using digital breeding, a tool where breeders can test many different experiments before deployment in their breeding programs. Finally, I developed a new tool that identifies genetic variants associated with specific environmental factors via network analysis of common datasets available to plant breeders.
  • Thumbnail Image
    Item
    Development of a Guideline for Work Zone Diversion Rate and Capacity Reduction
    (Minnesota Department of Transportation, 2016-03) Kwon, Eil; Park, Chongmyung
    This study develops a comprehensive guideline to estimate the traffic diversion rates and capacity reduction for work zones. The analysis of the traffic diversion patterns with data from past work zones in the metro freeway network in Minnesota resulted in a set of the diversion-estimation models that relate the diversion rates at freeway ramps with the travel times and speed levels on a freeway and alternative routes during construction. The interrelationship between diversion and work-zone traffic conditions has led to the development of an iterative process, where a freeway simulation model interacts with the diversion-estimation models until a convergence is achieved between diversion and resulting freeway delays. Freeval is adopted in this study as the simulation tool for freeways. The test results of the iterative process with the work zone data showed promising results in determining both the diversion rates and freeway delay for a given work-zone. Due to the types of the work zones used in developing the diversion models, the iterative process developed in this study can be applicable to only "two-to-one" lane reduction cases in estimating the diversion rates for the mainline exit flows, while the diversion rates at entrance ramps can be determined without such restrictions. The capacity analysis of the lane-closure sections performed in this study has also resulted in a set of the suggested capacity values for the work zones with two-to- one lane reduction.
  • Thumbnail Image
    Item
    Experimental and simulated cell migration in 1D and 2D nanofiber microenvironments
    (2017-03) Estabridis, Horacio
    Understanding how cells migrate in fibrous environments is important in wound healing, immune function, and cancer progression. A key question is how fiber orientation and network geometry influence cell movement. Here we describe a quantitative, modeling-based approach toward identifying the mechanisms by which glioblastoma cells migrate in fibrous geometries having well controlled orientation. Specifically, U251 glioblastoma cells were seeded onto STEP Fiber substrates that consist of networks of suspended 400 nm diameter nanofibers. Cells were classified based on the local fiber geometry and live cell migration was tracked, quantified and parameterized. Cells were found in three distinct geometries: adhering two a single fiber, adhering to two parallel fibers, and adhering to a network of orthogonal fibers. Cells adhering to a single fiber or two parallel fibers can only move in one dimension along the fiber axis, whereas cells on a network of orthogonal fibers can move in two dimensions. We found that cells move faster and more persistently in 1D geometries than in 2D, with cell migration being faster on parallel fibers than on single fibers. To explain these behaviors mechanistically, we simulated cell migration in the three different geometries using a motor-clutch based model for cell traction forces. Using nearly identical parameter sets for each of the three cases, we found that the simulated cells naturally replicated the reduced migration in 2D relative to 1D geometries. In addition, the modestly faster 1D migration on parallel fibers relative to single fibers was captured using a modest increase in the number of clutches to reflect increased surface area of adhesion on parallel fibers. Overall, the integrated modeling and experimental analysis indicates that cell migration response to varying fibrous geometries can be explained by a simple mechanical readout of geometry via a motor-clutch mechanism.
  • Thumbnail Image
    Item
    A Flexible Simulator for Oncolytic Viral Therapy
    (2015-05) Berg, David
    Developments in recombinant DNA technology have given researchers the ability to modify viruses so that they are highly selective towards cancer cells. Engineered viruses have successfully treated cancer in human trials. In an effort to better understand viral population dynamics in a temporal context, researchers have turned to mathematical models. Some of these viruses spread only by contact between virus-infected and uninfected tumor cells. Therefore, mathematical models that usually assume populations are well-mixed may not apply. This thesis describes a computational approach to modeling viral population dynamics that takes into account the spatial nature of viral spread by contact.
  • Thumbnail Image
    Item
    Full Simulation Data and Worked Examples from Specht et al. Conditional Occupancy Manuscript
    (2017-02-27) Specht, Hannah S; Iannarilli, Fabiola; Edwards, Margaret R; Johnson, Michael K; Stapleton, Seth P; Weegman, Mitch; Yohannes, Brittney J; Arnold, Todd W; Reich, Henry T; spech030@umn.edu; Specht, Hannah M
    Occupancy models are widely used to describe the distribution of rare and cryptic species— those that occur on only a portion of the landscape and cannot be detected reliably during a single survey. However, occupancy models often provide inaccurate estimates of occupancy (ψ ̂) and detection probabilities (p ̂) under these circumstances. We developed a new "conditional" occupancy design that more accurately estimates occupancy for rare species. Here we provide the full simulation dataset used to compare estimation properties of standard, removal and conditional designs. Data were simulated in R and analyzed using MCMC methods in package R2jags. See Specht et al. (in review) for description of methods. Please cite Specht et al. in further use of this data set.
  • Thumbnail Image
    Item
    Growing Certain: Students’ Mechanistic Reasoning about the Empirical Law of Large Numbers
    (2019-05) Brown, Ethan
    Extensive research has documented students’ difficulty understanding and applying the Empirical Law of Large Numbers, the statistical principle that larger random samples result in more precise estimation. However, existing interventions appear to have had limited success, perhaps because they merely demonstrate the Empirical Law of Large Numbers rather than support students’ conceptual understanding of why this phenomenon occurs. This dissertation developed a sequence of activities, Growing Certain, which provided support for two mechanistic explanations of the Empirical Law of Large Numbers for students in a simulation-based introductory statistics course: swamping, the decreasing influence of extreme values on the mean as sample size increases, and heaping, the increasing concentration of possible sample means around the population mean. Five students participated in over six hours of one-on-one clinical interviews, with analysis focused on one focal participant, “S”. S’s responses were analyzed using a detailed coding of S’s articulation of mechanism components. S already displayed strong inclination towards swamping in the pre-interview questions, and their articulation of swamping became more sophisticated as they progressed in Growing Certain. However, S’s understanding of the connections between population and sample were weak throughout, and S had a lot of difficulty reasoning about multiple sample means simultaneously in a sampling distribution. S’s lack of abstraction of the sample mean appeared to support them in attending to the dynamics of swamping, but hindered them in being able to reason about heaping. Future research could examine representations that bridge swamping and heaping, and to examine individual differences in attention to the mechanistic components of the Empirical Law of Large Numbers.
  • Thumbnail Image
    Item
    How anatomical details affect noninvasive brain stimulation in computational models
    (2023-01) Mantell, Kathleen
    Noninvasive brain stimulation (NIBS) is an exciting field of study that is becoming increasingly popular for its many therapeutic uses. Two of the most widely used types of NIBS are transcranial electric and magnetic stimulation (TES and TMS). NIBS takes advantage of the electrical properties of neurons by modifying neuronal behavior through externally applied electric fields. This is achieved by either passing a current through two or more electrodes (TES) or inducing electric fields via a time varying magnetic field (TMS). Today, the biggest problems facing the NIBS field are the variability of responses in experiments and clinical settings and translating findings from animal studies to humans. To work to address these problems, we employ the power of computational modeling, specifically finite element method (FEM) modeling. FEM modeling allows us to build head models and simulate TMS and TES induced electric fields. However, there are many factors that go into building accurate models and it is not always clear how important they are in estimating the NIBS induced electric fields. Therefore, in this dissertation I explain how we look at three different factors in building FEM models: inclusion of stroke lesions in pediatric models, changing head model size, and inclusion of muscle tissue. In this work we found that stroke lesions greatly influence variability of the TMS induced electric field, either increasing or decreasing the electric field strength depending on the TMS coil location. This indicates that individualized head models are key to planning future experiments because the complex morphology does not allow us to make a simple prediction about the electric field. Next, we found that head size plays a significant role in NIBS induced electric fields, both in spherical models and non-human primate (NHP) models. For TES the electric field strength exponentially decreases with increasing head size. But the TMS induced electric field strength first increases with head size and then decreases after a critical point based on the TMS coil size. Finally, we determined that muscle tissue is an important feature in NHP models for TES simulations and it increases the electric field strength, but the percent change can be influenced by anisotropic properties of the muscle. Overall, these results from modeling nonstandard cases suggest that individualized modeling with careful consideration of the model setup is vital to accurately predicting NIBS induced electric fields.
  • Thumbnail Image
    Item
    Improving the Precision and Application of Speech Diagnostic Tests
    (2018-11) Yu, Tzu-Ling
    Background: Diagnostic speech recognition tests are the most direct way to quantify the distortion component of hearing loss and to evaluate the outcome of hearing prostheses. Purpose: The primary purpose of this dissertation was to evaluate the diagnostic precision of the spoken word recognition (WR) tasks that differed in listeners’ response formats (the closed- and open-set tasks). The second purpose was to improve the precision through a refined analysis of WR performance where the chance performance for listening parts (phonemes) of a word was considered. Method: WR performance for closed- and open-set tasks was obtained from seventy listeners with normal hearing. Hearing loss was simulated by presenting words in noise or in a sinewave vocoder condition. The percentage of correct phonemes in response word for each test word was computed to derive the distribution of chance performance based on an assessment of 15,000 iterations of the randomly paired response and test words. Results: Analyses found the following for the most to least precise and efficient conditions in detecting a change in hearing: open-set task scored by percent correct phonemes, open-set task score by percent correct words, 6-alternative closed-set task, and 4-alternative closed-set task. When the range of phoneme chance performance was accounted for in an open-set WR task, listeners with identical word scores were found to have different abilities to perceive phonemes. Conclusions: Closed-set WR testing has distinct advantages for implementation but its poorer precision for identifying a change in hearing than open-set WR testing must be considered. The analysis of scoring WR by phonemes on an open-set task with the estimates of chance performance reveals meaningful differences in perception that are not possible based on word scores.
  • Thumbnail Image
    Item
    Investigation of Performance Requirements of Full-Depth Reclamation Stabilization
    (Center for Transportation Studies, University of Minnesota, 2016-03) Le, Jia-Liang; Marasteanu, Mihai; Milavitz, Rose
    This research investigates the relationship between the mechanical properties of SFDR and the final performance of the rehabilitated pavements. The study involves two computational tools (MEPDG and MnPAVE) for the simulation of the long-term rutting behavior of pavements containing SFDR layers. Based on the simulations of three existing MnROAD cells, it is shown that for MEPDG the SFDR layer is best modeled as a bounded asphalt layer. To further investigate the applicability of MEPDG, a series of laboratory experiments are performed on cores taken from several sites constructed with different stabilizers including engineered emulsion, foamed asphalt with cement and CSS-1 with cement. The experiments include IDT creep and tension, semi-circular bending, dynamic modulus and disc compact tension tests. The measured mechanical properties are inputted into MEPDG to predict the rutting performance of these sites and it is shown that the simulated rut depth agrees well with the site measurement. However, it is found that MEPDG may suffer a convergence issue for some ranges of the values of the mechanical properties of SFDR. Due to this limitation, MnPAVE was used as an alternative. It was shown that the results simulated by MnPAVE are consistent with those obtained by MEDPG. A parametric study was performed on the three sites constructed with SFDR to determine the relationship between the long-term reliability of the rut performance and the mechanical properties of the SFDR.
  • Thumbnail Image
    Item
    Molecular Simulation and Design of High-χ Low-N Block Oligomers for Control of Self-Assembly
    (2022-02) Shen, Zhengyuan
    Multi-component oligomer systems are exciting candidates for nanostructured functional materials, due to the wide variety of their self-assembled morphologies with extremely small feature size. However, experimentally screening through the vast design space of molecular architectures can be extremely laborious. Therefore, guidance from predictive modeling is essential to reduce the synthetic effort. This dissertation discusses the predictive design of self-assembling block oligomer systems using molecular simulations, and the development of computer vision models for automated morphology detection for simulation trajectories. Work presented in this thesis creates a roadmap for efficient computational screening of shape-filling molecules, thus accelerating the design and discovery of nanostructured functional materials. First, with the aid of experimentally-validated force fields, molecular dynamics simulations were exploited to design: 1) a series of symmetric triblock oligomers that can self-assemble into ordered nanostructures with sub-1 nm domains and full domain pitches as small as 1.2 nm, 2) Blends of a lamellar-forming diblock oligomer and a cylinder-forming miktoarm star triblock oligomer leading to stable gyroid networks over a large composition window. Similarities and distinctions between the self-assembly phase behavior of these block oligomers and block polymers are discussed. Second, existing simulation data were used to train deep learning models based on three-dimensional point clouds and voxel grids. The pretrained neural networks can readily detect equilibrium morphologies, and also give rich insights of emerging patterns throughout new simulations with different system sizes and molecular dimensions.
  • Thumbnail Image
    Item
    Moving contact lines in dynamic wetting phenomena: Wetting failure, elastocapillarity, and droplet evaporation
    (2021-05) Charitatos, Vasileios
    Dynamic wetting refers to a system where a fluid displaces another fluid (usually air), while remaining in contact with a solid. This displacement involves a moving fluid-solid-fluid junction, referred to as the dynamic contact line. While dynamic wetting phenomena are encountered in an everyday setting (e.g., rain sliding on car windows, coffee droplets evaporating on the table), the physics of moving contact lines is quite complex as their motion is governed by several physical factors. In this thesis we study four different model problems to advance our fundamental understanding of moving contact lines in dynamic wetting: (i) dynamic wetting failure of non-Newtonian liquids, (ii) droplet spreading on soft solids, (iii) droplet evaporation on inclined substrates and (iv) droplet evaporation on soft solids. The diversity of problems studied in this thesis, emphasizes the importance of moving contact lines in dynamic wetting phenomena. Motivated by the use of non-Newtonian liquids in coating processes, we study the effect of two non-Newtonian rheologies, shear thinning and shear thickening, on the onset of dynamic wetting failure. Flow-visualization experiments using a curtain coating geometry and a hydrodynamic model of liquid displacing air between two parallel plates are developed. We find that shear thinning postpones the onset of wetting failure while shear thickening promotes it. Our results provide insight into how rheological properties of non-Newtonian liquids can be fine-tuned to postpone wetting failure as much as possible. We then investigate the spreading of liquid droplets on soft viscoelastic substrates. A theoretical model is developed to study the effect of various solid properties (e.g., softness, thickness, wettability) on the spreading of perfectly wetting and partially wetting droplets. Our simulations show that softer substrates speed up droplet spreading for perfectly wetting droplets but slow down spreading for partially wetting droplets. Our findings can provide insight into the design of soft substrates for desired applications. The effect of substrate inclination on droplet evaporation is studied in the third problem. We develop a mathematical model based on lubrication theory and investigate pure-solvent and particle-laden droplet evaporation on smooth and rough inclined substrates. We find that on smooth substrates, steeper inclination speeds up evaporation. On rough substrates, the effect of substrate inclination depends on the Bond number Bo, measuring the relative importance of surface-tension forces to gravity forces. At low Bo, a steeper substrate inclination slows down evaporation whereas at high Bo it speeds up evaporation. Additionally, we investigate the effect of substrate inclination and initial particle loading on the final particle deposition patterns. Lastly, we develop a lubrication-theory-based model to study droplet evaporation on soft solid substrates. Our simulations show that on softer substrates droplets exhibit pinning, leading to faster evaporation. Results from our model qualitatively reproduce similar trends observed in experiments. We believe results of this work can provide guidelines toward engineering of soft solid substrates, designed for droplet-evaporation-related applications.
  • Thumbnail Image
    Item
    Multi-resolution Modeling and Simulation of Marine Hydrokinetic Turbine Arrays at Site Scale
    (2017-04) Chawdhary, Saurabh
    Marine and hydro-kinetic (MHK) energy hold promise to become significant contributor towards sustainable energy generation. Despite the promise, commercialization of MHK energy technologies is still in the development stage. While many simplified models for MHK site resource-assessment exist, more research is needed to enable efficient energy extraction from identified MHK sites. A marine energy company named Verdant Power Inc. was granted first federal license to install up to 30 axial hydrokinetic turbines in the East River in New York City under what came to be known as Roosevelt Island Tidal Energy (RITE) project. Therefore, in this study we investigate issues of relevance to post-site-identification stage for a real-life tidal energy project, the RITE project, using high-fidelity numerical simulations. An effective way to develop arrays of hydrokinetic turbines in river and tidal channels is to arrange them in TriFrame configurations where three turbines are mounted together at the apexes of a triangular frame. The TriFrames serve as the building block for rapidly deploying multi-turbine arrays. The wake structure of a TriFrame of three model turbines is investigated. We employ large-eddy simulation (LES) with the curvilinear immersed boundary method (CURVIB) for fully resolving the turbine geometry details to simulate turbine-turbine wake interactions in the TriFrame configuration. First, the computed results are compared with experiments in terms of mean flow and turbulence characteristics with overall good agreement with bed-flume experiments. The flow-fields are then analyzed to elucidate the mechanisms of turbine interactions and wake evolution in the TriFrame configuration. We found that the wake of the upstream TriFrame turbine exhibits unique characteristics indicating presence of the Venturi effect as the wake encounters the two downstream turbines. We finally compare the wakes of the TriFrame turbines with that of an isolated single turbine wake to further illustrate how the TriFrame configuration affects the wake characteristics and power production in an array of TriFrames. Lastly, we propose a large eddy simulation (LES)-based framework to investigate the site-specific flow dynamics past MHK arrays in a real-life marine environment. To this end, the new generation unstructured Cartesian flow solver, coupled with a sharp interface immersed boundary method for 3D incompressible flows, is used. Optimized data-structures and efficient algorithms were developed to enable faster simulation on high-resolution grids. Multi-resolution simulations on locally refined grids are then employed to model the flow in a section of the East River with detailed river bathymetry and inset turbines at field scale. The results are analyzed in terms of the wake recovery and overall wake dynamics in the array. Comparison with the baseline flow in the East River reveal the effects of tidal array installation.
  • Thumbnail Image
    Item
    New Rules for Simulation and Analysis in Ecology, Epidemiology, and Elsewhere
    (2012-06-13) Lehman, Clarence; Keen, Adrienne
    An invisible "phase change" has recently re-shaped computation, in only a few years. Vast memories allow new modeling methods–still being invented and deployed. We apply such methods to epidemiology and ecology, outlined here.
  • Thumbnail Image
    Item
    A numerical and theoretical study of drag reduction using superhydrophobic surfaces
    (2018-09) Li, Yixuan
    Motivated by the potential drag reduction benefits of superhydrophobic surfaces (SHS), direct numerical simulation (DNS) and theoretical analyses are used to explore the interaction between SHS and turbulent channel flow. First, DNS is used to study the drag reduction by SHS in laminar channel flow. Resolved multi-phase simulations using the volume of fluid (VOF) methodology are performed to study the effects of groove geometry, interface shear rate and meniscus penetration independently. An analytical solution for the flow in a laminar channel with grooved surface with gas-pocket within is obtained. The solution accounts for both the groove geometry and the trapped fluid properties, and shows good agreement with simulation results. The solution is used to propose a scaling law that collapses data across fully wetted to fully gas-filled regimes. The trapped gas is simulated as both flat and meniscal interfaces. The drag reduction initially increases with interface deflection into the groove and then decreases for large deflections as the interface velocity approaches zero due to the proximity to the bottom of the groove. Next, the geometric effect of SHS in turbulent flow is studied by performing DNS at friction Reynolds number $\Rey_\tau = 400$ over longitudinal grooves whose size is comparable to the viscous sublayer thickness. It is found that despite the bulk flow being close to that of a flat-wall channel, the slip effect of the grooves causes some differences within the viscous sublayer. Spectral analysis of the velocity transfer function between the interior and the exterior regions of the grooves shows that the grooves suppress the energy at low frequencies. The DNS reveals negligible Reynolds shear stress near the grooves, which motivates an unsteady Stokes flow model. It is assumed that the flow in the vicinity of the grooves is governed by the unsteady Stokes equations, forced by an oscillating external flow. The effects of streamwise, spanwise and vertical velocity, freestream wavenumber and the height of freestream perturbation above the groove are studied. The non-dimensional parameter $\omega L^2/\nu$ obtained from this model problem ($L$ is half of the groove span, $\omega$ is the frequency of the external turbulent signal and $\nu$ is the kinematic viscosity) is used to relate the model to the current DNS. Good agreement is seen with DNS at low frequencies. It is suggested that higher frequency disturbances are produced by smaller spanwise structures near the wall, and when this effect is accounted for, good agreement is also observed at higher frequencies. Finally, we study multiphase flow within grooved textures exposed to external unsteadiness. We derive analytical expressions for multiphase unsteady Stokes flow within periodic grooves driven by oscillating streamwise/spanwise freestream velocity. Good agreement is obtained between the analytical solution and DNS performed with the VOF method. The effect of oscillation frequency, Reynolds number, and the multiphase interface location on the transfer function between the input signal external to the groove and output near the interface, is examined. Also, the effective slip length and the shear stress over the grooved plane are studied.