You are here
Search results
(41 - 60 of 70)
Pages
- Title
- Modeling and simulations of evaporating spray, turbulent flow, and combustion in internal combustion engines
- Creator
- Srivastava, Shalabh
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
A multicomponent droplet evaporation model, which discretizes the one-dimensional mass and temperature profiles inside a droplet with a finite volume method and treats the liquid phase as thermodynamically real, has been developed and implemented into a large-eddy simulation (LES) code for evaporating and reacting spray simulations. Single drop evaporation results obtained by the variable property multicomponent model are shown to match with the constant property model in the limiting...
Show moreA multicomponent droplet evaporation model, which discretizes the one-dimensional mass and temperature profiles inside a droplet with a finite volume method and treats the liquid phase as thermodynamically real, has been developed and implemented into a large-eddy simulation (LES) code for evaporating and reacting spray simulations. Single drop evaporation results obtained by the variable property multicomponent model are shown to match with the constant property model in the limiting conditions. The LES code with the multicomponent model is used along with the Kelvin-Helmholtz - Rayleigh-Taylor (KH-RT) droplet breakup model to simulate realistic fuel sprays in a closed vessel and is found to reasonably well predict the experimentally observed non-linear behavior of spray penetration lengths with changing ambient conditions for n-hexadecane and 4 different multicomponent surrogate diesel fuels with 2-8 components. The effects of various modeling assumptions and gas and liquid parameters on the drop and spray evolution and evaporation are investigated in details.A previously studied single piston Rapid Compression Machine (RCM), extended to a twin-piston RCM, is simulated by LES for different stroke ratios of the two pistons, as a precursor to the study of opposed piston two-stroke engines. Opposed piston engines, which have recently generated interest due to their high power density and fuel economy, are mechanically simpler compared to conventional four-stroke engines but involve highly unsteady, turbulent and cycle-variant flows. LES of turbulent spray combustion in a generic single cylinder, opposed-piston, two-stroke engine configuration has been conducted with the two-phase filtered mass density function (FMDF) model, which is an Eulerian-Lagrangian-Lagrangian subgrid-scale probability density function (PDF) model for LES of two-phase turbulent reacting flows. The effects of various geometric parameters, operating conditions and spray parameters on the flow evolution, turbulence, spray and combustion in the engine are studied. The cycle-to-cycle variations in the flow variables like swirl and tumble are found to be significant while those in thermodynamic variables like temperature are negligible. The hybrid LES/FMDF methodology has been applied to simulate non-reacting turbulent spray for single-component and multi-component fuels and the consistency of the method has been established. The effects of spray parameters like nozzle hole diameter, injection pressure and injected fuel temperature on the spray penetration length are found to qualitatively follow experimental trends. Combustion simulations of n-dodecane fuel sprays are carried out for the opposed piston engine with a global kinetics mechanism and the consistency of the LES and FMDF components is demonstrated.
Show less
- Title
- The Köhler effect : intergroup competition using software-generated partners
- Creator
- Moss, Omotayo Micheal
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
Past research has examined the Köhler motivation gain effect (i.e., when an inferior team member performs better when paired with a more capable partner, from knowledge of his/her individual performance) in an active video game (AVG) using a superior, software-generated partner (SGP). The present investigation examined how incorporating a superior SGP into an AVG would affect an individual’s motivation when competing against one other human/virtual-partner team in an planking competition....
Show morePast research has examined the Köhler motivation gain effect (i.e., when an inferior team member performs better when paired with a more capable partner, from knowledge of his/her individual performance) in an active video game (AVG) using a superior, software-generated partner (SGP). The present investigation examined how incorporating a superior SGP into an AVG would affect an individual’s motivation when competing against one other human/virtual-partner team in an planking competition. Participants (N = 90 college-aged students) were randomly assigned to one of three conditions: individual control, conjunctive partner no competition (PNC), or conjunctive partner with opposing-team competition (PWT) in a 3 (conditions) x 2 (gender) factorial design. Participants performed the first series of five exercises alone, and after a rest period those in the partner conditions were told that they would do the remaining trials with a same-sex SGP whom they could observe during their performance. The partner’s performance was always superior to the participant’s. Participants were also told that they would work with their SGP as a team, and that the team’s score would be defined as the score of the person who stops holding the exercise first. Those in the opposing-team competition condition were also told that they and their virtual partner would be competing against one other human-virtual partner team. A significant motivation gain was observed in all partnered conditions compared to the control, F(2,89) = 15.63, p < .001, but the PNC and PWT groups were not significantly different from each other (p = 0.35). These findings suggest that competing against an opposing team does not ultimately boost the Köhler effect in AVGs.
Show less
- Title
- Elucidating the evolutionary origins of collective animal behavior
- Creator
- Olson, Randal S.
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
Despite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled...
Show moreDespite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled behavioral evolution experiments. In particular, I use these models to directly explore the evolutionary consequence of the selfish herd, predator confusion, and the many eyes hypotheses, and demonstrate how the models can lend key insights useful to behavioral biologists, computer scientists, and robotics researchers. This dissertation lays the groundwork for the experimental study of the hypotheses surrounding the evolution of collective animal behavior, and establishes a path for future experiments to explore and disentangle how the various hypothesized benefits of collective behavior interact over evolutionary time.
Show less
- Title
- Predictive control of a hybrid powertrain
- Creator
- Yang, Jie
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
Powertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This...
Show morePowertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This dissertation focuses on driving torque prediction, battery boundary management, and fuel economy optimization.For a hybrid powertrain, when the desired torque (driver torque demand) is outside of battery operational limits, the internal combustion (IC) engine needs to be turned on to deliver additional power (torque) to the powertrain. But the slow response of the IC engine, compared with electric motors (EMs), prevents it from providing power (torque) immediately. As a result, before the engine power is ready, the battery has to be over-discharged to provide the desired powertrain power (torque). This dissertation presents an adaptive recursive prediction algorithm to predict the future desired torque based on past and current vehicle pedal positions. The recursive nature of the prediction algorithm reduces the computational load significantly and makes it feasible for real-time implementation. Two weighting coefficients are introduced to make it possible to rely more on the data newly sampled and avoid numerical singularity. This improves the prediction accuracy greatly, and also the prediction algorithm is able to adapt to different driver behaviors and driving conditions.Based on the online-predicted desired torque and its error variance, a stochastic predictive boundary management strategy is proposed in this dissertation. The smallest upper bound of future desired torque for a given confidence level is obtained based on the predicted desired torque and prediction error variance and it is used to determine if the engine needs to be proactively turned on. That is, the engine can be ready to provide power for the “future” when the actual power (torque) demand exceeds the battery output limits. Correspondingly, the battery over-discharging duration can be reduced greatly, leading to extended battery life and improved HEV performance.To optimize powertrain fuel economy, a model predictive control (MPC) strategy is developed based on the linear quadratic tracking (LQT) approach. The finite horizon LQT control is based on the discrete-time system model obtained by linearizing the nonlinear HEV and only the first step of the solution is applied for current control. This process is repeated for each control step. The effectiveness of the supervisory control strategy is studied and validated in simulations under typical driving cycles based on a forward power split HEV model. The developed MPC-LQT control scheme tracks the predicted desired torque trajectory over the prediction horizon, minimizes the powertrain fuel consumption, maintains the battery state of charge at the desired level, and operates the battery within its designed boundary.
Show less
- Title
- Multiscale modeling of composite laminates with free edge effects
- Creator
- Cater, Christopher R.
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
ABSTRACTMULTISCALE MODELING OF COMPOSITE LAMINATES WITH FREE EDGE EFFECTSByChristopher R. CaterComposite materials are complex structures comprised of several length scales. In composite laminates, the mechanical and thermal property mismatch between plies of varying orientations results in stress gradients at the free edges of the composites. These free edge stresses can cause initial micro-cracking during manufacture, and are a significant driver of delamination failure. While the...
Show moreABSTRACTMULTISCALE MODELING OF COMPOSITE LAMINATES WITH FREE EDGE EFFECTSByChristopher R. CaterComposite materials are complex structures comprised of several length scales. In composite laminates, the mechanical and thermal property mismatch between plies of varying orientations results in stress gradients at the free edges of the composites. These free edge stresses can cause initial micro-cracking during manufacture, and are a significant driver of delamination failure. While the phenomenon of free edge stresses have been studied extensively at the lamina level, less attention has been focused on the influence of the microstructure on initial cracking and development of progressive damage as a consequence of free edge stresses. This work aimed at revisiting the laminate free edge problem by developing a multiscale approach to investigate the effect of the interlaminar microstructure on free edge cracking. First, a semi-concurrent multiscale modelling approach was developed within the commercial finite element software ABAQUS. An energetically consistent method for implementing free edge boundary conditions within a Computational Homogenization scheme was proposed to allow for micro-scale free edge analysis. The multiscale approach was demonstrated in 2D tests cases for randomly spaced representative volume elements of unidirectional lamina under tensile loading. Second, a 3D multiscale analysis of a [25N/-25N/90N]S composite laminate, known for its vulnerability to free edge cracking, was performed using a two-scale approach: the meso-scale model captured the lamina stacking sequence and laminate loading conditions (mechanical and thermal) and the micro-scale model predicted the local matrix level stresses at the free edge. A one-way coupling between the meso- and micro-scales was enforced through a strain based localization rule, mapping meso-scale strains into displacement boundary conditions onto the micro-scale finite element model. The multiscale analysis procedure was used to investigate the local interlaminar microstructure. The results found that a matrix rich interlaminar interface exhibited the highest free edge stresses in the matrix constituent during thermal cooldown. The results from these investigations assisted in understanding the tendency for pre-cracks during manufacture to occur at ply boundaries at the free edge and the preferential orientation to the ply interfaces. Additionally, analysis of various 90/90 ply interfaces in the thicker N=3 laminate found that the free edge stresses were far more sensitive to the local interlaminar microstructure than the meso-scale stress/strain free edge gradients. The multiscale analysis helped explain the relative insensitivity of free edge pre-cracks to progressive damage during extensional loading observed in experiments. Lastly, the multiscale analysis was extended to the interface between the -25 and 90 degree plies in the [25N/-25N/90N]S laminate. A micro-model representing the dissimilar ply interface was developed, and the homogenized properties through linear perturbation steps were used to update the meso-scale analysis to model the interlaminar region as a unique material. The analysis of micro-scale free edge stresses found that significant matrix stresses only occurred at the fiber/matrix boundary at the 90 degree fibers. The highest stresses were located near the matrix rich interface for both thermal and mechanical loading conditions. The highest matrix stresses in the case of extensional loading of the laminate, however, were found at the interior of the micro-model dissimilar ply micro-model within the -25 degree fibers.
Show less
- Title
- A particle-in-cell method for the simulation of plasmas based on an unconditionally stable wave equation solver
- Creator
- Wolf, Eric Matthew
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
In this dissertation, we present a particle-in-cell method for the simulation of plasmas based on an unconditionally stable solver for the second-order scalar wave equation, that is, a wave equation solver that is not subject to a Courant-Friedrichs-Lewy (CFL) stability restriction, typical of explicit methods, while maintaining a computational cost and code complexity comparable to such explicit solvers. This permits the use of a time step size many times larger than allowed by widely-used...
Show moreIn this dissertation, we present a particle-in-cell method for the simulation of plasmas based on an unconditionally stable solver for the second-order scalar wave equation, that is, a wave equation solver that is not subject to a Courant-Friedrichs-Lewy (CFL) stability restriction, typical of explicit methods, while maintaining a computational cost and code complexity comparable to such explicit solvers. This permits the use of a time step size many times larger than allowed by widely-used explicit methods.
Show less
- Title
- Design and simulation of single-crystal diamond diodes for high voltage, high power and high temperature applications
- Creator
- Suwanmonkha, Nutthamon
- Date
- 2016
- Collection
- Electronic Theses & Dissertations
- Description
-
ABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these...
Show moreABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these properties are crucial for a semiconductor that is used to make electronic devices that can operate at high power levels, high voltage and high temperature.Two-dimensional semiconductor device simulation software such as Medici assists engineers to design device structures that allow the performance requirements of device applications to be met. Most physical material parameters of the well-known semiconductors are already compiled and embedded in Medici. However, diamond is not one of them. Material parameters of diamond, which include the models for incomplete ionization, temperature-and-impurity-dependent mobility, and impact ionization, are not readily available in software such as Medici. Models and data for diamond semiconductor material have been developed for Medici in the work based on results measured in the research literature and in the experimental work at Michigan State University. After equipping Medici with diamond material parameters, simulations of various diamond diodes including Schottky, PN-junction and merged Schottky/PN-junction diode structures are reported. Diodes are simulated versus changes in doping concentration, drift layer thickness and operating temperature. In particular, the diode performance metrics studied include the breakdown voltage, turn-on voltage, and specific on-resistance. The goal is to find the designs which yield low power loss and provide high voltage blocking capability. Simulation results are presented that provide insight for the design of diamond diodes using the various diode structures. Results are also reported on the use of field plate structures in the simulations to control the electric field and increase the breakdown voltage.
Show less
- Title
- Unconstrained 3D face reconstruction from photo collections
- Creator
- Roth, Joseph (Software engineer)
- Date
- 2016
- Collection
- Electronic Theses & Dissertations
- Description
-
This thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input...
Show moreThis thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input is a long-standing computer vision problem. Traditional photometric stereo-based reconstruction techniques work on aligned 2D images and produce a 2.5D depth map reconstruction. We extend face reconstruction to work with a true 3D model, allowing us to enjoy the benefits of using images from all poses, up to and including profiles. To use a 3D model, we propose a novel normal field-based Laplace editing technique which allows us to deform a triangulated mesh to match the observed surface normals. Unlike prior work that require large photo collections, we formulate an approach to adapt to photo collections with few images of potentially poor quality. We achieve this through incorporating prior knowledge about face shape by fitting a 3D Morphable Model to form a personalized template before using a novel analysis-by-synthesis photometric stereo formulation to complete the fine face details. A structural similarity-based quality measure allows evaluation in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on Internet, synthetic, and personal photo collections.
Show less
- Title
- Modeling and simulation of strongly coupled plasmas
- Creator
- Chowdhury, Rahnuma Rifat
- Date
- 2016
- Collection
- Electronic Theses & Dissertations
- Description
-
The objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the...
Show moreThe objective of this work is to develop new modeling and simulation tools for studying strongly coupled plasmas (SCP). Strongly coupled plasmas are different from traditional plasmas as potential energy is larger than the kinetic energy. The standard plasma model does not account for some major effects in SCP: 1) the change in the permittivity 2) the impact on relaxation of the charged particles undergoing Coulomb collisions in a system with weakly shielded long range interactions3) the impact of statistical fluctuations in strongly coupled plasmas that leads to non-Markovian effects. Proper modeling of such systems through consideration of Lévy flight processes gives rise to fractional derivatives in time that result in an incorporation of time history in the model. A Lévy flight is a random walk in which the steps are defined in terms of the step-lengths, which have a certain probability distribution, with the directions of the steps being isotropic and random. Lévy processes in the plasma give rise to fluctuations in medium through which the electromagnetic waves are propagating. Averaging over the Lévy processes will allow us to relate to other important parameters in the plasma.
Show less
- Title
- Advances in metal ion modeling
- Creator
- Li, Pengfei (Chemist)
- Date
- 2016
- Collection
- Electronic Theses & Dissertations
- Description
-
Metal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte...
Show moreMetal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte Carlo orMolecular Dynamics simulation protocol. However, there are various metal ions and it isstill challenging to model them using available force field models. Generally there areseveral models available for modeling metal ions using the force field approach such asthe nonbonded model, the bonded model, the cationic dummy atom model, the combinedmodel, and the polarizable models. Our work concentrated on the nonbonded and bondedmodels, which are widely used nowadays. Firstly, we focused on filling in the blanks ofthis field. We proposed a noble gas curve, which was used to describe the relationshipbetween the van der Waals radius and well depth parameters in the 12-6 Lennard-Jonespotential. By using the noble gas curve and multiple target values (the hydration freeenergy, ion-oxygen distance, coordination number values), we have consistentlyparameterized the 12-6 Lennard-Jones nonbonded model for 63 different ions (including11 monovalent cations, 4 monovalent anions, 24 divalent cations, 18 trivalent cations,and 6 tetravalent cations) combined with three widely used water models (TIP3P, SPC/E, and TIP4PEW). Secondly, we found there is limited accuracy of the 12-6 model, whichmakes it hard to simulate different properties simultaneously for ions with formal chargeequal or larger than +2. By considering the physical origins of the 12-6 model, weproposed a new nonbonded model, named the 12-6-4 LJ-type nonbonded model. Wehave systematically parameterized the 12-6-4 model for 55 different ions (including 11monovalent cations, 4 monovalent anions, 16 divalent cations, 18 trivalent cations, and 6tetravalent cations) in the three water models. It was shown that the 12-6-4 model couldreproduce several properties at the same time, showing remarkable improvement over the12-6 model. Meanwhile, through the usage of a proposed combining rule, the 12-6-4model showed excellent transferability to mixed systems. Thirdly, we have developed theMCPB.py program to facilitate building of the bonded model for metal ion containingsystems, which can largely reduce human efforts. Finally, an application case of ametallochaperone - CusF was shown, and based on the simulations we hypothesized anion transfer mechanism.
Show less
- Title
- A global modeling framework for plasma kinetics : development and applications
- Creator
- Parsey, Guy Morland
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
The modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this...
Show moreThe modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this thesis centers around the development of an open-source global model frame-work for the focused study of non-equilibrium plasma kinetics. After verification and validationof the framework, it was used to study two physical phenomena: plasma-assisted combustion andthe recently proposed optically-pumped rare gas metastable laser.Global models permeate chemistry and plasma science, relying on spatial averaging to focusattention on the dynamics of reaction networks. Defined by a set of species continuity and energyconservation equations, the required data and constructed systems are conceptually similar acrossmost applications, providing a light platform for exploratory and result-search parameter scan-ning. Unfortunately, it is common practice for custom code to be developed for each application-an enormous duplication of effort which negatively affects the quality of the software produced.Presented herein, the Python-based Kinetic Global Modeling framework (KGMf) was designed tosupport all modeling phases: collection and analysis of reaction data, construction of an exportablesystem of model ODEs, and a platform for interactive evaluation and post-processing analysis. Asymbolic ODE system is constructed for interactive manipulation and generation of a Jacobian,both of which are compiled as operation-optimized C-code.Plasma-assisted combustion and ignition (PAC/PAI) embody the modernization of burning fuelby opening up new avenues of control and optimization. With applications ranging from engineefficiency and pollution control to stabilized operation of scramjet technology in hypersonic flows,developing an understanding of the underlying plasma chemistry is of the utmost importance.While the use of equilibrium (thermal) plasmas in the combustion process extends back to the ad-vent of the spark-ignition engine, works from the last few decades have demonstrated fundamentaldifferences between PAC and classical combustion theory. The KGMf is applied to nanosecond-discharge systems in order to analyze the effects of electron energy distribution assumptions onreaction kinetics and highlight the usefulness of 0D modeling in systems defined by coupled andcomplex physics.With fundamentally different principles involved, the concept of optically-pumped rare gasmetastable lasing (RGL) presents a novel opportunity for scalable high-powered lasers by takingadvantage of similarities in the electronic structure of elements while traversing the periodic ta-ble. Building from the proven concept of diode-pumped alkali vapor lasers (DPAL), RGL systemsdemonstrate remarkably similar spectral characteristics without problems associated with heatedcaustic vapors. First introduced in 2012, numerical studies on the latent kinetics remain immature.This work couples an analytic model developed for DPAL with KGMf plasma chemistry to bet-ter understand the interaction of a non-equilibrium plasma with the induced laser processes anddetermine if optical pumping could be avoided through careful discharge selection.
Show less
- Title
- Computational chemistry : investigations of protein-protein interactions and post-translational modifications to peptides
- Creator
- Jones, Michael R. (Graduate of Michigan State University)
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
Computational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit...
Show moreComputational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit β (IKKβ) or NF-κB Inducing Kinase (NIK), initiates this cascade of events. Understanding the structure-property relationships associated with IKKβ and NIK is essential for the development of prevention strategies. Although the signaling pathways are known, how the molecular mechanisms respond to changes in the intracellular microenvironment (i.e., pH, ionic strength, temperature) remains elusive. In this dissertation, computer simulation and modeling techniques were used investigate two protein kinases complexed with either small molecule activators or inhibitors in the active, inactive, and mutant states to correlate structure-property and structure-function relationships as a function of intracellular ionic strength. Additionally, radical-induced protein fragmentation pathways, as a result of reactions with reactive oxygen species, were investigated to yield insight into the thermodynamic preference of the fragmentation mechanisms. Analyses of the relationship between structure-activity and conformational-activity indicate that the protein-protein interactions and the binding of small molecules are sensitive to changes in the ionic strength and that there are several factors that influence the selectivity of peptide backbone cleavage. As there are many computational approaches for predicting physical and chemical properties, several methods were considered for the predictions of protein-protein dissociation, protein backbone fragmentation, and partition coefficients of drug-like molecules.
Show less
- Title
- Suicide, signals, and symbionts : evolving cooperation in agent-based systems
- Creator
- Vostinar, Anya E.
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
Cooperation is ubiquitous in nature despite the constant pressure for organisms to cheat by receiving a benefit from cooperators, while not cooperating themselves. The continued evolution and persistence of countless forms of cooperation is a central topic in evolutionary theory. Extensive research has been done on the theoretical dynamics of cooperation through game theory and the natural examples of cooperation. However, it remains difficult to understand thoroughly the evolution of many...
Show moreCooperation is ubiquitous in nature despite the constant pressure for organisms to cheat by receiving a benefit from cooperators, while not cooperating themselves. The continued evolution and persistence of countless forms of cooperation is a central topic in evolutionary theory. Extensive research has been done on the theoretical dynamics of cooperation through game theory and the natural examples of cooperation. However, it remains difficult to understand thoroughly the evolution of many cooperative systems, due in part to the ancient origins of these systems and the long time scales required to see cooperation evolve in any natural populations. I have systematically analyzed the evolution of three broad types of cooperation: programmed cell death, quorum sensing, and mutualisms (cooperation across species). I have provided evidence that programmed cell death can originate due to kin selection. I have also created two new systems to enable the extensive exploration of factors that affect the evolution of public goods cooperation and mutualism. Using these systems, I determine the effects of environmental factors on the evolution of public goods cooperation and mutualism. By uniting the expansive theoretical work on these forms of cooperation with a fully-controlled experimental system, I contributed to our understanding of how these forms of cooperation can emerge and be maintained in industrial and medical applications that rely on bacterial cooperation.
Show less
- Title
- Data-driven and task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
- Creator
- Ashtawy, Hossam Mohamed Farg
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
Molecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation ...
Show moreMolecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation (docking task), binary activity label (screening task), and binding affinity (scoring task) of ligands against a critical protein in the disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for the three tasks to solve three different, but related, prediction problems. The vast majority of these predictive models are knowledge-based, empirical, or force-field scoring functions. The fourth family of SFs that has gained popularity recently and showed potential of improved accuracy is based on machine-learning (ML) approaches. Despite intense efforts in developing conventional and current ML SFs, their limited predictive accuracies in these three tasks have been a major roadblock toward cost-effective drug discovery. Therefore, in this work we present (i) novel task- specific and multi-task SFs employing large ensembles of deep neural networks (NN) and other state-of-the-art ML algorithms in conjunction with (ii) data-driven multi-perspective descriptors (features) for accurate characterization of protein-ligand complexes (PLCs) extracted using our Descriptor Data Bank (DDB) platform.We assess the docking, screening, scoring, and ranking accuracies of the proposed task-specific SFs with DDB descriptors as well as several conventional approaches in the context of the 2007 and 2014 PDBbind benchmark that encompasses a diverse set of high-quality PLCs. Our approaches substantially outperform conventional SFs based on BA and single-perspective descriptors in all tests. In terms of scoring accuracy, we find that the ensemble NN SFs, BsN-Score and BgN-Score, have more than 34% better correlation (0.844 and 0.840 vs. 0.627) between predicted and measured BAs compared to that achieved by X-Score, a top performing conventional SF. We further find that ensemble NN models surpass SFs based on other state-of-the-art ML algorithms. Similar results have been obtained for the ranking task. Within clusters of PLCs with different ligands bound to the same target protein, we find that the best ensemble NN SF is able to rank the ligands correctly 64.6% of the time compared to 57.8% obtained by X-Score. A substantial improvement in the docking task has also been achieved by our proposed docking-specific SFs. We find that the docking NN SF, BsN-Dock, has a success rate of 95% in identifying poses that are within 2 Å RMSD from the native poses of 65 different protein families. This is in comparison to a success rate of only 82% achieved by the best conventional SF, ChemPLP, employed in the commercial docking software GOLD. As for the ability to distinguish active molecules from inactives, our screening-specific SFs showed excellent improvements over the conventional approaches. The proposed SF BsN-Screen achieved a screening enrichment factor of 33.90 as opposed to 19.54 obtained from the best conventional SF, GlideScore, employed in the docking software Glide. For all tasks, we observed that the proposed task-specific SFs benefit more than their conventional counterparts from increases in the number of descriptors and training PLCs. They also perform better on novel proteins that they were never trained on before. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from three tasks to simultaneously predict binding poses, affinities, and activity labels. MT-Net is composed of shared hidden layers for the three tasks to learn common features, task-specific hidden layers for higher feature representation, and three outputs for the three tasks. We show that the performance of MT-Net is superior to conventional SFs and competitive with other ML approaches. Based on current results and potential improvements, we believe our proposed ideas will have a transformative impact on the accuracy and outcomes of molecular docking and virtual screening.
Show less
- Title
- Integration of topological data analysis and machine learning for small molecule property predictions
- Creator
- Wu, Kedi
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
Accurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH...
Show moreAccurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants and is different from conventional chemical and physical representations. Based on ESPH and its modified version, element-specific topological descriptors (ESTDs) are constructed. The advantage of ESTDs is that they are systematical, comprehensive, and scalable with respect to molecular size and composition variations, and are readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between different small molecule properties, multi-task frameworks are further employed to simultaneously predict related properties. Deep neural networks, along with ensemble methods such as random forest and gradient boosting trees, are used to develop quantitative predictive models. Physical based molecular descriptors and auxiliary descriptors are also used in addition to ESTDs. As a result, we obtain state-of-the-art results for various benchmark data sets of small molecule properties. We have also developed two online servers for predicting properties of small molecules, TopP-S and TopTox. TopP-S is a software for topological learning predictions of partition coefficient and aqueous solubility, and TopTox is a software for computing element-specific tological descriptors (ESTDs) for toxicity endpoint predictions. They are available at http://weilab.math.msu.edu/TopP-S/ and http://weilab.math.msu.edu/TopTox/, respectively.
Show less
- Title
- Sensitivities of simulated fire-induced flows to fire shape and background wind profile using a cloud-resolving model
- Creator
- Stageberg, Marshall S.
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
Wildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud...
Show moreWildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud Model 1 (CM1) because it is designed to simulate high resolution, cloud scale processes that are comparable in scale to fire-induced flows. A surface sensible heat flux is added to CM1 to simulate the effect of a fire and the resultant fire-induced circulations and complex flows are examined. Using CM1 allows us to produce simulations with fine spatial and temporal resolution with a detailed representation of the evolution of the fire-atmosphere system. For the purpose of this study, we perform a series of simulations to examine the sensitivity of fire-induced flows to the shape of the simulated fire and to background wind profile. We show how fire shape and the background wind profile affect the intensity and extent of fire-induced perturbations to the lower atmosphere. The results from these numerical simulations, when combined with field observations, help improve our understanding of fire-atmosphere interactions. The results from this study can potentially help fire managers with decision-making when fighting wildland fires.
Show less
- Title
- The quest for active media models : a self-consistent framework for simulating wave propagation in nonlinear systems
- Creator
- Glosser, Connor Adrian
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
This work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the...
Show moreThis work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the long-wavelength limit. Variations in the local surface pressure caused by scattering from each microsphere set each microsphere into motion following Newton’s second law. By expanding this pressure in terms of spherical harmonics—natural eigenfunctions of the unretarded radiation kernel—I recover an analytic description of the force on individual microspheres due to an incident waveform. High-order numerical integrations then relate the surface potential on one microsphere to the surface pressure on the others, thereby coupling the microspheres’ trajectories. These simulations predict a dominant translational effect along the direction of propagation of the incident waveform, though they also reveal significant dipolar interactions between microspheres that produce secondary expansions and contractions of the collective microsphere system.Extending my approach from acoustic to electromagnetic systems, I apply it to a collection of quantum dots: “artificial” two-level atoms with a size-dependent energy structure. The optical Maxwell-Bloch equations give the evolution of quantum dots under the influence of electromagnetic fields; this evolution then produces secondary radiation that couples a collection of quantum dots together. In my computational model, I castmy secondary electromagnetic fields in terms of a point-to-point integral operator that accurately recovers both near- and far-field effects. These fields, then, drive a set of implicitly coupled Bloch equations (solved with an exponentially-fitted predictor/corrector scheme) to give the dynamics of the system as a whole. In ensembles of up to 10 000 quantum dots, my model predicts synchronized multiplets of particles that exchange energy, quantum dots that dynamically couple to screen the effect of incident external radiation, localization of the polarization due to randomness and interactions, as well as wavelength-scale regionsof enhanced and suppressed polarization.The remainder of the work uses the same physical quantum dot system while moving towards efficient computer-aided device design. I detail an improved propagation algorithm to reduce the time and space complexity of the simulation dramatically, thereby facilitating rapid analysis of promising device structures. The algorithm makes use of physical and numerical approximations to effect large-scale calculations in reasonable CPU time. A rotating-frame approximation removes high-frequency components in the evolution of the system while simultaneously preserving accurate interference phenomena in space,thereby affording far larger simulation timesteps. Additionally, projecting the source current distribution onto a regular spatial grid makes use of a low-rank approximation to the field propagator to communicate radiation information between distant groups of particles via fast Fourier transforms in a manner reminiscent of fast multipole methods.
Show less
- Title
- Balancing exploration and exploitation in bottom-up organizational learning contexts
- Creator
- Walker, Ross Ian
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
In order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term...
Show moreIn order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term learning. In March’s lineage, this thesis examines the effects of various exploratory strategies (i.e., individual experimentation, codification frequency, structural modularity, and employee turnover) on organizational learning in a bottom-up, networked, interpersonal learning context. Results demonstrate the complex interdependency of these variables in the exploration/exploitation tradeoff. Exploratory analyses suggest that a small degree of random individual experimentation has a favorable reward-to-risk ratio and that it is preferable to turnover as an exploratory strategy.
Show less
- Title
- Modeling and control of pre-chamber initiated turbulent jet ignition combustion systems
- Creator
- Song, Ruitao
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
"Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion....
Show more"Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion. A one-zone gas exchange model is developed to simulate the gas exchange process in both pre- and main-combustion chambers. The combustion process is modeled by a two-zone combustion model, where the ratio of the burned and unburned gases flowing between the two combustion chambers is variable. To simulate the influence of the turbulent jets to the rate of combustion in the main-combustion chamber, a new parameter-varying Wiebe function is proposed and used for mass fraction burned (MFB) calculation in the main-combustion chamber. The developed model is calibrated using the Least-Squares fitting and optimization procedure. The RCM model was then extended to a TJI engine model. The combustion process is modeled by a similar two-zone combustion model based on the newly proposed parameter-varying Wiebe function. The gas exchange process is simulated by one-zone model considering piston movement and intake and exhaust processes. Since the engine uses liquid fuel, a pre-chamber air-fuel mixing and vaporization model is developed. And correspondingly, the pre-chamber uses a chemical kinetics based model for combustion rate calculation. The model was validated using the experimental data from a single cylinder TJI engine under different operational conditions, and the simulation results show a good agreement with the experimental data. For control design, a nonlinear state-space engine model with cycle-to-cycle dynamics is developed based on the previous crank-angle-resolved (CAR) TJI engine model. The state-space model successfully linked the combustion processes in the two chambers using the parameter-varying Wiebe function. The validated CAR model is used to calibrate and validate the state-space engine model. The simulation results of the two engine models show a good agreement with each other. Thereafter, a linear-quadratic tracking controller is developed for combustion phasing control. Simulation results are presented and a baseline controller has been implemented on the research engine. Combustion phasing control is very important for internal combustion engines to achieve high thermal efficiency with low engine-out emissions. Traditional open-loop map-based control becomes less favorable in terms of calibration effort, robustness to engine aging, and especially control accuracy for TJI engines due to the increased number of control variables over conventional spark-ignition engines. In this research, a model-based feedforward controller is developed for the TJI engine, and a feedback controller is also designed based on the linear quadratic tracking control with output covariance constraint. Since the TJI main-chamber combustion is influenced by the pre-chamber one, the proposed controller optimizes the control variables in both combustion chambers. The proposed feedforward and feedback controllers show significant performance improvement over a group of baseline controllers through a series of dynamometer engine tests."--Pages ii-iii.
Show less
- Title
- Adaptation to visual perturbations while learning a novel virtual reaching task
- Creator
- Narayanan, Sachin Devnathan
- Date
- 2019
- Collection
- Electronic Theses & Dissertations
- Description
-
"Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn...
Show more"Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn to adapt to perturbations. Methods: 13 college-age participants (females = 7, Mean = 21.74 +/- 2.55) performed upper body movements to control a computer cursor. Visual rotation of the cursor position was introduced as a perturbation for one-half of the practice trials. Movement time and normalized path length were calculated. One way repeated measures ANOVA was performed to analyze significance between the performance at different times of the task. Results: Significant learning seen while learning the initial baseline task (p<0.0001) and a significant drop in performance upon immediate exposure to the perturbation (p =0.005). No significant adaptation over practice with the perturbation (p = 0.103) or significant after-effects on removal of the perturbation (p = 0.383). Conclusions: Results suggests differences in adaptation when the task is novel when compared to other adaptation studies and such novel tasks trigger a different type of learning mechanism when compared to adaptation."--Page ii.
Show less