1,720,985 research outputs found
Process Characterization and Optimization of Roll-to-roll Plasma Chemical Vapor Deposition for Graphene Growth
Get PDFThe main purpose of this thesis is to develop an efficient and scalable technique for depositing graphene on various flexible substrates. Hence, a custom-built roll-to-roll capacitively coupled plasma chemical vapor system for deposition of graphene on flexible substrates is thoroughly described in this work. Graphene quality on Cu foil has been optimized for a roll-to-roll process using statistical optimization methods. Since graphene quality and uniformity depend on plasma input parameters, such as plasma power, gas pressure, and the gas mixture used, effects of input parameters have been explored to maximize graphene quality, as quantified by Raman spectroscopy using the I D/IG intensity ratio. Furthermore, in situ optical emission spectroscopy has been developed and utilized to determine the effects of several plasma species on graphene growth and quality. OES results demonstrate that graphene quality on Cu foil increases with CH radical emission; however, O and H atoms, C2 and CN radicals, and Ar + ion all negatively correlate to graphene quality. Results aid in developing a conceptual model for a graphene growth mechanism that indicates the adverse impact of ion bombardment on graphene quality in the low-frequency capacitively coupled plasma. However, the existence of active carbon species in the plasma, such as CH radical, accelerates the growth process and leads to moderate-quality graphene deposition on Cu foil at web speeds reaching as high as 1 m/min. Plasma plays a crucial role in heating the foil for graphene deposition in the roll-to-roll process, without the need of a supplemental heating source. Thus, accurate measurement of the translational gas temperature in the plasma is vital, since gas temperature strongly influences the foil temperature distribution, which, in turn, affects graphene growth kinetics. Optical emission spectroscopy (OES) is used to measure the rotational temperatures of N2+ (B-X), CN (B-X) and H2 (d3Πu → a3 Σg+), and to determine accurate translational gas temperatures. Power dissipation in the plasma is also measured to understand gas temperature variation for the experimental input conditions. Thus, the effects of plasma power, gas pressure and the addition of nitrogen, oxygen and methane gases on power dissipation and gas temperature in a hydrogen plasma are assessed. The rotational temperatures measured from the gas species have different values due to the non-equilibrium nature of the plasma. Graphene quality significantly depends on gas pressure since our plasma roll-to-roll system is sustained by a capacitively coupled plasma that operates in two modes, depending on the gas pressure and discharge gap. The modes are identified as alpha and gamma modes, and are sustained by volume ionization and secondary electron emission processes, respectively. Up to our knowledge, the presence of both modes at 80 kHz plasma frequency has not previously been reported. Thus, a detailed characterization of argon plasma is attempted to determine the underlying plasma physics of the low-frequency plasma. Due to strong ion bombardment on the electrodes, the gamma mode coexists with the alpha mode, resulting in a hybrid mode. The voltage square waveform is found to play an important role in sustaining this hybrid mode. The hybrid mode exists at low gas pressures of 5.5 and 9.5 mbar in the plasma set power ranges from 300 to 1100 W. However, the plasma at 13.8 mbar gas pressure transforms from hybrid to gamma mode when the plasma set power is beyond 750 W due to increased secondary electron emission processes. The emission spectra measured from optical emission spectroscopy reveal the presence of non-Ar species in the gamma mode, such as H, CH, and C2. These species are sputtered from the graphite electrodes by ion bombardment to produce secondary electrons that sustain the gamma discharge. Results show the possibility of sustaining the hybrid mode at a low plasma frequency using a tailored waveform. As a results of these plasma characterization tools, we report a continuous and rapid roll-to-roll deposition of thin graphite film on Cu foil. The composition of the Ar/H2/CH4/N2/O2 plasma plays significant role in the successful direct growth of the thin graphite film on copper foil. Optical emission spectroscopy is used to characterize the plasma during graphite synthesis and show that the addition of N 2 enhances the plasma reactivity, and O2 was found to increase the deposition rate of the graphite film. The film was characterized by Raman spectroscopy, scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy. The described large-scale graphite production can produce a graphite-Cu-graphite structure or uniform thin graphite films for thermal management applications in electronics devices. (Abstract shortened by ProQuest.
Developing Artificial Intelligence-Based Decision Support for Resilient Socio-Technical Systems
No full textDuring 2017 and 2018, two of the costliest years on record regarding natural disasters, the U.S. experienced 30 events with total losses of $400 billion. These exuberant costs arise primarily from the lack of adequate planning spanning the breadth from pre-event pre- paredness to post-event response. It is imperative to start thinking about ways to make our built environment more resilient. However, empirically-calibrated and structure-specific vulnerability models, a critical input required to formulate decision-making problems, are not currently available. Here, the research objective is to improve the resilience of the built environment through an automated vision-based system that generates actionable information in the form of probabilistic pre-event prediction and post-event assessment of damage. The central hypothesis is that pre-event, e.g., street view images, along with the post-event image database, contain sufficient information to construct pre-event prob-abilistic vulnerability models for assets in the built environment. The rationale for this research stems from the fact that probabilistic damage prediction is the most critical input for formulating the decision-making problems under uncertainty targeting the mitigation, preparedness, response, and recovery efforts. The following tasks are completed towards the goal. First, planning for one of the bottleneck processes of the post-event recovery is formulated as a decision making problem considering the consequences imposed on the community (module 1). Second, a technique is developed to automate the process of extracting multiple street-view images of a given built asset, thereby creating a dataset that illustrates its pre-event state (module 2). Third, a system is developed that automati- cally characterizes the pre-event state of the built asset and quantifies the probability that it is damaged by fusing information from deep neural network (DNN) classifiers acting on pre-event and post-event images (module 3). To complete the work, a methodology is developed to enable associating each asset of the built environment with a structural probabilistic vulnerability model by correlating the pre-event structure characterization to the post-event damage state (module 4). The method is demonstrated and validated using field data collected from recent hurricanes within the US. The vision of this research is to enable the automatic extraction of information about exposure and risk to enable smarter and more resilient communities around the world
Efficient Computation of Accurate Seismic Fragility Functions Through Strategic Statistical Selection
No full textA fragility function quantifies the probability that a structural system reaches an undesirable limit state, conditioned on the occurrence of a hazard of prescribed intensity level. Multiple sources of uncertainty are present when estimating fragility functions, e.g., record-to-record variation, uncertain material and geometric properties, model assumptions, adopted methodologies, and scarce data to characterize the hazard. Advances in the last decades have provided considerable research about parameter selection, hazard characteristics and multiple methodology for the computation of these functions. However, there is no clear path on the type of methodologies and data to ensure that accurate fragility functions can be computed in an efficient manner. Fragility functions are influenced by the selection of a methodology and the data to be analyzed. Each selection may lead to different levels of accuracy, due to either increased potential for bias or the rate of convergence of the fragility functions as more data is used. To overcome this difficulty, it is necessary to evaluate the level of agreement between different statistical models and the available data as well as to exploit the information provided by each piece of available data. By doing this, it is possible to accomplish more accurate fragility functions with less uncertainty while enabling faster and widespread analysis. In this dissertation, two methodologies are developed to address the aforementioned challenges. The first methodology provides a way to quantify uncertainty and perform statistical model selection to compute seismic fragility functions. This outcome is achieved by implementing a hierarchical Bayesian inference framework in conjunction with a sequential Monte Carlo technique. Using a finite amount of simulations, the stochastic map between the hazard level and the structural response is constructed using Bayesian inference. The Bayesian approach allows for the quantification of the epistemic uncertainty induced by the limited number of simulations. The most probable model is then selected using Bayesian model selection and validated through multiple metrics such as the Kolmogorov-Smirnov test. The subsequent methodology proposes a sequential selection strategy to choose the earthquake with characteristics that yield the largest reduction in uncertainty. Sequentially, the quantification of uncertainty is exploited to consecutively select the ground motion simulations that expedite learning and provides unbiased fragility functions with fewer simulations. Lastly, some examples of practices during the computation of fragility functions that results i n undesirable bias in the results are discussed. The methodologies are implemented on a widely studied twenty-story steel nonlinear benchmark building model and employ a set of realistic synthetic ground motions obtained from earthquake scenarios in California. Further analysis of this case study demonstrates the superior performance when using a lognormal probability distribution compared to other models considered. It is concluded by demonstrating that the methodologies developed in this dissertation can yield lower levels of uncertainty than traditional sampling techniques using the same number of simulations. The methodologies developed in this dissertation enable reliable and efficient structural assessment, by means of fragility functions, for civil infrastructure, especially for time-critical applications such as post-disaster evaluation. Additionally, this research empowers implementation by being transferable, facilitating such analysis at community level and for other critical infrastructure systems (e.g., transportation, communication, energy, water, security) and their interdependencies
Predicting Energetic Material Properties and Investigating the Effect of Pore Morphology on Shock Sensitivity via Machine Learning
No full textAn improved understanding of energy localization (“hot spots”) is needed to improve the safety and performance of explosives. In this work I establish a variety of experimental and computational methods to aid in the investigation of hot spots. In particular, focus is centered on the implicit relationship between hot spots and energetic material sensitivity. To begin, I propose a technique to visualize and quantify the properties of a dynamic hot spot from within an energetic composite subjected to ultrasonic mechanical excitation. The composite is composed of an optically transparent binder and a countable number of HMX crystals. The evolving temperature field is measured by observing the luminescence from embedded phosphor particles and subsequent application of the intensity ratio method. The spatial temperature precision is less than 2% of the measured absolute temperature in the temperature regime of interest (23-220 ◦C). The temperature field is mapped from within an HMX-binder composite under periodic mechanical excitation.Following this experimental effort I examine the statistics behind the most prevalent and widely used sensitivity test (at least within the energetic materials community) and suggest adaptions to generalize the approach to bimodal latent distributions. Bimodal latent distributions may occur when manufacturing processes are inconsistent or when competing initiation mechanisms are present.Moving to simulation work, I investigate how the internal void structure of a solid explosive influences initiation behavior – specifically the criticality of isolated hot spots – in response to a shock insult. In the last decade, there has been a significant modeling and simulation effort to investigate the thermodynamic response of a shock induced pore collapse process in energetic materials. However, the majority of these studies largely ignore the geometry of the pore and assume simplistic shapes, typically a sphere. In this work, the influence of pore geometry on the sensitivity of shocked HMX is explored. A collection of pore geometries are retrieved from micrographs of pressed HMX samples via scanning electron microscopy. The shock induced collapse of these geometries are simulated using CTH and the response is reduced to a binary “critical” / “sub-critical” result. The simulation results are used to assign a minimum threshold velocity required to exhibit a critical response to each pore geometry. The pore geometries are subsequently encoded to numerical representations and a functional mapping from pore shape to a threshold velocity is developed using supervised machine-learned models. The resulting models demonstrate good predictive capability and their relative performance is explored. The established models are exposed via a web application to further investigate which shape features most heavily influence sensitivity.Finally, I develop a convolutional neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential represented as a 4D tensor. This method effectively bypasses the need to construct complex representations, or descriptors, of a molecule. This is beneficial because the accuracy of a machine learned model depends on the input representation. Ideally, input descriptors encode the essential physics and chemistry that influence the target property. Thousands of molecular descriptors have been proposed and proper selection of features requires considerable domain expertise or exhaustive and careful statistical downselection. In contrast, deep learning networks are capable of learning rich data representations. This provides a compelling motivation to use deep learning networks to learn molecular structure-property relations from “raw” data. The convolutional neural network model is jointly trained on over 20,000 molecules that are potentially energetic materials (explosives) to predict dipole moment, total electronic energy, Chapman-Jouguet (C-J) detonation velocity, C-J pressure, C-J temperature, crystal density, HOMO-LUMO gap, and solid phase heat of formation. To my knowledge, this demonstrates the first use of the complete 3D electronic structure for machine learning of molecular properties
Gaussian processes with built-in dimensionality reduction: Applications in high-dimensional uncertainty quantification
Get PDFUncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP). However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases. This curse of dimensionality can only be addressed, if the response exhibits some special structure that can be discovered and exploited. A wide range of physical responses exhibit a special structure known as an active subspace (AS). An AS is a linear manifold of the stochastic space characterized by maximal response variation. The idea is that one should first identify this low dimensional manifold, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the AS is low enough, then learning the link function is a much easier problem than the original problem of learning a high-dimensional function. The classic approach to discovering the AS requires gradient information, a fact that severely limits its applicability. Furthermore, and partly because of its reliance to gradients, it is not able to handle noisy observations. The latter is an essential trait if one wants to be able to propagate uncertainty through stochastic simulators, e.g., through molecular dynamics codes. In this work, we develop a probabilistic version of AS which is gradient-free and robust to observational noise. Our approach relies on a novel Gaussian process regression with built-in dimensionality reduction. In particular, the AS is represented as an orthogonal projection matrix that serves as yet another covariance function hyper-parameter to be estimated from the data. To train the model, we design a two-step maximum likelihood optimization procedure that ensures the orthogonality of the projection matrix by exploiting recent results on the Stiefel manifold, i.e., the manifold of matrices with orthogonal columns. The additional benefit of our probabilistic formulation, is that it allows us to select the dimensionality of the AS via the Bayesian information criterion. We validate our approach by showing that it can discover the right AS in synthetic examples without gradient information using both noiseless and noisy observations. We demonstrate that our method is able to discover the same AS as the classical approach in a challenging one-hundred-dimensional problem involving an elliptic stochastic partial differential equation with random conductivity. Finally, we use our approach to study the effect of geometric and material uncertainties in shock propagation in a one dimensional granular system
Game-Theoretic Modeling of Multi-Agent Systems: Applications in Systems Engineering and Acquisition Processes
No full textThe process of acquiring the large-scale complex systems is usually characterized with cost and schedule overruns. To investigate the causes of this problem, we may view the acquisition of a complex system in several different time scales. At finer time scales, one may study different stages of the acquisition process from the intricate details of the entire systems engineering process to communication between design teams to how individual designers solve problems. At the largest time scale one may consider the acquisition process as series of actions which are, request for bids, bidding and auctioning, contracting, and finally building and deploying the system, without resolving the fine details that occur within each step. In this work, we study the acquisition processes in multiple scales. First, we develop a game-theoretic model for engineering of the systems in the building and deploying stage. We model the interactions among the systems and subsystem engineers as a principal-agent problem. We develop a one-shot shallow systems engineering process and obtain the optimum transfer functions that best incentivize the subsystem engineers to maximize the expected system-level utility. The core of the principal-agent model is the quality function which maps the effort of the agent to the performance (quality) of the system. Therefore, we build the stochastic quality function by modeling the design process as a sequential decision-making problem. Second, we develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. We cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a contract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. We study how different parameters in the acquisition procedure affect the bidders’ behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior
Bayesian global optimization approach to the oil well placement problem with quantified uncertainties
Get PDFThe oil well placement problem is vital part of secondary oil production. Since the calculation of the net present value (NPV) of an investment depends on the solution of expensive partial differential equations that require tremendous computational resources, traditional methods are doomed to fail. The problem becomes exceedingly more difficult when we take into account the uncertainties in the oil price as well as in the ground permeability. In this study, we formulate the oil well placement problem as a global optimization problem that depends on the output of a finite volume solver for the two-phase immiscible flow (water-oil). Then, we employ the machinery of Bayesian global optimization (BGO) to solve it using a limited simulation budget. BGO uses Gaussian process regression (GPR) to represent our state of knowledge about the objective as captured by a finite number of simulations and adaptively selects novel simulations via the expected improvement (EI) criterion. Finally, we develop an extension of the EI criterion to the case of noisy objectives enabling us to solve the oil well placement problem while taking into account uncertainties in the oil price and the ground permeability. We demonstrate numerically the efficacy of the proposed methods and find valuable computational savings
Bayesian Identification of Nonlinear Structural Systems: Innovations to Address Practical Uncertainty
No full textThe ability to rapidly assess the condition of a structure in a manner which enables the accurate prediction of its remaining capacity has long been viewed as a crucial step in allowing communities to make safe and efficient use of their public infrastructure. This objective has become even more relevant in recent years as both the interdependency and state of deterioration in infrastructure systems throughout the world have increased. Current practice for structural condition assessment emphasizes visual inspection, in which trained professionals will routinely survey a structure to estimate its remaining capacity. Though these methods have the ability to monitor gross structural changes, their ability to rapidly and cost-effectively assess the detailed condition of the structure with respect to its future behavior is limited.Vibration-based monitoring techniques offer a promising alternative to this approach. As opposed to visually observing the surface of the structure, these methods judge its condition and infer its future performance by generating and updating models calibrated to its dynamic behavior. Bayesian inference approaches are particularly well suited to this model updating problem as they are able to identify the structure using sparse observations while simultaneously assessing the uncertainty in the identified parameters. However, a lack of consensus on efficient methods for their implementation to full-scale structural systems has led to a diverse set of Bayesian approaches, from which no clear method can be selected for full-scale implementation. The objective of this work is therefore to assess and enhance those techniques currently used for structural identification and make strides toward developing unified strategies for robustly implementing them on full-scale structures. This is accomplished by addressing several key research questions regarding the ability of these methods to overcome issues in identifiability, sensitivity to uncertain experimental conditions, and scalability. These questions are investigated by applying novel adaptations of several prominent Bayesian identification strategies to small-scale experimental systems equipped with nonlinear devices. Through these illustrative examples I explore the robustness and practicality of these algorithms, while also considering their extensibility to higher-dimensional systems. Addressing these core concerns underlying full-scale structural identification will enable the practical application of Bayesian inference techniques and thereby enhance the ability of communities to detect and respond to the condition of their infrastructure
Surrogate Modeling for Uncertainty Quantification in Systems Characterized by Expensive and High-Dimensional Numerical Simulators
No full textPhysical phenomena in nature are typically represented by complex systems of ordinary differential equations (ODEs) or partial differential equations (PDEs), modeling a wide range of spatio-temporal scales and multi-physics. The field of computational science has achieved indisputable success in advancing our understanding of the natural world - made possible through a combination of increasingly sophisticated mathematical models, numerical techniques and hardware resources. Furthermore, there has been a recent revolution in the data-driven sciences - spurred on by advances in the deep learning/stochastic optimization communities and the democratization of machine learning (ML) software.With the ubiquity of use of computational models for analysis and prediction of physical systems, there has arisen a need for rigorously characterizing the effects of unknown variables in a system. Unfortunately, Uncertainty quantification (UQ) tasks such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying physical models. In order to deal with the high cost of the forward model, one typically resorts to the surrogate idea - replacing the true response surface with an approximation that is both accurate as well cheap (computationally speaking). However, state-ofart numerical systems are often characterized by a very large number of stochastic parameters - of the order of hundreds or thousands. The high cost of individual evaluations of the forward model, coupled with the limited real world computational budget one is constrained to work with, means that one is faced with the task of constructing a surrogate model for a system with high input dimensionality and small dataset sizes. In other words, one faces the curse of dimensionality.In this dissertation, we propose multiple ways of overcoming the curse of dimensionality when constructing surrogate models for high-dimensional numerical simulators. The core idea binding all of our proposed approach is simple - we try to discover special structure in the stochastic parameter which captures most of the variance of the output quantity of interest. Our strategies first identify such a low-rank structure, project the high-dimensional input onto it, and then link the projection to the output. If the dimensionality of the low dimensional structure is small enough, learning the map between this reduced input space to the output is a much easier task in comparison to the original surrogate modeling task
Bayesian Methods For Uncertainty Quantification
Get PDFComputer codes simulating physical systems usually have responses that consist of a set of distinct outputs (e.g., velocity and pressure) that evolve also in space and time and depend on many unknown input parameters (e.g., physical constants, initial/boundary conditions, etc.). Furthermore, essential engineering procedures such as uncertainty quantification, inverse problems or design are notoriously difficult to carry out mostly due to the limited simulations available. The aim of this work is to introduce a fully Bayesian approach for treating these problems which accounts for the uncertainty induced by the infinite number of observations
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