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A theoretical and experimental study on broadband asymmetric light interfaces to reduce top surface losses in luminescent solar concentrators
No full textAugust 2022School of EngineeringA shift from traditional energy sources to renewable energy sources, such as photovoltaics (PV), is occurring across the planet. PV installations and usage has increased rapidly in recent years and there is becoming even more of a focus on building-integrated photovoltaics (BIPV). A plethora of research can now be found on new ways to incorporate renewable systems into the fabric of buildings themselves. Luminescent solar concentrators (LSCs) are concentrating devices that focus sunlight through total internal reflection that were first conceptualized in the 70’s. LSCs have not been able to find widespread commercial success due to their low efficiency, mostly attributed to losses via the escape cone of the top surface. A number of possible solutions to this problem have been proposed and researched, however there is not currently a broadband effective solution. Asymmetric light transmission (ALT) is a phenomenon first observed over a century ago, but is only now becoming more popular in the realm of optical physics. Most research for ALT revolves around creating optical diodes for future computers, however this concept can be applied to LSCs to enable a top surface that allows more light in than it allows to exit and therefore create a broadband solution to the escape cone losses. This thesis proposes a pyramidal nanostructure that exhibits ALT effects as a modification to the top surface of LSCs. This nanostructure was optimized and numerically tested in COMSOL Multiphysics for a wavelength range ideal for silicon solar cells. Over a wavelength range between 400nm-1200nm and an incident angle between 0-80°, this ALT surface was found to have a directionally averaged spectral transmissivity difference of 50% comparing the transmissivity of the forward and backward directions. To illustrate the potential of the COMSOL results to enhance the optical efficiency of LSCs, the data was integrated into a ray-tracing Monte Carlo code that predicts LSC performance. The method to integrate COMSOL data with the Monte Carlo code was developed, rigorously tested using COMSOL data for a plain LSC interface, and validated against other numerical solutions before testing the efficacy of a nanostructured interface. This code predicts an LSC with an ALT top surface will have an optical efficiency 82% higher than a plain interface LSC. Beyond these various numerical solutions, a procedure was developed for a simple, cost-effective fabrication process of ALT interfaces. ALT interface samples were developed in a nanofabrication facility and experimentally tested. Spectrometer testing for nanostructures with periodicities of 800nm and 900nm showed a “reduced” transmissivity difference of approximately 21% and 10%, respectively. The transmissivity value is “reduced” compared to the total transmissivity value because the spectrometer can only capture some of the scattered diffraction orders caused by the nanostructure. These trends were compared and validated through COMSOL simulations.
The results contained in this thesis provide a proof-of-concept solution to reduce LSC top surface losses. Numerical results show that this solution is broadband and effective over all realistic incident angles. Experimental results compare well to numerical expectations and showcase the ease in manufacturing of a nanopattern the is flexible with geometric variation and surface defects.Ph
Biomechanical ecologies: towards urban air remediation through indoor environments.
No full textDecember 2017School of ArchitectureIndoor air quality (IAQ) and inhabitant health is often adversely affected by ele-vated levels of Carbon Dioxide (CO2) and Volatile Organic Compounds (VOCs), especially formaldehyde and benzene rings. Even though these aspects of indoor air have been shown to detrimentally affect human cognitive function, health, and wellbe-ing, conventional filtration and ventilation systems, designed to remediate indoor air, have the potential to exacerbate levels of these toxic components in the air stream. The modern removal-only approach to IAQ remediation may compound another IAQ trend: decreased indoor microbial diversity. These removal-only approaches to IAQ may well have contributed to the observed development of human-mediated microbial communi-ties in indoor environments, meaning the microbial communities on the surfaces of the environments in which we live and work are often inoculated only by human interac-tion, and have very few diversifying inputs. Decreased exposure to high microbial and environmental bio-diversity has been associated with negative effects on human inhab-itant health including increased allergic reactions and atopic skin conditions, and decreased immune health overall. With these variables in mind, the beginning chapters in this thesis examine how the interaction between urban/architectural development patterns and biological evolution may have led to the development of these issues. Within this context, hypotheses and design propositions are discussed and developed that attempt to integrate plant and associated microbial communities with standard ventilation and climate control equipment as a solution to these modern IAQ issues. These meta-analyses were augmented by physical testing of some of the solu-tions proposed in the initial chapters in temporal, matrix-based, controlled experiments designed to test some of the dependent variables at different scales; from the qualities of the plant-based ecosystems to their cumulative effect on the room in which the experiment was conceived. The dependent variable tested at the pot scale was the CO2 remediation potential of each trio of pots with specified growth media and plant treat-ments, along with the relative biomass of each pot. These metrics were iterated tempo-rally to track changes over time. In addition to pot-scale testing, room-scale testing led to insights into how these systems affected the CO2 of the room in which the experi-ment is occurring. Sampling of CO2 was taken at seven locations throughout the room before and during the experiment, and was correlated to biomass increases in the room to determine how increases in biomass changed the air ecology of an entirely built-environment with no external inputs. The experimental section of this project indicated that water availability, growth media, plant biomass, and plant diversity are all statistically significant drivers of the respiration/photosynthesis balance of a plant-based ecosystem. While the integration of these results into an open system such as those found in indoor air streams is complex, this preliminary study indicates that if these drivers of the balance between cellular respiration and photosynthesis in these systems were optimized, significant changes to the CO2 levels of the room could be achieved. Integrating the results of this experiment with the previously discussed literature reviews and thought experiments may lead to pathways towards evaluating the efficacy of various plant species and growth media’s ability to support diverse indoor microbial communities, and evolve healthier indoor air streams from the single plant to the room scale. Some of the next large questions in this line of questioning pertains to the potential of extrapolating these proposed frameworks into the scales and cycling of buildings and cities, tackling global problems through the way we approach buildings and local ecologies.M
Characterization of protein surface hydrophobicity using molecular dynamics simulations and deep learning
No full textDecember 2022School of EngineeringIn the past decade, there has been a significant growth in the market size for protein based therapeutics such as monoclonal antibodies, enzymes, bispecifics and Fc-fusion proteins. Protein based therapeutics provide many advantages over small molecule drugs, and are used to treat a wide range of diseases such as cancers, autoimmune disorders and infectious diseases. The design, manufacturing and purification of protein therapeutics require careful consideration of its physicochemical properties, such as immunogenicity, toxicity, and aggregation propensity. Many of these properties are closely connected with the hydrophobicity of these molecules. Hydrophobic domains on proteins and antibodies act as hotspots that seed aggregation and interactions. Hydrophobicity also governs complex phenomena such as protein folding and its stability. The understanding and evaluation of all of these processes requires a diligent method for the quantification of protein surface hydrophobicity. The hydrophobic nature of protein surface patches has been linked to their residue hydropathy values, however, research has shown that hydrophobicity of complex heterogeneous surfaces, is a function of their chemistry and topographical features and the response of water to these. Specifically, hydrophobicity of a heterogeneous surface is closely related to the density fluctuations of its interfacial waters and their response to perturbations. In this work, we have used a fast and computationally efficient enhanced sampling method called sparse sampling INDUS to identify hydrophobic domains for a set of proteins. True to our hypothesis, density fluctuations for hydrophobic patches identified by sparse sampling INDUS, bear large fat tails that represent their proximity to dewetting. These fat tails are indicative of patches that are placed near a dewetting transition due to the unique combination of the chemistry of constituent groups and their topography. Further, we compare and contrast our results to those obtained from traditional methods of hydrophobicity calculation and present an analysis for the similarities and differences observed. Our investigations shed light on the complex nature of hydrophobicity, its underpinnings to density fluctuations and the role of chemical and topographical context in modulating it.Ph
The use of mathematical models to assess the probability of an autism spectrum disorder diagnosis in children
No full textMay 2022School of EngineeringAutism spectrum disorder (ASD) is a complex, multisystem disorder whose symptoms can range in severity, as well as be confounded by symptoms of co-occurring conditions (COCs). 1 in every 44 eight-year-old children in the United States is diagnosed with ASD, and 95% of children diagnosed are also diagnosed with at least one COC. This can lead to a diversity in ASD presentation and difficulties in diagnosis especially with current ASD diagnostic standards limited to a clinical evaluation on physical or behavioral abnormalities. This work explores the use of mathematical models to assess the risk of an ASD diagnosis in children. The similarities between biomarker identification and biological pathway models are discussed, highlighting the shared problem of regularization used to avoid overfitting. Solutions to this problem translates across model types and an example is shown using a candidate biomarker for ASD diagnosis. Fisher discriminant analysis and support vector machines are compared on their biomarker identification using a dataset comprising metabolic measurements from four separate studies. This work also uses risk analysis models to identify prenatal factors that occur during pregnancy and are associated with (1) having a child diagnosed with ASD and (2) belong to the three following pre-identified subgroups of children based on COCs: children with a High-Prevalence of COCs, children with mainly developmental delays and seizures (DD/Seizures COCs), and children with a Low-Prevalence of COCs. These retrospective analyses on maternal medical claims are made up of more than 100,000 mothers with a diverse mixture of ages, ethnicities, and geographical regions across the United States. Logistic regression analysis revealed that having a biological sibling with ASD, maternal use of antidepressants or psychiatric services, as well as non-pregnancy related claims such as hospital visits, surgical procedures, and radiology exposure were associated with an increased risk of having a child diagnosed with ASD. It also found that while some risk factors were shared between all three COC-based subgroups, unique factors were identified distinguishing the three groups. Anti-inflammatories, infections, or other complex medications were associated with the High-Prevalence COCs group; immune deregulatory conditions such as asthma or joint disorders were associated with the DD/Seizures COCs group; and overall pregnancy complications were most influential with the Low-Prevalence COCs group. Thus, previously identified subgroups of children with ASD have distinct associated prenatal risk factors. The use of mathematical models to assess ASD diagnoses in children will further develop both the diagnostic standards and current understanding of ASD pathophysiology.Ph
Studies of transition metal oxides in electrocatalytic oxygen reactions
No full textMay 2022School of EngineeringABSTRACTTransition metal oxides are an important class of materials with many interesting properties such as oxygen electrocatalysis, metal-insulator transition, pseudo-capacitance, and electrochromism. The central aim of this dissertation is to develop a more efficient and robust electrocatalyst for oxygen reactions based on transition metal oxide. The dissertation work consists of three projects. In the first study, a new design strategy for the development of bifunctional electrocatalysts capable of catalyzing both the oxygen evolution reaction (OER) and the oxygen reduction reaction (ORR) is proposed. In this strategy, a single MnOx lattice is doped with either electropositive (Sr, Ba) or electronegative (Bi, Pb) elements along with oxygen vacancies to create both electron-rich donor (Mn2+) and electron-poor acceptor (Mn4+) defects in the same parent (Mn3+) lattice. These defects effectively catalyze the reduction (ORR) and oxidation (OER) processes on the same electrode surface. The study is based on the results of a previous study on Mn2O3 that showed Mn2+ and Mn4+ as the active sites for ORR and OER processes, respectively. Our results show that BiMnOx is the most promising bifunctional catalyst with OER/ORR activities that are comparable to the individual activities of state-of-the-art commercial Pt or RuO2 catalysts. Stability tests show the catalyst to be stable for more than 3 h of continuous OER or ORR polarization. This work provides a pathway for the individual tuning of defects to control electrocatalytic activities, which opens up new possibilities for the rational design of many perovskite-based oxides.
The second study focuses on the electrical-field induced metal-insulator transition property of transition metal oxides and demonstrates its correlation to bifunctional ability for oxygen reactions. We conducted extensive electrochemical tests on three categories of oxides: bifunctional, single-functional, and pseudo-capacitive oxides. Our results suggest that bifunctional oxides undergo a metal-insulator-metal transition together with a switch in the type of majority charge carrier during the anodic-to-cathodic voltage scan. On the other hand, single-functional oxides undergo a metal-insulator transition during anodic and cathodic scan. Pseudo-capacitive oxides have exceptionally low resistance in capacitive potential ranges. Beyond this potential range, they either show metallic conductivity at potentials of OER/ORR or become an insulator with low activity for OER/ORR.
The third study focuses on a special type of transition metal oxide, namely iridium oxide, which is the state-of-the-art-catalyst for OER in acidic medium. However, the nature of Ir intermediates involved in the OER mechanism is not well established. In this study, we prepared various Ir compounds of varying oxidation state to serve as standards for spectroscopic studies. Our in-situ optical absorbance and photoluminescence measurements suggest that not only Ir intermediates but also lattice oxygen intermediates are created during electrochemical polarization that might be responsible for both the OER and the electrochromic processes. More in-depth investigation is still needed to unveil the nature of oxygen intermediates and oxidation states of Ir species involved under a wider range of applied potentials.Ph
Semantically enabling clinical decision support recommendations
No full textBackground
Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice.
Results
We introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework:
(i) guideline modeling to characterize diseases,
(ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and
(iii) study cohort modeling to identify relevant research publications for complicated patients.
Conclusions
We have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients’ unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results
Adaptive large eddy simulation for complex unsteady flows
No full textMay 2023School of EngineeringLarge eddy simulation (LES) is an attractive turbulence modeling approach due to thebalance it provides between computational cost and turbulence/scale-resolving capabilities.
LES is shown to be robust and provides accurate predictions for complex flow problems
including unsteady aerodynamic flows with spatiotemporal inhomogeneity. In this work,
the specific problem of interest involves flow over surging airfoils, which arises in many
aerodynamic applications. For example, for a rotorcraft in a forward flight, or for wind
turbines in non-uniform flows (e.g., shear). Mesh resolution requirements for such complex and unsteady flow problems are notknown a priori. In this work, we develop an adaptive approach for LES where the mesh
resolution is changed (refined or adapted) based on an a posteriori error estimator leading to
error-driven/controlled adaptive LES. In addition, a flow feature-based adaptation criterion
that can use the error estimator is developed. Both the LES methodology and the error
estimator used here are based on the variational multiscale (VMS) framework. A range of Reynolds numbers and advance ratios is considered in adaptive LES forsurging airfoils. Three different adaptive strategies based on VMS error estimator are ex-
plored: (i) zonal-based refinement/adaptation, (ii) nodal size field-based adaptation, and (iii)
feature-based refinement/adaptation. The zonal-based strategy is found to be most effective.
This strategy is applied for adaptive LES of flow over surging airfoils to construct a series
of adapted meshes and to demonstrate mesh convergence. In particular, for quantities of
interest including pressure coefficient and leading edge vortex (LEV) evolution. In addition,
very high advance ratios are considered, where a massive trailing edge separation also occurs
due to flow reversal. In these cases, LES includes active flow control in the form of active
reflex camber that is applied to dynamically morph the airfoil shape during flow reversal (i.e.,
only for a part of the surging cycle). Active reflex camber results in a significant reduction
in drag force and its fluctuations.Ph
Interactional aerodynamic modeling and analysis of multi-rotor aircraft using computational fluid dynamics
No full textMay 2023School of EngineeringOver the past decade, advancements in battery, electronics, and motor technology have led to the rapid popularization of electric vertical takeoff and landing (eVTOL) aircraft which use multiple rotors for thrust generation and control. The distributed-electric propulsion architecture allows for flexible placement of multiple lifting and propulsive rotors, leading to a variety of potential advanced configurations. However, when rotors operate in close proximity aerodynamic interactions can lead to degraded (or enhanced) performance. The current batteries powering most eVTOL aircraft exhibit low energy density relative to the hydrocarbon fuels used by conventional helicopters. Under this limitation, it is especially important to maximize the aerodynamic efficiency of eVTOL aircraft in order to realize meaningful payload capacity, endurance and range. In this body of work, computational fluid dynamics (CFD) is used to simulate the aerodynamics of multiple rotors (and wings) operating in close proximity. Simulations of numerous multi-rotor/rotor-wing systems are compared to those of isolated rotors and isolated wings operating under the same conditions in order to extract the interactional aerodynamic effects. Through these simulations, the physical mechanisms driving the interactions are established and potentially advantageous designs/configurations are identified.
In particular, the aerodynamic interactions between two in-line rotors operating in edgewise flight is established and an associated aft-rotor performance penalty is identified. This analysis is extended to systems with varying vertical and longitudinal hub-hub separation where the aft rotor thrust and torque at each separation distance is evaluated. The interactions of in-line rotors are also investigated for when they are canted (and each rotor's rotation axis is tilted). Laterally canted and longitudinally canted rotors are considered and the most aerodynamically efficient configuration is identified.
While much of the existing literature on rotor-rotor interactions pertains to forward flight, close-proximity rotors are also expected to interact when operating close to the ground. Pairs of side-by-side rotors are simulated in ground effect (IGE) at two ground heights and two hub-hub separation distances and their thrust production is compared to that of an isolated rotor out of ground effect (OGE). Canted side-by-side rotors are also simulated IGE, and their thrust production is compared to uncanted side-by-side rotors at the same height above the ground and hub-hub spacing.
Beyond rotor-rotor interactions, the influence of a rotor operating below and behind a wing is also considered. This rotor placement is found to improve system performance by enhancing wing lift through rotor induced suction. Wing lift enhancement is investigated at a range of wing incidence angles, rotor disk loadings, flight speeds and rotor positions and the most advantageous configurations/conditions are identified.
Rotor-wing interactions are further investigated for a full vehicle by simulating a quad rotor tail sitter (QRTS) using CFD. Rotor-wing and wing-rotor aerodynamic interactions are identified and compared for three rotor mounting positions. These results are used to inform CFD-CSD coupled simulations of the QRTS which show reduced power over the QBiT even with interactional aerodynamic penalties.Ph
Numerical methods for the mean-field game and its inverse problems
No full textMay 2023School of ScienceMean-field games (MFG) study the behavior of a large number of rational agents in a non-cooperative game. An important task in mean-field games is to study the flow of all the agents in the state space and to understand the behavior of mean-field Nash equilibrium. It has wide applications in various fields. But it is not easy to solve the mean-field game numerically because of its complicated structure. And conventional studies on mean-field games focus on Euclidean spaces, which limits the application. In addition, while many real-life applications can be cast as an inverse mean-field game problem, there are not enough studies in this direction. In this thesis, we attempt to solve the aforementioned problems related to mean-field games. The theme is numerical methods for the mean-field game in Euclidean spaces, on manifolds, and for the inverse mean-field game.In the first chapter, we propose an efficient and flexible algorithm to solve dynamic mean-field planning problems based on an accelerated proximal gradient method. Besides an easy-to-implement gradient descent step in this algorithm, a crucial projection step becomes solving an elliptic equation whose solution can be obtained by conventional methods efficiently. By induction on iterations used in the algorithm, we theoretically show that the proposed discrete solution converges to the underlying continuous solution as the grid becomes finer. Furthermore, we generalize our algorithm to mean-field game problems and accelerate it using multilevel and multigrid strategies. We conduct comprehensive numerical experiments to confirm the convergence analysis of the proposed algorithm, to show its efficiency and mass preservation property by comparing it with state-of-the-art methods, and to illustrate its flexibility for handling various mean-field variational problems.
In the second chapter, we explore the mean-field games on Riemannian manifolds. We formulate the mean-field game Nash Equilibrium on manifolds. We also establish the equivalence between the PDE system and the optimality conditions of the associated variational form on manifolds. Based on the triangular mesh representation of two-dimensional manifolds, we design a proximal gradient method for variational mean-field games. Our comprehensive numerical experiments on various manifolds illustrate the effectiveness and flexibility of the proposed model and numerical methods.
In the third chapter, we propose a bilevel optimization formulation for inverse mean-field games and study the numerical methods for solving the bilevel problem. With the bilevel formulation, we preserve the convexity of the objective function and the linearity of the constraint in the forward problem. This formulation permits us to solve the problem with a gradient-based optimization algorithm and to have a nice convergence of the algorithm. We focus on inverse mean-field games with unknown obstacles and unknown metrics and implement our alternating gradient algorithm to solve the inverse problems. For the inverse mean-field game with unknown obstacles, we also establish the local unique identifiability result and verify the result with numerical experiments.Ph
Demographic and socioeconomic determinants of access to care: A subgroup disparity analysis using new equity-focused measurements
No full textDisparities in healthcare access and utilization associated with demographic and socioeconomic status hinder advancement of health equity. Thus, we designed a novel equity-focused approach to quantify variations of healthcare access/utilization from the expectation in national target populations. We additionally applied survey-weighted logistic regression models, to identify factors associated with usage of a particular type of health care. To facilitate generation of analysis datasets, we built an National Health and Nutrition Examination Survey (NHANES) knowledge graph to help automate source-level dynamic analyses across different survey years and subjects’ characteristics. We performed a cross-sectional subgroup disparity analysis of 2013-2018 NHANES on U.S. adults for receipt of diabetes treatments and vaccines against Hepatitis A (HAV), Hepatitis B (HBV), and Human Papilloma (HPV). Results show that in populations with hemoglobin A1c level ≥6%, patients with non-private insurance were less likely to receive newer and more beneficial antidiabetic medications; being Asian further exacerbated these disparities. For widely used drugs such as insulin, Asians experienced insignificant disparities in odds of prescription compared to White patients but received highly inadequate treatments with regard to their distribution in U.S. diabetic population. Vaccination rates were associated with some demographic/socioeconomic factors but not the others at different degrees for different diseases. For instance, while equity scores increase with rising education levels for HBV, they decrease with rising wealth levels for HPV. Among women vaccinated against HPV, minorities and poor communities usually received Cervarix while non-Hispanic White and higher-income groups received the more comprehensive Gardasil vaccine. Our study identified and quantified the impact of determinants of healthcare utilization for antidiabetic medications and vaccinations. Our new methods for semantics-aware disparity analysis of NHANES data could be readily generalized to other public health goals to support more rapid identification of disparities and development of policies, thus advancing health equity