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Mechanics of network materials using discrete network models and gpu accelerated multiscale finite elements
No full textDecember 2021School of EngineeringThe focus of this thesis is on improving the state of modeling of tissue-scale biological network materials by addressing three component topics: First, discrete fiber models are used to probe the fundamentals of network physics. Second, numerical, and algorithmic enhancements are made to a multiscale finite element code, MuMFiM, to enable modeling tissue-scale network materials. Finally, MuMFiM is used to enhance the understanding of the underlying cause of anomalous realignment in the facet capsular ligament. Using a discrete fiber network model with a Voronoi structure, the small strain point force boundary value problem is solved. It is shown that with appropriate boundary conditions, the asymptotic scaling matches that of the linear elasticity solution outside a threshold distance. When the distance to the point force is less than approximately two times the mean distance between crosslinks, the solution diverges from the classical solution. It is postulated that this divergence is caused by the nonlocal behavior of the fiber network. Similar Voronoi networks are employed to investigate indentation of fibrous materials. With a small spherical indenter radius, the modulus recovered by fitting the classical Hertz solution is lower than the true modulus and the force displacement relationship is not consistent with the Hertzian model. It is proposed, through elimination of other potential mechanisms, that nonlocality is the cause of this indenter radius size effect. The limits of applicability of the Hertz solution are determined in terms of the network parameters. This assists the interpretation of data from indentation experiments with soft fibrous materials. Material size effects are of critical importance to multiscale modeling because for the microstructure to be homogenized into a representative volume element (RVE) the discrete fiber network must exhibit behavior which is representative of the macroscopic material behavior. Many authors use periodic boundary conditions to circumvent the large model sizes required to obtain a RVE, however fibrous materials are not typically periodic. To be able to properly reduce the RVE size, ``generalized boundary conditions'' are applied to RVEs composed from fibrous materials. To support the ability to employ fibrous RVEs in the study of large scale tissue applications, a multiscale finite element, MuMFiM, code was developed building on a previously initiated framework. Methods developments included introduction of incremental deformation gradients and a dynamic relaxation method on the microscale which improved robustness and allow modeling large global strains as required for the solution of biological tissues. Two level parallelism as required for use of accelerator driven exascale computers was introduced and a new approach for the performant parallel solutions of RVE’s on GPUs developed that produced up to a 1000x speed-up. MuMFiM is shown to scale well on up to 128 nodes of AiMOS using 6 NVIDIA V100 GPUs on each node. Lastly, MuMFiM is used to investigate the root cause of anomalous fiber realignment (AR) in the facet capsular ligament (FCL) and it is shown that the cause of AR is not the highly heterogeneous structure of the FCL including the presence of subdomains with preferentially aligned collagen, but rather microscopic damage or local instability.Ph
Effect of high glucose on glycosaminoglycans in cultured retinal endothelial cells and rat retina
No full textGlycobiology, in pressNote : if this item contains full text it may be a preprint, author manuscript, or a Gold OA copy that permits redistribution with a license such as CC BY. The final version is available through the publisher’s platform.Introduction The endothelial glycocalyx regulates vascular permeability, inflammation, and coagulation, and acts as a mechanosensor. The loss of glycocalyx can cause endothelial injury and contribute to several microvascular complications and, therefore, may promote diabetic retinopathy. Studies have shown a partial loss of retinal glycocalyx in diabetes, but with few molecular details of the changes in glycosaminoglycan (GAG) composition. Therefore, the purpose of our study was to investigate the effect of hyperglycemia on GAGs of the retinal endothelial glycocalyx. Methods GAGs were isolated from rat retinal microvascular endothelial cells (RRMECs), media, and retinas, followed by liquid chromatography-mass spectrometry assays. Quantitative real-time polymerase chain reaction was used to study mRNA transcripts of the enzymes involved in GAG biosynthesis. Results and Conclusions Hyperglycemia significantly increased the shedding of heparan sulfate (HS), chondroitin sulfate (CS), and hyaluronic acid (HA). There were no changes to the levels of HS in RRMEC monolayers grown in high-glucose media, but the levels of CS and HA decreased dramatically. Similarly, while HA decreased in the retinas of diabetic rats, the total GAG and CS levels increased. Hyperglycemia in RRMECs caused a significant increase in the mRNA levels of the enzymes involved in GAG biosynthesis (including EXTL-1,2,3, EXT-1,2, ChSY-1,3, and HAS-2,3), with these increases potentially being compensatory responses to overall glycocalyx loss. Both RRMECs and retinas of diabetic rats exhibited glucose- induced alterations in the disaccharide compositions and sulfation of HS and CS, with the changes in sulfation including N,6-O-sulfation on HS and 4-O-sulfation on CS.https://login.libproxy.rpi.edu/login?url=https://doi.org/10.1093/glycob/cwac02
Theoretical model of a continuum manipulator
No full textDecember 2020School of ScienceContinuum manipulators have a higher degree of maneuverability in constrained environments compared to conventional robots. Unconventional actuators along with soft structures increases their flexibility and make them favourable for uncertain environments. Different types of continuum manipulators are available which are modeled differently. In this work, we present a theoretical model which has been inspired by muscular hydrostats. We focus on one such muscular hydrostat, the elephant trunk and try to emulate its behaviour. We formulate the forward kinematics of our model and explore the inverse kinematics along with other relevant characteristics.M
Trajectory optimization and control for autonomous helicopter shipboard landing
No full textAugust 2021School of EngineeringHelicopter shipboard landing presents a particularly challenging control problem because of (1) the limited landing time; (2) stringent safety constraints; (3) the turbulent shipboard motion resulted from the rough sea condition and (4) complex ship-airwake-helicopter interactions during the landing maneuver. While most shipboard landing operations are still carried out by human pilots, there is a critical need for the development of computer-assisted and/or fully autonomous landing strategies to relieve the pilot from significant workload and improve the operational safety and efficiency. Typically, autonomous flights are governed by the guidance, navigation and control (GNC) system, which replaces the role of the pilot in decision making, perception and action respectively. The GNC system comprises of hardware (e.g. sensors and actuators) and software (e.g. estimation, fault detection and control algorithms) components. This thesis concentrates on the development and validation of the guidance and control algorithms for landing a full-scale helicopter onto a ship deck in realistic maritime environments. For the purpose of autonomy algorithm design, a simplified nonlinear model has been developed to capture key characteristics of the full-state helicopter dynamics which are stabilized by a conventional dynamic inversion controller. Further, this simplified nonlinear model is shown to be differential flat, which enables the transformation of the original nonlinear system dynamics into an equivalent linear form with endogenous nonlinear constraints. For guidance, a trajectory optimization problem is formulated to achieve time-optimal landing, while complying with the simplified nonlinear dynamics and other operation constraints. Subsequently, the differential flatness property of this simplified dynamics is leveraged so that the original problem can be reformulated into a computationally-efficient form, where the handling of the free end-time end-state problem is streamlined by propagating the state trajectory in the linear flat output space. Furthermore, other than acquiring the numerical solution from the optimization problem using conventional temporal discretization technique, a basis parametrization technique is developed to further enhance the algorithm's computational performance. For outer loop trajectory tracking (horizontal position and velocity of the helicopter relative to the ship), a differential flatness-based outer loop controller has been designed to govern and stabilize the unstable zero dynamics. By canceling the nonlinearity using the endogenous mapping, the resultant error dynamics are rewritten as a linear fractional transformation. Based on this, robust stability and performance criteria are provided with respect to inner loop tracking performance, ship motion uncertainty and external disturbance. The effectiveness of this controller is then validated on the full-state nonlinear simulation platform. To manage landing under significant ship motion forecast error (in the heave direction), a reachability-based guidance and control method has been proposed. The key behavior of the vertical dynamics is encapsulated into the joint helicopter-ship dynamics, which consists of a nominal part and an error part. Accordingly, the reachable set and probabilistic reachable set are employed respectively to quantify the nominal boundary and actual distribution of the helicopter state relative the ship based on the knowledge of the dynamic disturbance and forecast error. Consequently, a shrinking horizon model predictive control strategy is designed to (1) minimize the expected terminal error of the relative position and velocity between the helicopter and the ship by choosing the optimal touchdown time, and (2) adjust the landing maneuver with regard to the updated ship motion forecast through recursive implementation. The capability of this strategy is then tested on the full-state nonlinear simulation platform.Ph
Compressive hyperspectral single-pixel imaging for lifetime imaging and tomography applications
No full textAugust 2022School of EngineeringThe present document summarizes the overall goal, specific objectives, preliminary results,current conclusions as well as the future aims to be addressed for completion of a PhD degree in Biomedical Engineering and is submitted to the Doctoral Committee to fulfill the PhD defense exam requisite. The main goal of this thesis project is to improve the quality and efficiency of single pixel based hyperspectral data acquisition and processing algorithms to
further its utility for planar widefield hyperspectral lifetime imaging of extrinsic and intrinsic fluorescence, as well as tomographic reconstructions of absorption contrasts. For planar lifetime imaging, deep learning algorithms are developed to improve and facilitate both the intensity and lifetime imaging reconstruction processes. The reconstruction algorithms
are optimized to extend data compression thereby shortening experimental acquisition time while maintaining image quality. Experimental validation has been accomplished in silico and with both extrinsic in vivo and intrinsic in vitro fluorescent markers. The imaging of intrinsic markers is a new avenue proposed to leverage the hyperspectral features that can be acquired with a single-pixel arrangement. Additionally, a deep learning algorithm has been developed to disentangle spectral overlaps of the marker’s emissions by using both intensity and lifetime information. This DL framework has been applied for the retrieval of relative abundance coefficients and further extended to deliver fluorophore concentrations. For widefield hyperspectral tomography, compressive sensing is used together with hyperspectral and time resolved data types for two different deep learning frameworks that aim at retrieving absorption contrast values and their spatial distribution, as well as concentration, without the need for an ill-posed inverse solved solution. The deep learning approaches, together with application driven optimization of the single-pixel optical setup, aim to display the multiple advantages of single-pixel hyperspectral strategies coupled to deep learning frameworks for planar and tomographic imaging.Ph
Analysis of natural and synthetic systems for photo-initiated water splitting
No full textMay 2021School of EngineeringThe search for new sustainable energy sources has seen a substantial increase inattention as our current reliance on fossil fuels reaches a critical point in terms of supply
and environmental impact. One promising avenue to sustainable energy is through the
electrolytic splitting of water using solar light as an energy source. The work for this thesis
focuses on exploring two different materials with the potential to perform and elucidate the
water splitting reaction: benzimidazole phenol-porphyrin (BiP-PF10) and manganese
oxides (MnOx). BiP-PF10 serves as a bio-mimic, modeling the oxygen-evolving complex
(OEC) of photosystem II (PSII) in plants and cyanobacteria and the proton-coupled
electron transfer (PCET) mechanism it uses for water splitting. Analysis of the PCET
intermediate of BiP-PF10 led to the determination of the electronic environment during
PCET, providing insight on further attempts to synthesize this reaction. Manganese is
present in many photocatalytic compounds, so a study on manganese oxides, especially
Mn2O3, would help us understand some of the mechanisms of water splitting. The results
of the manganese oxide experiments are preliminary and show promise in terms of future
analysis.M
Neural semantic structural modeling for complex question answering tasks
No full textMay 2022School of EngineeringQuestion answering (QA) is a task in which an automatic system is built to answer questions given a particular context in a certain format. Over the past few years, considerable progress has been made on QA systems with the advancement of neural networks in natural language processing (NLP). On one hand, researchers have attained human-level performance in some relatively simple reading comprehension tasks in which the context is short and the exact answer to the question can be found in the context. On the other hand, the systems still struggle with complex questions that can only be answered using more than one piece of evidence in the context or require external knowledge such as commonsense. This thesis aims at exploring and modeling structural semantics in various contexts, particularly with language models, in order to equip a QA system with a deeper understanding of context and improve the quality of answers to complex questions. In this dissertation, we discuss representation-based and model-based approaches to integrating semantic structures into deep neural networks and their application in answering complex questions in three challenging scenarios: multihop QA, book QA, and visual QA. As opposed to traditional QA tasks, these scenarios share the common challenge that the answer depends on sophisticated semantics in the textual and multimodal context. Specifically, multihop QA looks at an evidence chain that links the question to the true answer rather than a single piece of evidence. Book QA extends the evidence space to an extremely large extent, and evidence is intertwined and sometimes hierarchical. Visual QA is prominent for its multimodal nature as it reaches beyond a single evidence space and aims to find correlations within and across different modalities. To this end, we explore different types of semantic structures in each scenario and develop advanced approaches to integrating prior structural knowledge into deep neural networks. In particular, 1) we optimize dense representational learning to better model the dependencies among pieces of evidence when constructing a chain of reasoning for a multihop QA task; 2) we address a fundamental challenge in getting the system to understand long narrative contexts by first finding the event-centric nature in a story and correspondingly advancing the open-domain retrieval and reasoning technologies in order to comprehend the concepts and events, as well as their relationships, for a book QA task; 3) we investigate rich multimodal interactions and use question-led top-down structures to enhance model training to improve its ability to capture the most beneficial interactions related to a visual QA task.Ph
Fatigue and fracture in polymer networks and their composites
No full textMay 2022School of EngineeringFatigue is encountered in wide spectrum applications varying from biomaterials to energy storage devices. As we explore novel materials to meet stringent performance demands of new applications, strategies to tackle fatigue damage must be explored as well. Polymer networks like thermosets are ubiquitous as structural components and their fiber composites
are equally popular because of their superior strength to weight ratio. Hence, improving fatigue and fracture performance of these polymer networks and their composites is an important research question.
We demonstrate fracture performance improvement in a thermoset modified by nanofillers. A field of stiffness heterogeneity is observed in this nanocomposite which is created by stochastic dispersion of nanofillers. This heterogeneous filed activates a mesoscale toughening mechanism which is confirmed by a continuum scale simulation. Fatigue testing of the
nanocomposite shows that small scale interactions created by nonofillers increases fatigue crack propagation threshold force. However, the crack propagation in large cracks is independent of nanofiller loading. Fatigue perforrmance of carbon fiber composite made with nanomodified thermoset shows improvement in high cycle fatigue regime which increases
with nanofiller loading fraction.
Fatigue performance improvement in the thermoset networks and their composites observed thus far is irreversible, making the eventual rapid fatigue failure inevitable. Vitrimers, which are novel epoxy based networks with reversible crosslinking ability, can potentially possess ability to heal fatigue damage. As vitrimers are crosslinked netwroks, they can possess healing ability while also retaining strength and stiffness comparable to conventional thermosets. We demonstrate that a vitrimer system can indeed undergo reversal of small scale fatigue damage when subjected to periodical heating to the characteristic temperature. Furthermore, carbon fiber composites made with the vitrimer also showed reversal of fatigue damage when subjected to similar intermittent heating strategy. This finding may pave way for composites which potentially have ultra high fatigue life.Ph
Excitonic physics in two-dimensional semiconductors
No full textAugust 2021School of EngineeringTwo-dimensional (2D) transition metal dichalcogenides (TMDCs) represent a new class of atomically thin semiconductors with superior optical and optoelectronic properties. TMDCs have extensively been investigated for potential applications in valleytronics, field-effect transistors, logic circuits, phototransistors, photodetectors, quantum information, and quantum computing. With the reduced dimension in one direction, 2D TMDCs show strong Coulomb interactions compared with bulk materials. The enhanced electron-electron interaction enables a new platform to study excitonic fine structures of different quasi-particles. Moreover, monolayer TMDCs possess a valley degree of freedom due to lack of spatial inversion symmetry, giving rise to applications like valleytronics. The strong spin-orbital coupling (SOC) results in spin-valley locking, leading to unique optical and electronic properties under the magnetic field. Besides, the angular controlled stacking of 2D TMDCs opens a new era for manipulating the electron-electron interactions in the artificial 2D structures, introducing a universal platform to study correlated states like Mott insulators and generalized Wigner crystals. A fundamental understanding of the excitonic states in TMDCs and their artificial structures is crucial for both fundamental physics studies and potential applications in the future.First, the excitonic fine structures due to strong electron-electron Coulomb interaction are investigated with helicity resolved photoluminescence (PL), reflectance and photocurrent spectroscopy under a high magnetic field in the hexagonal boron nitride (hBN) encapsulated monolayer tungsten diselenide (WSe2). The true biexciton state is identified in charge neutral WSe2 through the control of efficient electrostatic gating, indicating a strong Coulomb interaction in the 2D material. The new dark trion states are unveiled with magneto photoluminescence (PL) and back focal plane imaging technique. The helicity-resolved magneto-photocurrent spectroscopy is introduced to study the excited states of neutral exciton in which the excited exciton states are observed to 11s, the highest excited state of exciton ever reported for any 2D semiconductor. Besides, exciton fine features and exciton-polaron are observed under a high magnetic field. The quantized excitonic resonance using the optical method is proof of strong many-body interactions in the 2D system.
Second, the phonon-exciton interactions in monolayer WSe2 are explored. A phonon-assisted circularly polarized replica has been discovered by magneto PL, where the spin-forbidden dark exciton is brightened by phonons. The exciton-phonon replica inherits large magneto-tunability and a long lifetime from dark exciton. The replica has an efficient radiative recombination channel, providing a chirality dictated emission channel for both phonons and photons. In addition, the momentum-dark intervalley exciton is observed through the interaction between intervalley exciton and chiral phonon. The pseudo angular momentum (PAM) of the chiral phonon is shown to play an important role in determining the helicity of the emitted photon through phonon-exciton interaction. The phonon-exciton interaction in a high magnetic field also shows new excitonic states, including the phonon replica of the dark trions. The inter-LL transition selection rule of dark trions is modified by the exciton-phonon interaction.
Finally, the excitonic physics of hetero-bilayers of TMDCs is studied. An electrical switchable effect, transferring between exciton dissociation and funneling, is observed in MoSe2/WS2 heterostructures by using a highly efficient ionic back gate substrate. In WSe2/MoSe2 heterostructures with magneto-PL, a near-unity valley polarization of the interlayer exciton is observed, inspiring future exploration of applications in valleytronics and spintronics. In angle aligned WSe2/WS2 heterostructures, a series of correlated insulating states at integer fillings and a series of fractional fillings are investigated with scanning microwave impedance microscopy (MIM) and PL spectra of interlay exciton which interacts with correlated insulating states.Ph
FAIR and Interactive Data Graphics from a Scientific Knowledge Graph
No full textGraph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework