3,641 research outputs found
Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR
In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy.
The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure.
The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest.
The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
A clustering approach for the analysis of InSAR Time Series: application to the Bandung Basin (Indonesia)
Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas
ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE
In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph
Towards Practical Triboelectric Nanogenerator for Mechanical Energy Harvesting and Self-powered Sensing
Triboelectric nanogenerator (TENG) offers a promising solution as a decentralised energy source to sustainably power the ever-growing mobile electronics. TENG’s characteristics such as high output, versatile working modes and broad material availability have made it viable but still there are a few issues that limit practical applications of TENG. This thesis aims to tackle some of the practical limitations of TENGs to be used as wearable energy harvesters or self-powered sensors.
Firstly, to address the poor stretchability of TENG with rigid metal / carbon electrodes for wearable applications, a soft and highly stretchable solid polymer electrode (SPE) based on biocompatible PVA infused with ionically conductive ingredient was developed as the TENG electrode. The use of SPE endows the TENG with high stretchability, superb transparency, environmental stability, and enhanced electrical output. Then the requirement of complex power management system of conventional TENG with AC output was simplified with a new design of DC-TENG boosted by a dual-breakdown mechanism. The DC output could be directly utilized by small electronics without the need of rectifiers, thus reducing complexity and energy loss. Lastly, for TENG-based sensors, to overcome the errors caused by TENG output variations under changing environment, a new method of extracting sensing signals independent of intrinsic TENG output variations is proposed.
This research has yielded several important scientific contributions in the device design, optimisation and application of TENG. These findings provide new opportunities to improve TENGs in terms of wearing comfort, system complexity and sensing reliability, thus greatly enhancing the practical application of TENGs in real-life scenarios
Designing LMPA-Based Smart Materials for Soft Robotics Applications
This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed
Acoustic Propagation Variation with Temperature Profile in Water Filled Steel Pipes at Pressure
Conventional pressure leak testing of buried pipelines compares measurements of pressure with pipe wall temperature. An alternative proposed method uses acoustic velocity measurements to replace pipe wall temperature measurements. Early experiments using this method identified anomalous results of rising acoustic velocities thought to be caused by air solution.
This research investigated the anomalous acoustic velocity measurements by evaluation of acoustic velocity variation with pressure, temperature and air solution. Quiescent air solution rate experiments were carried out in water filled pipes. Computer modelling of the air bubble shape variation with pipe diameter was found to agree with bubble and drop experiments over the pipe diameter range from 100 mm to 1000 mm. Bubbles were found to maintain constant width over a large volume range confirmed by experiments and modelling
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Vitrimer chemistry for 4D printing formulation
Vitrimerization is one of the new methods under development to convert polymer wastes into high-value compounds. The chemistry of vitrimers is such that the presence of dynamic chemical bonds changes the permanent covalent bonds into covalent adaptable networks, which are reversible. This allows for recycling and reprocessing of polymers by maintaining their initial properties after several cycles, which is included in the preparation of polymer resins to convert polymer waste into materials that can be formulated for three-dimensional (3D) printing resins. Four-dimensional (4D) printing has also been recently introduced as sustainable 3D printing of responsive polymers with dynamic applications, such as soft robotics, medicine, and medicals. Therefore, the synthesis of polymers with dynamic chemistry based on vitrimers can add unique properties such as shape memory, shape recovery, self-healing, and flexibility to the 3D printed products. Vitrimerization chemistry could contribute to polymer waste by producing 4D-printed resins. This article presents the vitrimerization chemistry used in different polymers to produce 4D printing resins with the mentioned capabilities and lists their recipes for the preparation of formulations used in 4D printing so that the researchers can use them in a practical way to possibly achieve simultaneous shape-programmable, self-healing, and recyclable features in printed structures
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