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Novel Bio-Inspired Shielding Design for Deployable Microreactors
No full textRapidly deployable microreactors are being considered as an effective approach to supplying power post-disaster. Concerns for rapid deployment arise when a flat, structurally adequate surface is not guaranteed. Moreover, environmental concerns are propagated when the risk of radiation exposure from nuclear energy is involved. This paper proposes a novel multifunctional design, referred to as basemat, that provides a level operating surface for a deployed reactor by capitalizing on lightweight shielding and high-strength-to-weight materials, at the same time providing shielding and reducing soil activation. The design was optimized using different materials and overall thickness. Each variation was evaluated for its ability to shield the soil underneath the reactor from activation, limiting the activation-resulting dose rate, and its ability to hold the weight of a deployed microreactor. The shielding performance was simulated using the SCALE code package in a complex multi-step coupled analysis. The strength of the design was determined through simple compression strength calculations. From the simulation, the best-performing material combinations for radiation shielding are High Density Polyethylene (HDPE) and borated polyethylene, and HDPE and water, with a basemat thickness of 2 feet. These materials are capable of limiting the dose rate in the air to the United States Nuclear Regulatory Commission (NRC) yearly dose standards for the general population, while conservatively assuming someone is receiving the dose 24/7 for an entire year. Overall, the basemat design composed of lightweight shielding materials, such as water, HDPE, and borated polyethylene, is an effective solution for limiting the activation of the soil underneath a deployed microreactor, resulting in an overall dose that meets NRC standards, assuming continuous exposure
Advances in Understanding and Modeling of Heat Transfer across Earth's Surface
No full textThis work integrates theoretical and modeling approaches to advance understanding of heat transfer across Earth’s surface. It provides new insights into surface temperature dynamics, thermal stratification in natural water bodies, and cryospheric processes, thereby contributing to the development of physically based models with broad applications. Key contributions include: (i) a unified dynamic equation of surface temperature applicable to all surface types; (ii) a mechanistic study of the formation and diurnal cycle of the inverse temperature layer (ITL) in water bodies; (iii) the first analytical solutions for temperature distribution and active layer thickness (ALT) under general boundary conditions; and (iv) an analytical model of snow temperature profiles that elucidates the mechanisms underlying the snow isothermal process.Ph.D.Civil Engineerin
Bridging the Gap from Research to Real-World Implementation: Deep Learning-Based Dynamical Systems Modeling Approaches to Overcome Practical Barriers for Intracortical Brain Computer Interfaces
No full textFor individuals with neuromotor impairments, intracortical brain-computer interfaces (iBCIs) can restore movement and communication capabilities by recording neural activity and decoding it to a control signal for an intended output. Despite high interest amongst potential users, very few individuals have had access to use an iBCI in the last 25 years. The work in this dissertation aims to address practical considerations of iBCI use in the hopes of improving future device availability and adoption. To do so, we leveraged powerful deep-learning models of neural population dynamics to develop approaches that may solve three primary obstacles to iBCI translation. First, we used neural dynamics models to improve the stability of iBCI decoder performance over long timescales, reducing the need for lengthy daily recalibration procedures and shortening device setup time. In addition, we developed a set of standardized datasets and metrics to benchmark decoder stabilization approaches and released a platform for evaluating future modeling innovations. Next, we proposed a modeling approach to reduce the power requirements of neural signal acquisition and transmission while maintaining high decoding accuracy. This effort may be useful for wireless iBCIs, which are largely preferred by potential users but have design constraints regarding battery life. Lastly, we applied neural dynamics models to investigate the neural basis of corrective movements and used our findings to develop a decoding strategy that may improve iBCI accuracy during precise movements when mistakes and subsequent corrections are common. Our work proposes solutions to practical barriers in iBCI translation from research settings to everyday use and establishes a framework for further improvements using neural dynamics models.Ph.D.Biomedical Engineerin
Theoretical and Experimental Investigation of Ultra High-Strength Joining in Steel-Short Fiber Reinforced Composites
No full textThe increasing demand for lightweight, high-performance, and fuel-efficient structures in industries such as automotive and aerospace has driven significant research into hybrid metal-composite materials. However, effectively joining these dissimilar materials remains a primary obstacle. Traditional methods like adhesive bonding and mechanical fastening suffer from limitations in environmental durability, process efficiency, and strength. While modern laser-textured joining has shown promise, it is critically deficient in two key areas: weak joint strength under cross-tension loading. Furthermore, existing multi-step joining processes are often complex and ill-suited for high-volume industrial production.
This work presents a theoretical and experimental investigation into a novel, laser textured injection molded Joining (LIJ), which overcomes these fundamental limitations. The LIJ method integrates advanced laser texturing; employing a custom angular and wobble-beam system to machine deep, undercut grooves; with a single-step, in-situ injection molding process. A fiber-reinforced thermoplastic is molded directly onto a textured steel substrate, establishing a robust, fastener-free mechanical interlock. A comprehensive experimental optimization demonstrated the process's efficacy, achieving a maximum cross-tension strength of 13.8 MPa, a result that dramatically exceeds previously reported values. The process also yielded an exceptional lap-shear strength of 41.7 MPa. Failure analysis and durability testing are performed to analyze the behavior of the joint in both manufacturing and service environments.
To predict and understand the joint's performance, a multi-faceted modeling approach was employed. A computationally efficient, reduced-order thermo-phase field model was developed to successfully simulate the formation of line-scan grooves, capturing complex recast and narrowing phenomena. For the more complex "wobble" geometry, a hybrid data-driven model was built, which accurately predicts the groove morphology from laser inputs. Finally, a study combining finite element analysis with a novel chemical polishing validation experiment revealed the fundamental source of the joint's high strength. The model quantitatively proves the existence of a multi-scale interlocking mechanism, wherein the joint's integrity is a synergistic product of mesoscale interlocking (overall groove architecture) and microscale interlocking (surface roughness)
Piezoelectric Acoustic Identification Tags with Frequency Multiplexed Energy Harvesting and Backscatter Communication Operation for Underwater Applications
No full textThe oceans cover a majority of Earth’s surface, yet a vast proportion remains unexplored
due to the enormous physical scale and technical complexities. Ocean exploration and
mapping offer immense returns through improved shipping routes, renewable energy
generation, and accurate ocean modeling for understanding Earth’s climate processes.
Autonomous Underwater Vehicles (AUVs) offer adaptability, compactness and power
efficiency while minimizing human oversight, making them ideal for ocean exploration.
However, AUV operations are limited by currently achievable underwater localization and
navigation solutions; hence the development of low-cost and passive (i.e., operable
without an active power supply) acoustic underwater markers (or tags) can provide
accurate localization information to AUVs improving their situational awareness, especially
when operating in small scales or confined missions.
This work presents an Acoustic Identification (AID) tag that can be powered wirelessly with
ultrasonic power transfer from a remote acoustic source (e.g. mounted on an interrogating
AUV) and provide localization information using backscatter communication. The AID tag
harvests energy from the acoustic signal generated from the AUV and communicates by
modulating the reflected signals from an embedded piezoelectric transducer.
As a feasibility demonstration, this work develops a scaled (range of ≈ 100mm) broadband
AID tag prototype that achieves concurrent acoustic energy harvesting (tuned around 1.3
MHz) and backscatter communication (in wider frequency band 600 kHz and 800 kHz)
using frequency domain multiplexing. This scaled AID tag achieves data rates up to 200
kbit/s using Amplitude and Frequency based modulation communication.
Next, a full-scale AID tag is developed for short-range AUV missions (≈ 10 m) that uses a
lower frequency piezoelectric transducer tuned in the broadband ultrasonic range (200
kHz-500 kHz) to achieve highly efficient power transfer (source-to-tag electrical power
efficiency of > 2% at 6 m), and concurrent high data rate and backscatter level
communication (> 83.3 kbit/s, > 170 dB SPL at 5.5 m) with potential operating range ≈ 10 m
based on analytical extrapolations. Experimental tests benchmarking performance
sensitivity to source and tag misalignment are presented. Finally, experiments are
proposed to demonstrate device suitability for AUV routing and navigation applications.Ph.D.Mechanical Engineerin
Ensemble Kalman filtering and conditional normalizing flows for seismic monitoring via data assimilation
No full textThis research focuses on the application of the ensemble Kalman filter
(EnKF) for monitoring subsurface injected carbon dioxide (CO2) using seismic
measurements and physical models. Monitoring CO2 plumes in underground
storage reservoirs is critical to avoid failure scenarios, e.g., by early
detection of overpressuring that could lead to leaks or seismic activity, and
also enables real-time optimization of CO2 injection using computer
simulations.
Seismic measurements provide a non-intrusive method for determining the
spatial distribution of the CO2 based on changes in the subsurface density
and wave velocity. Alternatively, fluid flow models can predict the CO2
spread by simulating the system based on estimates of the subsurface flow
parameters, such as permeability. These two sources of
information---observation and transition physics---can be combined using
Bayesian data assimilation (DA). This statistical framework mathematically
describes how to combine information from multiple sources over time to
estimate hidden states---in this case, the evolving CO2 plume.
The EnKF is a scalable ensemble-based algorithm for sequential Bayesian DA. It
is exact for linear transition and observation operators in the limit of
infinite ensemble size and has demonstrated practical value for nonlinear
systems with modest ensemble sizes, as established in weather forecasting.
The research described herein improves upon existing DA works in the
seismic-CO2 domain by applying the EnKF to a synthetic high-dimensional CO2
reservoir that incorporates two-phase flow dynamics and realistic time-lapse
full waveform seismic data. We show more accurate estimates of the CO2
saturation field with the EnKF compared to using either the seismic data or
the fluid physics alone. Moreover, we examine the few hyperparameters of the
standard EnKF, giving guidance on their selection for seismic CO2 reservoir
monitoring.
Furthermore, we explore conditional normalizing flow filters (CNFFs) as a
nonlinear extension of the EnKF. We examine the typical affine coupling layer
used in CNFFs and compare it to the EnKF for a small Gaussian system. Through
this theoretical comparison, we propose a different, easier parametrization
of the affine coupling layer, which we call the "decorrelation" coupling
layer. On small systems, we empirically show that this layer produces
networks that are much more robust to noise compared to the standard affine
coupling layers, while an comparison on the large geophysical system shows
similar expressiveness between the two CNFF methods.
The empirical comparison for the large system shows a major shortcoming of the
EnKF---spurious correlations drastically reduce the posterior variance. We
attribute the lack of this issue in the two CNFF methods to their
convolutional structures. Convolutions give the CNFFs builtin covariance
localization, whereas the EnKF requires extra implementation work to apply
force covariance localization. These comparisons demonstrate that there is
benefit to hybridizing CNFF methods with ideas from classical filters and
vice versa
Light Yield and F-O-M Irradiation Degradation of Polysiloxane, Polyvinyltoluen, EJ200, and EJ276D Organic Scintillators Via a 6MV, 40kgy Absorbed Dose
No full textSixteen samples of organic scintillators fabricated from polysiloxane and polyvinyltoluene bulk solvents were taken to quantify their radiation hardness and irradiation degradation mechanisms. These samples received an absorbed dose of 40 kGy of 6MV irradiation via a Varian Clinical Linear Accelerator and their light yield and figure-of-merit were analyzed in increments of 10 kGy. It was found that the polysiloxane bulk solvent has a slower rate of light yield degradation, but a faster rate of figure-of-merit degradation when compared to the PVT matrix.
At 10kGy, control samples EJ200 and EJ276 were among the highest rates of percent light yield and F-O-M degradation when compared to all samples. Additionally, secondary dopant, Bis-MSB, had the highest rates of light yield degradation across both bulk solvents at 40kGy, suggesting a radio-sensitivity
High Energy Efficiency Dynamic Amplifier with Correlated Level Shifting Technique for High Resolution Analog to Digital Converter
No full textThis work focuses on designing a low-power, PVT-robust dynamic amplifier for high-performance data-converter system
Modeling of Language-Universal Speech Attributes for Multilingual Speech Recognition and Processing
No full textThe performance of multilingual automatic speech recognition (ASR) systems critically depends on their ability to generalize across diverse languages and linguistic environments. Conventional ASR approaches typically involve training separate models for each language, which can be challenging due to the inherent variability in phonetic and linguistic structures across languages. Alternatively, some methods train a single model on data from multiple languages, but these models often struggle to effectively capture the unique characteristics of each language, particularly when there is a significant imbalance in training data. This disparity can lead to substantial performance degradations, especially in scenarios involving languages with limited or no training data. To address these issues in multilingual speech recognition, it is essential to develop approaches that operate uniformly across multiple languages without being constrained by language-specific characteristics. Current systems often fail to scale effectively due to their reliance on language-dependent tokens such as phonemes and characters, which are not universally applicable. This limitation poses significant challenges in building robust multilingual systems capable of accommodating a wide variety of languages and dialects.
In this dissertation, we aim to establish a language-universal framework for ASR that overcomes language-specific limitations by leveraging universal speech attributes, such as manner and place of articulation. These attributes, which remain consistent across all languages, serve as a foundation for building multilingual models capable of performing effectively across diverse linguistic settings. Our approach seeks to address the lack of knowledge sharing across languages due to linguistic distance, where traditional language-dependent tokens are insufficient. By utilizing a compact set of language-universal speech attributes, we aim to bridge the performance gap for low-resource and unseen languages, enhancing the adaptability and scalability of ASR systems.Ph.D.Electrical and Computer Engineerin
Predictive Modeling of Aircraft Arrival Times in the Terminal Maneuvering Area Through Data-Driven Techniques
No full textRecently, the consistent increase in domestic air travel within the United States has exerted significant pressure on the National Airspace System (NAS). The arrival phase is especially impacted, as operations in the Terminal Maneuvering Area (TMA) face limited airspace, high traffic, and changing weather conditions. Although aircraft systems and navigation have improved, delays still pose a major challenge. Approximately 20 percent of commercial flights are delayed annually, highlighting the continued difficulty of handling busy terminal areas. A large part of these delays happens within the TMA, where operational coordination is heavily influenced by traffic congestion and environmental
conditions.
This dissertation aims to improve the accuracy of arrival time predictions in terminal airspace, where operations are often limited by congestion, restricted airspace, and unpredictable weather. To achieve this goal, the research is divided into three interconnected studies. Each study employs a distinct methodological approach to support the shared goal of enhancing ETA prediction in terminal airspace, with a focus on traffic patterns, trajectory behavior, and key factors such as weather and congestion. Specifically, the three studies focus on (1) Spatiotemporal Modeling for Airport Traffic Forecasting, (2) Bi-Level Clustering of Arrival Trajectories, and (3) ETA Prediction with Traffic and Weather Features.
Our first study examined short-term forecasting of airport traffic volume at both the individual airport and network levels. A Long Short-Term Memory (LSTM) model was trained to forecast traffic at individual airports using two years of 30-minute interval data. The model was trained with various feature combinations, including traffic volume, dely indicators, weather conditions, and aircraft composition. Chicago O’Hare International Airport (ORD) was used as a case study to assess the model's performance after hyperparameter tuning.
To extend the forecasting to the airport network, a Graph Convolutional Network (GCN) was integrated into the LSTM model. The LSTM-GCN model effectively captured spatial dependencies among airports. Adjacency matrices were created to represent the connectivity between airports. Seven types of adjacency were analyzed to illustrate spatial relationships among airports, encompassing both static and dynamic forms. Static adjacency matrices were constructed using geographic distance, total flight volume, and airport size. Dynamic matrices used monthly flight volumes to reflect temporal changes in airport connectivity. The results showed that how spatial features were defined impacted the prediction accuracy.
The second study focused on identifying flow patterns of arrival trajectories within the TMA. Arrival paths differed depending on traffic levels, weather, runway use, and aircraft type. A bi-level clustering framework was created to categorize these trajectories by operational conditions and shape. In the upper-level clustering, arrival trajectories were grouped based on operational factors present at the time of TMA entry. These factors included traffic volume, weather conditions, aircraft composition, and delay statistics, which collectively represented the broader arrival environment. Using K-means clustering for this categorization, trajectories were classified within shared flow environments, effectively capturing how traffic and environmental conditions influenced the structure of approach paths.
In the lower-level clustering, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used within each cluster to identify different trajectory onfigurations. The Dynamic Time Warping (DTW) technique was applied to measure the similarity of trajectories. To assess how data preprocessing affected clustering results, three resampling methods were compared: distance-based, time-based, and key point-based. This analysis helped examine how different representations of trajectory shapes influenced cluster formation.
The third study explored how adding traffic and weather features can improve arrival time prediction accuracy within the TMA. Two main types of features were examined. The first included traffic conditions and aircraft characteristics when entering the TMA. The second consisted of weather data collected from different parts of the terminal airspace. Weather information was sourced from multiple locations, such as the airport surface, TMA entry points, airspace grid areas above the airport, and sectors defined based on historical trajectory patterns. These weather features describe conditions that influence aircraft movement within the TMA. Linear regression, Lasso regression, and Random Forest were used, each with different combinations of these features. The results indicated that using traffic and weather data relevant to the aircraft’s TMA entry time improved ETA
prediction.
Together, the three studies help build a practical framework for predicting arrival times in complex terminal airspace. Each study addresses a different part of the problem: forecasting short-term traffic, identifying arrival flow patterns, and improving ETA prediction with weather and traffic features. Together, they provide essential insights for the development of tools that facilitate real-time traffic management as well as long-term planning