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Recent Submissions

A SPECTRAL DATA ASSIMILATION SYSTEM FOR WAVEWATCH III USING THE FOUR-DIMENSIONAL LOCAL ENSEMBLE TRANSFORM KALMAN FILTER
(2026) DiPasqua, Samuel; Ide, Kayo; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Ocean waves are created from energy passing through water with severe weather, such ashurricanes, creating hazardous waves. These hazardous waves pose risks to offshore operations, maritime operations, and coastal communities. Accurate wave forecasting provides critical information to these fields. Conventional wave data assimilation (DA) systems typically update mean wave parameters such as significant wave height, which limits their ability to represent the full wave spectrum and enforce physical consistency. This dissertation develops and evaluates a spectral wave data assimilation system for WAVEWATCH III based on the Local Ensemble Transform Kalman Filter. The LETKF analysis directly updates weights of the ensemble members, which makes it a versatile analysis for updating different analysis control variables. This proposed system introduces a logarithmically transformed spectral analysis control variable to weakly constrain the non-negative model state from WAVEWATCH III. Then, the 3D and 4DLETKF are compared to determine the impact of incorporating the model trajectory into the analysis. Lastly, shortest path and shortest distance localization are compared to determine the impact of observations across landmasses. Together, these factors result in a unique wave DA system that focuses on improving the physical characteristics of the wave spectrum. Observing system simulation experiments (OSSEs) were performed using significant wave height observations from buoys and satellite altimeters. Results show that spectrally-averaged root mean square error (RMSE) is similar across the different tested analysis control variables at the time of analysis. However, the analysis in significant wave height space reveals more meaningful corrections in RMSE and bias over a 24 hr forecast period. In significant wave height forecasts, changing the analysis control variable leads to significantly different forecasts. Comparisons between 3D and 4D-LETKF configurations demonstrate that 4D DA achieves persistent improvements to significant wave height forecast skill across each of the analysis control variables. The comparison between shortest path and shortest distance localization shows minimal aggregate differences because of the limited regions affected by the change in localization method and because the model, WAVEWATCH III, is wind-driven. These findings demonstrate the importance of the analysis control variable and wave evolution for wave DA systems.
Disequilibrium in Methane Clumped Isotopologue Signatures as a Tool for Assessing HIstorical Models and Thermometry
(2026) Turner, Austin R.; Farquhar, James; Geology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
As the simplest organic compound, methane is an important component in environments that contain evidence of life – including both the troposphere and deep ocean hydrothermal vents. In the atmosphere, methane acts as a greenhouse gas and contributes to the changing climate. In hydrothermal vent environments, methane acts as a source of electrons and powers these ecosystems via chemosynthesis. Methane can have thermogenic, abiotic, and microbial sources with overlapping bulk isotope signatures, which causes sources to be difficult to distinguish; additionally, methane undergoes sink and equilibrations processes that further change its isotopic signatures. In this thesis, new and updated methods of analyzing methane for its clumped isotopologue (∆13CH3D and ∆12CH2D2) signatures were developed to further disentangle contributing sources and constrain isotope effects. These were explored in two ways: 1) by using methane in archived ambient air sampled from different latitudes (25 °N to 60 °S) aboard a polar expedition called Deep Freeze ‘02 that took place almost 25 years ago to assess the validity of previous historical models of clumped isotopologue trends (projected backwards form modern values), and 2) by generating methane from catalyzed and uncatalyzed high temperature/pressure reactions (using the methyl-bearing tetrakis(trimethylsilyl)silane as a reactant and simulating hydrothermal conditions) in water of varying δD to assess to what extent the resulting isotopic signatures reflect an approach towards theoretical thermodynamic equilibrium. The analysis of methane in archive air found expected latitudinal trends in concentration (high concentrations in the Northern Hemisphere and a gradient going towards lower values in the Southern Hemisphere) as well as bulk isotopic signature (a shift towards more positive isotopic values going from N-S). No significant trend in clumped isotopologue signatures was found along the latitudinal range present in the samples, which should be investigated further. Mixing and Rayleigh sink fractionation models were also employed, and it was found that both bulk and clumped isotopic data from the archived samples showed agreement with models (except for the Rayleigh sink model of 13CH3D trends). Historical models which assumed higher and/or underreported fossil fuel methane were also supported by the data. Future work will involve the construction of multiple-box models (to account for the latitudinal gradient and interhemispheric transport) as well as repeated measurements of these samples and analysis of other latitudinal samples from different time periods. The analysis of methane from hydrothermal experiments found that the δDCH4 signatures display an approach towards equilibrium with increasing temperature. There was also an observed approach towards equilibrium in ∆12CH2D2 signatures when excluding outliers at extreme initial δDH2O, though ∆13CH3D data show no significant trend towards equilibrium. Additionally, limited comparisons between catalyzed and uncatalyzed samples support the formation of CH4 by the addition of 1 H/D from H2O, as well as a potential interaction between methane and the catalyst which shifts bulk carbon signatures. This prompts more investigation into gaps in the temperature and initial δDH2O composition ranges (and into the precipitates that form in the reactions) as well as the kinetics of the equilibration (which may be addressed by timed experiments).
Essays in Public Finance and Transportation
(2026) Braslavskiy, Arseniy; Cropper, Maureen; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Economic models often focus on optimal governmental actions and abstract from the actual implementation. This dissertation considers practical implications of public policies in the complex system of U.S. fiscal federalism. These policies may have unexpectedly large effects due to intergovernmental interactions, as in Chapter 1. At the same time, the incentives of local officials tasked with spending money might not be aligned with what the federal government intended, as I show in Chapter 2. Finally, some policies might have unintended consequences and the patchwork of state laws, instead of being a policy laboratory, may conceal this, as I demonstrate in Chapter 3. While transportation is the joint theme of these three chapters, their conclusions can be applied to a wide range of policy domains. Chapter 1 examines how targeted federal grants affect spending on public transit in a three-level fiscal system. The analysis uses comprehensive U.S. expenditure data from 2000–2019 and exploits an exogenous shock from the 2009 American Recovery and Reinvestment Act (ARRA). ARRA funds were apportioned to Urbanized Areas through preexisting formula programs, independent of potential changes in transit investment. I find that each $1 of ARRA apportionment led to an additional $1.76 of capital spending from all sources over 2009–2019, or $2.31 when scaled by the first-stage effect on federal capital grants. This reflects two distinct phases: an initial rise in federally funded expenditures with no displacement of state or local spending (the flypaper effect), followed by substantial crowding-in of state funding to the same localities. This additional spending was directed primarily toward maintaining existing bus fleets and did not increase service provision or ridership. Cross-state variation in institutional characteristics points to the negotiation channel as the primary mechanism: ARRA lowered the cost for local officials of securing flexible state funding. Chapter 2 studies how local transit agencies spent $8.4 billion of one-time capital from the 2009 stimulus, distributed by a formula and left to local discretion. Using a large language model to extract project-level data from grant texts, I find that 22 percent of projects added service capacity, 30 percent improved the quality of existing service, and 48 percent maintained it, while agencies' pre-existing characteristics explain almost none of this split. On the other hand, the type of spending mattered for the outcomes: areas that spent on expansion fared no differently from those that spent on maintenance, while areas that spent on quality improvement provided more service afterward. A simple model explains why. A system with excess capacity already carries the riders its quality attracts, so added vehicles sit unused and only an improvement in quality can attract new passengers. Empirical results thus suggest that the average American transit system overinvests in capacity and underinvests in quality. A back-of-the-envelope reallocation toward quality implies roughly 3 to 4 percent more national ridership. Chapter 3 re-examines the finding by Anderson, Hansen, and Rees (2013) that medical marijuana laws decrease traffic fatality rates by 10.4%. I document that legalizing states were already experiencing declining fatalities prior to legalization, even after controlling for state-specific linear trends in a Two-Way Fixed Effects model. To address these pre-trends, I apply the Imputation Procedure (IP) by Borusyak, Jaravel, and Spiess (2024), which estimates state-specific trends using only not-yet-treated observations. Depending on the inclusion of potentially confounding covariates, my IP estimates suggest either a 12% increase or a zero effect on fatalities. I also show that the average state effect differs substantially from the average individual effect, indicating large heterogeneity across states. Much of the original negative result is driven by California, which accounts for over half of the population-weighted estimate. This state consistently exhibits one of the largest estimated negative effects and one of the steepest negative pre-trends.
A Genetic Algorithm and Monte Carlo Simulation-Based Framework for Maintenance Policy Optimization of Multi-Component Aircraft Systems
(2026) Collins, Matthew; Zhao, Yunfei; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This thesis presents a maintenance policy optimization framework for multi-component sys-tems that are subject to stochastic degradation and imperfect maintenance. The objective of the thesis is to determine the optimal inspection interval as well as the repair and replace- ment maintenance thresholds that minimize the expected long-run cost of operation. The optimization framework considers component degradation, maintenance actions, and system reliability requirements. The multi-component system is modeled as a series-parallel architecture that consists of multiple subsystems with k-out-of-n redundancy structures. Component degradation is represented using a few different degradation models to reflect different physical failure mech- anisms. Examples include Paris’ Law for fatigue crack growth and Wiener process models for wear accumulation. The system components are inspected periodically, and maintenance decisions are determined using condition-based thresholds that define repair and replacement actions. Repairs are assumed to be imperfect, restoring components to a condition that is better than old but worse than new. A Monte Carlo simulation framework was developed to evaluate maintenance policies under uncertainty. The simulation tracks component degradation, maintenance events, cor- rective actions, and system failures over a specified operating horizon. A genetic algorithm was also developed to optimize the inspection interval and component-level maintenance thresholds. The overall objective function aims to minimize the expected cost per unit time, including inspection, repair, replacement, corrective maintenance, and system failure costs. The proposed framework is ultimately demonstrated through two aircraft-inspired case studies consisting of a turbofan engine, hydraulic pump, electrical generator, and actuator subsystem. In the simplified single-component-per-subsystem case study, the optimized pol- icy resulted in an inspection interval of 211.9 hours and an expected operating cost of $303.79 per hour. For the complex k-of-n system case study, the optimized inspection interval de- creased to 146.5 hours with an expected operating cost of $494.76 per hour. In addition, a sensitivity analysis was conducted to demonstrate how the optimized policy changes when the cost parameters are individually increased and decreased by ±5%. The final results illus- trate the ability of the optimization framework to identify cost-effective maintenance policies while accounting for stochastic degradation behavior and system-level reliability constraints. The methodology provides a flexible approach for condition-based maintenance optimization that can be extended to a wide range of engineering systems.
Portals to Hidden Sectors
(2026) Flood, Ina; Chacko, Zackaria; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Discrepancies between Standard Model (SM) predictions and experimental observationspoint to the need for new physics beyond the SM. However, many potential new physics models are tightly constrained by data from particle colliders, in particular the Large Hadron Collider (LHC). Therefore the possibility of a hidden sector—a collection of particles and fields that is uncharged under SM gauge groups—is of great interest for model building, as models which incorporate a hidden sector are relatively unconstrained by LHC and other collider data. Interactions between the SM and hidden sectors are only possible through terms in the Lagrangian, called portals, which couple SM gauge invariant terms to operators in the hidden sector. This dissertation performs a study of four hidden sector portal scenarios: the Higgs, neutrino, and hypercharge portal, as well as a Z′ gauge boson that couples to the SM via a linear combination of hypercharge and B − L (where B is baryon number and L is lepton number) currents. It is shown that for the first three portals, the forms of the leading dimension-six terms in the low-energy effective theory are fixed and independent of hidden sector dynamics. Additionally, it is shown that the signs of these terms that are of most relevance to experimental detection are fixed, under certain conditions, when dynamics in the hidden sector are required to be causal and unitary. Current bounds on the coefficients of these low-energy effective theory terms are found by performing a global fit to electroweak precision observables, Higgs measurements, and diboson production data. For the fourth portal, the form of the leading dimension-six terms in the low-energy effective theory are determined. Constraints are placed on these terms, independent of hidden sector dynamics, using data from LEP-II and electroweak precision observables. Constraints on the Z′ parameter space dependent on the hidden sector branching ratio are also found using resonant Z′ search data from CMS and jets + invisible search data from ATLAS.