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Development, Numerical Simulation, and Optimization of Quiet Supersonic Transport Aircraft and Extension of the Wray-Agarwal Algebraic Transition Model to Include Compressibility and Crossflow Effects
No full textIn the first part of this thesis, the goal of the research is to implement and extend multi fidelity methods for conceptual design and optimization of a commercial supersonic transport (SST). Most modern commercial aircraft operate at a cruise condition between Mach 0.7 and 0.9, as this is the optimal trade-off of flight efficiency, operational cost, and adherence of noise regulations for overland travel. Advancements in aviation propulsion fuel efficiency and design materials suggests a primary restraint on commercial supersonic transport is noise regulations. At speeds above Mach 1, the shock waves generated by the airframe are strong enough to propagate from the aircraft to the ground. The amount of time between the pressure increases and decrease (compression and expansion) correlates with how loud the acoustic disturbance is. The Concorde flight profile and performance is used as a benchmark for the conceptual design presented here. Empirical methods have been implemented to size conceptual aircraft that can meet the minimum identified criteria for a modern supersonic transport. SUAVE, a multi-fidelity conceptual design tool, has been coupled with OpenVSP to analyze sonic boom overpressure of concepts at cruise condition. The sonic boom over pressure estimation implemented has been validated for the Concorde and the F5E Tiger II at supersonic conditions. SUAVE’s supersonic vortex lattice method is implemented to evaluate the concept cruise efficiency, takeoff and landing field lengths, and to identify op timal wing aspect ratio, wing sweep, and total wing area. Following preliminary sizing and evaluation, a baseline configuration is subjected to shape optimization aimed at minimizing sonic boom disturbance by employing a discrete adjoint approach in conjunction with invis cid flow solutions from the Euler equations. Relaxation of initial design constraints identifies a concept capable of 40 passengers, 4100 nmi range, cruise efficiency of 11, and reduction of sonic boom by 10 PLdB from the Concorde before shape optimization. In the second part of this thesis, the Wray-Agarwal (WA) and Wray-Agarwal Algebraic Tran sition (WAAT) model are validated for 2D benchmark validation cases. The one-equation WA turbulence model has shown to offer the numerical accuracy of multi equation turbu lence models at the computational expense of 1 transport equation. The second part of this thesis addresses validation and extension of the Wray-Agarwal Algebraic Transition (WAAT) model in Ansys Fluent and open-source solver SU2. The WAAT model is a local correlation model that delays turbulence production through an intermittency term that is calibrated based on local vorticity. The WAAT model has been validated for various ERCOFTAC and NASA Turbulence Modeling Resource (TMR) benchmark validation cases. In Ansys Fluent this model demonstrated similar and improved accuracy over higher order transition models for subsonic symmetric and asymmetric 2D airfoils, and for a simplified fuselage geometry (6:1 prolate spheroid). Implementation of a compressibility correction has shown the WAAT model is capable of accurate transition location prediction in both compressible and incompressible 2D flows. The Wray-Agarwal family of turbulence and transition mod els was recently implemented in SU2. Basic validation and verification work for the SU2 implementation is provided. A crossflow correction is implemented and calibrated for the WAAT model in SU2. The correction shows significant improvement in transition location prediction for the simplified fuselage
Harnessing Metabolic Modeling and Artificial Intelligence for Next-Generation Bioproduction
No full textBioproduction in microbial hosts faces two main challenges: (1) unique metabolism of each species is difficult to generalize, and it requires precise quantification before metabolic engineering. (2) Past experiment data are fragmented in different studies, making it difficult to combine these non-standardized data for future experiment design. These challenges lead to high costs and risks in scaling up biomanufacturing processes, where R&D requires substantial budgets and iterative experiments are expensive. This thesis addresses both challenges through two research pillars. Part I uses 13C metabolic flux analysis (MFA) and genome-scale model (GSM) to investigate metabolic limitations in bioproduction hosts. In Escherichia coli producing silk fibroin, a toxic positive feedback loop was identified, in which acetate overflow inhibits protein synthesis and reduces TCA cycle flux. Supplementing key amino acids can help meet precursor demand and alleviate thermodynamic constraints. In oleaginous yeast Yarrowia lipolytica producing polyhydroxybutyrate (PHB) from volatile fatty acids (VFAs), 13C MFA and GSM identified disadvantages of acetate metabolism, including high carbon loss (\u3e50% as CO₂), high enzyme usage, and NADPH limitation. Co-utilization with glucose can reduce these problems by providing reducing power and alleviating thermodynamic constraints. In Lipomyces tetrasporous, 13C MFA and dynamic labeling showed robust TCA cycle activity and NADH production during acetate metabolism. This strain was engineered to produce malate using sustainable alternative feedstocks such as microbe-friendly CO2 fixation electrolyte VFA and corn stover hydrolysate. These studies provide a good mechanistic foundation for rationally engineering microbial hosts. Part II introduces artificial intelligence (AI) and large language model (LLM) tools for knowledge mining and bioprocess optimization. Generative AI GPT-4 was used to extract structured datasets from 176 synthetic biology publications, enabling machine learning (ML) models to predict fermentation titers in Y. lipolytica with high accuracy (R² = 0.86). Transfer learning extended ML to nonconventional yeasts, such as Rhodosporidium toruloides. To expand AI/LLM tools to general bioproduction topics, a NEKO (Network for Knowledge Organization) workflow was developed for knowledge mining by generating knowledge graphs and actionable summaries. NEKO streamlines tasks like literature review, hypothesis generation, and experimental design. Using open-source LLM Qwen augmented with PubMed search, NEKO outperforms proprietary LLMs such as GPT-4 in zero-shot Q and A. By automating data standardization and hypothesis generation, AI/LLM tools reduce the risks associated with fragmented datasets and accelerate R&D cycles for biomanufacturing. Together, this work establishes a framework for researching microbial metabolism. By combining mechanistic and data-driven approaches, this thesis advances next-generation bioproduction
Code Stories for Software Evolution
No full textProgrammers spend more than half of their time comprehending code, and in particular struggle to answer questions about the rationale, intent, and history behind software artifacts. Through this dissertation, I explore how history-aware tools can help programmers understand unfamiliar software artifacts. First, I investigated how providing additional context -- historical code changes grouped by the original developer\u27s stated subgoals and the web foraging activity of the original developer impacted the process of code reuse. I found that programmers utilized these resources to 1) make better analogies between their reuse scenario and what code was already written, and 2) anchor into the code base, but that separating history from the IDE introduced extraneous load. I then show that presenting this additional contextual information to programmers in the form of a narrative story improved their recollection of the information. Finally, I investigated how programmers actually utilized web foraging activity, historical subgoals, and narrative summaries of this information directly embedded into their IDE during various software evolution tasks. I show that programmers turn to narrative stories when answering questions about the temporal relationships between history items, prefer subgoal labels to quickly map high level intentions to code, and use web documentation to understand the rationale of the original developer
The Blacker the Berry: Food Environments and Intake associated with Perinatal Depression and Secondary Breastfeeding Outcomes among Black Americans in the St. Louis Metropolitan Area
No full textThe relationship between the food environment, intake, and health is complex. The purpose of this study was to evaluate if food environments in proximity to Black pregnant women in the St. Louis Metropolitan Area were associated with prenatal eating habits and if these eating habits were associated with the risk of developing depressive symptoms in pregnancy and postpartum. The study also aimed to understand if there was a relationship between prenatal eating habits and the decision to breastfeed and if the onset of postpartum depressive symptoms has any effect on this relationship. Due to the importance of nutrient-dense, anti-inflammatory foods on maternal mental health and breastfeeding intention, select foods high in critical nutrients with anti-inflammatory properties are included in aims one, two, and three. Aim one was to evaluate the relationship between the distance and density of residential food environments and prenatal dietary intake of nutrient-dense, anti-inflammatory foods in Black American participants. Aim two was to evaluate if daily prenatal intake of nutrient-dense, anti-inflammatory foods moderates the relationship between neuroinflammation biomarkers and prenatal depressive symptom onset and severity in Black American participants. Aim three was to evaluate if postpartum depressive symptom frequency and severity moderates the relationship between daily prenatal intake of nutrient-dense, anti-inflammatory foods and breastfeeding outcomes at four months postpartum. The study was a secondary analysis of the multi-wave, longitudinal eLABE study, which consists of 395 pregnant women aged 18 years or older without known pregnancy complications or substance use during pregnancy. Participants included in the current study identified as Black Americans (n=245, 67%), completed the National Institutes of Health (NIH) National Cancer Institute (NCI) Diet History Questionnaire II (DHQ-II) in pregnancy, the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy and postpartum, and the CDC Infant Feeding Practices Study II questionnaire (IFQ-R) at four months postpartum. The results of this study indicated that living closer to fast-food restaurants and further from grocery stores in pregnancy was associated with prenatal legume intake; prenatal legume intake moderated the independent effects of neuroinflammation biomarker TNF-α on the onset of “feeling anxious or worried for no good reason;” and the frequency of “feeling sad and miserable” at four months postpartum moderated the independent effects of daily prenatal citrus, melon, and berry intake on breastfeeding outcomes. The results suggest that food environments and intake are associated with perinatal depression determinants and outcomes in a sample of Black American women living in the St. Louis Metropolitan Area. Therefore, the identification of determinants and interventions that can effectively reduce perinatal depressive symptoms among Black Americans is critical. The current study identified legumes as a potential moderator of a key determinant in the pathology of perinatal depression, and future research is needed to identify if adequate and sustainable legume uptake moderates prenatal and postpartum outcomes of perinatal depression
Lessons Learned from the Trump Rule of Law Stress Test: Beware Bad Faith Actors
No full textThe second Trump Administration has engaged in a vast swath of actions that press against the outer boundaries of legality. These actions provide a unique opportunity to identify potential vulnerabilities and expose how the rule of law can be undermined. While I will elucidate well-known points along the way, my primary aim is to bring out aspects of the rule of law that usually go unnoticed or under the radar. After setting out basic aspects of the rule of law, I cover three topics: 1) adhering to the truth, 2) pretextual arguments, and 3) flooding the field with actions and using time to its advantage; discussed against the backdrop of a fourth point: 4) extreme pressure on the judiciary, including harsh criticisms, misleading or false statements, and defiance of judicial orders. This analysis reveals various ways in which a bad faith administration can exploit existing doctrines and a structural difference between the executive and courts to effectively undermine the rule of law
Thirty Productive Years and a Very Promising Future: The Annual Letter of the Center for Social Development
Full text linkIn the 2025 annual letter of the Center for Social Development (CSD), Founding Director Michael Sherrraden discusses the integration of the Washington University Social Policy Institute with CSD, summarizes developments at the center, introduces the new team of experts, shines a light on some of the accomplishments during the past year, and offers a vision for the future
Phone Floatation Device
No full textA problem of extreme frustration for boaters, kaykers, and lake-goers is that of taking an expensive new phone, with its water resistant capabilities, and watching it slip from their hand to the bottom of a deep body of water, never to be seen again. This project seeks to solve this problem by designing a new attachment for phones that when dropped, causes the phone to be buoyant enough that it quickly floats back to the surface of the water. To solve this problem, several approaches were analyzed from chemical reactions and compressed air that created or released air into a balloon, to a foam based solution. Each idea focusing specifically on reliability mixed with user comfort. This project ultimately ends with a simple solution that could one day become a product to solve this common challenge
Fossil Fuel Wealth for Human Development: CDAs Continue in Kazakhstan with Second Year Deposits
Full text linkIn January of 2024, Kazakhstan implemented a national Child Development Account (CDA) policy: The National Fund for Children. Through the policy, assets accrue for every child or youth under age 18. This policy brief discusses developments in the operation of the policy and the allocation of funding as of January 2025
The Keys to the Kingdom: The Unexpectedly Unsettled Definitions of Security and Sale and the Overruling of Chevron
Full text linkThis article explores two important unresolved questions for the Federal Securities regulation under the Securities Act of 1933 and the Securities Exchange Act of 1934. The definition of security and the definition of sale were increasingly contested and potentially unstable before the United States Supreme Court decision in Loper Bright Enterprises v. Raimondo overruled the Chevron doctrine
Expanding the druggable proteome by integrating deep learning with molecular simulations to predict cryptic pockets
Full text linkOf the protein structures deposited in the Protein Data Bank, less than half have pockets suitable for the binding of drugs. Even when proteins contain pockets in their ground state structures (e.g., the nucleotide-binding active site in myosin motors), achieving specificity remains a central challenge in drug design as many protein families share common structural motifs. Cryptic pockets are cavities absent in ligand-free experimental structures that form due to protein fluctuations in solution. They provide a means to specifically target proteins currently considered undruggable. While cryptic pockets are alluring drug targets, it remains difficult to predict which proteins will form cryptic pockets. It is also unclear how certain compounds that bind at cryptic pockets discriminate between similar targets, even though those targets all have closed pockets in experimental structures. To address these problems, I develop a graph neural network called PocketMiner that predicts whether a protein is likely to form a cryptic pocket based on its ground state structure. I demonstrate that PocketMiner achieves improved performance (ROC-AUC: 0.87) compared to existing methods at \u3e1,000-fold faster run times. To further accelerate cryptic pocket discovery, I leverage the protein structure prediction algorithm AlphaFold to generate ensembles of structures. I show that AlphaFold-generated ensembles often sample cryptic pocket opening, and that using these ensembles as starting structures for molecular dynamics simulations can enhance sampling of a rare cryptic pocket opening in an antimalarial drug target. To connect cryptic pocket opening to drug specificity, I show that differences in the probability of cryptic pocket opening underpin the specificity of a myosin inhibitor known to bind at a cryptic site. By combining Markov state models with molecular docking, we accurately predict the affinity of blebbistatin for different myosin proteins. Finally, to demonstrate the utility of these methods for drug discovery applications, I use simulations of a cancer drug target, PPM1D phosphatase, to discover a novel cryptic pocket. Docked poses of compounds bound to this cryptic pocket can be fed to a neural network that predicts affinities to accurately rank compounds by their experimental affinities. Taken together, these results represent an important advancement towards rational drug design against previously undruggable targets