POSTER SESSION: Tuesday, September 15, 2026 | Tulsa, OK | 101 Archer | 5:00 – 6:30 pm

U.S. LNG-CO₂ Bidirectional Maritime Supply Chains Under Proposed U.S.-Flag Shipping Objectives: Strategic Value Chain and Technical Implications

The global energy transition requires energy systems that simultaneously address energy security and greenhouse gas emissions. Liquefied natural gas (LNG) is expected to remain an important transitional fuel, particularly for European markets, while the increasing deployment of carbon capture and storage (CCS) creates a growing need for reliable and cost-effective CO₂ transportation and sequestration infrastructure. These parallel trends present an opportunity to integrate energy and carbon management supply chains through maritime logistics. This study investigates a two-fold, bidirectional maritime supply chain that couples outbound LNG exports from the United States to European markets with the inbound transportation of captured CO₂ for geological sequestration in eastern offshore basins. By utilizing LNG carriers for both energy and carbon transportation, the proposed framework seeks to improve fleet utilization, reduce transportation inefficiencies, and support the large-scale deployment of CCS. The study evaluates the techno-economic feasibility of this integrated supply chain and examines its potential to enhance the efficiency and reliability of transatlantic LNG and CO₂ logistics.

Student: Jake Eddy, University of Tulsa, McDougall School of Petroleum Engineering
Advisor: Prof. Buford Pollett

Effects of Liquid Droplet Impact Parameters on the Erosion of Metallic and Non-Metallic Materials

Liquid droplet erosion (LDE) is a significant wear mechanism that impacts materials exposed to continuous high-speed liquid impacts in various engineering applications, including wind turbines, turbomachinery, aerospace parts, and fluid transport systems. In wind energy, erosion on the leading edge caused by repeated raindrop impacts can drastically lower aerodynamic performance, increase repair costs, and reduce the overall lifespan of components. The material’s reaction to droplet impacts is influenced by multiple factors such as the angle of impact, droplet properties, and the inherent characteristics of the material itself. Although water droplet erosion has been explored in prior research, investigations focusing on non-metallic materials remain scarce, with most studies offering qualitative insights rather than comprehensive, controlled evaluations. In this study, conducted at the University of Tulsa, a specially designed whirling-arm liquid droplet erosion testing apparatus was used to examine how metallic and non-metallic materials respond to repeated water droplet impacts, replicating conditions typical of wind turbine operations. The materials were tested at different impact angles and droplet sizes to investigate the influence of different impact condition on degradation mechanisms under 20 m/s impact velocity. This research seeks to provide a comparative analysis of erosion resistance between metallic and polymeric materials, while also clarifying the roles of droplet size and impact angle in controlling erosion processes. The results are intended to inform material selection and improve durability for wind turbine components and other systems exposed to liquid impacts.

Student: Noushin Azimy, University of Tulsa, Department of Mechanical Engineering
Advisor: Dr. Soroor Karimi

Investigating Churn Flow Behavior and Sand Particle Erosion in Pipelines

This project investigates two important phenomena in industrial pipeline systems: multiphase flow behavior and solid particle erosion. The study combines experimental measurements and Computational Fluid Dynamics (CFD) simulations to better understand interactions among gas, liquid, and solid particles.
Multiphase flows, in which two or more phases flow simultaneously, are common in oil and gas transportation and other industrial applications. Depending on operating conditions, different flow regimes can develop. Among these, churn flow is particularly complex due to its chaotic gas–liquid interactions, irregular phase distribution, and highly transient behavior. Despite extensive research on multiphase flows, their behavior through pipe fittings, particularly elbows in series, remains insufficiently understood.
To address this gap, churn flow is investigated experimentally in a 76.2-mm-diameter vertical pipe containing two elbows in series. Wire Mesh Sensors (WMS) are used to characterize gas–liquid phase distributions and provide high-resolution experimental data. CFD simulations are then applied to investigate flow behavior numerically and are validated against experimental measurements to improve prediction reliability.
The second stage focuses on solid particle erosion, which occurs when particles such as sand impact pipeline surfaces and gradually remove material. Erosion under multiphase conditions is strongly influenced by flow behavior, particle size, velocity, and pipeline geometry. This study specifically investigates the influence of superficial flow velocities and particle size on erosion under liquid-dominated churn flow conditions.
This study aims to develop validated experimental and computational approaches to improve mechanistic models for solid particle erosion, supporting safer, more reliable, and more cost-effective pipeline design and operation.

Student: Saeid Pour Nemat, University of Tulsa, Mechanical Engineering Department
Advisors: Dr. Soroor Karimi and Dr. Siamack A. Shirazi

ClearSay – AI Speech Transcription for William

Cerebral palsy is a condition which can limit motor skills, making writing, typing, and speaking more challenging. For many, this can severely limit independence and make certain tasks much more arduous. Recent advances in AI and machine learning have enabled new solutions to some of the problems individuals with this condition may face. ClearSay is an accessibility-first app and speech model system built around a fine-tuned version of OpenAI’s open-source automatic speech recognition system, Whisper. Individual speech-to-text models are trained from several hours of user-specific speech data capturing unique vocal patterns to improve transcription accuracy. Coupled with a simple, accessible interface, our models allow more accurate and personalized transcription, which can be used to speed up processes like completing homework, writing emails, or even texting others. Our first user-specific fine-tuned model yielded promising results, with marked improvements in transcription accuracy over existing non-personalized speech-to-text systems. Expanding our work to a wider range of individuals facing similar difficulties can empower more people to communicate and work more independently.

Students: Ty Devito and Sahara Smith, University of Tulsa, Computer Science Department
Advisor: Dr. John Henshaw

AI-Assisted Contingency Screening for Three-Phase Distribution Networks using Graph Neural Networks

The heart of our research was to develop a way to make power grid analysis and surveillance more efficient, while keeping reliability. Traditional utilities only analyze their grids for vulnerabilities roughly one to two times a day, sometimes once every other day. This is because the power flow solving methods used are so extremely computationally expensive for large, realistic grids. To assuage this, we developed a graph neural network, learning on physics-based synthetic grids with various realistic parameters, that can accurately predict the severity classification (NERC standards) of different grid contingencies, without having to solve manually. Safe contingencies pass through this screen, whereas severe ones get pushed to a full solve to fully identify the extent of the severity.
Behind the machine learning component is an end-to-end synthetic grid generator, along with in-depth analysis and contingency testing capabilities. These are built on the back of Python’s physics-based, industry-compliant library, PandaPower. This ensures that the data the model is learning on is actually indicative of what it might see when implemented with real grids. Currently, the model has a 97% accuracy rate across all severity classes.

Students: Matthew Reynolds and Andrew Vidana, University of Tulsa, Computer Science / Electrical Engineering
Advisor: Dr. Suman Rath

Clathrate Hydrate-Based Carbon Capture from Pre-Combustion Gas Mixtures

The increasing demand for efficient carbon capture technologies has intensified interest in hydrate-based carbon capture (HBCC) as a promising approach for selective CO2 separation from fuel-gas mixtures. HBCC is based on the formation of crystalline gas hydrates, in which guest gas molecules are enclosed within hydrogen-bonded water cages under suitable pressure and temperature conditions. In CO2/H2 mixtures, the preferential hydrate-forming tendency of CO2 over H2 provides the thermodynamic basis for selective CO2 capture. However, slow hydrate nucleation and growth kinetics remain major limitations for practical implementation. In this study, L-tryptophan, an amino-acid-based green kinetic promoter, was evaluated for enhancing hydrate formation performance in a CO2/H2 system. L-tryptophan primarily influences the kinetics of hydrate formation by improving gas-liquid interactions and facilitating hydrate nucleation and crystal growth, thereby increasing the rate of gas incorporation into the hydrate phase. The effect of L-tryptophan concentration was evaluated using normalized gas uptake and hydrate formation rate as key performance parameters. The promoted system exhibited a substantial improvement compared with the unpromoted condition, achieving a maximum normalized gas uptake of approximately 0.0828 mol gas/mol water and a normalized formation rate of 255.56 mol min-1 m-3. These results demonstrate that combining the inherent thermodynamic selectivity of CO2 hydrate formation with L-tryptophan-assisted kinetic enhancement can significantly improve HBCC performance. The use of a sustainable amino-acid-based promoter further supports the potential development of continuous, cyclic, and scalable hydrate-based processes for energy-efficient CO2 capture from fuel-gas streams.
Keywords: Hydrate-based carbon capture; CO2 capture; Gas hydrates; L-tryptophan; Kinetic promoter; CO2/H2 separation; Pre-combustion capture

Student: Binathy Jayaveeran Saritha, University of Tulsa, Russell School of Chemical Engineering
Advisor: Dr. Nagu Daraboina