Energy Institute Graduate Fellowships

As next-generation energy systems, such as modular nuclear reactors and hybrid systems, grow in complexity, traditional computational workflows remain heavily reliant on labor-intensive expert knowledge. My research addresses this scalability barrier by developing advanced artificial intelligence methods that support the design, analysis, and operation of complex energy systems.

The work focuses primarily on two core research areas. The first area involves the development of specialized Large Language Model agents designed to automate complex modeling and simulation workflows, including multi-modal input generation, error diagnosis, simulation orchestration, and results interpretation. The second area utilizes scientific machine learning by integrating physics-informed neural networks (PINNs), uncertainty quantification (UQ), and reinforcement and continual learning to support modeling & simulation and experimental setups. This research aims to enhance operational efficiency and accelerate the transition toward a more reliable, lower-carbon energy future.

This research investigates the degradation of subsurface materials, including wellbore cement and rock formations, under long-term exposure to reactive fluids, elevated temperature, and pressure. Such degradation critically impacts the integrity of systems used in geothermal energy and enhanced oil recovery, where direct monitoring and remediation are limited.

The study integrates laboratory experiments, field-exposed samples, and physics-based modeling to examine the coupled chemo-mechanical processes governing material deterioration. Cement retrieved from a CO2-enhanced oil recovery site and shale samples
subjected to controlled high-pressure, high-temperature conditions are characterized using advanced multiscale multi-physics experiments. These techniques are used to quantify microstructural and compositional changes that contribute to mechanical weakening across scales.

The results indicate that coupled chemical and mechanical processes significantly accelerate material degradation, particularly in quartz-rich formations, highlighting the importance of incorporating multiscale interactions in predictive models of long-term subsurface performance. A machine learning-assisted framework is also introduced to map spatial variations in mechanical properties, enabling efficient upscaling from microscale measurements to continuum-scale behavior.

Overall, this work advances understanding of long-term subsurface stability by linking microscale chemo-mechanical evolution to macroscale mechanical response, supporting improved prediction of well and reservoir integrity in subsurface energy systems. This research aligns with Chevron’s efforts to improve the reliability and safety of subsurface systems for lower-carbon energy technologies, including carbon capture and storage.

Ahmet Birkan Demir’s research focuses on generating hydrogen from petroleum reservoir systems using microwave technology as a potential lower-carbon energy pathway. Specifically, the study investigates how reservoir rocks, crude oil, and brine environments respond to microwave heating and how mineral–fluid interactions influence hydrogen production. A key motivation behind this research is the idea that existing petroleum reservoirs can serve not only as sources of hydrocarbons but also as engineered subsurface environments for hydrogen production. By concentrating on carbonate-rich reservoir systems, Ahmet Birkan Demir aims to develop a method that enhances hydrogen generation while also supporting CO₂ storage in subsurface formations. This work introduces a new perspective to the literature by integrating petroleum reservoir engineering, subsurface energy conversion, and lower-carbon hydrogen generation.

Touka Elsayed’s research develops experimentally grounded modeling workflows for CO2 Plume Geothermal and CO2 storage systems. Her work investigates how thermal shock affects the microstructure and geomechanical response of igneous rocks, including granite and ultramafic formations, using micro-CT imaging, sonic velocity measurements, elastic property evaluation, and mechanical testing. These laboratory observations are integrated into thermo-hydro-chemo-mechanical modeling frameworks to evaluate injectivity, thermal efficiency, reactive effects, and mechanical integrity during subsurface energy operations. By linking rock-scale thermal damage to reservoir-scale performance, this research supports safer and more efficient design of scalable lower-carbon energy systems, particularly those involving carbon utilization and novel geothermal solutions.

My research focuses on developing MXene-based electrocatalysts for electrochemical energy conversion technologies, including hydrogen production and fuel cells. I investigate how composition, surface chemistry, and atomic structure influence catalytic activity and stability in hydrogen evolution, oxygen evolution, and oxygen reduction reactions. Current work explores titanium-based carbide, carbonitride, and nitride MXenes synthesized through minimal- or no-hydrofluoric acid routes to improve the safety and scalability of MXene production. By establishing composition–structure–property relationships, this research aims to provide design principles for advanced materials that support scalable, lower carbon-intensity energy technologies.

Power electronic converters are fundamental building blocks of modern energy systems, including renewable energy installations, electric vehicles, battery energy storage systems, and DC microgrids. My research focuses on the development of Conversion Function Theory (CFT), a novel framework for modeling and analyzing switching power converters and converter-dominated power systems.

CFT enables accurate and computationally efficient representation of converter behavior while preserving key dynamic characteristics of physical systems. The resulting models can be applied to stability assessment, control design, system optimization, and the integration of renewable energy and energy storage technologies. The long-term goal of this work is to provide scalable modeling tools that accelerate the design and deployment of next-generation electrified energy systems.

Hydraulic fracturing is the backbone of unconventional shale development, yet designing effective treatments remains one of the hardest problems in completion engineering. The numerical simulations used to model fracture growth and proppant transport are computationally expensive, often taking hours for a single run, so engineers cannot evaluate enough design alternatives to find genuinely optimal completions. My research addresses this gap by developing deep transfer learning surrogate models that generate two-dimensional maps of hydraulic fracture conductivity and proppant concentration at a fraction of the computational cost. These maps serve as direct inputs for optimizing completion design, allowing engineers to refine pumping schedules, fluid volumes, and proppant placement to maximize production and resource efficiency. The work scales from single-stage prediction toward multi-stage and pad-scale multi-well modeling, where field development decisions are actually made, and applies transfer learning so that a model trained in one basin can be adapted to other shale plays without retraining from scratch.

Hydrogen storage remains a major bottleneck in hydrogen energy systems due to hydrogen’s inherently low volumetric energy density. Safe and efficient storage is essential not only to mitigate operational hazards, such as fires and explosions, but also to improve system profitability by maximizing performance, reducing energy consumption, and minimizing operational downtime. Metal hydrides present a promising alternative by enabling hydrogen storage at lower pressures and in solid form through adsorption. However, this adsorption process is highly exothermic and generates a substantial amount of heat, increasing the risk of thermal runaway. To address this, a safety-intelligent model predictive control (MPC) strategy was developed to maintain thermal stability in metal hydrides while maximizing storage efficiency. The controller was implemented on a microcontroller and validated experimentally under closed-loop conditions. Results demonstrate that the metal hydride system exhibits minimal temperature overshoot while achieving maximum hydrogen storage. These findings indicate that the safety-intelligent MPC effectively optimizes operation and has the potential to advance metal hydride storage. Overall, this work aligns with Chevron’s aim of advancing a lower-carbon H2 value chain through process intensification.

Dimitrios’s research focuses on advancing scalable decision-support workflows for enhanced geothermal systems and subsurface energy storage through hybrid reduced-order and physics-guided reservoir models. He develops integrated workflows to characterize coupled pressure, temperature, saturation, plume, and fracture-sensitive behavior under changing controls, permeability fields, and well configurations. His work supports critical components of safe and efficient deployment in EGS, CO₂ sequestration, and underground hydrogen storage, including injection and production optimization, pressure management, adaptive thermal management, monitoring, containment-risk assessment, and fracture/fault reactivation analysis. He also explores the use of data-driven reduced-order models, DMD variants, and scientific machine learning techniques to accelerate scenario evaluation and optimize subsurface operations while preserving the dominant physics needed for field-scale forecasting and decision-making.

As modern power grids increasingly rely on power electronics-interfaced generation, loads, and battery energy storage, maintaining system stability and operational reliability has become significantly more challenging, particularly under weak-grid and autonomous off-grid operating conditions. These challenges are further intensified by the highly dynamic characteristics of advanced AI computing loads, which can introduce strong interactions across multiple power electronic interfaces. My research develops AI-enabled coordination and control frameworks for power electronics-interfaced energy resources in modern power systems. A primary focus is on modeling and controlling the power electronic interfaces of emerging large loads, including AI data centers, crypto-mining facilities, and hydrogen electrolyzers. By integrating physics-based electromagnetic transient modeling with AI-driven adaptive control, my work enhances the grid-friendly integration of large loads. Ultimately, my research aims to enable scalable, resilient, and reliable coordination of large-scale power electronics-interfaced resources for next-generation AI computing infrastructure and electrified low-carbon industrial systems.

Kassem’s research focuses on advancing large-scale geological carbon storage through fully coupled thermo-hydro-mechanical-chemical (THMC) modeling. He develops integrated workflows to characterize pressure-saturation plume dynamics, caprock integrity, trapping mechanisms, geochemical alterations in the near-wellbore region and fracture and fault activation. His work supports critical components of safe and scalable CCS deployment, including the delineation of the Area of Review, pressure management through brine production, and regulatory compliance with EPA Class VI permitting. He also explores the use of machine learning techniques to accelerate scenario evaluation and optimize CO₂ injection strategies in saline aquifers.

To ensure CO₂ storage integrity, regulatory compliance, and long-term environmental sustainability, measurement, monitoring, and verification (MMV), are essential to CO₂ sequestration. The ability to predict a storage integrity issue before it occurs would be valuable in supporting CO₂ storage site owners in making decisions that can minimize the detrimental effects of loss in storage integrity. While several MMV technologies exist, there is a gap in predictive tools that can utilize real-time surface measurements to anticipate subsurface issues before they occur. Thus, my research focuses on developing a High-Performance Digital Twin Technology for CO₂ MMV that combines a proxy model (based on a physics-informed numerical simulation model) that accounts for the thermohydrochemomechanical processes that occur during CO₂ storage with a deep learning framework that has identified patterns leading to CO₂ storage integrity issues based on surface data, such as temperature and pressure. By combining real-time subsurface data with AI-driven models, I aim to improve the accuracy of storage integrity predictions and reduce risks. This approach helps ensure that carbon capture and storage (CCS) projects are both reliable and scalable for long-term low-carbon energy solutions.

This research focuses on advancing industrial energy efficiency as a key strategy for decarbonization, with a particular emphasis on optimizing compressed air systems in manufacturing. Despite their ubiquity and significant energy consumption—accounting for up to 10% of U.S. manufacturing electricity use—compressed air systems are often inefficient and poorly monitored. The work leverages Industry 4.0 technologies to implement a “3M” (Measure, Monitor, Manage) approach, developing smart assessment tools, user-friendly prescriptive diagnostics, and data-driven models to detect inefficiencies such as air leaks and artificial demand. Contributions include the development of a smart data logger, an automatic air leak measurement device, experimental evaluation of engineered air nozzles, and the creation of indirect flow monitoring methods using statistical analysis of power and pressure data. Future research will enable waste heat recovery from air compressors and integrate machine learning for more robust diagnostics. The overarching goal is to make energy assessment more accessible and actionable, supporting measurable progress toward industrial decarbonization and sustainability.

Utilizing renewable energy and lowering our carbon footprint are the cornerstones of sustainable development. Single-use plastics, while versatile and affordable, have a significant carbon footprint and negatively impact our environment when improperly disposed of. My research is focused on heterogeneous catalysis to upcycle post-consumer plastic waste into higher-value compounds, including sustainable aviation fuel components, oils, and platform chemicals. My research utilizes the chemistries of hydrogenolysis and solvolysis to depolymerize prominent post-consumer plastics, including polyethylene, polyethylene terephthalate, and polycarbonate. In addition to creating a circular carbon economy through catalytically upcycling plastics, I utilize liquid organic hydrogen carriers in place of gas-phase hydrogen to facilitate these reactions, thereby creating a hydrogen value chain that utilizes biomass-derived alcohols as hydrogen sources. I use experimental and computational techniques to probe the active site requirements and reaction mechanisms for these reactions. Through this research, I aim to elucidate the key reaction mechanisms and catalyst design principles for efficiently upcycling large volumes of mixed plastic waste and contributing to sustainable fuel production pathways.

Halil Iseri’s research focuses on the design and operation of resilient and environmentally conscious energy systems and supply chains under uncertainty through mathematical modelling and optimization.

Mr. Iseri began his research by addressing the growing need to transport renewable energy and hydrogen from resource-rich regions to industrial demand centers. To this end, he developed a multi-objective optimization framework that incorporates cost, environmental impact, and transportation risk objectives, and considers multiple production locations, energy carriers, and transportation modes to determine the optimal energy transportation strategy. His findings reveal that optimal energy transportation strategies depend on distance, energy demand, and the potential to repurpose existing infrastructure. He is currently extending this framework into a “Data-Driven Design and Operation of Resilient Renewable Energy Supply Chain Networks Under Uncertainty” model, which leverages resilience metrics and machine learning for adaptive planning and operations.

Mr. Iseri is also engaged in collaborative research on the integration of renewable energy with energy-intensive material supply chains, such as steel and ammonia, using the spatial-temporal variability of renewable energy to investigate cost-emission trade-offs and long-term grid balancing. Additionally, through the NSF-funded SOLAR project, he develops mathematical models for reverse logistics network design for PV recycling, adopting a circular economy approach to enhance the resilience of critical material supply chains, including silicon, silver, and aluminum.

This research transforms the traditional in-situ combustion process into a low-emission, high-efficiency recovery method for light oil shale reservoirs. While ISC has strong recovery potential, it typically generates significant CO₂ emissions. This project addresses that challenge by integrating experimental combustion studies with machine learning, mineral-based carbon capture, and CO₂ reinjection strategies. Analytical and numerical models are also developed to screen reservoir suitability and predict emission profiles. The outcome is a scalable, field-deployable framework that aligns enhanced oil recovery with sustainable carbon and thermal management, advancing cleaner energy solutions in unconventional resource development.

Hydrogen is a viable option for future sustainable energy needs. Hydrogen can be sourced naturally from the subsurface reaction of olivine-rich rock formations with water under high-temperature and high-pressure conditions, in a process known as serpentinization. This reaction of olivine with water oxidizes the rock leading to a volumetric expansion of the rock while generating hydrogen. Even though this reaction has been described in the literature as a volume-expanding process, studies focus on generating more hydrogen while limited studies have studied the poromechanical response of these rocks due to the reaction. My research aims to experimentally optimize hydrogen generation from olivine rocks while also assessing the poromechanical response of these rocks during the hydrogen generation process. This includes assessing changes in rock stiffness, strength, and elasticity due to the volume expansion accompanying the serpentinization reaction. Understanding this coupling between geochemistry and geomechanics is important for the safe implementation of natural hydrogen generation projects. This study aligns with Chevron’s aim in new sustainable sources of materials for energy transition and research in future energy systems.

My Ph.D. research focuses on converting carbon dioxide (CO₂) and green hydrogen (H₂) into sustainable fuels using bifunctional catalysts. Specifically, I work on tandem catalytic systems that pair metal oxides, which first convert CO₂ into methanol, with zeolites, which then convert methanol into hydrocarbons like gasoline or aromatics — all in a single reactor. A key focus of my research is understanding how the spatial arrangement of these two catalyst components affects the reaction rates and product yields. I am also investigating how mass transport effects influence the reaction mechanisms and how to rationally integrate metal oxide and zeolite components for efficient CO₂ conversion. My work is particularly relevant to the production of sustainable fuel alternatives for hard-to-electrify sectors like aviation fuels, a topic of considerable current research interest.

The global energy sector is undergoing significant transformations as nations strive to address climate change and achieve net zero-emission goals. Nuclear energy, known for its capacity to deliver large-scale, reliable, and low-carbon power, is poised to play a crucial role in this transition. Advanced Small-scale Reactors, or Generation IV (Gen IV) Reactors – Small Modular Reactors (SMRs) and Microreactors (MRs), offer unique advantages such as lower capital costs, rapid construction, enhanced safety, and suitability for small grids. These features make them ideal for electrifying isolated regions, remote mines, and increasingly data-hungry AI-driven infrastructures, as well as for use in developing countries and various industrial applications like process heat for water desalination and hydrogen production. My research leverages advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the performance, safety, and efficiency of advanced small-scale nuclear reactor systems (SMRs and MRs). My focus is on two integrated areas: (i)-developing hybrid Digital Twin (DT) models to enhance autonomous reactor operations, improve predictive maintenance, and address human expertise limitations, and (ii)-utilizing Generative AI (GenAI) especially Larg Language Models (LLMs) and AI agents to automate nuclear research workflows and enable real-time operational monitoring. Recently, these two approaches have been fused to create a hybrid, more intelligent and resilient framework where insights from a DT-based simulator inform AI agents for optimized control, extending the application beyond simulators to real-world thermal-fluid facilities. This work directly contributes to Chevron’s goals for scalable, lower carbon-intensity energy solutions by advancing emergent technologies like AI/ML and DTs, and supporting integrated energy systems for reliable, autonomous modular nuclear reactors, thus driving the transition to a sustainable energy future.

As cyber-physical power systems grow increasingly complex and interconnected, their resilience to cyber threats, physical failures, and renewable integration challenges is critical to ensuring continuous, reliable energy delivery. My research develops scalable, bio-inspired methodologies to enhance the resilience of these systems by integrating ecological network principles, graph theory, and multi-objective optimization. Inspired by the structure of pollination networks, I identify critical nodes and interdependencies within power grids to mitigate cascading failures and improve operational robustness. My work emphasizes decentralized, low-carbon systems such as microgrids and grid-edge infrastructure, modeling real-world failure scenarios using Python and MATLAB. These models incorporate cybersecurity risk, energy flow dynamics, and system costs to inform resilient system design. In parallel, I contribute to infrastructure policy by integrating artificial intelligence and resilience strategies into critical infrastructure planning, offering actionable insights for national security and energy stakeholders. Ongoing work expands these frameworks to real-time optimization of renewable integration and adaptive CPS architectures. Collectively, my research bridges technical innovation with practical deployment, supporting the secure and sustainable transformation of modern energy infrastructure.

Process intensification refers to the development of chemical processing techniques and equipment that lead to substantially smaller, cleaner, safer, and more energy-efficient processes compared to their traditional counterparts. Often times, intensification alternatives are not known beforehand and identification of such novel solutions during the conceptual design stage, where the initial layout of the chemical plant is decided, necessitates systematic design methodologies. To this end, we developed an optimization-based method for the discovery of new chemical process designs. This method relies on an original representation of chemical process operations using building blocks. These blocks represent the fundamental physicochemical phenomena, tasks and unit operations that constitute most of the processes used in the chemical process industry (CPI). Various combinations of building blocks can yield a plethora of new equipment and flowsheets. A mixed-integer nonlinear programming (MINLP) optimization model is used to describe the systematic selection of optimal block combinations towards generating an intensified process system. The MINLP model incorporates mass and energy conservations, and descriptions of reaction, separation and material selection. With this generic optimization model, building block-based design methodology provides an automated approach for generation and screening of novel intensified process alternatives.

Turbulent flows are ubiquitous in nature and engineering systems and are critical to the efficient production and consumption of energy in conventional and future energy systems. Turbulence has first-order effects on critical processes, such as mixing of fuel and oxidizers, drag over cars and airplanes, and power generation from wind energy. Yet, turbulence is a notoriously difficult problem, especially at realistic conditions, due to a wide range of interacting spatial and temporal scales. Fundamental understanding of these interactions is currently lacking and is critical to developing predictive capabilities for turbulent systems. We use massively parallel supercomputers for direct numerical simulations (DNS) to solve the exact equations of fluid motion across all scales, without any modeling, and with a resolution an order of magnitude larger than traditional DNS. This provides a new window into the finest scales of turbulence, largely unstudied yet, and their interactions. We are also developing a theoretical formalism to predict the statistical behavior of turbulence at realistic flow conditions by understanding the behavior of the finest scales at smaller parameter ranges. These parameter ranges and resolutions are computationally accessible on the largest supercomputers today, unlike flows at realistic conditions. In doing so, we aim to further our understanding of dynamics within different scales of turbulent flows and advance the theoretical development of exact theories as well as low-fidelity models of turbulence for engineering design and computational predictive capabilities.

Algae-based biofuels have been regarded as an ultimate solution for renewable energy, considering the potentially high productivity, the efficient energy conversion from sunlight, the capacity to capture and utilize CO2 at high conversion rates, and the replacement of fossil fuels with limited land usage. Despite extensive efforts, the scale-up and commercialization of algal biofuel are still hindered by several fundamental challenges, including low light penetration, costly dewatering and the requirement to extract algal oil. In my graduate research, I designed an auto-flocculation-based continuous hydrocarbon and carbohydrate co-production platform to address these challenges. In the platform, the cell surface structure of cyanobacteria was modified to enable auto-flocculation, which can be applied to reduce costs for biomass harvesting and transform cyanobacterial biofuel from batch production to continuous production. Meanwhile, a limonene synthase was introduced into the strain to catalyze limonene biosynthesis, enabling continuous limonene and glycogen (in biomass) co-production. Since the cell density of cyanobacteria can be tightly controlled in continuous production, light penetration efficiency could be significantly improved, leading to dramatic increases in both limonene and glycogen productivities. Next, I am focusing on: 1) to continue to optimize and scale up the Continuous Ultra-high-yield Co-production of Hydrocarbon and Carbohydrate to achieve long-term sustainable production; and 2) to define the biochemical and metabolic limits for terpene production and further improve terpene and glycogen productivities by systemically optimizing carbon and energy partition from photosynthesis to hydrocarbon and carbohydrate biosynthesis.

Citing energy and environmental reasons, many individuals argue that it is time to adopt renewable energy sources (RES) and transform grids into smarter grids. The deployment and integration of fluctuating renewable energy into the power grid and the eventual transformation into a smart grid will require the use of advanced technologies and management strategies. My research is focused on the development of a real-time big data analytics platform for both the current grid and for future smart grids. The project focuses on developing big data analytics, predictive analytics, forecasting analytics, cyber-physical security and privacy for accurate and efficient energy consumption forecasting. This will lead to the development and testing of a dynamic energy management system with a big data analytics platform, which will help in the optimization of energy resources and load management for both energy efficiency and demand response programs based on real-time processing. Big data analytics and machine learning will help integrate renewable energy sources with energy storage and forecast services while considering uncertainty and customer behavior. Also, mining information from big data available from smart grids makes it possible to prevent blackouts, as it enables the discovery of underlying patterns and an understanding of the highly complex structures and thousands of system variables of a power network, thus paving the way to accurately predict the unstable status of power network components in-time. Currently, I am working on the applications of load forecasting and voltage stability analysis using deep learning models. In tandem, centralized and distributed control strategies are being worked upon and a combination of the RTP program with the IBR model will be used as the most efficient model for energy demand management.

The loss of energy through friction at sliding interfaces is a significant issue. Graphene, a single-atom thick, two-dimensional lattice of honeycomb structured carbon, has been shown to be a potentially useful lubricant to decrease friction and mitigate wear at sliding interfaces. However, studies have shown that when conformed to nanoscopically rough surfaces, the graphene lattice is strained, leading to an increased risk of mechano-chemically accelerated oxidation and breakdown. My research is a fundamental examination of the reactivity of graphene under varying amounts of strain. Lattice strain can be tailored by transferring graphene to silica nanoparticle films of varying particle diameter, or by applying pressure to suspended graphene membranes. Raman spectroscopy and ambient pressure XPS can then be used to monitor the reactivity of these systems with O₂, or with a diazonium compound. Studying strained and suspended graphene helps build a stronger understanding of the role strain has on graphene reactivity, and ultimately the viability of graphene as a lubricant to cut down on energy losses from friction and wear. Furthermore, we plan to apply these concepts to other 2D lubricants, such as MoS₂.

Geophysics, especially seismic studies, can contribute to drilling and development of unconventional shale gas reservoirs by defining ‘fracability’ and ‘organic carbon content’ of shale. Un Young Lim’s doctoral research is mainly focused on the estimation of geomechanical properties (i.e., Young’s modulus and Poisson’s ratio) and total organic carbon (TOC) content in shales using a seismic inversion method, AVO inversion. He applied a new approach using an exact equation for seismic reflections, the Zoeppritz equation for PP reflection, instead of widely used approximations of the equation into the research problem. Consequently, noticeable improvements are achieved for the estimation. Specifically, anisotropy of acoustic parameters such as P– and S-wave velocities, and density are more accurately determined. This leads to more accurate estimations of Young’s modulus, Poisson’s ratio, and TOC of target shale. He also recently developed a new inversion method that jointly uses PP and PS seismic reflections together in order to improve inversion results.

Enhanced Geothermal Systems (EGS) produce energy from hot (i.e. >100°C) deep (i.e. >500m) reservoirs. Hot Dry Rocks (HDR) are generally deficient in permeability and water, so the injection of cold fluids (e.g. water) will generate thermal shock in the rock and trigger the formation of fractures that will enable geothermal-energy production from hot water. A 2006 MIT report estimated that EGS resources could reach 13,000 million exajoules in the USA, which would be able to meet worldwide energy demand for centuries. A good understating of fracture formation and the thermo-hydro-mechanical (THM) processes controlling water/vapor flow in fractured rocks is necessary for safe and economic energy production from EGS. Equally important is to develop numerical tools that allow a proper modeling of these phenomena and the engineering of EGS to optimize geothermal energy production. However, current computational tools are (typically) not able to deal with the presence of discontinuities as they are based on the theory of continuous media. In this research, we propose an advanced THM framework to model evolving cracks in rocks that combines the Finite Element (FE) method with the Mesh Fragmentation Technique (MFT). The MFT inserts solid FE with high aspect ratio in between the regular (standards) finite element of the original mesh. The technique has shown to be very promising for tackling this type of problem. For example, it has assisted to select the target temperature and flow rate of the injected fluid; as well as to design the number of wells required for a given reservoir, and to estimate the optimal distance between them to produce geothermal energy efficiently.

Sorghum has been one of the important energy crops in the world due to its high level of biomass production capacity, but the most challenging part that can significantly affect biomass production is sorghum damage caused by pests and there are no techniques to detect pest damage both quickly and accurately. Therefore, the overall objective for my dissertation research is to develop a new concept of infestation detection technique based on the determination of herbivore-induced plant volatile organic compounds (VOCs), which can prevent the infestation from spreading out throughout the field and keep the field healthy. Several specific research topics include: 1) Fundamental Sorghum VOCs analysis induced from sugarcane aphid damage by adsorbent-GC/MS technique; 2) Proof of concept that can determine many kinds of plant VOCs with adsorbent coupled Raman spectroscopy; 3) Fabrication of phase transferred Ag-nanosphere and its application of surface-enhanced Raman spectroscopy (SERS) for VOCs determination; and 4) Development of adsorbent coupled SERS technique for VOCs detection. I hope that these lab-scale VOCs sensor platforms will be employed in the field as a reliable health monitoring system in the near future.

As we continue to move toward sustainable sources of energy (e.g. solar, wind), new challenges arise in the engineering of materials for more efficient energy conversion and storage devices. In order to improve the performance of such devices, it is crucial to gain a comprehensive understanding of how they operate, requiring joined efforts from first-principles modeling and in situ experiments. At Professor Balbuena’s research laboratory in the department of chemical engineering, I am carrying out research using computational methods at the nanoscale to study physical-chemical properties needed for design of novel materials used in diverse energy applications. A few specific topics of my research include: 1) the design of anode electrocatalysts for water splitting to improve hydrogen production; 2) evaluation of tunable electronic and optical properties of semiconductor transition metal dichalcogenides – a 2D type of materials with a diverse number of applications such as nano batteries, green electronics, and photonics; and 3) the elucidation and control of the solid-electrolyte interphase (SEI) growth at anode materials for Li-ion and Li-sulfur batteries to enhance the performance and lifetime of rechargeable batteries. This theoretical understanding combined with experimental data can help develop effective ways to tailor-make materials for crucial energy applications.

Magnetic gears use the interaction of modulated magnetic fields to transform mechanical energy between low-speed, high-torque rotation and high-speed, low-torque rotation. Thus, they perform the same function as mechanical gears while providing benefits from contactless power transfer, such as reduced maintenance requirements, higher reliability, and reduced acoustic noise. Magnetic gears are ideal for high-torque applications when maintenance and reliability are significant concerns, such as during production of wind energy, wave energy, as well as various downhole applications. In addition to simply replacing mechanical gears, magnetic gears can also be integrated directly with an electric machine (motor or generator) to form a magnetically geared machine – a single, compact device capable of producing significantly more torque than a comparably sized conventional electric machine. This magnetically geared machine can then be used directly, without any further gearing. This research has involved the development of novel magnetic gear and magnetically geared machine topologies; the development of analytical and numerical tools to evaluate magnetic gear performance; the optimization of magnetic gear and magnetically geared machine performance for different applications; and the design, fabrication, and testing of prototype magnetic gears and magnetically geared machines.

Electrochemical energy storage devices (EESDs) are in demand for portable electronic devices, smart grid, hybrid or electric vehicles, and energy recovery systems. To date, lithium ion batteries (LIBs) and supercapacitors (SCs) are two typical mediums of storage. This research aims at design, fabrication, and characterization of electrodes made of novel hierarchical nanocomposites. Firstly, various types of metallic current collectors with highly porous morphology are fabricated and characterized. Nanostructured, shape-specific, and electrochemically active transition metallic oxide (TMO) particles are subsequently synthesized and directly deposited on such porous current collector. This combination of nanostructured TMO particles and porous current collectors establishes the hierarchical micro-architecture of advanced electrodes. Meanwhile, facile and novel binder-free processing is applied during the assembly. Electrochemical characterization and analysis are conducted to understand the mechanisms of electrochemical interactions, ion transport, and energy storage. The ultimate purpose of this research is to design better electrodes with greater energy density, longer lifespan, enhanced cyclic stability, and lower cost.

Gas turbines are typically used for land-based power plant and aircraft propulsion applications. Demand for higher Turbine Inlet Temperature (TIT) has risen steadily over the last decades for better performing engines with higher efficiency. In the meantime, engine manufacturers have developed advanced designs to provide improved reliability. A portion of this improved temperature capability is due to migration toward enhanced component cooling schemes. As part of the research, experimental investigation is being conducted on the performance of advanced external and internal cooling designs for the first stage turbine blade and endwall at engine-like conditions.

The research project evaluates the technical and economic feasibility of using CO2 to enhance recovery from unconventional liquid reservoirs (ULR) such as oil shale. Horizontal drilling and hydraulic fracturing have enabled the economic exploitation of ULR plays, but the ultimate recovery obtained from the application of such techniques is expected to be marginal. CO2 has proven to be a powerful enhanced oil recovery (EOR) agent in conventional reservoirs, but such success cannot be directly extrapolated to ULR due to fundamental differences in conformation and composition of the rock matrix, that results in important alterations in fluid transport, storage, and phase behavior. The project is mainly experimental. The principal aspects being addressed are incremental oil recovery, CO2 utilization and storage, the effect of the presence of meso- and micro-pores on phase behavior changes, and some operational considerations such as injection scheme and pressure.

Novel materials are at the forefront of advancement and energy research in all aspects of energy generation and storage to optimization of energy use in processes and devices. Working with Professor Perla Balbuena in the Artie McFerrin Department of Chemical Engineering’s Balbuena research laboratory has enabled me to perform research that focuses on understanding materials behavior and phenomena occurring at the nanoscale using molecular simulation techniques such as quantum mechanics and molecular dynamics. Elucidation of nucleation and growth mechanisms of single-walled carbon nanotubes to produce selective structures to be used in energy applications including storage, photovoltaics and electronic devices; studying reactions occurring at the electrode-electrolyte interface and their effect on performance in Li-ion battery systems, and investigation of the separation capabilities of metal-organic frameworks for gas separation have been specific subjects of this research. The combination of the information obtained from these techniques with experiments is able to provide a clear picture of the phenomena studied and contribute to the advancement of rational materials design.

Energy conservation and storage have become issues of extreme importance as society begins to move away from reliance on fossil fuels. The diversity of properties and structure within the vanadium oxide family offers desirable materials to address these issues, especially when leveraged by scaling to nanometer sizes. Synthesizing gram scale batches of the highest quality of vanadium dioxide nanowires has allowed for the development of thermochromic glazings that significantly reduce the amount of heat transmitted through windows in hot environments thus reducing the costs of cooling large buildings. The study of lithium-ion storage in nanowires of vanadium pentoxide has also allowed me to develop a mechanistic understanding of how cations move through the host material. Understanding these mechanisms provided a blueprint for developing nanowires of vanadium pentoxide with a novel crystalline structure, reducing barriers to cation mobility and allowing for the inclusion of magnesium-ions, one of only a few known materials capable of accommodating the larger ions.

As part of my research, I have been working on simulating different aspects of heat and mass transfer in ultra-low permeability shale reservoirs. This is achieved by proposing a state-of-the-art multiple porosity approach with the objectives of decomposing shale matrix into different porosity systems of distinctive hydraulic and transport properties and applying appropriate physics to capture the complicated nature of oil and gas production in such reservoirs. The proposed model provides a more realistic picture of the production mechanisms, leading to a more reliable prediction of the reservoir behavior.

In recent years, lithium batteries, such as lithium-ion and lithium-sulfur batteries are leading the race towards meeting the energy and power requirements for the next generation of hybrid and electric vehicles. The majority of the research has focused on the performance improvement and degradation analysis of lithium batteries. Since the electrode microstructure affects the electrode properties, such as effective ionic conductivity and solid-phase diffusion, the cell performance and degradation phenomena (e.g. formation microcrack and solid electrolyte interphase) vary with the microstructure design. The objective of this research is to develop a microstructure-aware electrochemical model to conduct a fundament study of microstructural effects on cell performance and degradation. The influence of microstructure was observed from the cell performance and electrochemical impedance spectroscopy (EIS).

A novel laser diagnostic technique is being utilized to measure the thermochemistry of non-isobaric flows. Vibrational Raman scattering is being used to measure density and composition of major species, (CO2, O2, CO, N2, CH4, h4O, and h4) while rotational Raman scattering is being used to measure temperature. The independent measurements of density and temperature are allowing for the determination of pressure. This line imaging technique is being applied to flows with very high strain rates and Reynolds numbers emanating from a miniaturized combustor. Current work involves transitioning the miniaturized burner into a Moderate or Intense Low-oxygen Dilution (MILD) type of combustion in order to increase the burning efficiency.

Power system blackout is a fairly complicated phenomenon with a very low expectation of occurrence, but potentially devastating social and economic impacts. Studies have revealed that a series of cascading events, such as transmission line outages and malfunctions of protective relays have been among the main reasons of recent blackouts. My research is focused on the development of a wide area synchrophasor-based protection and control scheme to predict and mitigate cascade event outages to prevent major blackouts. Due to operation based on synchronized data from different part of system, the proposed method grasps better understanding of power grid behavior, such as stability margin, load change pattern, and generation reserve margin.

“Renewable” refers to the energy source being converted into electricity, not the significant quantities of raw materials—such as copper—required to implement and maintain the infrastructure performing the actual conversion. As the capacity for renewables increase, so too will the global demand for copper production. Unable to effectively regulate the relationship between copper mining and the renewable sector, the legal ramifications of developing substantial copper deposits, both in the U.S. and Mexico, illustrate the regulatory challenges arising from the energy-environment contradiction. Environmental issues challenge global resource development, constraining business objectives throughout the raw material supply-chain. Nevertheless, petroleum corporations utilize aspects of corporate sustainability to maximize efficiency and profitability—initiatives that could benefit the renewable energy industry and its dependence on copper production.

Pipelines are one of the most economical and reliable transportation methods for chemicals. However, catastrophes associated with leaks in pipelines have raised safety concerns. New software-based leak detection methods for natural gas pipelines are being designed. The natural gas pipeline leak detection problem is being tackled by considering the effect of thermal properties, leak detection in a pipeline with time-variant consumer usage, and as well as multiple leaks isolation and location estimation. Also, the practical issues associated with the leak detection such as pump station pressure oscillations and temperature changes are receiving consideration. Unlike the current Real-Time Transient Analysis method that needs extensive instrumentation, a new estimation algorithm is being applied to estimate the effect of thermal properties and eliminate their influence on leak detection without requiring new instrumentation.

In the past two years of Ph.D. research, I have been focusing on energy conversion and storage systems, of which the performances have been greatly improved by multi-dimensional nanostructured carbon materials. In particular, I have developed an easy-scale-up and low-cost method to produce 3D carbon nanotube sponges, which have been successfully used in energy conversion systems such as the proton exchange membrane fuel cells (PEMFC) and microbial fuel cells (MFC). Additionally, I am also working on designing new cathodes and anodes for Li-S batteries which have shown five times higher energy density than traditional Li-ion batteries – by using our carbon nanotube sponge-enabled novel electrodes.

MOFs, known as metal-organic frameworks, are a promising class of highly ordered porous materials with potential applications in gas storage, catalysis, and photoelectric devices. However, their practical applications in energy conversion and storage are hampered by their sensitivity to ambient moisture. My research is focused on the synthesis of stable MOFs for energy conversion and storage. It aimed at both the synthetic methodologies of stable MOFs and their clean energy related applications. Ultimately, we expect to commercialize our materials for energy conversion, harvesting, and storage.

As an emerging class of novel materials, metal-organic-frameworks (MOFs) have attracted great interest in the last few decades. Their modular nature endows these materials with structural diversity and tunable functionality. My research focus is on exploration of tailor-made MOFs for different energy related applications, including methane storage and CO2 capture. We have synthesized several MOFs with great stability, high performance and comparatively low cost, which make them potentially possible for commercial production and real-world applications.