Texas A&M Energy Institute Graduate Fellowships
The Texas A&M Energy Institute offers graduate fellowships to reward excellence in energy research, promote research that is important to our energy future, and encourage students to pursue careers in energy.
2023 Graduate Fellowships
The Texas A&M Energy Institute is pleased to announce the call for applications for the 2023 Texas A&M Energy Institute Graduate Fellowships. These graduate fellowships recognize outstanding energy research work performed by Ph.D. students under the supervision of Affiliated Faculty Members of the Texas A&M Energy Institute.
Up to five fellowships are available this year in the amount of $5,000 each. The fellowships, with a term of September through May, will be awarded in two equal payments, one in September 2023 and one in January 2024.
Five areas of interest will be given special consideration for this fellowship:
- Energy Digitization
- Resilient Energy and Manufacturing Supply Chains
- Carbon Capture, Utilization and Storage and the Hydrogen Economy
- Geothermal Energy
- The Energy Transition
All Ph.D. students who meet the following criteria may apply for a graduate fellowship:
- Be enrolled at Texas A&M University (College Station), Texas A&M University at Galveston, Texas A&M University at Qatar, and the Texas A&M University School of Law;
- Cumulative GPA of 3.7 or higher;
- Enrolled full time (9 hours or more);
- In academic good standing;
- Perform energy research; and
- Have a primary advisor who is an Affiliated Faculty Member of the Texas A&M Energy Institute. energy.tamu.edu/find-faculty-experts
The application should be submitted as separate PDF files and include the following:
- Nomination letter by the applicant’s primary faculty advisor (1 page)
- Description of applicant’s energy research (1 page)
- Applicant’s curriculum vitae (up 2 pages)
- Career Goals (up to 100 words)
11:59 p.m. on May 31, 2023
July 1, 2023
Sept 2023 – May 2024
Submission of Applications
Applications are due by 11:59 p.m. on May 31, 2023 and should be submitted electronically at https://forms.gle/dzgNXk9tz6p73WHj8
All questions should be addressed to the Texas A&M Energy Institute (firstname.lastname@example.org; 979-458-1644).
Spotlight on Student Research
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.