The Georgia Tech EcoCommons (Photo by Nick Hubbard)
The College of Sciences at Georgia Tech is proud to launch Science for Georgia’s Tomorrow, a new center focused on research that aims to improve life across the state of Georgia.
“From resilient communities and agriculture, to health and sustainable energy resources, Science for Georgia's Tomorrow will focus on improving the lives of Georgians and their communities,” Dean Susan Lozier says.
An expansion of the College’s strategic plan, Science for Georgia's Tomorrow — Sci4GT, for short — will serve as a statewide fulcrum, fostering research in direct service to Georgia cities, counties, and communities.
The center specifically addresses critical health and climate challenges throughout Georgia, and aims to pave the way for increased public-private partnerships. The initiative will also expand access — broadening participation opportunities for Georgia students and communities to engage with research.
The search for an inaugural faculty director has commenced, and will be followed by a dedicated cluster hire in 2025, funded by the Office of the Provost. Dean Lozier, who also serves as a professor in the School of Earth and Atmospheric Sciences, has reserved funds from the College of Sciences Betsy Middleton and John Clark Sutherland Dean’s Chair to initiate the center.
Selected from a pool of 17 faculty proposals, two dedicated faculty cluster hires will focus on improving the health of Georgians and Georgia’s communities — and the resilience of humans and ecosystems to current and anticipated climate change in the state. Appointments will be sought across the College’s six schools.
“These proposals address themes that are critically important right now for Georgia Tech research growth: sustainability and climate, along with health and well-being,” says Julia Kubanek, Vice President for Interdisciplinary Research at Georgia Tech and a professor in the School of Biological Sciences and the School of Chemistry and Biochemistry. “This is an opportunity for Georgia to be a model for the nation on how to solve health disparities.”
“These new cluster hires will strengthen the College’s existing research programs,” Lozier adds. “They will also facilitate large collaborations across campus, and educate the next generation of scientists who will tackle these problems in Georgia and beyond.”
An adjacent effort, the new College of Sciences Rising Tide Program, is selecting promising early-career scientists for a two-year virtual mentorship initiative.
The Rising Tide Program will work in tandem with the Sci4GT cluster hire, complementing the strong culture of mentorship in the College, while providing a pathway to support local research at the Institute.
“Rising Tide aims to help the College recruit scientists with professional or lived experiences in the Southeast — or focused on research with particular relevance to the Southeast,” explains Rising Tide Director Alex Robel, associate professor in the School of Earth and Atmospheric Sciences. “One of our key goals is to bring more faculty to Georgia Tech who can contribute to research and teaching that’s particularly relevant to communities in Georgia.”
“The reach of Georgia Tech is global,” Lozier adds. “Our fingerprints are on discoveries and innovations that benefit people and their communities around the world. As researchers at a leading public university in the state of Georgia, we are also cognizant of the responsibility and opportunity to focus our efforts more intently here at home.”
The College has launched an internal leadership search for the Science for Georgia’s Tomorrow center, with an expected appointment to be announced in February 2025. The inaugural director will have the opportunity to shape the direction of this new initiative by:
All faculty who hold a majority appointment within the College of Sciences are eligible and encouraged to apply. Learn more and apply via InfoReady.
Initial support for Sci4GT is generously provided by the College of Sciences Betsy Middleton and John Clark Sutherland Dean's Chair fund. Cluster hire funding has been awarded by Provost Steven W. McLaughlin.
Sci4GT will also seek funding from state, national and international organizations, private foundations, and government agencies to expand impact. Philanthropic support will also be sought in the form of professorships, programmatic support for the center, and seed funding.
Written by: Selena Langner
Media contact: Jess Hunt-Ralston
A new machine learning (ML) model from Georgia Tech could protect communities from diseases, better manage electricity consumption in cities, and promote business growth, all at the same time.
Researchers from the School of Computational Science and Engineering (CSE) created the Large Pre-Trained Time-Series Model (LPTM) framework. LPTM is a single foundational model that completes forecasting tasks across a broad range of domains.
Along with performing as well or better than models purpose-built for their applications, LPTM requires 40% less data and 50% less training time than current baselines. In some cases, LPTM can be deployed without any training data.
The key to LPTM is that it is pre-trained on datasets from different industries like healthcare, transportation, and energy. The Georgia Tech group created an adaptive segmentation module to make effective use of these vastly different datasets.
The Georgia Tech researchers will present LPTM in Vancouver, British Columbia, Canada, at the 2024 Conference on Neural Information Processing Systems (NeurIPS 2024). NeurIPS is one of the world’s most prestigious conferences on artificial intelligence (AI) and ML research.
“The foundational model paradigm started with text and image, but people haven’t explored time-series tasks yet because those were considered too diverse across domains,” said B. Aditya Prakash, one of LPTM’s developers.
“Our work is a pioneer in this new area of exploration where only few attempts have been made so far.”
[MICROSITE: Georgia Tech at NeurIPS 2024]
Foundational models are trained with data from different fields, making them powerful tools when assigned tasks. Foundational models drive GPT, DALL-E, and other popular generative AI platforms used today. LPTM is different though because it is geared toward time-series, not text and image generation.
The Georgia Tech researchers trained LPTM on data ranging from epidemics, macroeconomics, power consumption, traffic and transportation, stock markets, and human motion and behavioral datasets.
After training, the group pitted LPTM against 17 other models to make forecasts as close to nine real-case benchmarks. LPTM performed the best on five datasets and placed second on the other four.
The nine benchmarks contained data from real-world collections. These included the spread of influenza in the U.S. and Japan, electricity, traffic, and taxi demand in New York, and financial markets.
The competitor models were purpose-built for their fields. While each model performed well on one or two benchmarks closest to its designed purpose, the models ranked in the middle or bottom on others.
In another experiment, the Georgia Tech group tested LPTM against seven baseline models on the same nine benchmarks in a zero-shot forecasting tasks. Zero-shot means the model is used out of the box and not given any specific guidance during training. LPTM outperformed every model across all benchmarks in this trial.
LPTM performed consistently as a top-runner on all nine benchmarks, demonstrating the model’s potential to achieve superior forecasting results across multiple applications with less and resources.
“Our model also goes beyond forecasting and helps accomplish other tasks,” said Prakash, an associate professor in the School of CSE.
“Classification is a useful time-series task that allows us to understand the nature of the time-series and label whether that time-series is something we understand or is new.”
One reason traditional models are custom-built to their purpose is that fields differ in reporting frequency and trends.
For example, epidemic data is often reported weekly and goes through seasonal peaks with occasional outbreaks. Economic data is captured quarterly and typically remains consistent and monotone over time.
LPTM’s adaptive segmentation module allows it to overcome these timing differences across datasets. When LPTM receives a dataset, the module breaks data into segments of different sizes. Then, it scores all possible ways to segment data and chooses the easiest segment from which to learn useful patterns.
LPTM’s performance, enhanced through the innovation of adaptive segmentation, earned the model acceptance to NeurIPS 2024 for presentation. NeurIPS is one of three primary international conferences on high-impact research in AI and ML. NeurIPS 2024 occurs Dec. 10-15.
Ph.D. student Harshavardhan Kamarthi partnered with Prakash, his advisor, on LPTM. The duo are among the 162 Georgia Tech researchers presenting over 80 papers at the conference.
Prakash is one of 46 Georgia Tech faculty with research accepted at NeurIPS 2024. Nine School of CSE faculty members, nearly one-third of the body, are authors or co-authors of 17 papers accepted at the conference.
Along with sharing their research at NeurIPS 2024, Prakash and Kamarthi released an open-source library of foundational time-series modules that data scientists can use in their applications.
“Given the interest in AI from all walks of life, including business, social, and research and development sectors, a lot of work has been done and thousands of strong papers are submitted to the main AI conferences,” Prakash said.
“Acceptance of our paper speaks to the quality of the work and its potential to advance foundational methodology, and we hope to share that with a larger audience.”
Bryant Wine, Communications Officer
bryant.wine@cc.gatech.edu
Portrait of Tequila Harris
Tequila A.L. Harris, a professor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech, leads energy and manufacturing initiatives at the Strategic Energy Institute. Her research explores the connectivity between the functionality of nano- to macro-level films, components, and systems based on their manufacture or design and their life expectancy, elucidating mechanisms by which performance or durability can be predicted. She uses both simulations and experimentation to better understand this connectivity.
By addressing complex, fundamental problems, Harris aims to make an impact on many industries, in particular energy (e.g., polymer electrolyte membrane fuel cells), flexible electronics (e.g., organic electronics), and clean energy (e.g., water), among others.
Harris has experience in developing systematic design and manufacturing methodologies for complex systems that directly involve material characterization, tooling design and analysis, computational and analytical modeling, experimentation, and system design and optimization. Currently, her research projects focus on investigating the fundamental science associated with fluid transport, materials processing, and design issues for energy/electronic/environmental systems. Below is a brief Q&A with Harris, where she discusses her research and how it influences the energy and manufacturing initiatives at Georgia Tech.
In graduate school, I aimed to become a roboticist but shifted my focus after realizing I was not passionate about coding. This led me to explore manufacturing, particularly scaled manufacturing processes that transform fluids into thin films for applications in energy systems. Subsequently, my expertise is in coating science and technology and manufacturing system development.
We often ask how we can process materials more cost-effectively and create complex architectures that surpass current capabilities. In energy systems, particularly with fuel cells, reducing the number of manufacturing steps is crucial, as each additional step increases costs and complexity. As researchers, we focus on understanding the implications of minimizing these steps and how they affect the properties and performance of the final devices. My group studies these relationships to find innovative manufacturing solutions. A major challenge in the manufacture of materials lies in scaling efficiently while maintaining performance and keeping costs low enough for commercial adoption. This is a pressing issue, especially for enabling technologies such as batteries, fuel cells, and flexible electronics needed for electric vehicles, where the production volumes are on the order of billions per year.
What interests me most is the inherent possibility of advancing energy technologies holistically, from materials sourcing and materials production to public policy. More specifically, my interests are in understanding how we can scale the manufacture of burgeoning technologies for a variety of areas (energy, food, pharmaceuticals, packaging, and flexible electronics, among others) while reducing cost and increasing production yield. In this regard, we aim to incorporate artificial intelligence and machine learning in addition to considering limitations surrounding the production lifecycle. The challenges that exist to meet these goals cannot be done in a silo but rather as part of interdisciplinary teams who converge on specific problems. Georgia Tech is uniquely positioned to make significant impacts in the energy and manufacturing ecosystem, thanks to our robust infrastructure and expertise. With many manufacturers relocating to Georgia, particularly in the "energy belt" for EVs, batteries, and recycling facilities, Georgia Tech can serve as a crucial partner in advancing these industries and their technologies.
The global impact of advancing manufacturing technologies is significant for processing at relevant economy of scales. To meet such demands, we cannot always rely on existing manufacturing know-how. The Harris group holds the intellectual property on innovative processes that allow for the faster fabrication of individual or multiple materials, and that exhibit higher yields and improved performance than existing methods. Improvements in manufacturing systems often result in reduced waste, which is beneficial to the overall materials development ecosystem. Another global and societal benefit is workforce development. The students on my team are well-trained in the manufacture of materials using tools that are amenable to the most advanced and scalable manufacturing platform, roll-to-roll manufacturing, with integrated coating and printing tools. This unique skill set equips our students to thrive and become leaders in their careers.
By leveraging the new modular pilot-scale roll-to-roll manufacturing facility that integrates slot die coating, gravure/flexography printing, and inkjet printing, I plan to continue reaching out to faculty and industrial partners to find avenues for us to collaborate on a variety of interdisciplinary projects. The goal is to create groups that can help us advance materials development more rapidly by working as a collective from the beginning, versus considering scalable manufacturing pathways as an afterthought. By bringing interdisciplinary groups (chemists, materials scientists, engineers, etc.) together early, we can more efficiently and effectively overcome traditional delays in getting materials to market or, worse, the inability to push materials to market (which is commonly known as the valley of death). This can only be achieved by dismantling barriers that hinder early collaboration. This new facility aims to foster collaborative work among stakeholders, promoting the integrated development and characterization of various materials systems and technologies, and ultimately leading to more efficient manufacturing practices.
I enjoy cooking and exploring my creativity in this space by combining national and international ingredients to make interesting and often delicious fusion cuisines. I also enjoy roller skating, cycling, and watching movies with my family and friends.
From a professional standpoint, my research team influences me the most. After I present them with a problem, they are encouraged and expected to think beyond our initial starting point. This ability to freely think and conceive of novel solutions sparks many new ideas on which to build future ideas. The best cases have kept me up at night, inspiring me to think about how to approach new problems and funding opportunities. I carry their experiences and challenges with me. Their influence on me is profound and is fundamentally why I am a professor.
Priya Devarajan || SEI Communications Program Manager
Cybersecurity researchers have discovered new vulnerabilities that could provide criminals with wireless access to the computer systems in automobiles, aircraft, factories, and other cyber-physical systems.
The computers used in vehicles and other cyber-physical systems rely on a specialized internal network to communicate commands between electronics. Because it took place internally, it was traditionally assumed that attackers could only influence this network through physical access.
In collaboration with Hyundai, researchers from Georgia Tech’s Cyber-Physical Systems Security Research Lab (CPSec) observed that threat models used to evaluate the security of these technologies were outdated.
The team, led by Ph.D. student Zhaozhou Tang, found that vehicle technology advancements allowed attackers to launch new attacks, improve existing attacks, and circumvent current defense systems.
For example, Tang’s findings included the possibility for attackers to remotely compromise the computers used in cars and aircraft through Wi-Fi, cellular, Bluetooth, and other wireless channels.
“Our job was to thoroughly review existing information and find ways to protect against these attacks,” he said. “We found new threats and proposed a defense system that can protect against the new and old attacks.”
In response to their findings, the team developed ERACAN, the first comprehensive defense system against this new generation of attackers. Designed to detect new and old attacks, ERACAN can deploy defenses when necessary.
The system also classifies the attacks it reacts to, providing security experts with the tools for detailed analysis. It has a detection rate of 100% for all attacks launched by conventional methods and detects enhanced threat models 99.7% of the time.
The project received a distinguished paper award at the 2024 ACM Conference on Computer and Communications Security (CCS 24) held in Salt Lake City. Tang presented the paper at the October conference.
“This was Zhaozhou’s first paper in his Ph.D. program, and he deserves recognition for his groundbreaking work on automotive cybersecurity,” said Saman Zonouz, associate professor in the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering.
The U.S. Department of Homeland Security has designated the transportation sector as one of the nation’s 16 critical infrastructure sectors. Ensuring its security is vital to national security and public safety.
“Modern vehicles, which rely heavily on controller area networks for essential operations, are integral components of this infrastructure,” said Zonouz. “With the increasing sophistication of cyberthreats, safeguarding these systems has become critical to ensuring the resilience and security of transportation networks.”
This paper introduced to the scientific community the first comprehensive defense system to address advanced threats targeting vehicular controller area networks.
The CPSec team is putting the technology it has developed into practice in collaboration with Hyundai America Technical Center, Inc., which sponsors the work. Tang hopes ERACAN’s success will raise awareness of these new threats in the research community and industry.
“It will help them build future defenses,” he said. “We have demonstrated the best practice to defend against these attacks.”
Tang received his bachelor’s degree at Georgia Tech, where he first performed security-related work for the automobile industry. While working with Zonouz on his master’s degree, he decided to change course and pursue research initiatives like vehicle security in a Ph.D. program.
“It is interesting how it came full circle,” he said. “I will continue on this path of automobile security throughout my Ph.D.”
ERACAN: Defending Against an Emerging CAN Threat Model, was written by Zhaozhou Tang, Khaled Serag from the Qatar Computing Research Institute, Saman Zonouz, Berkay Celik and Dongyan Xu from Purdue University, and Raheem Beyah, professor and dean of the College of Engineering. The CPSec Lab is a collaboration between the School of Cybersecurity and Privacy and the School of Electrical and Computer Engineering.
John Popham
Communications Officer II
School of Cybersecurity and Privacy
Department of Energy’s (DOE’s) Office of Science Under Secretary Geri Richmond with Georgia Tech Researchers
As head of the Department of Energy’s (DOE’s) Office of Science, the nation’s largest federal sponsor of physical sciences research, Under Secretary Geri Richmond understands the vital role of higher education in advancing U.S. science and innovation. On Monday, Nov. 18, she visited Georgia Tech with Chief of Staff in the Office of the Under Secretary for Science and Innovation Ariel Marshall, Ph.D. Chem 14, to meet with students and faculty and discuss future opportunities for collaboration.
During the visit, Richmond and Marshall toured Dr. Thomas Orlando’s electron and photo induced chemistry on surfaces lab; the Invention Studio; Dr. Akanksha Menon’s water-energy research lab; and the AI Maker Space.
Richmond also joined the Women+ in Chemistry student group for a roundtable discussion. An advocate for underrepresented groups in STEM fields, Richmond is the founding director of the Committee on the Advancement of Women Chemists (COACh). COACh is a grassroots organization dedicated to ensuring equal opportunities for all in science.
Georgia Tech’s longstanding partnership with the DOE is centered on research and technology development aimed at advancing energy systems and promoting sustainability. The Institute plays a key role in the DOE’s national initiatives, contributing to transformative work in energy efficiency, renewable energy, nuclear power, and environmental sustainability. Through joint research programs, grants, and initiatives, Georgia Tech continues to drive innovation and push the boundaries of energy solutions for a sustainable future.
Top Row (Left to Right): Michael Biehler, Winner of 2024 James G. Campbell Fellowship, Erin Phillips & Sanggyun Kim - 2024 Spark Award Winners
Bottom Row (Left to Right): Keun Hee Kim, Richard Asiamah, Erik Barbosa - 2024 Spark Award Winners
The Strategic Energy Institute and the Energy, Policy, and Innovation Center at Georgia Tech are proud to announce the winners of the James G. Campbell Fellowship and Spark Awards for 2024.
Michael Biehler, a fifth-year Ph.D. student in the H. Milton Stewart School of Industrial and Systems Engineering, has been selected as the recipient of the 2024 James G. Campbell Fellowship. The Fellowship is an annual award given to a Georgia Tech graduate student studying renewable energy systems. Candidates are nominated by their advisors in recognition of their exceptional academic achievements in the field of renewable energy.
Biehler’s research leverages multi-modal machine learning to tackle critical challenges in manufacturing, such as enhancing energy efficiency in manufacturing processes. He is advised by Jianjun (Jan) Shi, Carolyn J. Stewart Chair and Professor in the School of Industrial and Systems Engineering.
“I consider this award an incredible honor, and the support means a lot to me, especially with the recent arrival of our second daughter—it will make a significant difference for us,” says Biehler.
The Annual Spark Award recognizes current graduate students who have exhibited outstanding leadership in promoting student engagement with energy research at Georgia Tech, with evidence of broader impacts and service/leadership. The Spark Award recipients for 2024 include Georgia Tech graduate students Richard Asiamah, Erik Barbosa, Keun Hee Kim, Sanggyun Kim, and Erin Phillips.
Richard Asiamah is a third-year Ph.D. student in the School of Electrical and Computer Engineering (ECE). His research focuses on power systems optimization, emphasizing the efficient integration of renewable energy resources into the electricity grid. Asiamah has recently worked as a graduate electrical engineering intern at the National Renewable Energy Laboratory in Golden, Colorado, and is currently serving as the president of the ECE Graduate Students’ Organization.
Erik Barbosa is pursuing a doctorate in mechanical engineering and works under Akanksha Menon, assistant professor in the Woodruff School of Mechanical Engineering. His work in the Water Energy Research Lab focuses on utilizing inorganic salt hydrates to develop thermochemical energy storage, ranging from the material level to system scale, to decarbonize heat for building applications. Barbosa has been actively engaged with mentoring undergraduate students and high schoolers by exposing them to innovative technologies that decarbonize energy.
Keun Hee Kim is a Ph.D. candidate in the Woodruff School. Kim’s research focuses on developing solid polymer electrolytes and artificial interlayers for lithium metal batteries, and synthesizing oxygen evolution reaction and oxygen reduction reaction catalyst materials for proton exchange membrane fuel cells and water electrolyzers.
Sanggyun Kim is a fourth-year Ph.D. student in materials science and engineering, advised by Juan-Pablo Correa-Baena, assistant professor in the School of Materials Science and Engineering. Kim’s research focuses on understanding the complex interfacial interactions between hybrid organic-inorganic halide perovskite films and newly designed charge transport layers in perovskite solar cells (PSCs). His goal is to drive progress in solar energy technology by integrating novel polymer- and molecule-based interlayers, improving the efficiency and stability of PSCs to support more sustainable photovoltaic solutions.
Erin Phillips is a doctoral student in the School of Chemistry and Biochemistry. Her research addresses difficulties associated with lignin valorization, which includes controlling the isolation of lignin from the original irregular lignocellulose structure and depolymerizing lignin into aromatic monomer units via mechanocatalysis. These aromatics can further be valorized as renewable sources for the creation of biofuels and other green chemicals. Phillips is currently serving as the president of the Technical Association of the Pulp and Paper Industry (TAPPI) student chapter at Georgia Tech.
News Contact: Priya Devarajan || SEI Communications Program Manager
As greenhouse gases accumulate in the Earth’s atmosphere, scientists are developing technologies to pull billions of tons of carbon dioxide (CO2) from the air and inject it deep underground.
The idea isn’t new. In the 1970s, Italian physicist Cesare Marchetti suggested that the carbon dioxide polluting the air and warming the planet could be stored underground. The reality of how to do it cost-effectively and safely has challenged scientists for decades.
Geologic carbon storage — the subterranean storage of CO2 — comes with significant challenges, most importantly, how to avoid fracturing underground rock layers and letting gas escape into the atmosphere. Carbon, a gas, can behave erratically or leak whenever it’s stored in a compressed space, making areas geologically unstable and potentially causing legal headaches for corporations that invest in it. This uncertainty, coupled with the expense of the carbon capture process and its infrastructure, means the industry needs reliable predictions to justify it.
Georgia Tech researcher and Georgia Research Alliance Eminent Scholar Felix J. Herrmann has an answer. His lab, Seismic Laboratory for Imaging and Modeling (SLIM), uses advanced artificial intelligence (AI) techniques to create algorithms that monitor and optimize carbon storage. The algorithms work as “digital twins,” or digital replicas of underground systems, facilitating the safe, efficient storage of CO2 underground.
“The trick is you want the carbon to stay put — to avoid the risk of, say, triggering an earthquake or the carbon leaking out,” said Herrmann, professor in the School of Earth and Atmospheric Sciences and the School of Electrical and Computer Engineering. “We’re developing a digital twin that allows us to monitor and control what is happening underground.”
Predicting the Best Place
Waveform Variational Inference via Subsurface Extensions with Refinements (WISER) is an algorithm that uses sound waves to analyze underground structures. WISER runs on AI, enabling it to work more efficiently than most algorithms while remaining computationally feasible. To improve accuracy, WISER makes small adjustments using sound wave physics to show how fast sound travels through different materials and where there’s variation in underground layers. This helps to create detailed, reliable images of underground areas for better predictions of carbon storage.
WISER allows researchers to work with uncertainties, which is vital for understanding the risk of these underground storage projects.
Scaling the Algorithm
While Herrmann’s lab has been working to apply neural networks to seismic imaging for years now, WISER required them to increase the networks’ scale. Making multiple predictions is a much larger problem that requires a bigger, more potent network, but these types of neural networks only run on graphics processing units (GPUs), which are known for speed but are limited in memory.
To optimize the GPU, Rafael Orozco, a computational science and engineering Ph.D. student, created a new type of neural network that can train with very little memory. This open-source package, InvertibleNetworks, enables the network to train on very large inputs and create multiple output images conditioned on the observed seismic data.
WISER’s fundamental innovation is for the lab’s next concept: creating digital twins for carbon storage. These twins can act as monitoring systems to optimize and mitigate risks of carbon storage projects.
Devising the Digital Twin
Digital twins are dynamic virtual models of objects in the real world, capable of replicating their behavior and performance. They rely on real-time data to evolve and have been used to replicate factories, cities, spacecraft, and bodies, to make informed decisions about healthcare, maintenance, production, supply chains, and — in Herrmann’s case — geologic carbon storage.
Herrmann and his team have developed an “uncertainty-aware” digital twin. That means the tool can manage risks and make decisions in an uncertain, unseen environment — because it’s been designed to recognize, quantify, and incorporate uncertainties in CO2 storage.
Probing the Unseen
Subsurface conditions are diverse and complex, making the management of greenhouse gas storage a delicate process. Without careful monitoring, the injection of CO2 can increase pressure in rock formations, potentially fracturing the cap rock that is supposed to keep the gas underground.
“The digital twin addresses this through simulations in tandem with observations,” said Herrmann, whose team linked two different scientific fields — geophysics and reservoir engineering — for a more comprehensive understanding of the subsurface environment. Specifically, they combined geophysical well observations with seismic imaging.
Geophysical well observation involves drilling a hole in the subsurface in a geological area of interest and collecting data by lowering a probe into the borehole to take measurements. Seismic imaging, on the other hand, uses acoustic waves to create images based on the analysis of wave vibrations.
“Bridging the gap between different fields of research and combining various data sources allows our digital twin to provide a more accurate and detailed picture of what’s happening underground,” Herrmann said.
To integrate and leverage these diverse datasets built from observations and simulations, the team used advanced AI techniques like simulation-based inference and sequential Bayesian inference, a method of updating information as more data becomes available. The ongoing learning allows researchers to quantify uncertainties in the subsurface environment and predict how that system will respond to CO2 injection. The digital twin updates its understanding as new data becomes available.
Making Informed Decisions
Herrmann’s team tested the digital twin, simulating different states of an underground reservoir, including permeability, which is the measure of how easily fluids flow through rock. The goal was to find the maximum injection rate of CO2 without causing fractures in the cap rock.
“The work highlights how dynamic digital twins can play a key role in mitigating the risks associated with geologic carbon storage,” said Herrmann, whose research group is supported in part by large oil and gas companies, including Chevron and ExxonMobil. “Companies are now in the process of starting large offshore projects for which the digital twin is being developed.”
But there is still plenty of work to be done, he added. For instance, the digital twin can monitor the subsurface and provide critical information about that uncertain environment. It can inform. But it still needs adjustments by humans for each new CO2 injection site, and Herrmann and his team are working on further developing the technology — giving the digital twins the ability to quickly replicate themselves so they can be deployed massively and quickly to meet the demands of mitigating climate change.
“Our aim is to make them smarter,” Herrmann said. “To make them more adaptable, so they can control CO2 injections, become more responsive to risks, and adapt to a wide range of complex situations in real time.”
Writers: Jerry Grillo and Tess Malone
Media Contact: Tess Malone | tess.malone@gatech.edu
EnergyHack @GT is a hackathon designed to bring together passionate students to develop solutions addressing the critical challenges in the energy industry. Over the course of 36 hours, participants will collaborate in teams to brainstorm, design, and prototype projects (for example, AI/ML projects, web-based tools, mobile applications, etc.) that promote sustainable practices based on diverse problem statements. The projects will be evaluated by an esteemed panel of judges.