At the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab), researchers are investigating and installing digital twins of highly sophisticated instruments that have the potential to dramatically speed up scientific discoveries.
A digital twin is a dynamic, virtual replica of a complex physical system such as a battery, manufacturing component, or a car. Digital twins have been used for decades in aerospace, healthcare, and manufacturing. While traditional simulations model a system based on fixed inputs, a digital twin uses real-time data from the physical system to model performance and predict future behavior.
What is a digital twin?
Digital twins complement complex instruments by creating a continuous feedback loop. This allows the digital twin to adjust autonomously using real-time data updates and measurements — scientists accomplish this by combining advanced simulation, advanced sensors, and AI technology. The real-world counterpart delivers the precise measurements that feed and validate the models, while the digital twins use those measurements to explore scenarios and suggest real-time updates that would be impractical or time-consuming to complete without them.
What’s next for digital twins?
While some of Berkeley Lab’s digital twins are already set to guide experiments, others are still in research and development. Researchers across disciplines are adapting methods from existing physics and energy models to unlock more powerful experiments.
Developing biological digital twins to accelerate biofuel production
The Advanced Biofuels and Bioproducts Process Development Unit (ABPDU) is developing a biological digital twin to model the scaled production of lipids for jet fuel from engineered microbes. Modeling living microbes within a bioreactor is more complex than modeling purely physical or chemical systems, since microorganisms constantly grow, divide, produce materials, and die, all within communities that interact in ways that are more difficult to predict.
Funded by DOE’s Advanced Fuels and Feedstocks Office, the researchers are developing novel methods to obtain imaging and phenotypic data to integrate with large, genome-wide omics datasets. Separately, the team will combine mechanistic models, which evaluate bioreactor design and performance, with machine-learning models that can analyze datasets to develop their digital twin. This hybrid approach uses high-performance computing facility Perlmutter at NERSC for large-scale simulations and will yield a richly structured ensemble model — much more predictive of technology performance than either type of model alone — allowing it to capture the complicated dynamics of microbial cultures. This research is a natural next step in ABPDU’s efforts to advance the science of scale-up and energy innovation.
“The global economy is changing as countries and markets rush to put biology to work in new ways. The U.S. has both the expertise and the opportunity to use AI and machine learning to gain a technological edge,” said James Gardner, Program Manager at the ABPDU. “This could help secure new intellectual property and grow domestic biomanufacturing capacity.”