In this episode of the Applied Imperative podcast, Dale Goulding interviews John Nixon—vice president, global strategy, energy chemicals and infrastructure—and William Hahn—director solutions consulting—to explore the intersection of artificial intelligence, model-based systems engineering (MBSE), and next-generation nuclear energy.
Stream the full episode here.
AI is fueling a nuclear renaissance
The rise of AI and data centers is fueling the demand for nuclear energy. “We are in the process of experiencing a nuclear renaissance,” John said.
Companies ranging from Google to X-energy are investing in nuclear with a goal of scalable and carbon-free power to keep pace with digital transformation, John said.
Siemens is at the middle of this renaissance and is helping nuclear projects come to life with its digital portfolio.
MBSE meets industrial AI
The biggest barrier to adopting MBSE is how tedious the process can be. William pointed to his experience at NASA, where system modeling efforts were often disconnected and time-consuming. This made widespread adoption difficult.
William offers his perspective on how AI can change that process.
By implementing AI in MBSE, you will be able to tell the AI model to, “‘Take all this legacy data and build me a system model,’” in SysML, William said.
Instead of manually building and updating system models, engineers could prompt AI to handle that complexity.
The capability, William said, would “greatly accelerate the adoption of MBSE by speeding up the process.” John noted that Siemens is already seeing companies saving “30, 40, and 50% in engineering time” when they combine AI with MBSE.
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