Reducing the amount of carbon released to the atmosphere is a political priority. The current U.S. administration plans to achieve net-zero carbon emissions…
Reducing the amount of carbon released to the atmosphere is a political priority. The current U.S. administration plans to achieve net-zero carbon emissions from the power grid by 2035, and industry-wide by 2050.
To achieve this goal, a variety of techniques are being developed that capitalize on the efficiency of AI to fight against climate change. For power plants, developing techniques to reduce carbon emissions, carbon capture, and storage processes requires a detailed understanding of the associated fluid mechanics and chemical processes throughout the facility. This requires scientifically accurate simulations of fluid mechanics, heat transfer, chemical reactions, and their degree of interaction.
Figure 1. To achieve net-zero carbon emissions, digital twins can be used to scale up several carbon capture, storage, and removal systems. Credit: National Energy Technology Laboratory media team
One important focus for industrial use cases is the development of more efficient fuel conversion devices. The goal is to create devices that are more flexible so that the equipment can integrate with renewable resources in a more reliable way.
It is crucial to have better design optimization, uncertainty quantification, and accurate digital twins so that design and control of energy conversion devices can be handled adequately without causing billions of dollars in damage. AI is a natural choice to be used for developing such digital twins that can provide near real-time predictions without compromising the accuracy.
This post explains how the physics-informed machine learning (ML) framework, NVIDIA Modulus, is being used to bypass the conventional methods to enable large-scale scientific modeling, and to develop power plant digital twins that can help move ..
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