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Siemens Energy is enlisting NVIDIA technology to develop a new workflow to reduce the frequency of planned power plant shutdowns while maintaining safety. Real-time data — water inlet temperature, pressure, pH, gas turbine power and temperature — is preprocessed to compute pressure, temperature and velocity of both water and steam. The pressure, temperature and velocity are fed into a physics-ML model created with the NVIDIA Modulus framework to simulate precisely how steam and water flow through the pipes in real time.
The flow conditions in the pipes are then visualized with NVIDIA Omniverse, a virtual world simulation and collaboration platform for 3D workflows. Omniverse scales across multi-GPUs to help Siemens Energy understand and predict the aggregated effects of corrosion in real time.
Accelerating Industrial Digital Twin Development
Using NVIDIA software frameworks, running on NVIDIA A100 Tensor Core GPUs, Siemens Energy is simulating the corrosive effects of heat, water, and other conditions on metal over time to fine-tune maintenance needs. Predicting maintenance more accurately with machine learning models can help reduce the frequency of maintenance checks without running the risk of failure. The scaled Modulus PINN model was run on AWS Elastic Kubernetes Service (EKS) backed by P4d EC2 instances with A100 GPUs.
Building computational fluid dynamics models for each heat recovery steam generator (HRSG), takes as long as eight weeks each to estimate corrosion within pipes at HRSGs plants. This process is required for a portfolio of more than 600 units. Faster workflow using NVIDIA technologies can enable Siemens Energy to accelerate corrosion estimation from weeks to hours.