Powering safety and sustainability in energy with industrial and scientific digital twins.
Energy companies can develop physically accurate, AI-enabled digital twins to design, simulate, and optimize products, equipment, and processes in real time before going to production. With virtual assets perfectly synced with their real-world counterparts, this approach maximizes energy production, reduces unplanned downtime, and lowers costs for both enterprises and their customers.
Learn how Siemens Energy developed AI surrogate models for complex engineering systems using NVIDIA Modulus, an open-source framework for building, training, and fine-tuning physics-informed machine learning (physics-ML) models.
Discover how the new CUDA® math library NVIDIA cuDSS 0.2.0 was integrated into Honeywell Unisim Design to achieve up to a 78X performance increase over traditional sparse linear equation solvers.
Simulating power plants, grid assets, and distributed energy resources.
Energy companies are building digital twins to accurately model industrial equipment, processes, and workflows—–such as power plants, wind farms, solar grids, and more—for safe, autonomous operations. The solution uses NVIDIA Modulus, a framework for developing physics-informed machine learning (physics-ML) models, and NVIDIA Omniverse, the platform for developing OpenUSD applications for industrial digitalization and physical AI simulation. Using industrial digital twins, utilities can enhance safety, improve power generation, and save billions of dollars per year in maintenance.
Predicting renewable energy generation and speeding up workloads.
As 3D simulations increase in size and scale, accelerated computing delivers higher fidelity and performance for true-to-reality climate, weather, and engineering modeling. This includes estimating the impact of cloud cover on solar energy generation, wind speeds for wind energy generation, and computational fluid dynamics (CFD) workloads. Faster time to results leads to less electricity consumed, reducing operating costs and improving energy efficiency.
Protecting worker health and safety while maximizing productivity.
Edge AI enables energy providers to deliver functional safety and security. NVIDIA IGX Orin™ is an industrial-grade platform that combines enterprise-level hardware, software, and support. With IGX, enterprises can ensure proactive safety in autonomous environments, utilize high-performance and energy-efficient systems built for low-latency, real-time applications, and apply the latest in embedded device security, remote provisioning, and management.
Detecting leaks and intrusions with edge AI and geospatial data analysis.
Energy companies are using edge AI and geospatial data to actively monitor oil and gas infrastructure, ensuring real-time detection of risks from leaks, digging, vehicles, and unauthorized access. By reducing false positives and auto-generating alerts, pipeline operators save costs from oil or gas loss and unplanned downtime.
Identifying potential hazards from industrial equipment and processes.
Protecting workers is critical on offshore oil rigs and other industrial sites using heavy machinery. Intelligent video analytics uses computer vision and AI at the edge to identify potential hazards, determine maintenance and repair needs, and enable real-time alerts to prevent industrial accidents.
Success Stories
Learn how AI is building industrial digital twins for more efficient industrial operations in energy.
AWS
Siemens Energy developed AI surrogate models for complex engineering systems using NVIDIA Modulus, an open-source framework for building, training, and fine-tuning physics-ML models. The systems include bushings used in power grids and gas-insulated switchgears.
Siemens Energy
AWS Batch and the NVIDIA Earth-2 platform are enabling near-real-time predictions for wind energy production, combining machine learning weather models with physics-based simulations for rapid, cost-efficient results.
Honeywell
Discover how Honeywell Unisim Design integrated the NVIDIA cuDSS 0.2.0 (Preview) to achieve up to a 78X performance increase over traditional sparse linear equation solvers for process simulation.
Gurobi Optimizer, which identifies optimal solutions for complex nonlinear problems for over 1,200 global customers across industries, achieved a 23% faster runtime while using 46% less energy on the NVIDIA Grace™ CPU.
Sygnia
Sygnia announced a collaboration with NVIDIA to transform cybersecurity in energy and industrial with AI-powered edge solutions, combining Sygnia’s Pathfinder sensors and the Velocity XDR platform with NVIDIA BlueField DPUs and NVIDIA Morpheus cybersecurity AI framework.
The adoption of the metaverse can greatly accelerate the development of new facilities by allowing stakeholders to visualize and collaborate on design in a more immersive and interactive way. This technology enables real-time simulation of various scenarios and provides valuable insights. Additionally, the adoption of the metaverse can reduce the need for physical prototyping or travel due to collaboration across multiple devices and systems with better data availability.
— Maurizio Galardo, CTO 3D Visualization, Schneider Electric/AVEVA
We will continue to collect data, not just on how our wind turbines operate, but also weather forecasting, site planning, and other areas to optimize wind turbine sites. We're exploring augmented reality and extended reality as wind turbines are complicated machines with many types of failure modes. It's imperative to make sure the wind turbines operate safely and service technicians know how to do service repairs in the right way.
— Lasse Lundberg Nowack, Vice President, Engineering Development Power Solutions, Vestas
We started using virtual reality and augmented reality for site visits and visualization of safety studies. We are at the tipping point where to deliver these projects the ecosystem will have to come together.
— Vishal Mehta, Senior Vice President, Digital, Worley
NVIDIA GPUs are faster, higher resolution, and less costly than CPUs for forecasting weather and predicting renewable power. This is important to help reduce carbon-based power generation, enable efficient grid management, and lower energy costs.
— Gene Pache, Founder, President, and CEO, TempoQuest
AceCAST on one NDm A100 (GPU) virtual machine with eight NVIDIA GPUs runs 7 percent faster at 75 percent lower cost than Weather and Research Forecasting (WRF) on 16x HBv3 (CPU) virtual machines.
— Amirreza Rastegari, Senior Program Manager, Azure Specialized Compute, Microsoft
Technology
Learn about AI and high-performance computing (HPC) hardware, software, and networking solutions for surface operators.
NVIDIA OVX™ systems are purpose-built to power the creation and operation of real-time, physically accurate, AI-enabled Omniverse applications at data center scale. Digital twins revolutionize how enterprises design, simulate, and optimize complex systems and processes.
NVIDIA Omniverse is a platform for developing OpenUSD applications for industrial digitalization and physical AI simulation. Omniverse combined with Modulus, a framework for developing physics-ML models, enables digital twins for wind farms, power plants, electric grids, and someday Earth itself.
With NVIDIA AI Enterprise, energy companies can speed up development of use case applications, such as reservoir simulation, seismic processing, demand forecasting, predictive maintenance, and power grid management. Learn how to get access to the software platform with free curated labs.
NVIDIA Modulus is an open-source framework for building, training, and fine-tuning physics-ML models with a simple Python interface. With Modulus, you can build models for enterprise-scale digital twin applications across multiple physics domains, from computational fluid dynamics to structural analysis to electromagnetics to climate science.
NVIDIA® Jetson™ brings accelerated AI performance to the edge in a power-efficient and compact form factor. Together with the NVIDIA JetPack™ SDK and NVIDIA Isaac™ software for Robotics Operating System, these Jetson modules, including the new NVIDIA Jetson Orin Nano™, support a full range of edge AI and robotics applications.
NVIDIA IGX is an industrial-grade edge AI platform that delivers high performance, advanced functional safety, and security. Purpose-built for industrial and medical environments, IGX enables organizations to confidentially deliver AI safely and securely to support human and machine collaboration.
NVIDIA DGX™ Cloud is a multi-node AI-training-as-a-service solution optimized for the unique demands of enterprise AI. Access NVIDIA DGX Cloud to experience a combined software and infrastructure solution for AI training that includes a full-stack AI developer suite, leadership-class infrastructure, and concierge support, allowing businesses to get started immediately with predictable, all-in-one pricing.
NVIDIA Metropolis features GPU-accelerated SDKs and developer tools that help developers optimally build, deploy, and scale AI-enabled video analytics and IoT applications from the edge to the cloud.
Resources
Videos
Webinars
Gain an understanding of the various building blocks of NVIDIA Modulus, the basics of physics-informed deep learning, and how the framework integrates with the overall Omniverse platform.
Get introduced to NVIDIA Isaac Sim™, NVIDIA Omniverse’s solution for simulation and robotics. Learn how to tap into the simulation loop of a 3D engine and initialize experiments with objects, robots, and physics logic.
Learn how to use NVIDIA Base Command™ Platform to accelerate your containerized AI training workloads, discover the tools necessary to build an AI center of excellence, and get the basics of working with, modifying, and running containers from NVIDIA NGC™.
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