Digital Innovation

Center of Excellence

 

Inventing Our Energy Future in the Digital Frontier

Digital Innovation Center of Excellence (DICE)

 

MEGAPROJECTS AND DIGITAL ENGINEERING

In the world of industry and research, megaprojects are the ambitious efforts that can change the world, e.g., space exploration technology, defense systems, power plants, complex buildings. The benefits of such projects are enormous, but undetected design errors and unforeseen production issues can cascade into delays that put a project behind schedule and over budget.

Digital engineering (DE) helps prevent such calamities. By using artificial intelligence and the latest data models to coordinate engineering and construction, DE keeps costs down and work on track while dramatically reducing overall program risk.

Out of this emerged DICE – the Digital Innovation Center of Excellence. DICE serves to design next generation power and energy systems with digital engineering principals through the Water Power Office, National Reactor Innovation Center, Versatile Test Reactor, and National Nuclear Security Administration.

What is DICE?

DICE serves as a virtual center to formalize and coordinate digital engineering, digital twinning, and digital transformation activities across next generation energy systems. DICE will serve the following key functions:

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Leadership for a strategy on digital innovation for the world’s energy future

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Recognition as a national center of excellence through research accomplishments

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Coordination to share community best practices across the laboratory

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Outreach to universities, industry partners, and other national laboratories

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Enhance training and education materials on digital engineering and digital twinning

Examples of Success

Mortenson Construction cumulative day direct schedule reductions

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Boeing improvements in first-time quality through use of Digital Twins

General Electric savings in cost avoidance through digital innovations

National Nuclear Security Administration (NNSA) in savings through digital innovations

DICE Pillars

DICE is organized into eight key pillar areas with tight collaboration between pillars to realize our digital future. Each pillar will meet at least quarterly with event-based (proposal opportunities / standards development) meetings arranged between regularly scheduled meetings.

Next Gen Artificial Intelligence

Next-gen AI encompasses emerging architectures, methodologies, and tools that will drive revolutionary advancements in scientific applications that include real-time chemical analysis, battery and catalysis mechanistic models, and high-consequence nuclear energy and security missions.

Operational Artificial Intelligence

The application of artificial intelligence (AI) in energy systems has a game-changing potential in automating expensive and manual human activities in various types of industries. This pillar focuses on the application of AI to improving asset operations.

Practice and Culture

The practice/culture is the realization of digital innovation and digital engineering within our national laboratory system, industry, and academic partners.

Digital Twin

Digital twins are the computational simulation of a physical process or system that has a live link to the physical system, enabling enhanced verification of the simulation, control of the physical system, and analysis of trends via artificial intelligence and machine learning.

Digital Thread

The digital thread is defined as integrating systems and data together to trace connections in data across data sources and across the lifecycle of a program or asset. The result of this effort is a holistic view that empowers new insights and prevents losses caused by siloed data.

Decision Sciences and Visualization

Using behavioral economics, game theory, statistics, and risk analysis to develop models and simulations that shed light on how human behavior affect decisions.

 Cyber & Data Resilience

Data resilience represents the safeguards and validation of integrity with data which is utilized in the training of machine learning models.

Computing Platforms

Computing platforms represent the computational architectures with the right-sized deployments of high performance computing, public/community cloud, and edge device computing.

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