As is true with any emerging field of technology, Digital Twins is just now coalescing into a clearer definition of what it actually means in practice. Many market observers and vendors have offered up their views on what Digital Twins means to them. The ingredients have been around for a while, but their synergistic combination is just now bearing initial business fruit. Key enablers are the increasing accuracy of CAE models, the declining costs of high-performance computing, expanding cloud accessibility, and low-cost sensors.
What is IIoT? IoT (Internet of Things) is just connecting physical devices/systems to each other via the internet (think: your home internet-connected thermostat and an app on your smartphone that enables you to observe and change the temperature in your home from anywhere on earth if you have an internet connection). If the devices and connections and data transfer via the internet are for business/industrial systems, that’s the INDUSTRIAL Internet of Things. A lot more detailed and involved, but the same basic idea. When one of the “things” is a current/updated physics/simulation-based digital model, that’s where IIoT plays a role in Digital Twins.
This “How It Works” area of the Revolution In Simulation website is your go-to source for more in-depth background information on Digital Twins and the latest relevant end-user enterprise and vendor case studies and success stories.
Physics-based simulation models (CAE) have proven their mainstream business value during the development and manufacturing phases of the Product Performance Life-cycle (PPL).
Now they are moving beyond those phases into the entire life cycle of products and systems by being updated/modified to reflect the ongoing true in-field status/condition of such products and by being subject to the actual in-field loadings and boundary conditions obtained from sensors (the Industrial Internet of Things – IIoT). The resulting Digital Twins can then be used to anticipate needed maintenance and predict the behavior of future proposed operational changes to the physical structures and systems they represent in digital form.
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This panel discussion outlines strategic approaches for integrating AI/ML into CAE, with a focus on real-life experiences and insights from industry experts. Topics include CAE simulations, virtual animation data, and product design iterations and optimization.
CAE fatigue methods for design optimization and virtual product validation
Simulation-based digital twins can ensure your facility maximizes performance and reclaims unused capacity without risk or downtime. Learn to use 6SigmaDCX to study the impact of new cooling technologies and optimize your data center facilities.