Jun 27 2019
Digital Twins is #6 in our series on corporate data management and digital transformation tech trends. A digital twin is a real-time digital replica of a physical object. Data twins integrate the internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that continually learn and update to represent real-time status. Digital Twins is a concept, not a single product or a piece of technology. Technologies such as 3D modeling, edge computing, cloud computing, artificial intelligence and IoT offer components of this problem-solving approach. Industry applications include manufacturing, healthcare, and automotive for uses including 3D modeling, monitoring, diagnostics, prognosis, and performance optimization.
The first application of digital twins was used by NASA to prepare to operate, maintain, and repair systems in outer space, well beyond one’s ability to see or monitor physically. In fact, when disaster struck Apollo 13, the mirrored innovation on earth allowed engineers and astronauts to rescue the mission. Consider that in 1970, after the explosion of an oxygen tank, engineers modeled a solution on the ground using only the physical components available to the astronauts in the capsule. They created a twin, tested and found a solution, and applied it in space.
Traditionally, digital twins were used to improve the performance of a single object such as a jet engine. Today, the possibilities for digital twins are much more complex, connecting systems of assets and organizations to solve more complex problems. For example, in order to address climate change and over-population, the UK is creating a digital twin of its entire infrastructure to test its resilience to the challenges of each. The Living Heart Project is using digital twin technology to model the cardiovascular systems for research, treatment, and clinical trials. Stakeholders include Intel, Bayer, Hewlett-Packard Enterprise, and Pfizer. Gartner reports that digital twins are reaching proliferation with over 75% of organization implementing IoT already using digital twins or having plans to do so within a year.
Whether you are talking about engine parts, coupled with maintenance, wear and tear, or modeling the infrastructure for the entire infrastructure of the UK, there is a massive amount of data involved. The desire for real-time analytics and the need to pull only the relevant data makes computational storage the logical solution for these petabyte scale datasets. The speed, efficiency, small infrastructure footprint addresses these application needs. For more information, contact me.