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Results of Altair’s 2022 Technology Predictions

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Altair created a survey to find the good, the bad, and the ugly from its technology predictions for 2022.

Altair, a company that focuses on simulation design software and data analytics, offered Design News a look back at some of the company’s predictions from the beginning of 2022 to see how the predictions fared. Some we spot on, some are still gaining momentum, and others were completely off the mark. Below, Altair offers some reflections on the expectations of specific technologies. Also, check out the article where engineerings and executives from Altair will predict the direction of technology in 2023.

Digital Twins Will Break New Ground

Altair believes this prediction came to fruition this past year. According to the company’s 2022 survey, manufacturers around the world are adopting digital twins at record speeds. Nearly three in four (69%) organizations are now leveraging digital twins for a wide range of use cases including improved product design and risk assessment, predictive maintenance, and even sustainability. As many as 71% of those businesses began investing in the technology in just the past year. For organizations not using digital twins, nearly half expect their company to adopt the technology within the next three years.

Altair noted that in 2023 we will see digital twin technology hit further milestones, as a game-changing technology develops and companies begin to see digital twins as a competitive driver.

Digital Twins Will Unlock Sustainable Production and Drive Efficiency

As we head into 2023, sustainability will continue to be a top priority for businesses across industries in the fight against climate change. For engineers and manufacturers, digital twin technology will be the key to making their processes more energy efficient and designing their products with fewer materials — a benefit cited by over 90% of respondents in Altair’s survey. Overall, 85% of respondents said their organization either already does, or is planning to, use digital twin technology to decrease their carbon footprint by improving energy and production efficiency, while reducing costs – especially critical in the new year with inflation on the rise.

Say Goodbye to the Crash Test Dummy

With advancements in digital twins and the convergence of simulation with data analytics and HPC, physical prototyping could soon become a thing of the past. According to Altair’s survey, 43% of respondents believe digital twins will make the need for physical prototypes obsolete within four years, and 67% said this would happen in six years.

High-Powered Computing (HPC) Will Go On-Demand

This prediction stated that we would see a new world of high-powered computing where teams can seamlessly connect with different platforms, collaborate, and accelerate new ideas forward with few limitations. Altair’s chief scientist Rosemary Francis believes this has come true and sees much more potential for high-performance computing in the coming year.

Altair also noted that the availability of exascale computing also fulfills this prediction. With the Polaris and Aurora machines at Argonne National Labs going online, exascale computing is finally here. Polaris is already live, and Aurora aims to deliver two exaflops of computing resources in 2023, giving researchers a new tool to accelerate scientific breakthroughs and to solve some of the world’s biggest issues, from climate change and cancer research to the development of new materials and energy resources.

As HPC workloads are taking on big data applications, such as in life sciences and particle accelerators like the UK’s Diamond Light Source (for greater research and experimentation), we’re seeing an explosion in workflow tools. Going into 2023, this transformation into multidimensional scheduling will be the biggest driver of change within HPC as the industry seeks to modernize itself and adapt to these big connected applications.

As deep learning becomes more prevalent in 2023, we will see a further shift in HPC workloads. While initially most machine learning workloads were run on Kubernetes or other container orchestration frameworks, it’s become clear that these systems are designed for microservices, not for the bursty, computer-intensive machine workloads now required for deep learning. Commercial HPC workload managers need comprehensive container support so organizations can spool their compute and start to take advantage of batch scheduling, cloud bursting, and fare share — all key aspects of efficient HPC.

Urban Air Mobility Becomes a Reality – Even Human-Carrying Drones

Altair saw this prediction as a major flop. Although we have seen some advancements in drones for field work, security, and inspection, creating an electronic propulsion system with the power and energy density needed to carry heavy objects or a group of passengers remains a challenge and not something we can expect to see in the next year, anyway.

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