A virtual model can help streamline operations, maximising throughput for its physical counterpart, say engineers at Wistron, a global designer and manufacturer of computers and electronics systems.
In fact, a manufacturing plant near Hsinchu, Taiwan’s Silicon Valley, is among facilities worldwide boosting energy efficiency with Artificial Intelligence (AI)-enabled digital twins.
In the first of several use cases, the company built a digital copy of a room where NVIDIA DGX systems undergo thermal stress tests (pictured above). Early results were impressive.
Making Smart Simulations with Wistron, Digital Twins, and NVIDIA
Using NVIDIA Modulus, a framework for building AI models that understand the laws of physics, Wistron created digital twins that let them accurately predict the airflow and temperature in test facilities that must remain between 27°C and 32°C.
A simulation that would have taken nearly 15 hours with traditional methods on a CPU took just 3.3 seconds on an NVIDIA GPU running inference with an AI model developed using Modulus, a whopping 15,000x speedup.
The results were fed into tools and applications built by Wistron developers with NVIDIA Omniverse, a platform for creating 3D workflows and applications based on OpenUSD.
With their Omniverse-powered software, Wistron created realistic and immersive simulations that operators interact with via VR headsets. And thanks to the AI models they developed using Modulus, the airflows in the simulation obey the laws of physics.
“Physics-informed models let us control the test process and the room’s temperature remotely in near real time, saving time and energy,” said John Lu, a Manufacturing Operations Director at Wistron.
Specifically, Wistron combined separate models for predicting air temperature and airflow to eliminate risks of overheating in the test room. It also created a recommendation system to identify the best locations to test computer baseboards.
The digital twin, linked to thousands of networked sensors, enabled Wistron to increase the facility’s overall energy efficiency up to 10%. That amounts to using up to 121,600 kWh less electricity a year, reducing carbon emissions by a whopping 60,192 kilograms.
An Expanding Effort
Currently, the group is expanding its AI model to track more than a hundred variables in a space that holds 50 computer racks. The team is also simulating all the mechanical details of the servers and testers.
“The final model will help us optimise test scheduling as well as the energy efficiency of the facilities’ air conditioning system,” said Derek Lai, a Wistron Technical Supervisor with expertise in physics-informed neural networks.
“The tools and applications we’re building with Omniverse help us improve the layout of our DGX factories to provide the best throughput, further improving efficiency,” said Lu as he looked ahead.
Efficiently Generating Energy
Half a world away, Siemens Energy is demonstrating the power of digital industrialization using Modulus and Omniverse.
The Munich-based company, whose technology generates one-sixth of the world’s electricity, achieved a 10,000x speedup simulating a heat-recovery steam generator using a physics-informed AI model (see video below).
Using a digital twin to detect corrosion early on, these massive systems can reduce downtime by 70%, potentially saving the industry USD $1.7 billion annually compared to a standard simulation that took half a month.
“The reduced computational time enables us to develop energy-efficient digital twins for a sustainable, reliable and affordable energy ecosystem,” said Georg Rollmann, head of advanced analytics and AI at Siemens Energy.
Digital Twins Drive Science and Industry
Automotive companies are applying the technology to the design of new cars and manufacturing plants. Scientists are using it in fields as diverse as astrophysics, genomics, and weather forecasting. It is even being used to create a digital twin of Earth to understand and mitigate the impacts of climate change.
Every year, physics simulations, typically run on supercomputer-class systems, consume an estimated 200 billion CPU core hours and 4 terawatt hours of energy. Physics-informed AI is accelerating these complex workflows 200x on average, saving time, cost, and energy.
For more insights, listen to a talk from GTC describing Wistron’s work and a panel about industries using generative AI.
Learn more about the impact accelerated computing is having on sustainability.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)