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Thanks to their use of virtual models, digital twins are sometimes regarded as the same as simulations. Of course, this is not the reality. Despite some similarities in purpose and use cases, digital twins are far more advanced than most simulation tools.

Digital twins enable companies to preemptively check the real-time performance of their finished product. For instance, a “physical” solar panel (fitted with sensors) can produce and transmit data to the digital twin (or model), which can then:

  • Run data-driven simulations.
  • Measure the performance and health of the solar panel.
  • Identify areas of improvement to be applied to the physical product.

Unlike simulations, digital twins can effectively drive business strategy to:

  • Improve operational efficiency.
  • Automate human or manual tasks.
  • Improve data analysis and training.

On the other hand, product companies can run simulations to test their product features – without having to create a “physical” prototype. This is more useful for product designers and engineers, who can create 2D (or 3D) computer-aided models of the product (or process).

How are Digital twins different from Simulations?

Essentially, both digital twins and simulations can replicate a product (or process) in a digitalized environment. However, both of these approaches have a host of differences. For instance, a digital twin uses a “virtual” environment for simulations, along with a real-time flow of data between the physical object and the twin.
Here are some of the notable differences between digital twins and simulations:
1.Simulations are static, digital twins are active.
A simulation model is more static and tested using fixed design parameters or elements. To change this static model, simulation engineers need to constantly add more elements or parameters.

Digital twins are active as they can use real-time data to modify models. With more real-time data, digital twin models can keep evolving (or maturing) around the physical product.
2.Simulations provide “possibilities,” digital twins are “actual.”
Simulations can replicate what a product can do. Largely, this is limited by the imagination of the simulation engineer. On the flip side, they cannot test the product in various environments.

Digital twins do not provide “possibilities,” but present what’s happening with the product. This is possible due to the continuous flow of information between the physical product and its digital twin. Based on this real feedback, engineers can check if the product is performing as expected.
3.Simulations don’t have real-time data integration like digital twins.
Typically, simulations use predefined datasets to design the model – and don’t have real-time data integration. This limits their predictive analytics capabilities.
On the other hand, digital twins feature real-time data integration, which can update the virtual model continuously. They also have better predictive capabilities, thus enabling real-time decision-making.

5 real-world applications of digital twins

To understand the difference between digital twins and simulations, let’s explore some real-world applications:

1. Discrete manufacturing
Digital twins are richly suited for discrete manufacturers like automotive companies. Automotive companies manufacture vehicles by assembling each part to make the finished product.

For discrete manufacturing, digital twins are more flexible for testing each product part separately across various settings. In short, a digital twin can evaluate the product across its entire lifecycle.

Here’s a case study of how an India-based discrete manufacturer leveraged digital twin technology to improve their machine utilization and planning.

2. Solar-powered renewable energy
In the renewable energy sector, digital twins can leverage real-time data from sensors installed on solar panels and other equipment. For instance, digital twins can utilize solar panel data – including its temperature and energy output – to measure its performance and identify areas of improvement.
Here are some use cases of digital twins in the renewable energy industry:
● Storage and distribution of energy
● Analyzing energy consumption
● Optimizing the design of renewable energy sources

Here’s a recent case study of how a patented digital twin solution for energy analytics improved the performance of a solar power plant by 5-7%.
3. Product designing
Traditionally, computer-aided simulations have provided their value in product designs. However, digital twins are capable of much more. For example, when integrated with an IoT platform, digital twins can process real-time product data faster. This allows product designers to virtually “see” their product’s performance.

Among other benefits, digital twins can analyze the product design through its entire lifecycle and different environments. This enables product designers to improve the product design and make them more resilient. Besides, digital twins can also “simulate” the entire product manufacturing process – much better than “traditional” simulations.

Here’s a real-life case study of how semiconductor company Altum RF reduced its product designing process by 30% with digital twins.

4. Autonomous vehicles
Autonomous (or self-driving) vehicles need real-time data for their predictive abilities. For maintenance purposes, autonomous vehicles can provide garage operators with real-time data about:
● The current performance levels of the vehicle
● Previous servicing records
● Replaced or repaired parts
● Current potential problems in the vehicle

Along with IoT technology, digital twins can capture and analyze autonomous vehicle data, thus saving both maintenance time and effort. They can also provide predictive analytics on the maintenance issues likely to affect the vehicle.

An example is that of Tesla creating a digital twin for every autonomous vehicle that it sells. Connected sensors from thousands of Tesla cars stream real-time vehicle data into its digital twin model, thus helping in determining its current performance levels.

5. Patient care
In the healthcare domain, medical professionals are constantly looking to improve patient treatment, while keeping it safe at the same time. Digital twin solutions in healthcare are the best way of improving patient outcomes. For instance, doctors can now “simulate” the best form of patient treatment – before “physically” making the care.

Here’s how U.S.-based Cleveland Clinic is leveraging digital twins to understand how a patient’s neighborhood can influence their health.

Conclusion

In general, simulations are useful for understanding “what may happen” with products or processes. Digital twins are useful for knowing “what’s really happening” with its virtual model.

At Pratiti Technologies, we have developed in-house expertise in digital twin technology for providing solutions across industries. Are you looking for a partner to consult, build, and deploy your next digital twin? We can partner with you. Contact us today!

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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