BimScaler Blog – How to create digital twins has become a pivotal question as many firms are starting to see the transformative power of these virtual replicas. But what exactly does this process involve?
Why should you spend your time learning about this process?
Digital Twins (DTs) give you a unique chance to gain insights, improve performance and make smart decisions throughout the life of a built asset.
From the design and construction stages to operations and maintenance, digital twins can completely change how we approach the built environment.
What are the Stages of Creating a Digital Twin?
The first thing you need to do is collect the data. This means collecting data in real time from different sources, including satellite images, environmental sensors, and social and economic indicators.
F. R. Taghikhah, in “A conceptual framework for developing digital twins of human-environmental systems”, makes a good point about the need for comprehensive and accurate data.
In their paper ‘A systematic review of digital twin technology and applications’, Jun-Feng Yao and colleagues point out that high-precision sensors and distributed sensor networks are key for capturing the physical quantity information that characterises the system state.
Next, we create a detailed virtual representation of the physical entity using modelling.
This model has to show how the real-world system behaves and what it’s made of.
Taghikhah says using machine learning algorithms, like deep learning and reinforcement learning, is a good way to train the model and understand how different factors work together.
Once the virtual model is created, it’s important to integrate it with the physical system.
This means setting up real-time data flows and making sure the digital twin and its physical counterpart stay in sync all the time.
The analysis stage uses data analytics and machine learning to look at the data we’ve collected and work out what it all means.
Once we’ve got the insights from the analysis, we can start optimising. This means making changes to improve performance, efficiency and sustainability.
Yao et al. say optimisation can include design, configuration, performance, and process improvements based on feedback from the digital twin.
It’s really important to make sure that the digital twin accurately represents the physical system. This means keeping on testing and making changes as you go along.
Taghikhah’s framework is all about keeping the model up to date with the latest data. This means that as more real-world data comes in, the model can be updated to make better predictions over time.
The final step is to put the digital twin into the real world.
This also means putting the digital twin into existing systems and processes and training staff to make sure they know how to use and maintain it.
And don’t forget to keep an eye on things and make any necessary improvements so you can adapt to changing conditions and requirements.
If you’re looking for a solid starting point, kindly read “What is Digital Twin? Not Your Average Virtual Copy – Here’s Why It Matters.”
How to Create Digital Twins
This process is key for making sure the digital twin matches its physical counterpart exactly and can give you valuable insights and help you make improvements. So, let’s go through this in seven steps.
Step 1: Define Objectives
Before you start building a digital twin, it’s got to be clear what you’re trying to achieve.
Figuring out what you want to achieve with the digital twin is key, whether it’s improving operational efficiency, predictive maintenance, or enhancing product design.
In her conceptual framework, Firouzeh R. Taghikhah makes a good point about setting clear goals to guide the development and application of digital twins effectively.
Step 2: Data Acquisition
Gathering data from different sources, like sensors, IoT devices, and enterprise systems, is the basis for a digital twin.
This data is what the virtual model needs to work with.
According to Jun-Feng Yao et al., high-precision sensors and distributed sensor networks are key for gathering accurate and comprehensive data that characterises the system state.
It’s crucial to make sure the data we collect is accurate and comprehensive if we want our digital twins to be reliable.
Step 3: Develop the Digital Model
To create a virtual model that accurately represents the physical entity, you need to develop a detailed digital representation.
This model should include all the relevant physical and behavioural characteristics.
Taghikhah’s framework shows how machine learning algorithms, like deep learning and reinforcement learning, can be used to train the model and understand how different factors are connected.
Yao et al. also say at this stage, you need to create a multi-domain, multi-dimensional, and high-fidelity virtual model and dynamic simulations.
Step 4: Integrate Systems
The next step is to integrate the digital model with the physical entity.
This means setting up data flows and making sure that the physical and digital systems are in sync all the time.
Technologies like IoT and cloud computing are really important for this integration.
Yao et al. say this integration allows for real-time monitoring and feedback, which is really important for keeping the digital twin accurate and useful.
Step 5: Implement Data Analytics
Processing the data collected by the digital twin with data analytics is key to spotting patterns, predicting future behaviour and getting useful insights.
Taghikhah’s framework uses causal inference methods like instrumental variable techniques, counterfactual analysis, and propensity score matching.
These are the ways of finding out what causes things to happen and of working out what effect changes to policies or interventions will have.
Step 6: Optimise and Validate
Using what we’ve learned from the digital twin to make the physical system better is an ongoing process.
This means making changes to improve performance, efficiency and sustainability.
Yao et al. say optimisation can mean making changes to the design, configuration, performance and processes based on what we learn from the digital twin.
To make sure the digital twin stays an accurate representation of the physical entity, we need to test it regularly and make any necessary adjustments.
Step 7: Deploy and Monitor
When you put the digital twin into the real world, you have to get it working with the existing systems and processes.
This stage is about training staff to make sure they know how to use and look after the digital twin.
It’s important to keep an eye on things and make any necessary changes to adapt to changing conditions and requirements.
Having a feedback loop for continuous model refinement is key to keeping the digital twin up to date and effective over time.
How to Implement Digital Twins
The first thing to do is to take a look at the existing systems and see where digital twins could be useful.
This means getting to grips with what the company needs and what it’s facing, and working out how digital twins can help.
Starting with a few pilot projects lets organisations test out digital twins on a smaller scale before going all in.
Taghikhah reckons that pilot projects can be used for decision support and participatory planning.
Then, we need to plan for scalability to make sure the digital twin can handle more data and more complexity as the system grows.
Yao et al. say that we need to think about distributed storage and high-speed data management to support the lifecycle data of complex systems.
Also, make sure your staff get regular training so they know how to use and maintain the digital twin. This also includes training on how to collect data, how to interact with the model, and how to analyse the data.
And finally, it’s important to keep making improvements so you can adapt to changing conditions and requirements.
This means regularly updating the model with new data, refining the algorithms, and optimising system performance.
Taghikhah’s feedback loop for continuous model refinement is a key part of this stage. It makes sure the digital twin stays accurate and effective over time.
For another reference, please refer to “How do Digital Twins Work: A Cheat Sheet of Magic Behind Virtual Replica.”
Find Your Digital Twin Supporting System
As you can see, creating digital twins is a pretty complex process that needs to be improved all the time.
Yes, it’s really helpful to have a solid support system in place.
At BIM Scaler, we’ve got a team of experts who can help you integrate your digital twin with your existing systems without any fuss.
So, how can BIM Scaler help you with your digital twin workflow?
BIM Scaler’s got the CAD and BIM know-how to help you build accurate and detailed 3D models for your digital twin, so you’ve got a solid foundation for data integration and analysis.
Our IT support team makes sure your digital twin infrastructure runs smoothly.
And yes, they will get to the bottom of any technical issues quickly, so you don’t have to worry about downtime.
For sure, we can tailor our services to meet your specific needs, whether it’s data integration, model optimisation, or user interface development.
The idea is to make the most of your digital twin while we take care of the technical stuff.
If you want to know more about how BIM Scaler can help your digital twin projects, kindly read our CAD and BIM management page.
Or get in touch with us today to arrange a lunch and a discussion. Absolutely! We’re all set for you.
In Closing
This all-encompassing approach is the foundation for unlocking the potential of digital twins.
If you follow these steps and use the right tech and systems, you’ll be able to develop and implement digital twins successfully.
It’s only natural to see how to create digital twins as a complex puzzle. That’s why we at BIM Scaler are on hand 24/7 for your projects.
