Interscale Content Hub – Python’s got a lot going for it when it comes to digital twin development. Its versatility, rich libraries and user-friendly syntax make it an ideal choice. But, how to build a digital twin in Python?
Building a digital twin in Python means having a good grasp of both the theory and the practice of digital twin technology. However, building a digital twin in Python is a challenging but rewarding task.
Neha Karanjkar and Subodh M. Joshi (2021) make a good point in their paper, “A Python-based Mixed Discrete-Continuous Simulation Framework for Digital Twins.”
They say that if you want to work with Python, you’ve got to get to grips with the language’s core concepts.
If you’re just starting out, there are lots of online resources that can help you get to grips with the basics.
That’s why we’ve put together this guide, packed with practical tips and examples, to support you in creating an effective and functional digital twin.
Prerequisites and Tools
Make sure you’ve got Python 3.6 or above installed. Python is the backbone of the digital twin platform because it’s so versatile and has such a big library of support.
Now, get to grips with Python’s powerful libraries. If you’re looking to do any kind of numerical operations or scientific computing, especially when it comes to sensor data, then NumPy and SciPy are your best friends.
Pandas is there to help you analyse and manipulate your data, making sense of the information coming from your physical asset.
If you want to visualise this data and the insights it reveals, Matplotlib or Plotly are your best bet.
If your digital twin needs to make predictions, scikit-learn has a whole bunch of machine learning algorithms that can help.
The next thing to consider is which data sources are right for the job. These could be sensors in your asset that are connected to the Internet of Things (IoT), data from Building Information Modelling (BIM), maintenance logs, or even environmental factors.
Then, you might want to use tools like AutoCAD or Blender to create detailed 3D models of the physical asset.
And don’t forget the value of domain knowledge. It’s really important to have a good understanding of the asset you’re replicating.
This will help you choose the right things to monitor, the most useful data to collect, and the best models to use.
To see how useful this technology is, kindly read “Digital Twins Technology in Manufacturing: The Most Efficient of Them All.”
Step-by-Step Guide to Building a Digital Twin in Python
Let’s get started on building your digital twin in Python! Once you’ve got your prerequisites in place, we can jump right in.
Before you get started, it’s crucial to have the right mindset. Think about this: What are you hoping to achieve with your digital twins?
As Matthew S. Bonney and colleagues point out in their work, “Development of a digital twin operational platform using Python Flask,” you need to clearly define the purpose of your digital twin.
Decide which parts of the physical asset you want to copy and what functions you need, like monitoring, diagnostics, or predictive maintenance.
Now, the first step is data collection and preprocessing, as outlined by Karanjkar and Joshi.
This is where you collect the raw data from your chosen sources, get rid of any errors or inconsistencies, and format it into a format that can be used.
Trusted Win’s “Create a digital twin of a meter [Python]” is a great example.
They’ve created a digital twin for a water meter and open weather in Gdańsk, Poland.
Their study case shows how data can be collected, stored and retrieved using the Trusted Twin platform.
Now it’s time to create a virtual model. This is where you’ll create models that reflect the behaviour and characteristics of your physical asset.
These models can be descriptive (showing what’s going on right now), predictive (telling us what’s going to happen in the future), or even prescriptive (telling us what we should do).
As we mentioned earlier, Python’s scikit-learn library has a wide range of machine learning algorithms that can help with this process.
Now for the really exciting part: integrating the digital twin. This means you need to create a Python script that links the physical and digital worlds.
This script gathers data, processes it through your models, and updates the digital twin based on the model outputs.
If you want to create a visually immersive experience, you could add a visualisation layer.
Libraries like Blender or VTK can help you create a 3D representation of your asset, which you can then link to your Python script to display real-time data and model predictions.
Right, the next step is to get the digital twin platform up and running. Bonney shows us how you can use Flask to create a web-based interface for your digital twin.
The idea is for the interface to let users interact with the twin, view real-time data and perform necessary operations. This is a simple Flask setup:
| from flask import Flask, jsonify app = Flask(__name__) @app.route(‘/’) def home(): return jsonify({“message”: “Digital Twin Operational Platform”}) if __name__ == ‘__main__’: app.run(debug=True) |
You can also use RESTful APIs to integrate real-time data from sensors. For instance:
| from flask import request @app.route(‘/update’, methods=[‘POST’]) def update_data(): data = request.get_json() # Process data return jsonify({“status”: “success”}) |
Next, you can start using libraries like NumPy, Pandas and Matplotlib for data analysis and visualisation. As an example, implement predictive analytics for maintenance and operational efficiency:
| import numpy as np import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv(‘sensor_data.csv’) plt.plot(data[‘time’], data[‘value’]) plt.xlabel(‘Time’) plt.ylabel(‘Sensor Value’) plt.show() |
Next, be sure to run some thorough tests to make sure the digital twin is an exact match for the physical asset.
Use past data to check your predictions and make any necessary changes to the model.
Bonney et al. say it’s vital to keep making changes based on what people tell you and what they need.
Then, put your digital twin on a server and keep an eye on how it’s performing in real time.
It’s also a good idea to regularly update the system to incorporate new data and improve accuracy. Make sure you have solid security in place to keep data safe and private.
Learn how digital twin technology helps construction projects in “How Digital Twin Technology in Construction Help You Avoid Blockhead?”
Best Practices and Tips for Digital Twin Development
As you get to grips with the ins and outs of digital twin creation, remember these top tips.
So, start simple, as Karanjkar and Joshi suggest. Start with a simple model and then build on it as you get more insights.
Make sure you focus on what’s valuable by putting the most effort into data and models that give you useful information you can act on.
It’s not just about creating a replica. The goal is to gain valuable insights that can drive decision-making and optimisation.
Good data quality is the key to a successful digital twin. Make sure your data is accurate, reliable and up to date.
Bonney et al. also point out how important it is to keep data secure, especially on web-based platforms like DTOP-Cristallo.
Make sure your digital twin is safe from unauthorised access and potential breaches.
Teamwork is a big part of what makes the AEC industry tick. Get people from different areas involved so you can build a digital twin that works for architects, engineers and construction professionals.
And please remember, building a digital twin is an ongoing learning process.
Embrace the fact you can change your approach as you go along, try out different techniques and make the most of the resources you have at your fingertips.
If you’re dedicated and have the right tools, you can use digital twins to transform the way you design, build, and manage assets in the Australian AEC landscape.
Consider the Support System for Your Digital Twins
As we all know, the challenge is to start simple. To start simple, we need to break down the problem and solution.
This is why we’ve put together some specialised solutions to help you make the most of this amazing technology.
Our team of experts is here for you every step of the way, to guide you through your digital twin journey.
We’ve got your back from start to finish, with a support system that makes sure your projects are secure, reliable and deliver maximum value, from the first risk assessment to ongoing management and optimisation.
What do you think the outcome is going to be?
With our support, you can stay one step ahead of issues before they become problems, make the most of your assets with data-driven insights, and make smart choices that boost efficiency and sustainability.
So, let’s start more simply: Give us a shout today to arrange a one-on-one with our team.
You can count on us 24/7 to talk through your particular requirements and show you how Interscale can help you in digital twin challenges.
Or, you can also get a quick overview by reading our Interscale BIM Management Support page.
In Closing
Building a digital twin in Python requires data science, software engineering and domain expertise.
That’s why you should regularly update your digital twin to keep up with new technology and changing needs.
And with Interscale support, you can create a useful digital twin that gives you real-time insights and helps you make better decisions. This approach keeps you up to date on how to build a digital twin in Python.
