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Reusable Assets

The reusability of digital twin assets makes it easy for users to work with the digital twins. The reusability of assets is a fundamental feature of the platform.

Kinds of Reusable Assets

The DTaaS software categorizes all the reusable library assets into five categories:

Categories of Library Assets

Functions

The functions responsible for pre- and post-processing of: data inputs, data outputs, control outputs. The data science libraries and functions can be used to create useful function assets for the platform. In some cases, Digital Twin models require calibration prior to their use; functions written by domain experts along with right data inputs can make model calibration an achievable goal. Another use of functions is to process the sensor and actuator data of both Physical Twins and Digital Twins.

Data

The data sources and sinks available to a digital twins. Typical examples of data sources are sensor measurements from Physical Twins, and test data provided by manufacturers for calibration of models. Typical examples of data sinks are visualization software, external users and data storage services. There exist special outputs such as events, and commands which are akin to control outputs from a Digital Twin. These control outputs usually go to Physical Twins, but they can also go to another Digital Twin.

Models

The model assets are used to describe different aspects of Physical Twins and their environment, at different levels of abstraction. Therefore, it is possible to have multiple models for the same Physical Twin. For example, a flexible robot used in a car production plant may have structural model(s) which will be useful in tracking the wear and tear of parts. The same robot can have a behavioural model(s) describing the safety guarantees provided by the robot manufacturer. The same robot can also have a functional model(s) describing the part manufacturing capabilities of the robot.

Tools

The software tool assets are software used to create, evaluate and analyze models. These tools are executed on top of a computing platforms, i.e., an operating system, or virtual machines like Java virtual machine, or inside docker containers. The tools tend to be platform specific, making them less reusable than models. A tool can be packaged to run on a local or distributed virtual machine environments thus allowing selection of most suitable execution environment for a Digital Twin. Most models require tools to evaluate them in the context of data inputs. There exist cases where executable packages are run as binaries in a computing environment. Each of these packages are a pre-packaged combination of models and tools put together to create a ready to use Digital Twins.

Digital Twins

These are ready to use digital twins created by one or more users. These digital twins can be reconfigured later for specific use cases.

File System Structure

Each user has their assets put into five different directories named above. In addition, there will also be common library assets that all users have access to. A simplified example of the structure is as follows:

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workspace/
  data/
    data1/ (ex: sensor)
      filename (ex: sensor.csv)
      README.md
    data2/ (ex: turbine)
      README.md (remote source; no local file)
    ...
  digital_twins/
    digital_twin-1/ (ex: incubator)
      code and config
      README.md (usage instructions)
    digital_twin-2/ (ex: mass spring damper)
      code and config
      README.md (usage instructions)
    digital_twin-3/ (ex: model swap)
      code and config
      README.md (usage instructions)
    ...
  functions/
    function1/ (ex: graphs)
      filename (ex: graphs.py)
      README.md
    function2/ (ex: statistics)
      filename (ex: statistics.py)
      README.md
    ...
  models/
    model1/ (ex: spring)
      filename (ex: spring.fmu)
      README.md
    model2/ (ex: building)
      filename (ex: building.skp)
      README.md
    model3/ (ex: rabbitmq)
      filename (ex: rabbitmq.fmu)
      README.md
    ...
  tools/
    tool1/ (ex: maestro)
      filename (ex: maestro.jar)
      README.md
    ...
  common/
    data/
    functions/
    models/
    tools/

Tip

The DTaaS is agnostic to the format of your assets. The only requirement is that they are files which can be uploaded on the Library page. Any directories can be compressed as one file and uploaded. You can decompress the file into a directory from a Terminal or xfce Desktop available on the Workbench page.

A recommended file system structure for storing assets is also available in DTaaS examples.

Upload Assets

Users can upload assets into their workspace using Library page of the website.

Library Page

You can go into a directory and click on the upload button to upload a file or a directory into your workspace. This asset is then available in all the workbench tools you can use. You can also create new assets on the page by clicking on new drop down menu. This is a simple web interface which allows you to create text-based files. You need to upload other files using upload button.

The user workbench has the following services:

  • Jupyter Notebook and Lab
  • VS Code
  • XFCE Desktop Environment available via VNC
  • Terminal

Users can also bring their DT assets into user workspaces from outside using any of the above mentioned services. The developers using git repositories can clone from and push to remote git servers. Users can also use widely used file transfer protocols such as FTP, and SCP to bring the required DT assets into their workspaces.