The future of industrial operations.
Introducing the Digital Twin Model.
What is a digital twin?
A digital twin is a virtual model of an actual or intended physical product, system or process (a physical twin) that serves as an effectively indistinguishable digital counterpart of it for practical purposes such as simulation, integration, testing, monitoring and maintenance. This, has been conceived since its initial introduction as the underlying premise for product lifecycle management and exists throughout the entire lifecycle (create, build, operate/support and dispose of) of the physical entity it represents. It can and often does exist before a physical entity exists and this is the beauty of this model. The use in its creation phase allows to model and simulate the entire life cycle of the envisioned final entity. A digital twin of an existing entity can be used in real time and regularly synchronized with the corresponding physical entity. The evolving US DoD Digital Engineering Strategy initiative, first formulated in 2018, defines a digital twin as “an integrated multiphysics, multiscale, probabilistic simulation of an as-built system, enabled by a Digital Thread, that uses best available models, sensor information, and input data to reflect and predict activities/performance over the lifetime of its corresponding physical twin.” Digital twins can be further characterized as a digital representation of a physical object or system throughout its lifecycle, using real-time data to enable understanding, learning and reasoning.
What are the key features of a digital twin model in industrial operations?
-Digital representation: A digital twin is a virtual representation of a physical system, product or process, which provides a detailed and accurate model of the real-world counterpart.
-Real-time data integration: It involves the integration of real-time data from sensors, IoT devices, and other sources to provide up-to-date information about the physical system or process
-Simulation and analytics: Support simulation and analytics, enabling analysis of various scenarios, predictive maintenance, and optimization of industrial processes
-Life cycle: They can cover the entire product life cycle, from design to simulation, manufacturing, assembly, service and support, providing a complete view of the product or process.
-Interconnectivity: They are designed to model complex assets or processes that interact with various components, environments and unpredictable variables, allowing a holistic view of the system or process.
-Improved decision making: They enable improved decision making by providing insight into product performance, process optimization and operational details, resulting in improved efficiency and reduced costs.
What tools are needed to create a digital twin model?
-Fraunhofer-advanced AAS tools for digital twins (FA3ST): These tools support the engineering and management of digital twins by exporting parts of a digital twin model from relevant software and hardware systems, analyzing existing documentation, and providing functions for the development, sharing, use and validation of digital twins. https://www.iao.fraunhofer.de/
-MapleSim: MapleSim is an advanced modeling tool that supports digital twins for virtual commissioning and system-level modeling for complex engineering design projects. https://www.maplesoft.com/
What about AI powered tools?-Ansys Twin Builder: Simulation-based software that enables engineers to build, validate, deploy and scale digital twins. It integrates real-world data and enables the use of predictive analytics through a combination of machine learning-based analytics and a physics-based approach. https://www.ansys.com/
-C3 AI Suite: Enables organizations to leverage their existing enterprise systems, data warehouses and disparate data sources by unifying their data into a C3 AI digital twin in a unified object model. This virtualization enables customers to gain critical business insights through simulations of their processes, assets or systems. https://c3.ai/
Here are some real-world examples of companies leveraging AI and digital twins:
-General Electric: GE applies machine learning algorithms to digital twin models of jet engines, wind turbines and locomotives to improve predictive maintenance. Real-time analysis of sensor data against simulations enables earlier detection of problems.
-Airbus: Digital twin models of individual commercial aircraft are maintained throughout the product life cycle. AI helps design optimal cabin layout, predict electrical loads and customize maintenance schedules based on actual aircraft usage.
-Siemens: Leverages AI and digital twins to create dynamic simulations for the efficient design of automation systems. This applies to smart building infrastructure and advanced manufacturing plants.
-NASA: Its DELPHI system acts as a digital twin to provide real-time vehicle health monitoring and anomaly detection for spacecraft systems. AI adds intelligence to model deviations and predict failures.
-ArcherGrey: This startup partners with healthcare providers to build digital twin operating rooms. AI tracks usage patterns to optimize surgery planning and asset utilization based on data such as patient outcomes.
In these use cases, the fusion of physics-based digital twins with AI’s pattern recognition capabilities is leading to more predictive environments that adapt to real-world operating conditions. Technology enables smarter asset management.
That said, the challenges of implementing a digital twin model in industrial operations include data integration and interoperability, modeling complexity, security and privacy issues, resource intensity, lifecycle management, change management, and validation and verification, but the advantages once you have the system integrated into the organization can be the difference between flat or exponential growth, without losing sight of the fact that flat growth is ultimately death.
