What Is a Digital Twin?

What is Digital Twin?

A digital twin can be described as a virtual representation of a physical asset or process. These sophisticated models are developed using engineering simulation software, allowing them to replicate the behavior, performance, and environmental conditions of their real-world counterparts in real-time. The creation of a digital twin begins with engineers constructing a detailed 3D model, which is then integrated with relevant data, such as inspection results and sensor data. This comprehensive information is subsequently input into the model, resulting in the formation of an accurate digital twin.

What types of Digital Twin?

• Component Twins represent the most detailed category of Digital Twin technology, focusing on individual components or parts within a larger system. Examples include motors, sensors, and valves. This type of Digital Twin is invaluable for monitoring the health, performance, and operational efficiency of specific components. By leveraging both real-time and historical data analysis, organizations can detect wear, malfunctions, or inefficiencies at an early stage, facilitating predictive maintenance. This proactive approach not only extends the lifecycle of components but also significantly reduces the likelihood of unexpected downtime.
• Asset Twins are digital representations of physical assets, encompassing a network of interconnected components. These can range from machines to entire facilities. By offering a comprehensive view of an asset's performance and condition, Asset Twins enables organizations to optimize utilization, lower operational costs, and enhance uptime through the integration of data from various sources. Asset Twins can monitor a machine's performance throughout its lifecycle, facilitating improvements in efficiency and waste reduction.
• System Twins are a sophisticated representation of a network of interconnected assets that collaboratively fulfill a specific operational role. As a vital category of Digital Twin technology, System Twins play an integral role in analyzing and enhancing the performance of complex systems. Organizations can leverage this technology to simulate diverse operational scenarios, anticipate potential bottlenecks, and optimize system interactions for peak efficiency.
• Process Twins represent a highly sophisticated approach to capturing and analyzing workflows or processes in their entirety. These models encompass various domains, including manufacturing processes, supply chain operations, and service delivery pipelines. By integrating data from multiple sources, Process Twins provides a comprehensive view of owner processes. Organizations can leverage this technology to enhance productivity, ensure quality control, and achieve substantial cost savings. By identifying inefficiencies, predicting potential disruptions, and optimizing workflows, businesses can make informed decisions that drive success.

Applications of Digital Twins

NASA

The idea of a “digital twin” was born at NASA in the 1960s as a “living model” of the Apollo mission. In response to Apollo 13’s oxygen tank explosion and subsequent damage to the main engine, NASA employed multiple simulators to evaluate the failure and extended a physical model of the vehicle to include digital components. This “digital twin” was the first of its kind, allowing for a continuous ingestion of data to model the events leading to up to the accident for forensic analysis and exploration of next steps. Fast forward half a century and NASA, along with others in the aerospace community, continues to develop and utilize high-fidelity digital models of physical systems and components as well as the extreme environments in which they operate. NASA aims to travel further and stay longer in space as we realize the Artemis program, taking us from the moon to Mars by establishing a sustainable presence on the Moon to prepare for missions to Mars. We will no longer be able to rely on constant connectivity with an asset nor be in-the-loop for on-demand human intervention in the event of an anomaly. Further, the importance of digital twins is increasing as we seek alternatives for certification of structures so large that they cannot be fully evaluated in existing test facilities and autonomous systems that are not deterministic. The idea of a digital twin is not a new one. What is new is the scale, ordinality, and non-deterministic nature of the models that are critical to achieving NASA’s goals. Their number and autonomy from each other as well as the reference system is suggestive of a changing ecosystem, returning us the idea of a living model.

Oil and Gas Industry

The oil and gas sector generates about USD 3.3 trillion annually in revenue, making it one of the largest sectors in the world in terms of dollar value. The emergence of DT technology in the oil and gas industry has brought tremendous value to this industry by being an effective tool in, (i) reducing and a better understanding of risks, creating and managing executable work schedules, and identifying any changes in the process and responding accordingly. Additionally, DT can be a critical resource for oil companies when it comes to extracting offshore resources. Due to all these benefits of DT, Accenture, in one of their surveys from 2017, found that out of 200 refineries, 66% of them were planning to increase their investments in digital transformation in the coming years, and 57% already had invested more than the previous year.

BP (British Petroleum), one of the largest oil and gas companies in the world, has been using DTs for several of their plants in the North Sea called APEX. APEX helps them in safely optimizing the production, remote surveillance, predictive maintenance, saving time in optimizing, and testing. The most notable benefit contributed by DTs for BP was a boost in oil and gas production by 30,000 additional barrels per day globally.

Another oil and gas company to embrace the DT technology is Royal Dutch Shell. By collecting more than 10 million operational data per minute, they use it for maintenance, improvement in productivity and safety, and reduction in emissions by anticipating the conditions to optimize asset performance and managing them autonomously. They also developed an improved fatigue model for their assets using the DT concept by combining the data from sensors with a structural finite element model to accurately predict the life of assets so that they can optimize the inspection planning and safety cases around it. Other companies, namely Eni and Equinor, are also exploiting the technology to maximize productivity while keeping the costs and risks at a minimum.

Siemens also studied the benefits of deploying DTs in the oil and gas industry and found out that the project cycle was shortened by four–eighteen weeks in total: four weeks in stabilizing the operation and eight weeks in the engineering phase. Moreover, the capital expenditure and operation expenditure were also reduced by USD 4–7 million and USD 60–100 million, respectively. It was also suggested that creating a DT as a part of the project rather than retrofitting it to the existing plant is more economical.

Since the oil and gas industry relies on heavy and sophisticated plants as well as machinery located in remote areas under extreme environmental conditions, a DT makes it safer to monitor and control the activities/processes, thus reducing the risks involved. Moreover, by predicting the downtime, the DT improves the overall process, which directly translates into savings both in terms of time and money.

Maritime and Shipping

Anything or any activity related to the ocean, sea, ships, navigation of ships from one destination to another, seafarers, etc., is represented by the maritime industry which also includes the transportation of passengers and freight on both inland and international waters. Globally, around 80% of trade by volume and 70% by value is carried by sea, according to the United Nations (UN), making it the most important means of transport for goods. Like the aviation industry, the main application of DT in this industry focuses on increasing the reliability of the assets, improving maintenance, and reducing operational costs.

Military Sealift Command, the leading organization for providing military transport ships to the United States Navy and Department of Defense, with the help of GE, is building a DT for their cargo ammunition vessels. The data collected from marine equipment, such as variable frequency drives, propulsion motors, diesel engines, and generators, are used to check the real-time performance of the vessel against the calculated one, and any performance deviations that indicate potential failure in the engines or other critical infrastructure are reported and resolved before it ever happens, thus improving their assets’ and missions’ availability, efficiency, operations, and readiness. The DT also makes remote monitoring and diagnostics possible.

In 2019, DNV GL, the creator of the WindGEMINI, which is also the world’s leading classification society and a recognized advisor for the maritime industry, implemented DT technology on one of the world’s largest crane vessels. The vessel demands constant inspections due to its large lifting capability of 14,000 tons, which puts the vessel under high stress. The DT makes reporting, assessing problems, maintenance planning, and predictive analysis easier, which results in huge cost savings. DNV GL believes that DT can bring significant benefits to everyone involved in the maritime industry, including ship owners, equipment manufacturers, authorities, universities, maritime academies, and consultancy services.

The DT technology is not restricted to just vessels/ships, but it is finding its way to ports as well. The Port of Rotterdam has sensors throughout its docks that gather real-time data of the environment around and water conditions, which includes air temperature, wind speed, humidity, turbidity, water salinity, flow, levels, tides, and currents. The port even has ‘Digital Dolphins’, smart quay walls, and sensor-equipped buoys. In addition, they have a physical container, Container 42, fitted with a sensor, which uses AI to optimize the time for the vessel to sail and several cargos to carry. They are planning to become the first digital port by 2030.

Digital Twin Software

ANSYS Twin Builder

An analytics-driven, simulation-based digital twin is a connected, virtual replica of an in-service physical asset — in the form of an integrated multidomain system simulation —that mirrors the life and experience of the asset. Hybrid digital twins enable system design and optimization and predictive maintenance, and they optimize industrial asset management. By implementing Ansys Twin Builder, you can improve top-line revenue, manage bottom-line costs and both gain and retain a competitive advantage.

Bentle Systems iTwin

Stands out as a prominent platform extensively utilized by organizations to develop digital twins. Built on open APIs and sophisticated digital twin software, iTwin empowers companies to create innovative digital twins and Software as a Service (SaaS) solutions. This platform not only provides customers with access to extensive datasets but also ensures they retain ownership of their digital twins. Recognized as one of the foremost digital twin platforms in the industry, iTwin effectively manages data flows, security, and the overall maintenance of clients’ digital twins. Consequently, Bentley Systems iTwin has garnered significant interest from clients such as Allvision IO and SmartViz, who leverage the platform to construct digital twins for various buildings and infrastructure projects.

AVEVA

Whith an industrial platform, CONNECT, along with AI applications and visualization solutions forms the foundation of a digital twin. Digital twin that can begin with your conceptual design and mature with your project into operations and optimization. Aveva software connects engineering data with operations data and then brings it into industrial platform where it is consolidated, cleansed, and aggregated to build a representation of equipment, systems, or assets. Because CONNECT platform integrates seamlessly with IoT devices, AI algorithms, and data analytics tools—whether they’re AVEVA or third-party tools—companies are able to leverage their existing investments.

Digital Twin Services in Capnor

We are a company with deep ties to the oil and gas industry. With two decades of experience, we understand that delivering the highest quality products and maintaining efficiency are paramount for our partners. Utilizing cutting-edge equipment from Leica Geosystems and Aveva Software, we can seamlessly transform any installation into a sophisticated 3D model as part of our advanced 3D Digital Twin System.

Based on our experiences with Equinor, Transocean, and COPNO, we recognize that the primary responsibility for effectively operating the facility lies in the maintenance of existing systems. Through ongoing modifications, we can convert "blueprints" into a comprehensive 3D model, integrating it into an intricate Digital Twin system managed by our clients.

To ensure the complexity of a system we also deliver our in-house solutions AYELIX and IOM.  On Ayelix you have a visual representation of the site object that you are working on. 3D Laser scans data based on facility coordinates calibrated with a plot plan letting you quickly view specific areas, check coordinate position, and easily create measurements. You can feel like you are there by remotely reviewing Ayelix pointcloud data.

The future of digital twins is highly promising, driven by advancements in IoT, AI, and data analytics. These virtual replicas are set to revolutionize industries by enabling real-time monitoring, predictive maintenance, and simulation for decision-making. Key trends include: Enhanced AI Integration, IoT Proliferation, Expanded Use Cases, Edge Computing, and Sustainability.

These developments position digital twins as critical tools for innovation, operational efficiency, and strategic planning in the coming years that CAPNOR will be part of.