Digital Twin Technology

Digital twin technology involves creating a virtual replication of a physical object, system, or process. This digital counterpart is designed to accurately reflect the real-world entity, allowing for simulations, monitoring, and analysis in near real-time.

Here are some key aspects of digital twin technology:

• Near real-time data integration: Digital twins can be connected to real data sources, such as sensors on physical objects, which continuously update the digital model to reflect the current state of the physical counterpart.
• Simulation and analysis: They can simulate various scenarios to predict outcomes, optimize performance, and identify potential issues before they occur.
• Lifecycle management: Digital twins span the entire lifecycle of the physical entity, from design and manufacturing to operation and maintenance.
• Enhanced decision-making: By providing a comprehensive view of the physical entity, digital twins help in making informed decisions, improving efficiency, and reducing costs.

Digital twins are used in various industries, including manufacturing, healthcare, smart cities, and more, to enhance performance, predict maintenance needs, and optimize operations. GE Vernova leverages digital twins in several of its software solutions. For example, for energy industries digital twins are used for predictive analytics to reduce unplanned downtime. For power generation, a digital twin is enhanced with AI/ML for automated gas turbine tuning to significantly reduce missions. For manufacturing, digital twins are used for advanced analytics and near real-time data integration to improve industrial processes.

A digital twin in manufacturing is a virtual representation of a physical asset, process, or system. It integrates near real-time data from sensors and IoT devices to create an accurate simulation that reflects the current state of the physical counterpart. This allows manufacturers to finetune production processes, predict and prevent equipment failures, improve efficiency and quality of part production, and enable data-driven design and optimization. They can also enable near real-time diagnostics and prognostics and enable plug-and-play customization and modular improvement.

Key benefits of digital twins in the manufacturing industry include enhanced decision-making through data analytics, reduced downtime by anticipating failures, and improved product development cycles through testing and simulations. Digital twins enable manufacturers to experiment with changes in a virtual environment before applying them in the real world, leading to increased efficiency and innovation. This concept supports Industry 4.0 initiatives by fostering a more connected and responsive manufacturing ecosystem.

There are different methods and steps for creating and building a digital twin. The first step is to define the purpose of the digital twin, which will determine the model type. For example, is the digital twin for future state, present state, or predictive maintenance or process optimization? The next step varies on the standard work of the team. It could be collecting the basic data on how the physical product operates, creating a blueprint or building a detailed model, and then pairing it with the data.

Note: to accurately represent the real-world object or process, a deep understanding of the object or system's design, performance and maintenance requirements are critical. If data was collected first, developers then will create blueprints or simulations that are based on that data. Either way, the information is fed into the model to create a digital twin. If the digital twin is to provide near real-time data, developers can use coding to use real-time data to update and replicate what is happening in the real world. The digital twin is connected to the physical asset through data streams. Next, data analysis and algorithms are implemented. Machine learning, for example, will be used for predictive analytics. To visualize the data and insights, a developer will create interfaces and dashboards.

Digital twins simulate the behavior, performance, and interactions of its physical counterpart. Depending on the maturity of the digital twin technology, people may be able to visualize the data or receive insights and recommendation on how to optimize. In advanced software, digital twins can also be used for What-If scenarios and/or predict behavior.

When you purchase a solution that leverages digital twins, the vendor will likely ask your company to assemble a team that includes IT, operations, and users. This team is necessary to integrate data from various sources such as IoT sensors, systems, and other databases. A digital thread will be established to ensure a continuous flow of data between the physical and digital worlds. Before making the purchase, the vendor will check if your company has all the required technology. Sometimes, this technology, such as an Edge device, is included in the solution package. Other required technology, like IoT sensors and data storage, might need to be purchased separately.

Next, the digital twin model is built. This is followed by validation and testing to ensure accurate real-time or near-real-time data collection. A reliable data collection infrastructure is essential to capture and transmit the data. This process may involve multiple systems or a single cloud platform. Digital twin implementation may also require additional sensors.

To receive insights, alerts, and recommendations, deep domain knowledge is needed to build machine learning models and algorithms. Interoperability is crucial for integrating the digital twin into simulations for accurate modeling. The final step is to deploy the digital twin and implement it in your company’s operational environment.

Digital twins do not inherently come with Artificial Intelligence (AI). If the digital twin is integrated with AI, however, can be integrated with AI. If integrated with AI, the digital twin will be able to perform more complex tasks typically done by humans, such as making autonomous decisions and interacting with other AI systems. AI ranges in abilities and may include machine learning, natural language processing, robotics and more. AI can also simulate various scenarios and predict outcomes. Generative AI can create new data and generate possible future states of physical object or system. This can be used to test different methods or strategies.

Machine Learning is a subset of AI that focuses specifically on developing algorithms that can learn from data and includes pattern recognition. Since ML excels at recognizing patterns and correlations in data, ML digital twins can help with predictive maintenance, anomaly detection and optimization based on historical data. Domain expertise is essential for creating accurate and relevant models. The domain expert(s) will have knowledge about the physical object's behavior, constraints and operational environment, all of which improve the model performance.

Digital twins have numerous benefits, including:

• Improved decision-making with real-time data and insights. Advanced digital twins can simulate different scenarios and predict outcomes helping to optimize processes.

• Enhanced product development with virtual prototypes to identify potential issues and refining prior to physical production.

• Predictive maintenance by monitoring the condition of assets and equipment and alerting to an issue before it happens, reducing downtime, and extending the lifespan of assets.

• Operational efficiency with continuous analysis of data, digital twins can help to optimize operations and reduce costs.

• Risk management with simulating and analyzing potential risks enabling improved preparedness and mitigation strategies.

• Sustainability with help monitoring and reducing environmental impact by optimizing resource usage and minimizing waste.

The challenges that digital twins solve depend on the industry and purpose for which the digital twin was created. Here are some key challenges that digital twins help solve:

Operational efficiency to identify inefficiencies and optimize processes by simulating different scenarios and predicting outcomes.

Downtime reduction by monitoring systems in near real-time, digital twins can predict and prevent equipment failures.

Remote management can be enabled by digital twin software, which can be particularly useful for power generation in remote and hard-to-reach areas, construction, and manufacturing.

Cost savings by optimizing operations and maintenance schedules, digital twins can help to significantly reduce costs.

Data driven decision-making by providing real world insights.

Sustainability with visibility on ways to optimize resource usage to reduce greenhouse gas emissions.

In the oil and gas industry, a digital twin is a dynamic, virtual representation of a physical asset or system. It can be created using near real-time data. Digital twin technology offers several benefits and applications:

Predictive Maintenance: Digital twins continuously monitor equipment performance and can be designed to use predictive analytics to forecast potential failures, allowing for timely maintenance and reducing downtime.

Operational Optimization: By simulating various operational scenarios, digital twins help optimize processes, improve efficiency, and boost productivity. For example, they can model the flow of oil and gas through pipelines to identify bottlenecks.

Asset Performance Management: Digital twins provide a comprehensive view of asset performance, enabling better management and optimization of critical assets like oil rigs, refineries, and pipelines.

Safety and Emergency Preparedness: Digital twins can be designed for running simulations where the digital twin model output is projected into the future based on past data to simulate emergency situations, helping operators prepare and respond more effectively to potential hazard.

Environmental Monitoring: Digital twins help monitor environmental parameters, ensuring compliance with regulations and minimizing the environmental impact of operations.

A real example of a digital twin is GE Vernova's Asset Performance Management (APM) SmartSignal predictive analytics software. The solution leverages digital twins of critical assets such as turbines, compressors, and entire power stations. The digital twins in SmartSignal use near real-time data from various sensors to monitor and predict the performance and maintenance needs of these assets. By using digital twins connected to near real-time data, predictive maintenance and prevention of unplanned downtime is made possible. Total EP uses SmartSignal to achieve zero unanticipated failures by leveraging a monitoring center powered by this digital twin technology.

Another real-world example is STEG that caught a 4MW recovery using GE Vernova APM Performance Intelligence, which leverages physics-based digital twins to ensure entitlement. When assets are not reaching entitlement, the solution is able to provide visibility to where the issue is occurring and recommendations on how to fix it. This is all possible from digital twin models customized to the plant's equipment, system design and dispatch provide.

The four main types of digital twin are component twins, asset twins, system twins, and process twins. Each type of digital twin offers unique benefits and can be sued in various industries to improve efficiency, reduce costs and enhance performance.

Component twins are digital models of individual components or parts, such as motors, sensors or valves. They provide detailed information about a component's performance and behavior. If connected to sensors, this might be in real time or information over time.

Asset Twins represent entire assets, such as machines, gas turbine or vehicles and include all the components that make up the asset. Asset twins help in understanding how different components interact and affect the overall performance of the asset.

System twins are digital representations of entire systems such as a production line or a power plant. System twins provide insights into how different assets work together within a system, helping to optimize performance and efficiency.

Process twins focus on the process and workflows within a system. They help in understanding and optimizing the flow of operations, such as manufacturing processes or supply chain logistics.

Different mathematical models can be used in the four types of digital twins. For example, physics-based models in a component twin will model a motor understanding its thermal and mechanical behavior under different operating conditions, whereas in system twins, a digital twin may simulate the interactions between various assets (like gas turbines and generators) to optimize overall efficiency and predict potential failures.

Digital twins are used in a variety of applications across different industries. Key examples include:

Predictive maintenance: By continuously monitoring the condition of equipment, digital twins can predict when maintenance is needed, as well as extend the lifespan of assets. GE Vernova’s SmartSignal, for example, uses digital twin technology to help companies prevent unplanned downtime and unexpected failures.

Product Development: They allow engineers to test and refine designs in a virtual environment before physical prototypes are built, speeding up the development process and reducing costs.

Performance Optimization: Digital twins can simulate different scenarios to find the most efficient way to operate a system or process, leading to improved performance and reduced cost.

Improved Decision-making: With near real-time data and insights, advanced digital twins can simulate different scenarios and predict outcomes helping to optimize processes.

Operational efficiency: with continuous analysis of data, digital twins can help to optimize operations and reduce costs.

IoT (Internet of Things) and Digital Twins are closely related but serve different purposes.

IoT refers to the network of physical devices (sensors, meters, turbines, transformers, etc) connected to the internet, collecting and transmitting data. Examples of how Energy industries use IoT include: real-time monitoring, predictive maintenance using sensor data and energy consumption tracking and optimization.

Digital twins are a virtual replica of a physical asset, system, or process that uses real-time data (often from IoT devices) to simulate, analyze and optimize performance. Energy companies may use digital twins to simulate grid behavior under different conditions, optimize asset performance and lifecycle, and test scenarios without affecting the real system, such as load changes and outages. Predictive analytics is also a common methodology that uses digital twins, often powered by AI/ML.

IoT provides the data and digital twins use that data to create actionable insights. For example, SmartSignal takes sensor data and uses that data, along with historical data for early failure detection.

Digital twins are virtual replicas of physical assets, systems, or processes. In the energy industry, they are widely used for predictive maintenance, cost savings, and operational efficiency. Today, digital twins deliver transformative results across several domains, including data collection, near real-time simulation, predictive analytics, visualization, scenario testing, and optimization.

Digital twins, although very impactful today, still face growth opportunities. Key growth areas include interoperability between systems and ability to model complex biological or environmental systems, such as geothermal or biomass energy assets. The market outlook for digital twins remains strong, with significant growth projected through 2030. The energy sector is expected to be one of the fastest-growing adopters, particularly in areas like renewables, grid modernization, and smart infrastructure.

To prepare for the future of digital twins, energy companies should focus on:
• Cross-sector collaboration, such as integrating electricity, hydrogen, and mobility systems.
• Improving interoperability to scale digital twin ecosystems across assets and vendors.
• Investing in enabling technologies like artificial intelligence, edge computing, and cloud infrastructure to support real-time responsiveness and scalability.
• Embedding sustainability metrics into digital twin models to support ESG goals and decarbonization strategies.

The Role of Generative AI:
Generative AI is an emerging force in digital twin development, offering new capabilities such as:
• Automatically generating simulation models from raw or unstructured data.
• Conversational interfaces that allow users to interact with digital twins using natural language.
• Low-code/no-code development, enabling faster deployment and broader accessibility.

These innovations accelerate development cycles and enhance usability, but they also introduce risks.
Risks and challenges with GenAI and Digital Twins include:
• Inaccurate or misleading outputs due to over-reliance on synthetic data.
• Cybersecurity threats, including data leakage, model poisoning, and unauthorized access.
• Regulatory and compliance risks, especially in critical infrastructure.
• Ethical and workforce impacts, such as job displacement and bias in decision-making.

Preparing for the Future
To successfully leverage digital twins and emerging technologies, companies should:
• Invest in cybersecurity to protect data and models.
• Use hybrid models that combine GenAI with physics-based simulations for reliability.
• Establish governance frameworks for AI ethics, transparency, and compliance.
• Upskill the workforce to collaborate effectively with AI tools and digital twin platforms.

Digital twins enhance operational efficiency by enabling smarter, faster, and more informed decision-making across asset lifecycles. Here’s how:

1. Predictive maintenance: Digital twins use real-time sensor data and historical trends to predict equipment failures before they occur. This reduces unplanned downtime, lowers maintenance costs, and extends asset life.

2. Performance optimization: Operators can simulate different operating conditions to find the most efficient configurations. This helps optimize fuel usage, energy output, and load balancing.

3. Near real-time monitoring and control: Digital twins provide a live, virtual view of assets and systems, enabling faster response to anomalies or inefficiencies. They support remote diagnostics, reducing the need for on-site inspections and provide improved visibility and control over the health and status of critical equipment.

4. Scenario testing and risk mitigation: Digital twins allow teams to test “what-if” scenarios without disrupting operations. This helps in planning for outages, demand spikes, or integrating new technologies.

5. System integration and collaboration: Digital twins unify data from multiple systems (IoT, SCADA, ERP), creating a single source of truth. This improves cross-functional collaboration and reduces inefficiencies caused by siloed data.

6. Sustainability and ESG tracking: They help monitor energy consumption, emissions, and resource usage in near real-time. This supports compliance with environmental regulations and corporate sustainability goals.

Examples from the Real-World:

• Combined-cycle plant: A digital twin of a heat recovery steam generator (HRSG) can detect early signs of fouling or tube leaks, allowing maintenance teams to intervene before efficiency drops or damage escalates.

• Oil & gas: A digital twin of a compressor station can monitor vibration and temperature trends to predict seal wear, preventing costly shutdowns and environmental risks.

• Metals & mining: Simulating conveyor belt load balancing and crusher throughput using a digital twin integrated with SCADA and ERP systems to optimize production schedules and reduce energy consumption across departments.

Digital twins are being used across a wide range of industries, from energy to healthcare. They leverage the technology to improve efficiency, safety, and innovation. Here are some key industry adopters of digital twin technology:

1. Energy and Utilities for grid simulation, predictive maintenance for turbines, and optimization of renewable energy assets.

2. Manufacturing for near real-time monitoring of production lines, quality control, and virtual commissioning.

3. Oil & gas for use in asset integrity management, reservoir modeling, and emergency scenario testing. Digital twins are often used to help reduce downtime and improve safety in both upstream and downstream operations.

4. Metal & mining for drilling, hauling and processing operations. Digital twins are used to improve energy efficiency, predictive maintenance and ESG tracking.

5. Automotive for simulating vehicle performance, testing autonomous systems, and managing supply chains.

6. Aerospace & Defense for aircraft system simulation, maintenance planning, and mission readiness. This is used to enhances safety and reduces lifecycle costs.

7. Healthcare for patient-specific digital twins for personalized treatment and surgical planning. Can also used in hospital operations and medical device development.

8. Smart buildings and infrastructure for energy management, HVAC optimization, and occupancy modeling. Digital twins for this industry support sustainability and operational efficiency.

9. Logistics and supply chain for simulating warehouse operations, transportation routes, and inventory flows. Digital twins mainly used to improve responsiveness and reduces costs.

10. Urban planning and smart cities for modeling traffic, utilities, and environmental impacts. Digital twins can help cities plan infrastructure and respond better to emergencies.