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Advanced Manufacturing

Digital Twins in Aerospace

Digital twins are virtual replicas of physical assets that integrate real-time data, predictive analytics, and simulations. In aerospace, they allow for improved design validation, performance optimization, maintenance forecasting, and lifecycle management. By using digital twins, aerospace companies can reduce costs, increase operational efficiency, enhance safety, and accelerate innovation.

Aircraft Design & Development

Simulate the entire aircraft, including its aerodynamics, structures, and systems, allowing engineers to test different designs, materials, and configurations in a virtual environment. This reduces the need for physical prototypes and accelerates the design process.

Predictive Maintenance

Collect real-time data from onboard sensors to monitor the health of critical components (e.g., engines, landing gear). Predictive algorithms analyze the data to forecast potential failures and recommend maintenance actions before issues arise.

Operations Optimization

Digital twins of aircraft in operation analyze flight data, fuel consumption, weather conditions, and air traffic patterns to optimize flight routes and performance in real-time. Airlines can simulate and implement operational improvements for better fuel efficiency and reduced emissions.

Satellite Monitoring

Continuously monitor mission-critical systems such as propulsion, power, and thermal controls. Simulates how the spacecraft responds to different conditions and allows ground control to make adjustments to ensure optimal performance and longevity.

Digital twins to maximise maintenance efficiency

Manage maintenance programs by leveraging real-time data from aircraft systems, predictive analytics, and simulations to ensure timely, efficient, and proactive maintenance.

Continuous Health Monitoring: track the condition of critical components, such as engine performance, oil levels, and wear-and-tear on parts. Allow the maintenance team to monitor the health of the aircraft continuously, detecting even small deviations from normal operating conditions.

Predictive Analytics: By analyzing historical and real-time data, digital twins use predictive algorithms to forecast when specific components or systems are likely to fail. These predictions allow the company to schedule maintenance activities before a failure occurs, minimizing the risk of in-flight malfunctions.

Fleet-Wide Optimization: For companies managing large fleets of aircraft, digital twins allow them to compare the condition and performance of multiple aircraft. Maintenance schedules can be optimized based on the specific condition of each plane, reducing the number of planes out of service at any given time.

Spare Parts and Resource Allocation: The digital twin forecasts the need for specific spare parts and resources by predicting component failures. This allows for better inventory management and ensures that the right parts are available when needed, reducing wait times for repairs.

Virtual Training for Technicians: Technicians can use the digital twin to simulate maintenance procedures in a virtual environment before performing them on actual aircraft. This ensures that technicians are well-prepared and familiar with the equipment, reducing the risk of errors during maintenance tasks.

Digital Twins in Shipbuilding

The shipbuilding industry involves complex design, construction, and operational processes. As a virtual replica of a physical ship or its components, a digital twin helps streamline these processes by enabling real-time data analysis, simulations, and predictive maintenance. By leveraging digital twins, shipbuilders can optimize designs, reduce costs, improve operational efficiency, and ensure long-term reliability of ships. The technology enhances decision-making across the entire ship lifecycle, from design and construction to operation and maintenance.

Ship Design and Development

Digital twins simulate the ship’s design, including hull structure, propulsion systems, and interior layouts, allowing engineers to optimize designs and test various configurations in a virtual environment. This reduces the need for costly prototypes and shortens design cycles.

Predictive Maintenance of Ships

Monitor the condition of critical systems such as engines, hull structures, and propulsion systems in real time. Predictive analytics detect early signs of wear or potential failures, allowing ship operators to perform maintenance before issues arise.

Operational Optimization

Track real-time operational data, including fuel usage, engine performance, and environmental conditions. Using this data, the twin can be used to optimize routes, speeds, and operating conditions to enhance fuel efficiency and reduce emissions.

Safety and Risk Management

Simulate real-time conditions, such as storm impacts, structural stress, and emergency scenarios. A twin can also be used to test and validate safety protocols, helping operators understand how the ship will respond in various situations and ensuring they are prepared to handle potential risks.

Manage maintenance and upgrades

By creating virtual models of ships and their systems, which are continuously updated with real-time data from onboard sensors. This enables the company to monitor the condition of critical systems, predict potential failures, and optimize upgrade decisions

Continuous Condition Tracking: The digital twin monitors essential parameters such as temperature, vibration, pressure, and wear levels. For example, the twin can detect abnormal vibrations in the engine or changes in the stress levels on the hull.

Failure Forecasting: By continuously assessing component performance, the digital twin can forecast when parts like pumps, turbines, or propellers are likely to fail. This prevents unexpected breakdowns by allowing maintenance teams to intervene before the issue affects ship operations.

Lifecycle Management: Throughout the ship’s lifecycle, digital twins provide a comprehensive record of maintenance activities, upgrades, and performance data. This helps ship operators make decisions about when to perform major overhauls, retire components, or upgrade key systems to extend the ship’s operational life.

Upgrade Feasibility Testing: Before performing a major system upgrade or modification, the ship’s digital twin allows engineers to simulate the impact of these changes on ship performance. For example, upgrading propulsion systems or installing new equipment can be modeled to ensure compatibility and performance improvements.

Digital Twins in Automotive*

A digital twin is a virtual model of physical assets, systems, or processes that mirrors real-time data and allows for monitoring, optimization, and simulation. For water utility companies, digital twins are particularly beneficial because they help improve operational efficiency, reduce maintenance costs, enhance resource management, and support sustainability efforts. By leveraging real-time data and analytics, water utilities can predict issues, optimize resource distribution, and make informed decisions to ensure safe, reliable, and cost-effective water services.

Leak Detection and Prevention

A digital twin can simulate the entire water distribution system and use real-time data from sensors to detect pressure changes and anomalies. This helps in identifying leaks early and prioritizing repair actions. The system can also simulate different scenarios to predict future failures.

Demand Forecasting and Supply Optimization

A digital twin can forecast water demand by analyzing data from weather patterns, usage history, and population trends. This enables real-time adjustments in water supply, helping to optimize pumping schedules and water distribution.

Predictive Maintenance for Pumps and Equipment

Digital twins can monitor the performance of pumps and other equipment by collecting data on vibration, temperature, and flow rates. Machine learning algorithms can predict potential failures before they occur, enabling preemptive maintenance.

Water Quality Monitoring and Management

A digital twin integrates data from various water quality sensors that measure parameters like pH, turbidity, and chemical levels. It can simulate how contaminants might spread, enabling rapid response to water quality issues and ensuring compliance with safety standards..

Data Integration

Combine static and dynamic, spatial and non-spatial data together.

Drag and drop file importing

Connectors to ArcGIS, Maximo and other enterprise systems

API tools to build your own microservice connectors

Tools to build connections to IoT data sources

Hotspot link 3D models with 2D diagrams and drawings

Digital Twins in Steel Manufacturing

By leveraging digital twins, steel manufacturers can enhance production efficiency, improve product quality, reduce downtime, optimize resource usage, and ensure sustainability. Real-time monitoring, predictive maintenance, and process optimization are key benefits of integrating digital twins into steel manufacturing operations.

Process Optimization and Quality Control

Simulate and monitor the entire production process, from raw material input to final steel output. Optimize temperature controls, cooling rates, and other parameters to ensure the highest quality steel. Predict when variations in quality might occur and suggest adjustments.

Predictive Maintenance for Equipment

Continuously monitor equipment health using sensor data (e.g., temperature, vibration, and pressure), detecting early signs of wear or failure. Enable predictive maintenance by forecasting when parts will need repairs or replacements, reducing the risk of unexpected downtime.

Energy and Resource Optimization

Track energy consumption across the manufacturing process and identify areas where energy is wasted or underutilized. Simulate different operational scenarios to find the most energy-efficient settings, optimizing the use of electricity, water, and raw materials.

Supply Chain and Production Scheduling

Integrate production scheduling with supply chain management, providing a real-time overview of material availability, machine capacity, and production progress. Simulate and optimize production schedules to meet demand, reduce delays, and manage inventory effectively.

Intelligent maintenance planning

Manage maintenance by creating a virtual model of equipment and machinery, allowing for real-time monitoring, predictive analytics, and optimization of maintenance schedules.

Live Data Feed: Data is transmitted to the digital twin in real time, creating an up-to-date virtual model of the equipment’s current condition.

Condition-Based Maintenance: Instead of following a fixed schedule, the digital twin enables condition-based maintenance, where repairs or replacements are scheduled based on the actual condition of the equipment.

Resource Allocation: By predicting maintenance needs in advance, the digital twin allows the company to efficiently allocate resources like spare parts, personnel, and tools, ensuring they are available when needed.

Predicting Spare Part Needs: By analyzing the wear and tear of machinery, the digital twin can predict when parts will need to be replaced. This helps in ensuring that the correct spare parts are available when needed, preventing delays.