Maersk’s digital twin ecosystem

Maersk’s digital twin ecosystem integrates advanced algorithms, machine learning, AI, IoT sensor networks, and satellite connectivity for operational optimization, predictive analytics, and real-time decision-making; each component plays a specific technical role for their vessels and logistics platforms.

Algorithms and ML Techniques

Voyage Simulation Algorithms: Maersk’s digital twins simulate “ghost ship” voyages using data-driven algorithms that include time series analysis, regression models, and vessel hydrodynamics optimization; these help forecast fuel consumption, emissions, and routing efficiency before a voyage is booked.

Predictive Modeling: ML models (e.g. XGBoost, Random Forest, Neural Networks) are used to estimate future cargo demand, predict maintenance needs (predictive maintenance), detect anomalies (such as abnormal sensor readings), and optimize speed and course under varying weather and market conditions.metalab.

Prescriptive Analytics: Reinforcement learning and optimization algorithms help select the best speed and route for fuel and time savings, leveraging vessel, weather, and trade data.

Artificial Intelligence Capabilities

AI for Real-Time Operations: Computer vision, time-series forecasting, and optimization AIs continuously monitor sensor inputs, vessel status, and logistics data; these systems perform anomaly detection and health forecasting for engines, cargo holds, and supply networks.

Scenario Modeling: AI-enabled digital twins help planners run “what-if” scenarios: e.g., impact of supply chain disruptions, reroute simulations, and dynamic inventory management for resilience and risk mitigation.

Sustainability Optimization: AI models track and recommend operational changes to minimize emissions, optimize energy use, and schedule cargo transfers, enabling measurable carbon reduction and cost savings.

IoT Sensor and Edge Analytics

IoT Sensor Networks: Vessels are fitted with thousands of IoT sensors—engine, hull, weather, cargo, and environmental status—which stream real-time telemetry to Maersk’s analytics backend.

Edge Computing: Machine learning models run directly on vessels (“at the edge”), enabling instant analysis of fuel consumption, cargo condition, and climate impacts, often when vessels lack continuous shore connectivity.

AI-enabled Alerts: IoT analytics power automated risk detection and alerting—such as engine overheating, cargo temperature excursions, and hull damage—allowing prompt corrective action.

Satellite Connectivity and Communications

Global Satellite Networks: Maersk uses high-bandwidth Maritime satellite networks (Inmarsat) for fleet-wide, always-on connectivity for telemetry, cloud-based digital twin services, and crew welfare.

Cloud-Enabled Vessel Operations: Satellite links allow seamless data flows for real-time analytics, digital twin updates, and continuous crew and office communications, supporting both business processes and life aboard.

Remote Operations and Automation: Satellite connectivity unlocks “floating office” concepts a cloud-based applications, real-time fleet coordination, and even future autonomous vessel operations.

Example Data Pipeline Workflow

• Technology

• Operations

• Role

• Data collection

• IoT sensors, AIS feedsMeasure fuel, speed, weather, cargo, ship location

• Edge analytics Python ML models Forecast consumption, detect risks, local decisions

• Cloud integration Satellite, cloud APIs

• Sync vessel data, run digital twin simulations.

• Predictive insights

• ML/AI algorithms

• Optimize routing, maintenance, emissions.

• Prescriptive action

• Optimization

• AI Recommend speed/course, schedule, risk mitigations.

Maersk’s architecture blends data-driven AI, ML models, ubiquitous IoT analysis, and high-reliability satellite infrastructure to enable resilient, sustainable, and highly autonomous fleet and logistics management.

You can get more details here

Web Based Digital Twin with WebGL

 

                                                                        by researchgate

You can convert a Unity digital twin to a browser by using WebGL. WebGL is a JavaScript API that allows you to render 3D graphics in a web browser.

There are a few different ways to convert a Unity digital twin to WebGL. One way is to use the Unity WebGL exporter. The Unity WebGL exporter will export your digital twin to a set of HTML, CSS, and JavaScript files. You can then host these files on a web server and access them in a web browser.

Another way to convert a Unity digital twin to WebGL is to use a third-party tool, such as WebGL Studio. WebGL Studio is a cloud-based platform that allows you to convert Unity projects to WebGL without any coding required.

Once you have converted your Unity digital twin to WebGL, you can then access it in a web browser using any WebGL-compatible browser, such as Chrome, Firefox, or Edge.

Here are some additional tips for converting a Unity digital twin to WebGL:

• Make sure that your Unity project is compatible with WebGL. This means that you cannot use any Unity features that are not supported by WebGL.
• Optimize your Unity project for WebGL. This means reducing the number of polygons and textures in your project.
• Use a third-party tool, such as WebGL Studio, to convert your Unity project to WebGL without any coding required.
• Test your WebGL digital twin in a web browser before deploying it to production.

                                                            by mdpi

Here is an end-to-end example of how to convert a Unity digital twin to WebGL:

1. Create a Unity project and import your digital twin model.
2. Make sure that your Unity project is compatible with WebGL. This means that you cannot use any Unity features that are not supported by WebGL. You can find a list of supported features on the WebGL documentation page.
3. Optimize your Unity project for WebGL. This means reducing the number of polygons and textures in your project. You can use the Unity Profiler to identify areas of your project that need to be optimized.
4. Export your Unity project to WebGL. To do this, go to File > Build Settings and select WebGL from the Platform dropdown menu. Then, click the Build button.
5. Host the exported WebGL files on a web server.
6. Access your WebGL digital twin in a web browser using any WebGL-compatible browser, such as Chrome, Firefox, or Edge.

Here is a more detailed example of how to use WebGL Studio to convert a Unity digital twin to WebGL:

1. Create a WebGL Studio account.
2. Upload your Unity project to WebGL Studio.
3. Select the WebGL export option.
4. Click the Convert button.
5. Once the conversion is complete, download the exported WebGL files.
6. Host the exported WebGL files on a web server.
7. Access your WebGL digital twin in a web browser using any WebGL-compatible browser, such as Chrome, Firefox, or Edge.

Once you have converted your Unity digital twin to WebGL, you can then use it in a variety of ways. For example, you could use it to create a virtual showroom for your products, or you could use it to provide your customers with a way to visualize and interact with your products before they buy them. You could also use your WebGL digital twin to create a training simulator for your employees, or you could use it to create a marketing tool for your business.

Here is a simple code example of a WebGL digital twin:

<!DOCTYPE html>

<html>

<head>

  <title>WebGL Digital Twin</title>

  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>

  <script src="three.min.js"></script>

</head>

<body>

  <canvas id="myCanvas"></canvas>

</body>

</html>

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var canvas = document.getElementById("myCanvas");

var renderer = new THREE.WebGLRenderer({canvas: canvas});

// Create a scene

var scene = new THREE.Scene();

// Create a camera

var camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 1, 1000);

camera.position.set(0, 0, 100);

// Create a light

var light = new THREE.AmbientLight(0xffffff);

scene.add(light);

// Create a geometry

var geometry = new THREE.BoxGeometry(10, 10, 10);

// Create a material

var material = new THREE.MeshBasicMaterial({color: 0x00ff00});

// Create a mesh

var mesh = new THREE.Mesh(geometry, material);

scene.add(mesh);

// Render the scene

renderer.render(scene, camera);

This code will create a simple WebGL digital twin of a box. You can modify the code to create more complex digital twins, such as digital twins of factories, buildings, and other real-world objects.