Digital twin development for airport management

A digital twin is a realistic digital representation of something physical. A digital twin must represent physical reality at a level of accuracy suited to its purpose. It is an integrated model of an as-built product, including the physics, fatigue, life cycle, sensor information, performance simulations etc. It is intended to reflect all manufacturing defects and be continually updated to include wear and tear sustained while in use. Research has been done on digital twins in a more theoretical way.

The digital twin is a virtual representation that interacts with the physical object throughout its life cycle and provides intelligence for evolution, optimisation, predictions, analyses etc. According to Fei Tao, four parts can be considered as the core of the digital twin, namely model, data, connections and services. The model reproduces the real proprieties, behaviours and rules of the physical equivalent with high fidelity and thus is an exact digital duplicate that records all changes of the physical one.

Data can be considered as a driver that provides intelligence to the digital twin. The connections enable every element in the digital twin interact with each other. The services are the final presentations of the digital twin, which are generally expressed in standard formats to describe their inputs, outputs, basic information and functions, according to the demand. Figure 1 shows the interaction of the physical environment with the digital environment sensors to make continuous measurements that are transmitted to a digital platform, which performs analyses in almost real time to optimise a business process transparently.

DIGITAL TWINS: ECONOMIC BENEFITS

Digital twins link physical systems and virtual spaces on the internet of things. They connect physical systems to the digital world while collecting data from an asset to create business value.

The value of the digital twin can be seen by looking at the entire life cycle of the asset. For this, a strategic definition of the asset will help to reduce business risks, data management can improve collaboration and the digital twin can be used to perform predictive analysis and help reduce unscheduled downtime. Prescriptive maintenance will improve safety and reliability in maintenance and lower costs.

The value of the digital twin also helps making decisions based on more reliable information. Organisations based on a culture of silos can suffer from breaking down of silos. The lack of collaboration between teams is expensive. Asset data management can promote making more accurate decisions.

Using the digital twin can help break down information silos within an organisation and help obtain the right information at the right time. Inspection data, work history and extremely large datasets can be used in the context of a digital twin. Using extensive graphics and dashboards and real-time and reliable information helps to reduce the total cost of assets; better manage, make changes and obtain accurate returns; and ensure better asset performance and return on investment (ROI).

Asset managers will have to add software skills to their operations teams as they add smarter assets and address more complex digital twins in their operations. They must also add software terms and data to their contracts. As such, the costs of developing and maintaining digital twins must be driven by economic and commercial models. The concept and business model must be tested against an economic model — revenue, profits, ROI, cost optimisation — with defined performance indicators to measure progress as products/services are being delivered and implemented.

The different levels of complexity of digital twins — from simple devices like light sensors to complex assets — will have different ongoing development and maintenance costs, sophisticated chief information officer (CIO) design business cases and ROI analysis.

Issues such as digital ethics and property rights raised by different parties that interact with data not only from the company but also from its partners and customers should be considered. In some cases, complex systems require multiple digital twins with different business requirements to meet specific needs.

Complex assets are usually composed of several digital twins, usually organised into large composite digital twins. This will drive deployment development, integration and cost analysis because at each layer of a composite digital twin, there will be different business requirements to meet the needs of different constituents (eg manufacturer, customer and maintenance provider) that will be added to and increase the complexity, behaviour and cost of the digital twin.

AIRPORT DIGITAL TWINS’ PURPOSES

Airport digital twins can be developed for different purposes, operate at different scales or take different approaches to modelling. Several airport digital twins have begun to appear in the built environment, serving various purposes. Few digital twins are, however, currently connected or sharing data between organisations or geographic data. Lack of interoperability is an important constraint. The reasons to use digital airports for airports are:

● To create a data-based model of assets or processes, paving the way towards advanced techniques to anticipate and prevent equipment failures and find ways to improve performance. To capture data from old technology still in use and discover tendencies and patterns from it that feed into digital twin models.

● To obtain detailed information about large groups of key assets in complex systems so that it can help determine the best way to allocate scarce investment. This also highlights gaps and allows for informed and differentiated decisions to be made about where to invest in new assets or upgrade existing assets.

● To establish a culture of data-based decision-making, consequently supporting all sorts of ways to improve performance. Also, it helps perform more predictive and condition-based maintenance, which help to improve performance in the long term. Anomaly detection in place in digital twins can detect problems far earlier. This permits planning rather than reacting when problems arise. The proactive approach also allows businesses to prevent problems from occurring in the first place. Using digital twins provides a better way to manage airport business with more insights and databased analysis.

In conclusion, the development of digital twins for airport management represents a significant evolution in how we understand and optimize airport assets and processes. The ability to create realistic and interactive digital representations of physical infrastructure offers numerous advantages, from predicting and preventing equipment failures to efficiently allocating investments in assets. Furthermore, digital twins promote a culture of data-driven decision-making, resulting in long-term performance improvements, predictive maintenance, and early anomaly detection.

The economic benefits of digital twins are evident throughout the lifecycle of assets, including reducing business risks, cost optimization, and improvements in safety and reliability. However, the success of this concept requires considerations of interoperability, digital ethics, and robust business models. It is essential for organizations to continuously assess and optimize the cost, complexity, and added value of digital twins as they are implemented in complex environments such as airports.

Ultimately, creating digital twins for airports not only transforms how assets are managed but also empowers businesses to make more informed decisions, enhance performance, and provide more effective and efficient airport management. The integration of these innovative technologies into airport operations promises to bring lasting benefits, improving the passenger experience and ensuring a solid return on investment for all parties involved.