Designing smarter digital twins for engineering and industry
David Manrique Negr铆n developed methods to simplify the creation of digital twins, improve their reliability, and enhance their ability to support engineers鈥� decisions.
Digital twins are digital models that replicate the behavior of machines, vehicles, or industrial processes. They are used to predict problems, optimize performance, and plan maintenance. Imagine a virtual copy of a factory machine that can warn you before it breaks, suggest improvements, or test changes safely before applying them in the real world.
While digital twins have great potential, building them can be complicated because different models, sensors, software, and services all need to work together, and current development processes are often fragmented and inefficient.
PhD researcher David Manrique Negr铆n focused on making digital twins more systematic, modular, and reusable. Using model-driven engineering, which is a method that emphasizes high-level models to guide software development, he explored integrating and coordinating the many components that make up a digital twin. He defended his PhD thesis on Tuesday, November 18.
Connecting different models
Digital twins often combine models created with different tools, methods, or levels of detail. For example, one model might simulate a machine鈥檚 physical behavior, while another model approximates its software or control systems. These models can differ in format, scale, and how they represent information, thus making it difficult to connect them and share information between the models.
developed a method that standardizes how these models communicate with each other, allowing them to exchange information smoothly and operate together as a single system. This approach reduces the effort needed to connect diverse models and ensures that the overall digital twin behaves reliably, giving engineers a clearer and more accurate view of the system.
Documenting and reusing models
To help engineers understand, share, and reuse models, Manrique Negr铆n introduced a structured approach to document and explain how models work and how they relate to each other.
This method provides context for each model, making it easier to incorporate existing models into new digital twins, save time, and improve consistency. By capturing the connections and assumptions behind each model, engineers can reuse work more confidently and avoid errors caused by misunderstanding or misapplying a model.
Coordinating and automating digital twins
Beyond combining models, digital twins need orchestration, which is the coordinated execution of all their components, including models, data streams, sensors, and services. Manrique Negr铆n developed a new orchestration approach, called OrchTwin, supported by a domain-specific language known as LOTTS.
This system allows engineers to specify high-level requirements and automatically turn them into working and executable systems. This reduces the need for manual coding, minimizes errors, and makes it faster and easier to deploy and manage digital twins in practice.
Practical validation and impact
Manrique Negr铆n tested his methods through prototypes and real-world case studies. He showed that his approaches make digital twins easier to build, more consistent, and more robust.
His research bridges the gap between design and implementation, and it also paves the way for smarter, scalable digital twins that better support engineers in decision-making and system optimization.
His work has implications across industries, from manufacturing and energy to mobility and smart infrastructure. Digital twins can help improve efficiency, safety, and sustainability by helping engineers to anticipate problems and optimize performance before costly real-world interventions.
PhD researcher David Manrique Negr铆n. Photo: Vincent van den Hoogen
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Supervisors
Loek Cleophas, Mark van den Brand
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