#Virtual model with my measurements update
#Virtual model with my measurements verification

#Virtual model with my measurements update

At this stage, the usual procedure is to test the prototype and update the design based on the results, engineering understanding, and previous experience. The corollary of the requirement for the physical object to exist means that вЂњdigital twins for designвЂќ is only meaningful when the prototyping stage is reached. This concept of similarity of two things throughout development and evolution is key to the true digital twin, and for this similarity to be possible, the physical twin must also exist. Biological identical twins are created at the same time, are the same when they are created, and (for the sake of this argument) they continue to be the same as they develop and age. One of the key aspects of the parts listed above is that a digital twin has to be associated with an object that actually exists: a digital twin without a physical twin is a model. A validated model can provide a snapshot of the behaviour of an object at a specific moment, but using that model within a digital twin can extend the use of that model to timescales over which the object and its behaviour will change significantly. The use of evolving data means that a key strength of the digital twin approach is that it provides an accurate description of objects that change over time. The key features of a model within a digital twin are discussed in more detail in вЂњ What sort of model should a digital twin use?вЂќ section of this paper. The model used in a digital twin need not be a data-driven model, but it should produce results that are directly equivalent to a measured quantity (so that the model updating process is data-driven), and it is likely that the model will take in other measured quantities as boundary conditions, loads, or material properties. This definition merely renames technology that has existed for many years, leading many engineers wondering why they are being told that a practice they have been successfully employing for decades is a new and vibrant thing. вЂњWhile we see many definitions of вЂњDigital Twin,вЂќ LMS Research keeps it simple: A Digital Twin is an executable virtual model of a physical thing or system.вЂќ In this paper we highlight the essential differences between a model and a digital twin, outline some of the key benefits of using digital twins, and suggest directions for further research to fully exploit the potential of the approach. However, many interesting open questions exist, some connected with the volume and speed of data, some connected with reliability and uncertainty, and some to do with dynamic model updating.

#Virtual model with my measurements verification

Many of the aspects of using data in models are similarly well-understood, from long experience in model validation and verification and from development of boundary, initial and loading conditions from measured values. The basic components of a digital twin (essentially a model and some data) are generally comparatively mature and well-understood. The danger of this variety and vagueness is that a poor or inconsistent definition and explanation of a digital twin may lead people to reject it as just hype, so that once the hype and the inevitable backlash are over the final level of interest and use (the вЂњplateau of productivityвЂќ) may fall well below the maximum potential of the technology. In many of its usages, the distinction between a model and a digital twin is not made clear. Within engineering, claims are made regarding the benefits of using digital twinning for design, optimisation, process control, virtual testing, predictive maintenance, and lifetime estimation. Some differences are variations from sector to sector, but definitions within a sector can also vary significantly. вЂњDigital twinвЂќ is currently a term applied in a wide variety of ways. вЂњWhen I use a word, it means whatever I want it to meanвЂќ: Humpty Dumpty in AliceвЂ™s Adventures Through The Looking Glass, Lewis Carroll.