“When I use a word, it means whatever I want it to mean”: Humpty Dumpty in Alice’s Adventures Through The Looking Glass, Lewis Carroll. “Digital twin” is currently a term applied in a wide variety of ways. Some differences are variations from sector to sector, but definitions within a sector can also vary significantly. Within engineering, claims are made regarding the benefits of using digital twinning for design, optimisation, process control, virtual testing, predictive maintenance, and lifetime estimation. In many of its usages, the distinction between a model and a digital twin is not made clear. 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 “plateau of productivity”) may fall well below the maximum potential of the technology. The basic components of a digital twin (essentially a model and some data) are generally comparatively mature and well-understood. 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. 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. 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.

中文翻译：

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如何分辨模型和数字孪生之间的区别

“当我使用一个词时，它意味着我想要表达的意思”：刘易斯·卡洛尔（Lewis Carroll）的《爱丽丝梦游仙境》中的矮胖。当前，“数字孪生”是一个以多种方式应用的术语。某些差异因部门而异，但一个部门中的定义也可能有很大差异。在工程领域内，有人声称使用数字孪生技术可进行设计，优化，过程控制，虚拟测试，预测性维护和寿命估算。在许多用途中，模型和数字双胞胎之间的区别尚不清楚。这种多样性和模糊性的危险在于，对数字双胞胎的定义和解释不佳或不一致，可能会导致人们以炒作为由拒绝它，因此，一旦炒作和不可避免的反冲超过了最终的关注和使用水平（“生产力的平稳期”），就可能大大低于该技术的最大潜力。数字孪生的基本组成部分（基本上是模型和一些数据）通常都比较成熟并且容易理解。基于在模型验证和验证方面的长期经验以及从测量值得出边界条件，初始条件和加载条件，在模型中使用数据的许多方面都得到了很好的理解。但是，存在许多有趣的开放问题，其中一些与数据的数量和速度有关，一些与可靠性和不确定性有关，还有一些与动态模型更新有关。在本文中，我们重点介绍了模型和数字双胞胎之间的本质区别，