Twin Vs Twin Challenge
Beichen Poque
In Cambodia, I met Reatrey. Her mom had four to five kids if I remember right, and she was pregnant with another. And though none were twins, I could tell it was too much for her to handle. Her husband had gone to Thailand to work and send back money, but the job he had gone for had been a trick and he was trafficked, stuck in another country with no money to send home or to buy his way back.
The digital twin concept gained recognition in 2002 after Challenge Advisory has hosted a presentation for Michael Grieves in the University of Michigan on technology. The presentation involved the development of a product lifecycle management center. It contained all the elements familiar with the digital twin including; real space, virtual space and the spreading of data and information flow between real and virtual space. While the terminology may have changed over the years the concept of creating a digital and physical twin as one entity has remained the same since its emergence. While its commonly thought to be developed in 2002, digital twin technology itself has actually been a concept practiced since the 1960s. NASA would use basic twinning ideas during this period for space programming. They did this by creating physically duplicated systems at ground level to match the systems in space. An example is when NASA developed a digital twin to assess and simulate conditions on board Apollo 13.
2011 - 2024 CHALLENGE ADVISORY LLP, a UK limited liability partnership, is a member firm of the CHALLENGE ADVISORY network of independent member firms. Challenge Advisory LLP is a limited liability par tnership registered in England and Wales with registered number OC380630.Challenge Advisor y LLP
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In this report, DNV has interviewed organizations across the energy sector and beyond to understand how a connected digital twin ecosystem could become a reality. We have discussed and explored the opportunities, the barriers, the risks, and sought to understand how trust in digital twins can be developed, proven, and maintained.
Rising inequalities are hastening the divergence between thriving and stagnant cities around the world. Inequalities are also widening within fast-growing and mature cities alike. This is dangerous. Spatial and social inequalities are key determinants of everything from social mobility to life expectancy. The neighborhood in which a person is born and lives matters fundamentally to their (and their children's) life chances. Indeed, people living in environments characterized by high levels of social, economic, gender, and racial inequality tend to be more exposed to violence and victimization than those who are not. Study after study shows that neighborhoods with higher levels of income inequality and concentrated disadvantage experience higher levels of unrest and violent crime. The failure to address these issues has dramatically reduced equality of opportunity and outcomes across generations.
Another way that cities are addressing the twin challenges of inequality and violence is by addressing the specific needs of vulnerable groups. Improving access to jobs and life skills, especially for young men, is critical and can generate dramatic declines in social tension and violence. Likewise, some urban authorities are rethinking immigration, especially since there is a demonstrated positive correlation in North American and European cities between high concentrations of migrant populations and declines in violence. The reasons for this are various and likely connected to a high motivation to work, a desire to avoid flouting the law, and strong social ties. The key is to avoid reinforcing spatial segregation, however, since this can entrench inequality and related social and economic harms.
Interoperability between the Digital Twin and Asset Administration Shell is crucial for standardizing data exchange, aligning with Industry 4.0 objectives, facilitating manufacturer interoperability, supporting autonomous systems and AI, and navigating implementation challenges in the production environment.
The platform allows companies and consumers to participate in a data ecosystem in an easy and versatile manner. All information, such as needed for the digital product passport, can be provided along the the lifecycle of a product. Changes to product information can instantly be made available with events and push capabilities. Through this the digital twin can act as a single source of truth about product specifics in cross-company business scenarios.
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This issue of Nature Computational Science includes a Focus that highlights recent advancements, challenges, and opportunities in the development and use of digital twins across different domains.
Since then, there have been major developments in the technologies that surround digital twins. We have experienced an ever-growing amount of data being generated and made available, including in real time. We have implemented sophisticated modeling capabilities and observed the sharp rise in data-driven methodologies, including machine learning, which has allowed us to take advantage of the data deluge. While NASA made use of state-of-the-art telecommunications technology at the time of the Apollo 13 mission, we now have access to advanced Internet of Things networks that can substantially accelerate data movement. The list goes on and on.
Another important development is in regards to applications. The engineering and industrial domains have arguably leveraged digital twins for longer, such as for developing, testing, and maintaining aircraft and spacecraft in aerospace engineering, and for optimizing product life-cycle management in manufacturing systems (the concept of a digital twin in this context was first introduced by Michael Grieves4, before the term was coined). More recently, however, many other distinct areas of science have realized the potential of digital twins, from biomedical sciences to climate sciences and social sciences. For instance, digital twins could enable improved precision medicine, more accurate weather and climate predictions, and more informed urban planning.
Advancements and current challenges for industrial applications of digital twins are discussed in a Perspective by Fei Tao and colleagues. Digital twins have become very popular in industry and manufacturing, with different conceptual models proposed in the past and large investments from many well-known companies being made within this space. Nevertheless, according to the authors, we still have a long way to go to improve the maturity of digital twins and to facilitate large-scale industrial applications. Among the many challenges and opportunities that still need to be addressed, the authors argue that the trade-offs between overly simplistic models (which are less expensive, but less accurate) and overly complex models (which are more accurate, but can be prohibitively expensive) need to be well-understood and evaluated on a case-by-case basis; that the opportunities and risks brought by artificial intelligence need to be better assessed; and that validation benchmarks and international standards are urgently needed to make the field more mature.
The applications of digital twins in the biomedical sciences are explored in the Focus by Reinhard Laubenbacher and colleagues in a Perspective. The authors argue that, different from industry and engineering, there is no broad consensus as to what constitutes a digital twin in medicine, mainly due to some of the unique challenges faced in the field, including the fact that the relevant underlying biology is partially or completely unknown and that the required data are often not available or difficult to collect, with the latter challenge impacting the exchange of data between physical and digital twin. That said, the authors discuss many potential promising applications for medical digital twins in curative and preventive medicine, as well as in developing novel therapeutics and helping with health disparities and inequalities.
The definition of a digital twin is also examined by Michael Batty in a Perspective, this time in the context of urban planning. Batty argues that, while the coupling between real and digital tends to be strong and formalized for physical assets, the same is not true for social, economic, and organizational systems (such as cities), since the transfer of data is often non-automated. Batty also discusses the need of the human in the loop in the design and use of digital twins, and the fact that cities may be intrinsically unpredictable, which brings challenges to applying the standard definition of digital twins to the field. On the other hand, Lus M. A. Bettencourt argues in a Comment that cities do present many levels of predictability that can be leveraged and represented in digital twins, in particular related to processes that are being increasingly understood via statistical models and theory. Both authors talk about the fact that cities are very complex and are associated with long-term dynamics (in contrast to the typical short-term dynamics of many applications), as well as about the different computational challenges that come with building digital twins of cities, such as the high computational complexity and the required multiscale modeling support.
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