A digital twin has recently received considerable attention in various industry domains. The digital twin replicates physical objects (e.g., people, objects, spaces, systems, and processes) in the real world into digital objects in the digital world. It also provides various simulations to solve problems in the real world or to improve situational operations. Therefore, the digital twin is a convergence of various technologies, such as advanced machine-learning algorithms, data analytics, super-resolution visualization and modeling, and simulation. Because the digital twin is a complicated technology, a step-by-step implementation that includes many technology elements should be considered to create a digital twin model. In this study, implementation layers are introduced to guide practical implementations of the digital twin. In addition, technology elements were suggested for the presented implementation layers. Because the suggested technology elements include clear technology definitions, various application domains (e.g., energy, transportation, logistics, environment, manufacturing, and smart cities) can easily utilize the introduced implementation layers and technology elements according to the intended purpose. Furthermore, this paper describes the evolution of digital twins. Digital twin technology has evolved continuously since 2002, when the digital twin concept was first introduced. In the described evolution levels, we show the future aspects of digital twin technology, according to the technological evolution direction. Therefore, the digital twin model can be efficiently created by considering the evolution direction and future aspects by using the suggested digital twin evolution levels.

Natural windbreaks such as trees are very efficient barriers to high velocity winds. The windbreaks exert drag force causing a net loss of momentum and thus disturb the characteristics of flow. The main factors which can affect the efficiency of the windbreaks are tree height, width, tree arrangement, porosity, etc. However, tree porosity which is strongly related to the windbreak drag is very difficult to establish. In this study, the results of a wind tunnel test were introduced to find the aerodynamic porosity and resistance factor of a tree windbreak. Black pine tree (Pinus thunbergii), a typical tree windbreak in Korea was chosen as the experimental tree. With the main factors of wind velocity, static pressure and density of the tree, the aerodynamic porosity as well as the resistance factor of the tree was found. The average porosity at varied tree density were found to be 0.91, 0.69 and 0.42 for one, two and three trees, respectively. The resistance factors which can be equaled to the drag coefficient were 0.55, 0.82 and 1.08 for one, two and three trees, respectively. The determined aerodynamic porosity and windbreak drag will be used later as input data in computer simulation studies such as computational fluid dynamics (CFD). Moreover, the experimental procedure as well as the use of real trees in the wind tunnel experiment of finding the aerodynamic porosity and windbreak drag of various tree windbreaks was established through this study.