## 1 Introduction  
By integrating advanced information technology, the Cyber-Physical System (CPS) builds a complex system of efficient coordination between the physical and information space, and realizes on-demand response, rapid iteration, and dynamic optimization of the system operation [1]. In recent years, the development of multi-physics solvers, the Internet of Things, big data, and artificial intelligence have enabled us to make digital twins (DT) of large-scale physical systems [2, 3], using sensor and simulation data to solve problems, such as control, monitoring, and improved decision-making. These technologies have promoted the transformation of the manufacturing industry to digitalization and intelligence. The push-plate kiln is a continuous heating and sintering equipment that arranges the required temperature to meet the product's heat demand according to the sintered product's process requirements. Its working process is a typical multidisciplinary coupling process involving the related theoretical knowledge, such as physics, mechanics, and heat transfer [4, 5]. The state of its internal temperature field and pressure field has an important application value for the remote monitoring and real-time regulation of the production process. The failure of the kiln will lead to a huge loss of productivity and profits. However, the traditional manufacturing process is not transparent, and the degree of digitalization is low. The flow field distribution in the whole kiln cannot be seen. The concept of the digital twin provides an effective technical tool for the modeling, analysis, and monitoring of the push-plate kiln. A digital twin Is essentially a simulation model of the physical object. Unlike static simulations, it can evolve in real-time by receiving data from the physical object, thus remaining consistent with the physical object throughout its lifecycle [6]. Digital twins--precise, virtual copies of machines or systems--are revolutionizing the industry [7]. [PERSON] et al. [8] proposed a conceptual framework for digital twins in manufacturing, including physical entities, virtual models, intelligence layers, data layers, and enterprise layers. This framework enables the interconnection and ultra-high synchronization with physical assets. [PERSON] et al. [9] established a digital twin-driven framework consisting of a conceptual layer, an information layer, and a method layer. [PERSON] et al. [10] proposed a digital twin modeling method based on biomimicry principles, which adaptively builds a digital twin of the machining process. However, the CFD simulation digital twin model for key equipment is still lacking. High-precision simulation is the foundation for realizing the digital twin. With the development of computer technology and numerical computing technology, computational fluid dynamics (CFD) provides an alternative method to study the fluid flow in complex systems and is widely used in many fields, including aerospace engineering [11], environmental science [12], biomedicine [13], and industrial design [14]. [PERSON] et al. [4] used the ANSYS FLUENT software package to simulate the convective heat and fluid flow in the cooling zone of the tunnel kil. Compared with the physical manufacturing system, the cost and security of CFD are significantly reduced, and it is an important tool to solve the early design and engineering problems of product and production development. The high-fidelity modeling and the simulation of important equipment, high confidence simulation prediction, and the high real-time data interaction are challenges in the process of establishing digital twins in the production process [15]. The computational process of high-precision numerical simulation requires a lot of computational resources, especially when the number of flow fields to be solved is huge. In engineering, the time consumption is often unacceptable.