What Are the Different Types of Simulation Modeling?

Simulation is the simulation of the shape, operation, and visual perception. It uses real car models or other proportional aircraft and spacecraft models as the control platform for participants. Using VR technology (virtual reality technology), Participants have an immersive technology that is currently mainly used to simulate driving, training, demonstration, teaching, training; military simulation, command, virtual battlefields; architectural vision and urban planning.

Simulation

Simulation is the simulation of the shape, operation, and visual perception. It uses real car models or other proportional aircraft and spacecraft models as the control platform for participants. Using VR technology (virtual reality technology), Participants have an immersive technology that is currently mainly used to simulate driving, training, demonstration, teaching, training; military simulation, command, virtual battlefield; architectural vision and urban planning.

Chinese name

Simulation

Application

Driving simulation, etc.

Three major components

Building models, etc.

Types of

simulation

Definition

Shape, operation, visual perception simulation

Simulation driving, training, demonstration, teaching, training; military simulation, command, virtual battlefield; architectural vision and urban planning; scientific research

At present, there are not many companies with core intellectual property rights and strong technology, and few outstanding brand companies in the industry.

The simulation of an engineering technology system includes the three main steps of model building, experimental solution and result analysis.

Establish a mathematical model of the system

Simulation is a model-based activity, which uses the model to replace the real system for experiments and research. Therefore, we must first quantitatively describe the problem of simulation. This is to establish a mathematical model of the system.

The model is an imitation of the real world, and the real world is colorful, so the model is also full of variety;

According to whether the model contains random factors, it can be divided into random and deterministic models.

According to whether the model is time-varying, it can be divided into dynamic models and static models.

According to whether the model parameters continuously change in space, it can be divided into distributed parameter models and centralized parameter models.

According to whether the model parameters continuously change with time, it can be divided into continuous system models and discrete system models.

According to the mathematical description of the model, it can be divided into ordinary differential equations, partial differential equations, difference equations, discrete event models, and so on.

Regarding the different types of models mentioned above, I will not discuss them in depth here, but only discuss a few common problems in establishing a mathematical model of the system.

1) The process of modeling is a process of information processing. In other words, information is the "raw materials" used to construct the model. According to the different types of "raw materials" used for modeling, modeling methods can be classified into two categories:

One is deductive modeling, that is, modeling using prior technical information. The process is: starting from certain premises, assumptions, principles and rules, the model is established through mathematical logic derivation. Therefore, this is a process from general to special, that is, the special description of the simulated object is derived according to universal technical principles.

The other is inductive modeling, which uses experimental data information for real systems. The process is to obtain data through testing of real systems. These data contain information that reflects the nature of the real system, and then use data processing methods to derive a description of the regularity of the real system, such as the least-known least Multiplication regression model and so on. This is a process from special to general.

However, in practical applications, the establishment of a model is often accomplished through a combination of the above two types of methods, that is, hybrid method modeling.

No matter which method is used for modeling, the key lies in the understanding of the real system. If the real system is not fully and correctly understood, the model built will not accurately mimic the nature of the real system.

2) Credibility of the model. Since the model is an imitation of a real system, there is a problem of impersonation, which is the problem of model similarity and accuracy.

The credibility of the model depends on whether the information "raw materials" (prior knowledge, experimental data) used for modeling are correct and complete, and whether the modeling methods used (deduction, induction) are reasonable and strict. In addition, for many simulation software, the mathematical model must be transformed into a simulation model that can be processed by the simulation algorithm. Therefore, there is also a problem with the conversion accuracy of the model. Mistakes in any part of the modeling will affect the credibility of the model.

For this reason, after the model is established, it is an indispensable and important step to perform a credibility test on the model. The method for testing the model's reliability is usually: first, the model is analyzed and evaluated by experts familiar with the system being simulated, then the data used for modeling is statistically analyzed, and finally the model is tested, and the preliminary simulation results are compared with the estimated results .

Simulation calculation

Simulation calculation is the process of numerical experiment and solving the established simulation model. Different models have different solving methods. For example, for continuous systems, they are usually described by ordinary differential equations, transfer functions, and even partial differential equations. Because it is almost impossible to obtain analytical solutions of these equations, numerical methods are always used. For example, for ordinary differential equations, various numerical integration methods are mainly used. For partial differential equations, finite difference method, characteristic method, and Monte Carlo are used. Raffa or finite element methods.

Another example: For discrete event systems, probability models are usually used. The simulation process is actually a numerical experiment process, and these parameters must conform to a certain probability distribution law. There are different simulation methods for different types of discrete event systems (such as random service systems, random inventory systems, random network planning, etc.).

With the increase of the complexity of the simulated objects and the urgent requirements for the real-time performance of the simulation, researching new simulation algorithms has always been an important task, especially the various parallel simulation algorithms.

Analysis of simulation results

In order to draw correct and effective conclusions through simulation, a scientific analysis of the simulation results is necessary. Early simulation software output simulation results in the form of a large amount of data, so it is necessary to sort the simulation result data and perform various statistical analysis to obtain scientific conclusions. Modern simulation software widely uses visualization technology to vividly and vividly display the various states of the simulated object through graphics, charts, and even animations, so that the output information of the simulation is richer, more detailed, and more conducive to the scientific analysis of the simulation results .