The atmospheric turbulence simulation device is used to study the propagation effects of laser in atmospheric turbulence. Most previous turbulence simulation devices generate turbulence by adjusting parameters such as temperature difference and wind speed. Although they successfully simulate the basic characteristics of atmospheric turbulence, the environmental parameters that can be measured and controlled are limited, and the automatic control capability is relatively weak. To generate a stable and controllable turbulent state in the simulation chamber, it is necessary to create stable boundary conditions. Therefore, the measurement and control system of the atmospheric turbulence simulation device must have high precision, stable control capabilities, and intelligent characteristics. At the same time, a model linking turbulence intensity and control parameters should be established based on the measured data.

To simulate atmospheric turbulence under various conditions, we develop a turbulence simulation chamber that integrates control functions for temperature difference, wind speed, and air pressure. The chamber is equipped with hot and cold plates to create temperature differences, fans to adjust wind speed, and a vacuum pump to create a low air pressure environment by sealing the chamber. Based on the principles of turbulence generation, we design a system that considers both convective and hot air characteristics to simulate high-frequency turbulence. To meet control requirements, we develop an integrated measurement and control system based on a programmable logic controller (PLC) and host computer software (Fig. 3). This system combines various sensors and actuators to monitor temperature, humidity, air pressure, and wind speed throughout the simulation chamber. Additionally, it can be integrated with specialized test equipment to measure the atmospheric coherence length along the integral path within the turbulence simulation chamber. We calculate and analyze the control accuracy and uncertainty of the system. By measuring turbulence intensity under different temperature differences and air pressure conditions, we build a model that describes the relationship between turbulence intensity, air pressure, and temperature difference. The accuracy of this model has been analyzed based on the measured data.

Initially, we design the turbulence simulation chamber and its measurement and control system based on the principles of turbulence generation. We then analyze the control errors and uncertainties of the control variables (Figs.5,6,and 7). The results indicate that the absolute value of control errors for different temperature differences is less than 1.40%, and the absolute value of control errors for different air pressures is less than 0.425%. The control uncertainty of r₀ under different temperature differences is limited to a maximum of 0.0490 cm. Additionally, we establish a log-linear relationship between turbulence intensity and air pressure (Fig. 9), which can be used to calculate the input temperature difference required to achieve a specific r₀ at different air pressures. The correlation coefficient between the fitted values based on the turbulence model and the measured values exceeds 0.99, and the root mean square errors do not exceed 0.10854.

1) The measurement and control system has functions for measurement, control, real-time display, and data storage, with high automation and control accuracy, which effectively ensures that the turbulence state in the turbulence simulation chamber remains stable and controllable. 2) The turbulence intensity in the turbulence simulation chamber mainly depends on the temperature difference. As the temperature difference grows, the turbulence intensity becomes stronger. They exhibit a clear logarithmic linear relationship. At the same time, the turbulence intensity and the chamber air pressure also show a logarithmic linear relationship. 3) By logarithmic linear fitting of the measured data under different temperature differences and air pressure conditions, we build a turbulence state control function model for the simulation chamber. This model can be used to predict the required plate temperature difference within the allowable error range, based on the target turbulence intensity to be simulated under specific air pressure conditions.