Simulations of future Thailand tokamak plasmas are carried out using a CRONOS integrated predictive modelling code. The design of the reactor is based on nominal parameters of HT-6 M tokamak. The code consists of a 1D transport solver with general 2D magnetic equilibria, and includes several heat, particle and impurities transport models as well as heat, particle and momentum sources. In this work, a combination of a mixed Bohm/gyro-Bohm anomalous transport model and an NCLASS neoclassical transport model are used to calculate plasma core diffusivities. The boundary condition of the simulations is taken to be at the top of the pedestal which is calculated based on an international multi-tokamak scaling. Sensitivity analyses on plasma performance of the future Thailand tokamak are investigated by varying plasma current, toroidal magnetic field and external heating schemes. It is found that the performance in H-mode plasmas such as transport barrier at plasma edge and central temperatures are found to be sensitive to heating schemes and their magnitudes. Additionally, ICRH and LH methods appear to be the most effective scheme of heating for ion and electron temperatures, respectively. Central ion temperature in the range of 120-750 eV and central electron temperature in the range of 1,100-2,750 eV with heating are expected.

No abstract

No abstract

No abstract

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.