New article published in 12(4B) - ENFIR/INAC 2024

Response to nuclear and radiological emergencies - Brazil and the world

Abstract: Some nuclear and radiological accidents happened on last decades, providing to specialized personnel a huge change in terms of protocols and safety measures. In Brazil, the experience during the response to the radiologic accident in Goiânia/GO in 1987 showed the need for a rapid and intense mobilization of human resources to act in several areas of knowledge (radiological monitoring of personnel and areas, dosimetry, waste management, logistical support, social communications, among others). At that time, most of the people involved did not have the opportunity to have previously received even necessary training to act in an event of that nature and magnitude. Taking into account the global scenario, the International Atomic Energy Agency (IAEA) has issued a series of documents that aim to guide its Member States, to achieve an adequate level of preparedness to respond to emergency situations of nuclear or radiological origin. The paper brings an introduction, the methodology applied, a contextualization about nuclear accidents; emphasis on ocurrences in Peru; the accident in Goiânia. Finally, the preparation and response to accidents; the Nuclear and Radiological Emergency Response System (SAER), and a conclusion. Read full article. 

Efficient Acceleration in Solving the 2D Neutron Diffusion Equation with CUDA: Exploring the Collaborative Practicality of Colab

Abstract: This paper explores an approach to accelerate the finite difference method applied to solving the two-dimensional (2D) neutron diffusion equation for two energy groups (2G) independent of time. The main innovation lies in the implementation of a performance optimization method, emphasizing the practicality of development in Python using direct browser collaboration through Google Colaboratory (Colab). Utilizing CUDA (Compute Unified Device Architecture) for GPU acceleration, we achieve significant computational performance improvements. The study compares Python implementations using CuPy and NumPy libraries with traditional FORTRAN implementations utilizing the LAPACK library, highlighting the efficiency and precision of GPU-accelerated calculations. Results show that Python with CuPy significantly outperforms NumPy, both in a Colab environment and on a personal desktop computer. This demonstrates the practicality of cloud-based solutions for intensive computations, as the ability to run code directly in the browser through Colab eliminates the need for extensive local hardware resources. The results emphasize the convenience of executing complex simulations without relying on physical computers, promoting greater flexibility and accessibility in computational research.  All computational codes are available on GitHub for transparency and reproducibility. Read full article.