In the realm of nuclear energy, ensuring the safety and efficiency of nuclear reactors is paramount. A critical component in this endeavor is the fuel rod, a fundamental unit of a fuel assembly that directly impacts the safe operation of nuclear reactors. However, detecting internal defects in these fuel rods has long been a challenge due to the low contrast in X-ray digital radiography (DR) images. Enter Huang Fan, a researcher from CNNC Jianzhong Nuclear Fuel Co., Ltd, who has developed a groundbreaking algorithm that could revolutionize the way we inspect fuel rods.
Huang’s research, published in the journal ‘He jishu’ (which translates to ‘Nuclear Techniques’) addresses the issue of low contrast in fuel rod X-ray DR images by proposing a brightness fusion and multiscale optimized enhancement algorithm. This algorithm not only improves the overall and local contrast of the fuel rod DR image but also significantly highlights edge details, making internal defects more visible.
The process begins with logarithmic and gamma transformations, which are further refined by incorporating local information fusion to correct the brightness of the fuel rod DR image. Subsequently, a wavelet function is applied for multiscale decomposition, enhancing and sharpening low-frequency components with Retinex, and non-local means (NL Means) is applied to filtering high-frequency components. The image enhancement is then realized via wavelet reconstruction.
The results speak for themselves. Huang’s algorithm achieved the highest information entropy (IE) of 6.8345, which is 10.2%, 3.3%, and 12.6% higher than the NLIE, HMF, and LIME algorithms, respectively. This significant improvement in image quality means that internal defects in fuel rods are better highlighted, leading to more accurate and efficient inspections.
Huang explains, “Our algorithm not only enhances the overall contrast but also sharpens the edge details, making it easier to identify defects that might have been missed with traditional methods.” This advancement could have profound implications for the nuclear energy sector, where the early detection of defects can prevent costly repairs and potential safety hazards.
The commercial impact of this research is substantial. By improving the quality of X-ray DR images, nuclear power plants can conduct more reliable inspections, reducing downtime and maintenance costs. This could lead to more efficient operations and a safer working environment for nuclear power plant employees. Additionally, the enhanced image quality could extend the lifespan of fuel rods, further reducing operational costs.
Looking ahead, Huang’s research could pave the way for future developments in the field of nondestructive testing. As nuclear energy continues to play a crucial role in the global energy mix, advancements in inspection technologies will be vital for maintaining safety and efficiency. Huang’s algorithm represents a significant step forward in this direction, offering a more reliable and accurate method for inspecting fuel rods.
Huang Fan’s work, published in ‘He jishu’, underscores the importance of innovation in the nuclear energy sector. As we strive for a cleaner and more efficient energy future, advancements in inspection technologies will be crucial. Huang’s algorithm is a testament to the power of scientific research in driving progress and ensuring the safety and reliability of nuclear power.
