ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
Latest Magazine Issues
Latest Journal Issues
Nuclear Science and Engineering
Fusion Science and Technology
Nuclear energy: enabling production of food, fiber, hydrocarbon biofuels, and negative carbon emissions
In the 1960s, Alvin Weinberg at Oak Ridge National Laboratory initiated a series of studies on nuclear agro-industrial complexes1 to address the needs of the world’s growing population. Agriculture was a central component of these studies, as it must be. Much of the emphasis was on desalination of seawater to provide fresh water for irrigation of crops. Remarkable advances have lowered the cost of desalination to make that option viable in countries like Israel. Later studies2 asked the question, are there sufficient minerals (potassium, phosphorous, copper, nickel, etc.) to enable a prosperous global society assuming sufficient nuclear energy? The answer was a qualified “yes,” with the caveat that mineral resources will limit some technological options. These studies were defined by the characteristic of looking across agricultural and industrial sectors to address multiple challenges using nuclear energy.
Cihang Lu, Zeyun Wu, Xu Wu
Nuclear Technology | Volume 207 | Number 5 | May 2021 | Pages 692-710
Technical Paper | doi.org/10.1080/00295450.2020.1805259
Articles are hosted by Taylor and Francis Online.
Thermal stratification (TS) is a thermal-fluid phenomenon that can introduce large uncertainties to nuclear reactor safety. The stratified layers caused by TS can lead to temperature oscillations in the reactor core. They can also result in damages to both the reactor vessel and in-vessel components due to the growth of thermal fatigue cracks. More importantly, TS can impede the establishment of natural circulation, which is widely used for passive cooling and ensures the inherent safety of numerous reactor designs. A fast-running one-dimensional (1-D) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. The efficient 1-D model provided reasonable temperature predictions for the test conditions investigated, but nonnegligible discrepancies between the 1-D predictions and the experimental temperature measurements were observed. These discrepancies are attributed to the model uncertainties (also known as model bias or errors) in the 1-D model and the parameter uncertainties in the input parameters.
In this study, we first recognized through a forward uncertainty analysis that the observed discrepancies between the computational predictions and the experimental temperature measurements could not be explained solely by input uncertainty propagation. We then performed an inverse uncertainty quantification (UQ) study to reduce the model uncertainties of the 1-D model using a modular Bayesian approach based on experimental data. Inverse UQ serves as a data assimilation process to simultaneously minimize the mismatches between the predictions and experimental measurements, while quantifying the associated parameter uncertainties. The solutions of the modular Bayesian approach were in the form of posterior probability density functions, which were explored by rigorous Markov Chain Monte Carlo sampling. Results showed that the quantified parameters obtained from the inverse UQ effectively improved the predictive capability of the 1-D TS model.