Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation

Huang, Dongli; Abdel-Khalik, Hany S.

doi:10.1016/j.pnucene.2020.103443

Cited by 8 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computationally, forward UQ is typically tackled by Monte Carlo (MC) sampling [93], from which useful statistics such as the mean, covariance, probabilities of rare/failure events, and expectations of performance and health metrics may be obtained. For example, one major area of forward UQ in nuclear science involves propagating uncertainty from the cross section data of nuclear isotopes through reactor analysis calculations, with some recent examples found in [94,95]. However, MC sampling converges slowly and is considered prohibitively expensive.…”

Section: Forward Uqmentioning

confidence: 99%

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Kochunas

Huan

2021

Energies

View full text Add to dashboard Cite

Digital Twins (DTs) are receiving considerable attention from multiple disciplines. Much of the literature at this time is dedicated to the conceptualization of digital twins, and associated enabling technologies and challenges. In this paper, we consider these propositions for the specific application of nuclear power. Our review finds that the current DT concepts are amenable to nuclear power systems, but benefit from some modifications and enhancements. Further, some areas of the existing modeling and simulation infrastructure around nuclear power systems are adaptable to DT development, while more recent efforts in advanced modeling and simulation are less suitable at this time. For nuclear power applications, DT development should rely first on mechanistic model-based methods to leverage the extensive experience and understanding of these systems. Model-free techniques can then be adopted to selectively, and correctively, augment limitations in the model-based approaches. Challenges to the realization of a DT are also discussed, with some being unique to nuclear engineering, however most are broader. A challenging aspect we discuss in detail for DTs is the incorporation of uncertainty quantification (UQ). Forward UQ enables the propagation of uncertainty from the digital representations to predict behavior of the physical asset. Similarly, inverse UQ allows for the incorporation of data from new measurements obtained from the physical asset back into the DT. Optimization under uncertainty facilitates decision support through the formal methods of optimal experimental design and design optimization that maximize information gain, or performance, of the physical asset in an uncertain environment.

show abstract

Section: Forward Uqmentioning

confidence: 99%

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Kochunas

Huan

2021

Energies

View full text Add to dashboard Cite

show abstract

“…Therefore, it is crucial to accurately estimate radionuclide inventory, as well as the uncertainties associated with this inventory. Furthermore, it should be stressed that the estimation of uncertainties associated with in-core isotopic inventories is a vast and non-trivial subject [1] [2]. Indeed, the calculations of nuclear fuel depletion under irradiation require complex simulations, which include many sources of bias and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Estimation of biases and uncertainties on PWR nuclide inventories for severe accidents analyses

et al. 2023

View full text Add to dashboard Cite

The work presented in this paper deals with bias and uncertainty quantification on nuclear fuel inventory in a pressurized water reactors core during normal operation. This actinides and fission products inventory is used as input data for radiological releases evaluation in case of a severe accident. The different sources of bias and uncertainty, as well as their impacts for UO2 and MOX fuel at the assembly and core levels, are discussed. Uncertainty sources include technological uncertainties (e.g. dimensions, irradiation history, temperatures), modeling assumptions, uncertainties related to the resolution methods used in the calculation tools and nuclear data uncertainties. For each source of uncertainty investigated in this paper, an evaluation of the associated biases and uncertainties on nuclide inventory is performed. It is shown that, among the sources of bias and uncertainties studied, spread due to nuclear data as well as the bias and uncertainties due to “infinite lattice approximation” are the most significant ones, for the isotopes of interest.

show abstract

“…is will cause the hinge function to fail to perform regression predictions correctly, and this incomplete dataset also has high uncertainty. e loss function of the traditional SVM needs to be modified into a new loss function, which can obtain the uncertainty of the data [17,18]. Once the uncertainty of the data is captured, the researcher can ensure the correct utilization of the hinge function by adjusting the data for the location of the larger uncertainty [19].…”

Section: Introductionmentioning

confidence: 99%

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Tao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The loss function of the traditional support vector machine (SVM) method consists of hinge function and regularization, which is difficult to achieve the quality control of observation data. It requires a new loss function to measure the quality of the observed data. At this stage, researchers will use data cleaning or data preprocessing to process observational data. The data preprocessing method will normalize the data features, which will make the data process the same interval and a distribution. However, this method is also difficult to guarantee the accuracy of the data. This study uses the SVM method to study the integrity, unity, and accuracy of the observation data. This study also proposes a new loss function, which can obtain the uncertainty distribution of the observed data. The loss function of traditional SVM methods is that the uncertainty distribution of the data cannot be obtained. The results show that the SVM algorithm with the new loss function has better accuracy in processing the observed data. For the maximum prediction error of the observed data, the error with the new loss function is only 2.58%, which is reduced by 0.21% compared to the SVM method with the old loss function. However, the prediction error of the SVM algorithm with the old loss function is 2.78%, and the SVM algorithm with the new loss function has obvious prediction accuracy.

show abstract

Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation

Cited by 8 publications

References 19 publications

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Estimation of biases and uncertainties on PWR nuclide inventories for severe accidents analyses

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Contact Info

Product

Resources

About