Preliminary evaluation of the LASSO method for prediction of the relative power density distribution in mixed oxide (Pu,DU)O2 fuel pellets

2021 ◽  
Author(s):  
Catalina Anghel ◽  
Blair Bromley ◽  
Andrew A. Prudil ◽  
Mike Welland
Keyword(s):  
Machine Learning ◽  
Nuclear Fuel ◽  
Mixed Oxide ◽  
Power Distribution ◽  
Neutron Transport ◽  
Neutron Energy Spectrum ◽  
Fuel Performance ◽  
Test Set ◽  
Fuel Pellets ◽  
Analytical Approximations

Abstract Predicting the power distribution within nuclear fuel is essential for predicting reactor fuel performance, since power distributions can impact pellet temperature distributions and fission product transport and migration. Analytical expressions for radial power distribution in fuel pellets were sought using lattice physics calculations to generate data and a machine learning technique was applied to find representative expressions. Analytical approximations can be useful in nuclear fuel performance codes, such as ELESTRES/ELOCA for providing very rapid predictions of power distributions with reduced computational effort and memory requirements, relative to using an embedded or coupled neutron transport / burnup reactor physics code. Radial power distributions were calculated a priori using lattice physics codes to model mixed oxide (MOX) 37-element fuel bundles in pressure tube heavy water reactors (PT-HWRs). Such advanced fuels are of interest for future fuel cycles. Several datasets were generated with different amounts of PuO2 and variable neutron energy spectrum. Results of preliminary studies with the Least Absolute Shrinkage and Selection Operator (LASSO) regression machine learning method have obtained analytical fitting functions with a mean maximum relative error (MRE) of 0.056 and a maximum MRE of 0.152 on the test set. However, using LASSO to estimate the coefficients of a physically-motivated modified Bessel plus an exponential function, results in a lower MRE (mean MRE 0.041 and maximum MRE 0.11) on the same test set. Further potential improvements in both the curve fit and the machine learning methods are discussed.

scholarly journals An Integrated Process for Recycling of ThO<sub>2</sub> Based Mixed Oxide Rejected Nuclear Fuel Pellets

2017 ◽  
Vol 07 (04) ◽  
pp. 309-330 ◽  
Author(s):  
Gitender Singh ◽  
Prashal M. Khot ◽  
Pradeep Kumar ◽  
Chetan Baghra ◽  
Raj Bhushan Bhatt ◽  
...  
Keyword(s):  
Nuclear Fuel ◽  
Mixed Oxide ◽  
Integrated Process ◽  
Fuel Pellets ◽  
Nuclear Fuel Pellets

Space to Air High-Altitude Region Adjoint Neutron Transport

2021 ◽  
pp. 154851292110316
Author(s):  
Zachary W LaMere ◽  
Darren E Holland ◽  
Whitman T Dailey ◽  
John W McClory
Keyword(s):  
Energy Spectrum ◽  
High Altitude ◽  
Neutron Energy ◽  
Neutron Transport ◽  
Energy Sources ◽  
Source Spectrum ◽  
Detector Response ◽  
Neutron Energy Spectrum ◽  
True Source ◽  
Adjoint Transport

Neutrons from an atmospheric nuclear explosion can be detected by sensors in orbit. Current tools for characterizing the neutron energy spectrum assume a known source and use forward transport to recreate the detector response. In realistic scenarios the true source is unknown, making this an inefficient, iterative approach. In contrast, the adjoint approach directly solves for the source spectrum, enabling source reconstruction. The time–energy fluence at the satellite and adjoint transport equation allow a Monte Carlo method to characterize the neutron source’s energy spectrum directly in a new model: the Space to High-Altitude Region Adjoint (SAHARA) model. A new adjoint source event estimator was developed in SAHARA to find feasible solutions to the neutron transport problem given the constraints of the adjoint environment. This work explores SAHARA’s development and performance for mono-energetic and continuous neutron energy sources. In general, the identified spectra were shifted towards energies approximately 5% lower than the true source spectra, but SAHARA was able to capture the correct spectral shapes. Continuous energy sources, including real-world sources Fat Man and Little Boy, resulted in identifiable spectra that could have been produced by the same distribution as the true sources as demonstrated by two-dimensional (2D) Kolmogorov–Smirnov tests.

scholarly journals Exploring sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: a machine learning approach

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Tokodi ◽  
A Behon ◽  
E.D Merkel ◽  
A Kovacs ◽  
Z Toser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sex Differences ◽  
Cardiac Resynchronization Therapy ◽  
Cardiac Resynchronization ◽  
Funding Source ◽  
Resynchronization Therapy ◽  
Test Set ◽  
All Cause Mortality ◽  
Mortality Predictors

Abstract Background The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). Purpose We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in patients undergoing CRT implantation. We also aimed to assess the sex-specific differences and similarities in the predictors of mortality using ML approaches. Methods A retrospective registry of 2191 CRT patients (75% males) was used in the current analysis. ML models were implemented in 6 partially overlapping patient subsets (all patients, females or males with 1- or 3-year follow-up data available). Each cohort was randomly split into a training (80%) and a test set (20%). After hyperparameter tuning with 10-fold cross-validation in the training set, the best performing algorithm was also evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC) and the associated 95% confidence intervals. The most important predictors were identified using the permutation feature importances method. Results Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645–0.802] and 0.732 [0.681–0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction and QRS morphology had higher predictive power in females, whereas hemoglobin was less important than in males. The importance of atrial fibrillation and age increased, whereas the relevance of serum creatinine decreased from 1- to 3-year follow-up in both sexes. Conclusions Using advanced ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in patients undergoing CRT implantation. The in-depth analysis of features has revealed marked sex differences in mortality predictors. These results support the use of ML-based approaches for the risk stratification of patients undergoing CRT implantation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Office of Hungary

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

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