Abstract:Given the insufficient cross-sectional data regarding the 14-MeV-neutron experiment of molybdenum, the vital fusion reactor structural material, and the significant heterogeneities among the reported values, this study examined the (n,2n), (n,α), (n,p), (n,d), and (n,t) reaction cross sections in molybdenum isotopes based on the neutrons produced via a T(d,n)4He reaction carried out in the Pd-300 Neutron Generator at the China Academy of Engineering Physics (CAEP). A high-resolution gamma-ray spectrometer, whi… Show more
“…Gaussian process regression is a type of supervised learning algorithm that is based on Bayesian non-linear regression. The GPR algorithm is non-parametric, where probability distributions are surmised over all possible values of x. Theoretically, the data points can be represented as multivariate Gaussian [3,[17][18][19]. The neutron incident energy, E n is between 13 MeV to 17 MeV.…”
Section: Resultsmentioning
confidence: 99%
“…The machine learning model with R 2 closed to 1 and lowest RMSE is highlighted in yellow. [17,19] 133.6 −0.12 TALYS 1.9 + Borman et al [3,17] 133.6 −0.12 EMPIRE 3.2 + Borman et al [3,17] 133.6 −0.12…”
Section: Discussionmentioning
confidence: 99%
“…There are three main components to the input to feed into our algorithms, which are the incident energy of the neutron, E n , experimental cross-section data (EXP) and the computation cross-section, which consists of various output from EMPIRE 3.2 and TALYS 1.9 nuclear code done in the previous study [3,[16][17][18][19][20]. The output is the ENDF/B-VIII.0 library nuclear cross-section, and the list of inputs and outputs can be seen in Table 1.…”
Section: Methodsmentioning
confidence: 99%
“…The plasma-facing components are the first to be exposed to plasma generated from the D-T reaction in the reactor, which is heavily bombarded by fast neutrons (around 14 MeV). Thus, the materials used to fabricate plasma-facing components must be able to withstand the high neutron flux and are usually made from beryllium [1], tungsten [2], and molybdenum [3].…”
Section: Introductionmentioning
confidence: 99%
“…Figure 1. Graph of nuclear cross-section data of 92 Mo(n, 2N) 91 Mo nuclear reaction from various literature used in this work[3,[17][18][19]. The neutron incident energy, E n is between 13 MeV to 17 MeV.…”
In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes, 92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of 92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2=1 and RMSE =0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR.
“…Gaussian process regression is a type of supervised learning algorithm that is based on Bayesian non-linear regression. The GPR algorithm is non-parametric, where probability distributions are surmised over all possible values of x. Theoretically, the data points can be represented as multivariate Gaussian [3,[17][18][19]. The neutron incident energy, E n is between 13 MeV to 17 MeV.…”
Section: Resultsmentioning
confidence: 99%
“…The machine learning model with R 2 closed to 1 and lowest RMSE is highlighted in yellow. [17,19] 133.6 −0.12 TALYS 1.9 + Borman et al [3,17] 133.6 −0.12 EMPIRE 3.2 + Borman et al [3,17] 133.6 −0.12…”
Section: Discussionmentioning
confidence: 99%
“…There are three main components to the input to feed into our algorithms, which are the incident energy of the neutron, E n , experimental cross-section data (EXP) and the computation cross-section, which consists of various output from EMPIRE 3.2 and TALYS 1.9 nuclear code done in the previous study [3,[16][17][18][19][20]. The output is the ENDF/B-VIII.0 library nuclear cross-section, and the list of inputs and outputs can be seen in Table 1.…”
Section: Methodsmentioning
confidence: 99%
“…The plasma-facing components are the first to be exposed to plasma generated from the D-T reaction in the reactor, which is heavily bombarded by fast neutrons (around 14 MeV). Thus, the materials used to fabricate plasma-facing components must be able to withstand the high neutron flux and are usually made from beryllium [1], tungsten [2], and molybdenum [3].…”
Section: Introductionmentioning
confidence: 99%
“…Figure 1. Graph of nuclear cross-section data of 92 Mo(n, 2N) 91 Mo nuclear reaction from various literature used in this work[3,[17][18][19]. The neutron incident energy, E n is between 13 MeV to 17 MeV.…”
In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes, 92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of 92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2=1 and RMSE =0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR.
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