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Full-Text Articles in Physics
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …
Fast Fission Neutron Detection Using The Cherenkov Effect, Matthew Millard
Fast Fission Neutron Detection Using The Cherenkov Effect, Matthew Millard
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The Cherenkov effect in optically clear media of varying indices of refraction and composition was investigated for quantification of fast neutrons. The ultimate application of the proposed detection system is criticality monitoring. The optically clear medium, composed of select target nuclei, was coupled to a photomultiplier tube. Neutron reaction products of the target nuclei contained within the optical medium emit beta particles and gamma rays that produce Cherenkov photons within the medium which can be detected. Assessed media include quartz (SiO2), sapphire (Al2O3), spinel (MgAl2O4), and zinc sulfide (ZnS), which were irradiated with un-moderated 252Cf. Monte Carlo N-Particle (MCNP) code …