Best Estimate Plus Uncertainty (BEPU) approaches for nuclear reactor applications have been extensively developed in recent years. The challenge for BEPU approaches is to achieve multi-physics modeling with an acceptable computational cost while preserving a reasonable fidelity of the physics modeled. In this work, we present the core multi-physics computational framework developed for the efficient computation of uncertainties in Light Water Reactor (LWR) simulations. The subchannel thermal-hydraulic code CTF and the nodal expansion neutronic code PARCS are coupled for the multi-physics modeling (CTF-PARCS). The computational framework is discussed in detail from the Polaris lattice calculations up to the CTF-PARCS coupling approaches. Sampler is used to perturb the multi-group microscopic cross-sections, fission yields and manufacturing parameters, while Dakota is used to sample the CTF input parameters and the boundary conditions. Python scripts were developed to automatize and modularize both pre- and post-processing. The current state of the framework allows the consistent perturbation of inputs across neutronics and thermal-hydraulics modeling. Improvements to the standard thermal-hydraulics modeling for such coupling approaches have been implemented in CTF to allow the usage of 3D burnup distribution, calculation of the radial power and the burnup profile, and the usage of Santamarina effective Doppler temperature. The uncertainty quantification approach allows the treatment of both scalar and functional quantities and can estimate correlation between the multi-physics outputs of interest and up to the originally perturbed microscopic cross-sections and yields. The computational framework is applied to three exercises of the LWR Uncertainty Analysis in Modeling Phase III benchmark. The exercises cover steady-state, depletion and transient calculations. The results show that the maximum fuel centerline temperature across all exercises is $2474\mathrm{K}$ with $1.7\%$ uncertainty and that the most correlated inputs are the ²³⁸U inelastic and elastic cross-sections above 1 MeV.

With the improvement of computer performance, machine learning, and digital systems, interest in autonomous systems has increased in a variety of fields from manufacturing to unmanned space, ground vehicles, and nuclear reactors. Comparing to the classical human-centered systems, the new autonomous systems require a human-automation collaboration framework. The objective is to

With the improvement of computer performance, machine learning, and digital systems, interest in autonomous systems has increased in a variety of fields from manufacturing to unmanned space, ground vehicles, and nuclear reactors. Comparing to the classical human-centered systems, the new autonomous systems require a human-automation collaboration framework and make use of