Engineering topics
Baldwin, Christopher
Publications and source records attributed to Baldwin, Christopher.
Enhanced steam oxidation resistance of uranium nitride nuclear fuel pellets
Here, the steam oxidation resistance of UN and UN-(20 vol%)ZrN fuel pellets is evaluated to enhance understanding of steam corrosion mechanisms in advanced nuclear fuel materials. In situ neutron diffraction shows the modified UN fuel pellets form a (U 0.77 ,Zr 0.23 )N solid-solution and the sole crystalline oxidation product detected in bulk is (U 0.77 ,Zr 0.23 )O 2 . U 2 N 3 is not detected in significant quantities during the steam oxidation of UN or (U 0.77 ,Zr 0.23 )N and stable lattice parameters show that hydriding does not take place. Steam oxidation rates, obtained via sequential Rietveld refinement show how (U 0.77 ,Zr 0.23 )N has a higher activation energy (79 ± 1 kJmol -1 vs. 50 ± 5 kJmol -1 ), higher onset temperature (430 °C vs. 400 °C) and slower reaction rates for steam oxidation up to 616 °C, than pure UN. Throughout, both UN and (U 0.77 ,Zr 0.23 )N exhibit linear (non-protective) oxidation kinetics, signifying that degradation of the fuel pellets is caused by the evolution of gaseous products at the interface followed by oxide scale spallation. This quantitative and mechanistic understanding of material degradation enables better defined operating regimes and points towards (U,Zr)N solid solutions as a promising strategy for the design of advanced nuclear fuel materials with enhanced steam corrosion resistance.
Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator
Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.