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LuNi crystallizes in the orthorhombic Pnma space group. The structure is three-dimensional. Lu is bonded in a 7-coordinate geometry to seven equivalent Ni atoms. There are a spread of Lu–Ni bond distances ranging from 2.80–2.85 Å. Ni is bonded in a 9-coordinate geometry to seven equivalent Lu and two equivalent Ni atoms. Both Ni–Ni bond lengths are 2.48 Å.
Data on zero applied field measurements of remanent magnetization and magnetic relaxation in a BCS superconductor LuNi 2 B 2 C and several hard ferromagnets are presented and compared. Apparent similarities and differences, in particular in Thermoremanent Magnetization (TRM) - like, Isothermal Remanent Magnetization (IRM) - like, and remanent magnetization measurements with zigzag temperature sweep measurements are outlined. It is discussed how these results could be relevant for the magnetization measurements in diamond anvil cells.
The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.
Abstract not provided.