The application of Haynes 230 (HA230) by Laser Powder Bed Fusion (LPBF) is limited by its high susceptibility to process-induced cracking. TiB2 addition offers a promising route to suppress cracking; however, its effect on the fatigue behaviour of LPBF-processed HA230 remains unclear. In this work, an LPBF-processed HA230 superalloy modified with 1.5 wt.% TiB2 was investigated in terms of its microstructure, tensile properties, and Low-Cycle Fatigue (LCF) behaviour at 23 °C and 850 °C. The addition of 1.5 wt.% TiB2 effectively suppressed LPBF-related cracking and increased the yield strength by approximately 30 % at 23 °C and 20 % at 850 °C while maintaining high ductility. The enhanced strength is attributed to the transformation of TiB2 during LPBF, which resulted in the formation of M6(CB) carboborides and a fine dispersion of Ti-rich and La-rich nanoparticles. These nanoparticles impeded dislocation motion and contributed to strengthening. Under LCF loading, HA230 exhibited pronounced cyclic hardening at 23 °C, whereas cyclic softening predominated at 850 °C. Fatigue crack propagation was predominantly transgranular at both temperatures, although the damage mechanisms depended on the testing condition. At 23 °C, failure was mainly associated with brittle cracking of M6(CB) particles, whereas at 850 °C, oxidation-assisted surface crack initiation and propagation dominated. This study provides the first comprehensive correlation between the microstructure, tensile response, and LCF behaviour of TiB2-modified LPBF-processed HA230, demonstrating that controlled TiB2 addition can simultaneously improve LPBF processability and mechanical performance.
This study presents new U–Pb–Hf isotopic data and zircon ages from the Ediacaran to Ordovician Ötztal Complex of the Eastern Alps in Austria to provide new constraints on the evolution of the northern Gondwana active margin in the “proto-Alpine” realm. The multistage tectonic evolution of the complex started with siliciclastic deposition presumably in an accretionary wedge that may have lasted from ca. 600 Ma to ca. 517 Ma. The age spectra are dominated by Neoproterozoic zircon grains indicating that the complex was most likely sourced from the Arabian–Nubian shield, with a contribution of older Proterozoic and Archean grains from the more westerly Saharan metacraton. The deposition was partly overlapping in time with Cambrian to Early Ordovician mafic magmatism that formed either as mafic underplate below the accretionary wedge or outboard, being later accreted as part of the lower plate. The wedge was then intruded by compositionally diverse granitoids from ca. 500 Ma until ca. 440 Ma. By comparing the Ediacaran and Early Paleozoic evolution of the Ötztal Complex with originally more westerly Cadomian-basement terranes (e.g., those now found in the Bohemian Massif), we concluded that the Cenerian orogeny was generally younger in the proto-Alps than elsewhere in the former Cadomian belt. This was possibly due to a significantly curved geometry of the northern Gondwana margin and/or due to an eastward ridge–trench–transform triple point migration. Arrival of a warmer part of the oceanic plate then may have caused mantle melting, mafic underplating, and voluminous granitic plutonism in the forearc, perhaps finally terminated by ridge–trench interaction and slab break-off.
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.
Abstract Surveys and observations of lightning on Jupiter prior to the NASA Juno mission used night‐side imaging approaches, and a common conclusion was that the optical energy was similar to the highest energy terrestrial lightning flashes, or superbolts. We use data from the Juno Microwave Radiometer (MWR) to measure the first radio pulse power distribution of Jovian lightning. The power distribution measurement was enabled by unique meteorological conditions in Jupiter's North Equatorial Belt (NEB) in 2021–2022, as the belt transitioned from an anomalously quiescent (non‐convective) state to its more typical configuration with small moist convective plumes scattered in longitude. During this transition, convective plumes in the NEB occurred only in isolated storms we label “stealth superstorms.” The isolated nature of these storms (as lightning sources) resolved the degeneracy between pulse location and pulse strength, allowing measurement of a pulse power distribution with statistical median values ranging from 27 to 214 W over the MWR bandpass, well within the observational sensitivity range. The MWR thus measures typical pulse power in the storms, rather than high‐power outliers. Pulse power in the stealth superstorms may be comparable to terrestrial lightning radio emission, or up to a million times more powerful, depending on uncertainties in unresolved pulse duration and lightning spectral energy distributions. Future studies may determine whether the lightning pulse power in stealth superstorm is typical or anomalous of Jupiter's lightning in general.
Serpentinization significantly alters orogenic peridotites, but the processes controlling the magnetic fabric development remain incompletely understood, particularly the roles of mineralogy, fluid availability, and deformation. This study addresses this gap by combining petrography, rock magnetic measurements, and numerical modeling on samples representing moderate to complete serpentinization. Magnetic properties indicate a mix of paramagnetic serpentine and ferrimagnetic magnetite, with anisotropy of magnetic susceptibility fabrics showing spatial patterns linked to serpentinization degree and structural setting. Microstructural observations reveal that serpentine and magnetite grow parallel or at characteristic angles to primary fabrics, while deviations reflect post-serpentinization deformation. Numerical modeling confirms that magnetic fabric orientation depends on the interaction of primary mineral crystallographic preferred orientation and topotactic serpentinite growth. These results demonstrate the complex interplay of mineralogy, fluid-rock interaction, and deformation in controlling magnetic fabric evolution during serpentinization in orogenic peridotites.
