Selective laser melting (SLM) is used to produce a SiC reinforced aluminum metal matrix composite (AMMC, Al-12Si plus 10 vol% SiC) with laser energy densities ( E p ) between 20 and 80 J mm À3 . Microstructural analysis shows that at lower energies, SiC is present in the Al-12Si matrix; however, at higher energies there is a distinct lack of SiC particles and the extensive formation of Al 4 C 3 needles and primary Si particles. XRD analysis confirms a decrease in the volume of SiC and an increase in the amount of Al 4 C 3 and primary Si with increasing E p . This indicates that a reaction occurs between the Al and SiC during SLM. The underlying mechanism is attributed to the selective absorption of laser energy into the SiC particles, causing regions of extremely high temperatures. The formation of the reaction products cause errors in the theoretical density calculations. Therefore, X-ray micro tomography (XMT) is used to independently measure the relative density of the samples with a peak relative density %97.4%, which is much higher than that (relative density %93%) measured using the Archimedes method.

Probability distributions that describe metocean conditions are essential for design and operational decision making in offshore engineering. When data are insufficient to estimate these distributions an alternative is expert elicitation -a collection of techniques that translate personal qualitative knowledge into subjective probability distributions. We discuss elicitation of surface currents on the Exmouth Plateau, North-Western Australia, a region of intense oil and gas drilling and exploration. Metocean and offshore engineering experts agree that surface currents on the plateau exhibit large spatio-temporal variation, and that recorded observations do not fully capture this variability. Combining such experts' knowledge, we elicit the joint distribution of magnitude and direction by first focusing on the marginal distribution of direction, followed by the conditional distribution of magnitude given direction. Although we focus on surface currents, the direction/magnitude components are common to many metocean processes. The directional component complicates the problem by introducing circular probability distributions. The subjectivity of elicitation demands caution and transparency, and this is addressed by embedding our method into the established elicitation protocol, the Sheffield Elicitation Framework. The result is a general framework for eliciting metocean conditions when data are insufficient to estimate probabilistic summaries.

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<p>Coupled climate-ice sheet models are crucial to evaluating climate-ice feedbacks' role in future ice sheet evolution. Such models are calibrated to reproduce modern-day ice sheets, but current observations alone are insufficient to constrain the strength of climate-ice feedbacks. The extent of the Northern Hemisphere ice sheets during the last glacial maximum, ~20,000 years ago, is well known and could provide a benchmark for calibrating coupled climate-ice sheet models. We test this with the FAMOUS-ice coupled Climate-Ice Sheet model (Smith et al., 2020), a fast GCM coupled to the Glimmer ice sheet model. We ran Last Glacial Maximum simulations using FAMOUS-ice with interactive North American Ice Sheet, following the PMIP4 protocol (Kageyama et al., 2018). We find that the standard model setup, calibrated to produce a good present-day Greenland (Smith et al., 2020), produced a collapsed North American ice sheet at the Last Glacial Maximum. We ran ensembles of hundreds of simulations to explore the influence of uncertain ice sheet, albedo, atmospheric, and oceanic parameters on the ice sheet extent. The North American continent deglaciated rapidly in most of our simulations, leaving only a handful of useful simulations out of 280. We thus developed a method to efficiently identify regions of the parameter space that can produce a reasonable ice-sheet extent. This involved emulating the equilibrium ice volume and area as a function of the surface mass balance at the start of our simulations. We then ran three waves of short simulations for 20-50 years to identify parameter values and surface mass balance conditions potentially suitable to grow a realistic ice sheet. This enabled us to find ~160 simulations with good ice extent.</p><p>Through analysis of these simulations, we find that albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. The differences in cloud cover over the ablation zones of the North American and Greenland ice sheet explains why the ice sheets have different sensitivities to surface mass balance parameters. Based on our work, we propose that the Last Glacial Maximum can provide an “out-of-sample” target to avoid over calibrating coupled climate-ice sheet models to the present day.</p><p><strong>References:</strong></p><p>Kageyama, M. <em>et al.</em> The PMIP4 contribution to CMIP6 – Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments. <em>Geosci. Model Dev.</em> <strong>10</strong>, 4035–4055 (2017).</p><p>Smith, R. S., George, S., and Gregory, J. M.: FAMOUS version xotzt (FAMOUS-ice): a general circulation model (GCM) capable of energy- and water-conserving coupling to an ice sheet model, Geosci. Model Dev., 14, 5769–5787, https://doi.org/10.5194/gmd-14-5769-2021, 2021.</p><p> </p>