Norsk Hydro (Norway)

To assess the effects of using post-consumer scrap in common wrought aluminium alloys, simulated recycled 6082 and 6005 alloys were produced with added common impurity elements Fe, Cu and Zn in amounts of 0.1 to 0.2 wt pct. They were investigated and compared to their primary alloy counterparts with respect to microstructure, mechanical properties and intergranular corrosion resistance. Optical microscopy, scanning electron microscopy, transmission electron microscopy and atom probe tomography were used to measure grain structure, primary- and dispersoid particles, hardening precipitates and solute distribution in the microstructure. Through process modelling was conducted to rationalize the effects of the impurities on the microstructure constituents. In both alloy groups Fe was responsible for an increase in the density and volume fraction of primary particles. The increased Cu level in 6082 led to a finer microstructure of hardening precipitates in the peak hardness condition and to a small hardness increase. Low amounts of Cu and Zn incorporate into the hardening precipitate phases. However, most Zn remained in solid solution, including in the precipitation free zones close to grain boundaries. This has been associated with a deterioration of intergranular corrosion resistance due to the galvanic coupling with Cu segregated at the grain boundary planes.

A hybrid modeling framework for predicting the mechanical properties of Al-Mg-Si alloys, that blends physics-based and machine-learning models, is developed and tested. Motivated by a demand for post-consumer material (PCM) content in wrought aluminium applications, this work proposes, analyses, and discusses a parallel framework that applies an adaptive weighting coefficient derived from local observation density. Based on existing datasets from a range of Al-Mg-Si alloys, such a model is trained and tested in an iterative manner to study its robustness, by emulating a shift in observed alloy composition. The results indicate that the hybrid model is able to combine the interpolative strength of machine learning for cases similar to previous observations with the explorative strength of physics-based (Kampmann–Wagner Numerical) modeling for previously unobserved parameter combinations, as the hybrid model shows higher or similar accuracy than the best of its constituents across the majority of the sequence. The observed model characteristics are promising for predicting the effect of increased compositional variation inherent in PCM. Finally, possible future research is discussed.

<strong class="journal-contentHeaderColor">Abstract.</strong> About every 10 years, the Norwegian Centre for Climate Services publishes a national climate assessment report, presenting the updated historical climate change and climate projections towards the end of this century. This paper documents the model experiment used to generate high-resolution climate and hydrological projections for the new climate assessment report published in October 2025. The model experiment follows the standard modelling chain for hydrological impact assessment, i.e., climate model selection &ndash; downscaling and bias adjustment &ndash; hydrological modelling. However, compared with the model experiment for the climate assessment report published in 2015, all modelling components have been improved in terms of data availability, data quality and methodology. Specifically, a large climate model ensemble was available and new criteria were developed to select tailored climate projections for Norway. Two bias-adjustment methods (one univariate and one multivariate) were applied to account for the uncertainty of method choice. The hydrological modelling was improved by implementing a physically-based Penman-Monteith method for evaporation and a glacier model accounting for glacier retreat under climate change scenarios. Besides model description, this paper elaborates the effects of different bias-adjustment methods and the contribution of climate models and bias-adjustment methods to the uncertainty of climate and hydrological projections under the RCP4.5 scenario as examples. The results show that the two bias-adjustment methods can contribute larger uncertainty to seasonal projections than climate models. The multivariate bias-adjustment method improves hydrological simulations, especially in the reference period, but cannot conserve climate change signals of the original climate projections. The dataset generated by the presented modelling chain provides the most updated, comprehensive and detailed hydrometeorological projections for mainland Norway, serving as a knowledge base for climate change adaptation to decision makers at various administrative levels in Norway.

<strong class="journal-contentHeaderColor">Abstract.</strong> About every 10 years, the Norwegian Centre for Climate Services publishes a national climate assessment report, presenting the updated historical climate change and climate projections towards the end of this century. This paper documents the model experiment used to generate high-resolution climate and hydrological projections for the new climate assessment report published in October 2025. The model experiment follows the standard modelling chain for hydrological impact assessment, i.e., climate model selection &ndash; downscaling and bias adjustment &ndash; hydrological modelling. However, compared with the model experiment for the climate assessment report published in 2015, all modelling components have been improved in terms of data availability, data quality and methodology. Specifically, a large climate model ensemble was available and new criteria were developed to select tailored climate projections for Norway. Two bias-adjustment methods (one univariate and one multivariate) were applied to account for the uncertainty of method choice. The hydrological modelling was improved by implementing a physically-based Penman-Monteith method for evaporation and a glacier model accounting for glacier retreat under climate change scenarios. Besides model description, this paper elaborates the effects of different bias-adjustment methods and the contribution of climate models and bias-adjustment methods to the uncertainty of climate and hydrological projections under the RCP4.5 scenario as examples. The results show that the two bias-adjustment methods can contribute larger uncertainty to seasonal projections than climate models. The multivariate bias-adjustment method improves hydrological simulations, especially in the reference period, but cannot conserve climate change signals of the original climate projections. The dataset generated by the presented modelling chain provides the most updated, comprehensive and detailed hydrometeorological projections for mainland Norway, serving as a knowledge base for climate change adaptation to decision makers at various administrative levels in Norway.

Fluid flow has a major effect on the aggregation and fragmentation of bacterial colonies. Yet, a generic framework to understand and predict how hydrodynamics affects colony size remains elusive. This study investigates how fluid flow affects the formation and maintenance of large colonial structures in cyanobacteria, using an experimental technique that precisely controls hydrodynamic conditions. We performed experiments on laboratory cultures and lake samples of the cyanobacterium Microcystis, while their colony size distribution was measured simultaneously by direct microscopic imaging. We demonstrate that EPSembedded cells formed by cell division exhibit significant mechanical resistance to shear forces. However, at elevated hydrodynamic stress levels (exceeding those typically generated by surface wind mixing) these colonies experience fragmentation through an erosion process. We also show that single cells can aggregate into small colonies due to fluid flow. However, the structural integrity of these flow-induced colonies is weaker than that of colonies formed by cell division. We provide a mathematical analysis to support the experiments and demonstrate that a population model with two categories of colonies describes the measured size distributions. Our results shed light on the specific conditions wherein flow-induced fragmentation and aggregation of cyanobacteria are decisive and indicate that colony formation under natural conditions is mainly driven by cell division, although flow-induced aggregation could play a role in dense bloom events. These findings can be used to improve prediction models and mitigation strategies for toxic cyanobacterial blooms and also offer potential applications in other areas such as algal biotechnology or medical settings where the dynamics of biological aggregates play a significant role.

The ordered tetragonal FeNi L10 phase, tetrataenite, is a promising candidate for rare earth-free permanent magnets due to its competitive magnetic properties and the low cost of the constituent elements. In this work, we have investigated the effect of molybdenum and aluminum substitution on the formation of the ordered L10 phase. The alloys were prepared with die casting and melt spinning techniques, further processed using cold rolling and cryomilling, and finally annealed below the estimated order–disorder temperature (TOD). To study the influence of composition and processing of the alloys, structural characterization and microstructural analysis were performed with synchrotron radiation X-ray diffractometry (SR-PXD) and Scanning Transmission Electron Microscopy (STEM), respectively. The presence of tetrataenite in the alloys investigated in this work could not be confirmed. In situ SR-PXD and STEM indicated minimal structural changes in the temperature stability range of the materials. A full-loop hysteresis curve acquired using a vibrating sample magnetometer (VSM) indicated no signs of magnetic hardening of the alloys with the measured coercivity being below 10 Oe, and thus consistent with FeNi without ordering.

Abstract Forests are essential carbon stocks worldwide; however, they are losing their storage capacity due to deforestation, degradation, and land-use changes caused by anthropogenic activities in forest ecosystems. The soil organic carbon store (SOC) is the most critical reservoir, accounting for over 40% of the total carbon stored in forests. The present study provides information on the SOC storage capacity of forest ecosystems compared to other land uses in the Texcoco Forest Region, State of Mexico. Vegetation was classified into phases: Phase 1, primary or undisturbed land use and vegetation (LUV); Phase 2, modified LUVs with various disturbances; Phase 3, LUVs resulting from anthropogenic activities such as agriculture, forest plantations, and fruit trees. The largest SOC reserve was found in sites with the least anthropogenic disturbance. Forest ecosystems presented up to three times as much SOC as Phase 3 land uses; thus, coniferous forests were the largest SOC reservoirs, more efficient than agricultural and oak ecosystems, which did not show large SOC stocks.

The current standard of practice for sizing stormwater infiltration facilities typically relies on one-dimensional (1D) test methods that do not account for the full dynamics of groundwater flow, including lateral flow and capillary flow. Although some agencies allow methods that do account for lateral and capillary flow, these methods are relatively small-scale and may not replicate the effects of soil layering below a full-scale infiltration facility. The uncased and cased methods evaluated in this study account for lateral and capillary flow and can be used to evaluate both small-scale and large-scale infiltration tests in a broad range of test facilities, including excavated pits, uncased shallow boreholes, and deep cased wells. This study provides numerically calibrated shape factors for both glacially consolidated and normally consolidated soils that are generally considered suitable for stormwater infiltration [saturated hydraulic conductivity (Ks)>0.1 m/day]. Soil sorptive numbers (α*), which quantify the degree of soil capillarity, were also calculated for the 10 representative soils evaluated in this study. Using the α* estimates and calibrated shape factors developed for this study, these methods can provide estimates of Ks with a bias range of 0.87–1.13 and an average bias of 0.99. Bias is the calculated Ks based on the constant-head borehole permeameter method divided by the specified Ks used in the numerical model. As demonstrated in this study, the constant-head borehole permeameter methods are well-suited for predicting the flow capacity of full-scale infiltration facilities.