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We present a proof-of-concept for the adaptive mesh refinement method applied to atmospheric boundary-layer simulations. Such a method may form an attractive alternative to static grids for studies on atmospheric flows that have a high degree of scale separation in space and/or time. Examples include the diurnal cycle and a convective boundary layer capped by a strong inversion. For such cases, large-eddy simulations using regular grids often have to rely on a subgrid-scale closure for the most challenging regions in the spatial and/or temporal domain. Here we analyze a flow configuration that describes the growth and subsequent decay of a convective boundary layer using direct numerical simulation (DNS). We validate the obtained results and benchmark the performance of the adaptive solver against two runs using fixed regular grids. It appears that the adaptive-mesh algorithm is able to coarsen and refine the grid dynamically whilst maintaining an accurate solution. In particular, during the initial growth of the convective boundary layer a high resolution is required compared to the subsequent stage of decaying turbulence. More specifically, the number of grid cells varies by two orders of magnitude over the course of the simulation. For this specific DNS case, the adaptive solver was not yet more efficient than the more traditional solver that is dedicated to these types of flows. However, the overall analysis shows that the method has a clear potential for numerical investigations of the most challenging atmospheric cases.

We present a topology structural optimization framework with adaptive mesh refinement and stress-constraints. Finite element approximation and geometry representation benefit from such refinement by enabling more accurate stress field predictions and greater resolution of the optimal structural boundaries. We combine a volume fraction filter to impose a minimum design feature size, the RAMP penalization to generate “black-and-white designs” and a RAMP-like stress definition to resolve the “stress singularity problem.” Regions with stress concentrations dominate the optimized design. As such, rigorous simulations are required to accurately approximate the stress field. To achieve this goal, we invoke a threshold operation and mesh refinement during the optimization. We do so in an optimal fashion, by applying adaptive mesh refinement techniques that use error indicators to refine and coarsen the mesh as needed. In this way, we obtain more accurate simulations and greater resolution of the design domain. We present results in two dimensions to demonstrate the efficiency of our method.

We construct several stable finite element pairs for the Stokes problem on barycentric refinements in arbitrary dimensions. A key feature of the spaces is that the divergence maps the discrete velocity space onto the discrete pressure space; thus, when applied to models of incompressible flows, the pairs yield divergence-free velocity approximations. The key result is a local inf-sup stability that holds for any dimension and for any polynomial degree. With this result, we construct global divergence-free and stable pairs in arbitrary dimension and for any polynomial degree.

Axions are hypothetical particles that may explain the observed dark matter density and the non-observation of a neutron electric dipole moment. An increasing number of axion laboratory searches are underway worldwide, but these efforts are made difficult by the fact that the axion mass is largely unconstrained. If the axion is generated after inflation there is a unique mass that gives rise to the observed dark matter abundance; due to nonlinearities and topological defects known as strings, computing this mass accurately has been a challenge for four decades. Recent works, making use of large static lattice simulations, have led to largely disparate predictions for the axion mass, spanning the range from 25 microelectronvolts to over 500 microelectronvolts. In this work we show that adaptive mesh refinement simulations are better suited for axion cosmology than the previously-used static lattice simulations because only the string cores require high spatial resolution. Using dedicated adaptive mesh refinement simulations we obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, to within ~5% precision, leading to a mass prediction in the range (40,180) microelectronvolts. The question of what axion mass would give rise to the observed dark matter abundance requires proper modelling of non-linear dynamics of the axion field in the early Universe. Here, the authors use adaptive mesh refinement simulations to predict a mass in the range in the range (40,180) microelectronvolts.

It is well-known that grain refiners can tailor the microstructure and enhance the mechanical properties of titanium alloys fabricated by additive manufacturing (AM). However, the intrinsic mechanisms of Ni addition on AM-built Ti–6Al–4V alloy is not well established. This limits its industrial applications. This work systematically investigated the influence of Ni additive on Ti–6Al–4V alloy fabricated by laser aided additive manufacturing (LAAM). The results showed that Ni addition yields three key effects on the microstructural evolution of LAAM-built Ti–6Al–4V alloy. (a) Ni additive remarkably refines the prior- β grains, which is due to the widened solidification range. As the Ni addition increased from 0 to 2.5 wt. %, the major-axis length and aspect ratio of the prior- β grains reduced from over 1500 μ m and 7 to 97.7 μ m and 1.46, respectively. (b) Ni additive can discernibly induce the formation of globular α phase, which is attributed to the enhanced concentration gradient between the β and α phases. This is the driving force of globularization according to the termination mass transfer theory. The aspect ratio of the α laths decreased from 4.14 to 2.79 as the Ni addition increased from 0 to 2.5 wt. %. (c) Ni as a well-known β -stabilizer and it can remarkably increase the volume fraction of β phase. Room-temperature tensile results demonstrated an increase in mechanical strength and an almost linearly decreasing elongation with increasing Ni addition. A modified mathematical model was used to quantitatively analyze the strengthening mechanism. It was evident from the results that the α lath phase and the solid solutes contribute the most to the overall yield strength of the LAAM-built Ti–6Al–4V– x Ni alloys in this work. Furthermore, the decrease in elongation with increasing Ni addition is due to the deterioration in deformability of the β phase caused by a large amount of solid-solution Ni atoms. These findings can accelerate the development of additively manufactured titanium alloys.

Conventional ultrafine-grains can generate high strength in Mg alloys, but significant tradeoff of corrosion resistance due to inclusion of a large number of non-equilibrium grain boundaries. Herein, an ultrafine-grain structure consisting of dense ultrafine twins is prepared, yielding a high strength up to 469 MPa and decreasing the corrosion rate by one order of magnitude. Generally, the formation of dense ultrafine twins in Mg alloys is rather difficult, but a carefully designed multi-directional compression treatment effectively stimulates twinning nucleation within twins and refines grain size down to 300 nm after 12-passes compressions. Grain-refinement by low-energy twins not only circumvents the detrimental effects of non-equilibrium grain boundaries on corrosion resistance, but also alters both the morphology and distribution of precipitates. Consequently, micro-galvanic corrosion tendency decreases, and severe localized corrosion is suppressed completely. This technique has a high commercial viability as it can be readily implemented in industrial production. Conventional ultrafine grains can generate high-strength Mg alloys, but non-equilibrium grain boundaries deteriorates their corrosion resistance. Here, the authors present ultrafine grained Mg alloys with dense twins that display high strength and reduced corrosion rate by one order of magnitude.

The most common technique for the numerical implementation of peridynamic theory is based on a mesh-free approach, in which the whole body is discretized with a uniform grid and a constant horizon. As a consequence of that computational resources may not be used efficiently. The present work proposes adaptive refinement algorithms for 2D peridynamic grids. That is an essential component to generate a concurrent multiscale model within a unified approach. Adaptive grid refinement is here applied to the study of dynamic crack propagation in two dimensional brittle materials. Refinement is activated by using a new trigger concept based on the damage state of the material, coupled with the more traditional energy based trigger, already proposed in the literature. We present as well a method, to generate the nodes in the refined zone, which is suitable for an efficient numerical implementation. Moreover, strategies for the mitigation of spurious reflections and distortions of elastic waves due to the use of a non-uniform grid are presented. Finally several examples of crack propagation in planar problems are presented, they illustrate the potentialities of the proposed algorithms and the good agreement of the numerical results with experimental data.

Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidity of hard labels and the simplicity of label smoothing under-represent inter-class relations and compositional structures, degrading cross-domain robustness. While current domain generalization methods can alleviate these issues, they typically rely on multi-source domain data. However, considering the limitations of equipment operational conditions and data acquisition costs in industrial applications, only one or two independently distributed source datasets are typically available. In this work, an adaptive label refinement network (ALRN) was designed for learning with imperfect labels under source-scarce conditions. Compared to hard labels and label smoothing, ALRN learns richer, more robust soft labels that encode the semantic similarities between fault classes. The model first trains a convolutional neural network (CNN) to obtain initial class probabilities. It then iteratively refines the training labels by computing a weighted average of predictions within each class, using the sample-wise cross-entropy loss as an adaptive weighting factor. Furthermore, a label refinement stability coefficient based on the max-min Kullback–Leibler (KL) divergence ratio across classes is proposed to evaluate label quality and determine when to terminate the refinement iterations. With only one or two source domains for training, ALRN achieves accuracy gains exceeding 22% under unseen operating conditions compared with a conventional CNN baseline. These results validate that the proposed label refinement algorithm can effectively enhance the cross-domain diagnostic performance, providing a novel and practical solution for learning with imperfect supervision in cross-domain compound fault diagnosis.

Grain refinement is a widely sought-after feature of many metal production processes and frequently involves a process of recrystallization. Some processing methods use very high strain rates and high strains to refine the grain structure into the nanocrystalline regime. However, grain refinement processes are not clear in these extreme conditions, which are hard to study systematically. Here, we access those extreme conditions of strain and strain rate using single copper microparticle impact events with a laser-induced particle impact tester. Using a combined dictionary-indexing electron backscatter diffraction and scanning transmission electron microscopy approach for postmortem characterization of impact sites, we systematically explore increasing strain levels and observe a recrystallization process that is facilitated by nanotwinning, which we term nanotwinning-assisted dynamic recrystallization. It achieves much finer grain sizes than established modes of recrystallization and therefore provides a pathway to the finest nanocrystalline grain sizes through extreme straining processes. Extreme mechanical deformation processes can lead to nanograins in many metals, but the underlying mechanism remains unclear. Nanotwinning-assisted dynamic recrystallization is shown to facilitate grain refinement to the nanoscale at high strains and strain rates.

Acid fracturing is a technique to enhance productivity in carbonate formations. In this work, a thermal–hydraulic–mechanical–chemical (THMC) coupling model for acid fracture propagation is proposed based on a phase-field approach. The phase-field variable is utilized as an indicator function to distinguish the fracture and the reservoir, and to track the propagation of the fracture. The resulting system is a nonstationary, nonlinear, variational inequality system in which five different physical modules for the displacement, the phase-field, the pressure, the temperature, and the acid concentration are coupled. This multi-physical system includes numerical challenges in terms of nonlinearities, solution coupling algorithms, and computational cost. To this end, high fidelity physics-based discretizations, parallel solvers, and mesh adaptivity techniques are required. The model solves the phase-field and the displacement variables by a quasi-monolithic scheme and the other variables by a partitioned schemes, where the resulting overall algorithm is of iterative coupling type. In order to maintain the computational cost low, the adaptive mesh refinement technique in terms of a predictor-corrector method is employed. The error indicators are obtained from both the phase-field and concentration approximations. The proposed model and the computational robustness were investigated by studying fourteen cases as well as some mesh refinement studies. It is observed that the acid and thermal effect increase the fracture volume and fracture width. Moreover, the natural fractures and holes affect the acid fracture propagation direction.Highlightsthermal–hydraulic–mechanical–chemical coupling system was established for acid fracture propagation based on a phase-field method.The acid fluid equations including diffusion, transport and reaction were derived. The penalization method was introduced based on physical reality.Acid fracture problem is a kind of dynamic heterogeneous problem. The adaptive mesh refinement was extended to help researchers get smooth simulation results and save computation costs.