Meso to macro connections to capture fatigue damage in cemented materials

Available online 16 August 2023Fatigue-induced damage is a common issue in cemented materials, involving the progressive formation and complicated propagation of fatigue cracks. These cracks typically localise on weak or fracture planes, leading to inhomogeneous deformation within the material. Thus, accurately predicting the fatigue phenomenon becomes challenging due to the material’s inhomogeneity and the complex evolution of cracks, from initiation to propagation and eventual failure. To address this challenge, this paper presents a constitutive model that accounts for strain discontinuity across fracture planes by employing kinematic enrichment. This enhancement facilitates interaction between the material responses of cracks and the outer bulk, thereby contributing to the overall macro behaviour of the materials. Moreover, the proposed model incorporates a new cohesive-frictional fatigue model that couples damage mechanics and bounding surface plasticity to describe the fatigue behaviour of fracture planes/cracks. Since the proposed model features a characteristic length scale, it exhibits size-dependent behaviour and helps overcome the issue of mesh dependence. The model’s validity is demonstrated through its ability to capture nonlinear fatigue damage under constant/variable cyclic loading and to simulate the propagation of fatigue fracture process zones. Furthermore, the model effectively captures the significant influence of stress amplitudes on the fatigue lives of materials, making it an essential tool for predicting and mitigating fatigue-induced damage in cemented materials.Vinh T. Le, Ha H. Bui, Giang D. Nguyen, Jayantha Kodikara, Didier Bodin, James Grenfel

Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions. The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator: We exploit the non-Euclidean Isomap metric to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime

Semantic Interoperability in Body Area Sensor Networks and Applications

Crucial to the success of Body Area Sensor Networks is the flexibility with which stakeholders can share, extend and adapt the system with respect to sensors, data and functionality. The first step is to develop an interoperable platform with explicit interfaces, which takes care of common management tasks. Beyond that, interoperability is defined by semantics. This paper presents the analysis, design, implementation and evaluation of a semantic layer within an existing BASN platform for the purpose of improving the semantic interoperability among sensor networks and applications. We adopt an ontology-based approach but rather than having a single overall ontology, we find that using clear semantic domains and mappings between them improves composability and reduces interoperability problems. We discuss the design choices and a reference implementation on an Android phone and actual sensor devices. We show by a qualitative evaluation that this semantic interoperability indeed provides significant improvements in flexibility

The angular two-point correlation function of Planck SZ cluster catalog

International audienceWe present results from our analysis of the angular two-point correlationfunction of galaxy clusters detected via the Sunyaev-Zeldovich (SZ) effect bythe Planck mission. The Planck SZ survey selection function is a complexfunction of position on the sky, source size and flux. We focus on the MMF3 sub-sample for which we know the selection function to produce a reference randomcatalog. We get no significant clustering of Planck clusters with SN R > 6 andredshift z < 0.4. We find the clustering of Planck clusters with redshift z <0.2, compatible with the Abell clusters correlation as investigated by Bahcalland Soneira (1983)

Low-temperature atomic layer deposition delivers more active and stable Pt-based catalysts

We tailored the size distribution of Pt nanoparticles (NPs) on graphene nanoplatelets at a given metal loading by using low-temperature atomic layer deposition carried out in a fluidized bed reactor operated at atmospheric pressure. The Pt NPs deposited at low temperature (100 °C) after 10 cycles were more active and stable towards the propene oxidation reaction than their high-temperature counterparts. Crucially, the gap in the catalytic performance was retained even after prolonged periods of time (&gt;24 hours) at reaction temperatures as high as 450 °C. After exposure to such harsh conditions the Pt NPs deposited at 100 °C still retained a size distribution that is narrower than the one of the as-synthesized NPs obtained at 250 °C. The difference in performance correlated with the difference in the number of facet sites as estimated after the catalytic test. Our approach provides not only a viable route for the scalable synthesis of stable supported Pt NPs with tailored size distributions but also a tool for studying the structure-function relationship.ChemE/Product and Process EngineeringRST/Fundamental Aspects of Materials and EnergyChemE/O&O groepChemE/Catalysis EngineeringChemE/Chemical Engineerin