On discontinuities when computing the stress-field from the strain: a finite volume discretization

Recently, a widely applicable system of hyperbolic partial differential equations has been derived that enables the deterministic computation of a full heterogeneous stress field from a measured deformation field, for example, from a strain field obtained via digital image correlation. This information enables the determination of material properties, making this approach an alternative to finite element model updating or the virtual fields method. This article focuses on developing a finite volume discretization of this system of equations to address instabilities that arise from violations of the Courant-Friedrichs-Lewy condition. The developed discretization enables the system of equations to be applied to irregular geometries and finite deformation. We determine how, in general, one may translate knowledge of the traction at the boundary into boundary conditions, so that the numerical method can be applied to a variety of loading conditions. We analyse the solution structure in the case of deformation discontinuities, and the results are applied to account for discontinuities at the interfaces between finite volumes (this has relevance to other applications such as composite materials). Interestingly, the discontinuities cause reflection and transmission of the principal stresses. The finite volume discretization is validated using data output from commercial finite element software. Furthermore, the discretization is applied to an experimental uniaxial tension test with plastic deformation and necking. The strain field is obtained using digital image correlation, and the stress field is computed using the developed finite volume discretization. Together, these give the stress-strain behavior for each material element

Complexion-mediated martensitic phase transformation in Titanium

The most efficient way to tune microstructures and mechanical properties of metallic alloys lies in designing and using athermal phase transformations. Examples are shape memory alloys and high strength steels, which together stand for 1,500 million tons annual production. In these materials, martensite formation and mechanical twinning are tuned via composition adjustment for realizing complex microstructures and beneficial mechanical properties. Here we report a new phase transformation that has the potential to widen the application window of Ti alloys, the most important structural material in aerospace design, by nanostructuring them via complexion-mediated transformation. This is a reversible martensitic transformation mechanism that leads to a final nanolaminate structure of α″ (orthorhombic) martensite bounded with planar complexions of athermal ω (a–ω, hexagonal). Both phases are crystallographically related to the parent β (BCC) matrix. As expected from a planar complexion, the a–ω is stable only at the hetero-interface

Complexion-mediated martensitic phase transformation in Titanium

The most efficient way to tune microstructures and mechanical properties of metallic alloys lies in designing and using athermal phase transformations. Examples are shape memory alloys and high strength steels, which together stand for 1,500 million tons annual production. In these materials, martensite formation and mechanical twinning are tuned via composition adjustment for realizing complex microstructures and beneficial mechanical properties. Here we report a new phase transformation that has the potential to widen the application window of Ti alloys, the most important structural material in aerospace design, by nanostructuring them via complexion-mediated transformation. This is a reversible martensitic transformation mechanism that leads to a final nanolaminate structure of α″ (orthorhombic) martensite bounded with planar complexions of athermal ω (a–ω, hexagonal). Both phases are crystallographically related to the parent β (BCC) matrix. As expected from a planar complexion, the a–ω is stable only at the hetero-interface.European Commission. Framework Programme for Research and Innovation (FP7/2007–2013))/ERC Grant agreement 290998 'SmartMet’)Innovative Research Team in University (IRT13034)National Basic Research Program of China (973 Program) (2014CB644003)China. Ministry of Science and Technology. National Key Research and Development Program (2016YFB0701302)National Natural Science Foundation of China (51501145)National Natural Science Foundation of China (51320105014)National Natural Science Foundation of China (51621063

Integrated experimental and simulation analysis of stress and strain partitioning in dual phase steel

The mechanical behavior of multiphase steels is governed by the microscopic strain and stress partitioning behavior among microstructural constituents [1-3]. However, due to limitations in the characterization of the partitioning that takes place at the submicron scale, microstructure optimization of such alloys is typically based on evaluating the averaged response, referring to, for example, macroscopic stress–strain curves. Here, a coupled experimental–numerical methodology is presented and discussed to strengthen the integrated understanding of the microstructure and mechanical properties of complex alloys, enabling joint analyses of deformation-induced evolution of the microstructure, and the strain and stress distribution therein, down to submicron resolution. From the experiments, deformation-induced evolution of (i) the microstructure, and (ii) the local strain distribution are concurrently captured, employing in situ secondary electron imaging and electron backscatter diffraction (EBSD) (for the former), and microscopic-digital image correlation (for the latter) [3,4]. From the simulations, local strain as well as stress distributions are revealed, through full-field crystal plasticity (CP) simulations conducted with the advanced DAMASK spectral solver suitable for heterogeneous materials [5,6]. The simulated model is designed directly from the initial EBSD measurements, and the phase properties are obtained by additional inverse CP simulations of nanoindentation experiments carried out on the original microstructure. The experiments and simulations demonstrate good correlation in the proof-of-principle study conducted here on a martensite–ferrite dual-phase steel, and deviations are discussed in terms of opportunities and limitations of the techniques involved. C.C. Tasan et al. Strain localization and damage in dual phase steels investigated by coupled in-situ deformation experiments and crystal plasticity simulations, International Journal of Plasticity,63,198-210,2014 C.C. Tasan et al. An overview of dual-phase steels: advances in microstructure-oriented processing and micromechanically guided design, Annual Review of Materials Research,45,391-431,2015 D. Yan et al. High resolution in situ mapping of microstrain and microstructure evolution reveals damage resistance criteria in dual phase steels,Acta Materialia,96,399-409,2015 C.C. Tasan et al. Integrated experimental–simulation analysis of stress and strain partitioning in multiphase alloys,Acta Materialia,81,,386-400,2014 F. Roters, et al. 2012, DAMASK: The Düsseldorf Advanced Material Simulation Kit for studying crystal plasticity using an FE based or a spectral numerical solver, in Procedia IUTAM, Vol. III, pp. 3–10, Elsevier, Amsterdam. https://damask.mpie.d

Retardation of plastic instability via damage-enabled microstrain delocalization

Multi-phase microstructures with high mechanical contrast phases are prone to microscopic damage mechanisms. For ferrite-martensite dual-phase steel, for example, damage mechanisms such as martensite cracking or martensite-ferrite decohesion are activated with deformation, and discussed often in literature in relation to their detrimental role in triggering early failure in specific dual-phase steel grades. However, both the micromechanical processes involved and their direct influence on the macroscopic behavior are quite complex, and a deeper understanding thereof requires systematic analyses. To this end, an experimental-theoretical approach is employed here, focusing on three model dual-phase steel microstructures each deformed in three different strain paths. The micromechanical role of the observed damage mechanisms is investigated in detail by in-situ scanning electron microscopy tests, quantitative damage analyses, and finite element simulations. The comparative analysis reveals the unforeseen conclusion that damage nucleation may have a beneficial mechanical effect in ideally designed dual-phase steel microstructures (with effective crack-arrest mechanisms) through microscopic strain delocalization

Prioritizing causal disease genes using unbiased genomic features

Background: Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. Results: To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. Conclusion: Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0534-8) contains supplementary material, which is available to authorized users

Interpreting Cancer Genomes Using Systematic Host Perturbations by Tumour Virus Proteins

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer

Evidence for Network Evolution in an Arabidopsis Interactome Map

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing ~6,200 highly reliable interactions between ~2,700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops