Microstructure-Based Lifetime Assessment of Austenitic Steel AISI 347 in View of Fatigue, Environmental Conditions and NDT

The assessment of metallic materials used in power plants’ piping represents a big challenge due to the thermal transients and the environmental conditions to which they are exposed. At present, a lack of information related to degradation mechanisms in structures and materials is covered by safety factors in its design, and in some cases, the replacement of components is prescribed after a determined period of time without knowledge of the true degree of degradation. In the collaborative project “Microstructure-based assessment of maximum service life of nuclear materials and components exposed to corrosion and fatigue (MibaLeb)”, a methodology for the assessment of materials’ degradation is being developed, which combines the use of NDT techniques for materials characterization, an optimized fatigue lifetime analysis using short time evaluation procedures (STEPs) and numerical simulations. In this investigation, the AISI 347 (X6CrNiNb18-10) is being analyzed at different conditions in order to validate the methodology. Besides microstructural analysis, tensile and fatigue tests, all to characterize the material, a pressurized hot water pipe exposed to a series of flow conditions will be evaluated in terms of full-scale testing as well as prognostic evaluation, where the latter will be based on the materials’ data generated, which should prognose changes in the material’s condition, specifically in a pre-cracked stage. This paper provides an overview of the program, while the more material’s related aspects are presented in the subsequent paper

A Short-Time Approach for Fatigue Life Evaluation of AISI 347 Steel for Nuclear Power Energy Applications

AISI 347 austenitic steel is, as an example, used in nuclear energy piping systems. Piping filled with superheated steam or cooled water is particularly exposed to high stresses, whereupon local material properties in the pipes can change significantly, especially in the case of additional corrosive influences, leading to aging of the material. In the absence of appropriate information, such local material property variations are currently covered rather blanketly by safety factors set during the design of those components. An increase in qualified information could improve the assessment of the condition of such aged components. As part of the collaborative project “Microstructure-based assessment of the maximum service life of core materials and components subjected to corrosion and fatigue (MiBaLeB)”, the short-time procedure, StrainLife, was developed and validated by several fatigue tests. With this procedure, a complete S–N curve of a material can be determined on the basis of three fatigue tests only, which reduces the effort compared to a conventional approach significantly and is thus ideal for assessing the condition of aged material, where the material is often rare, and a cost-effective answer is often very needed. The procedure described is not just limited to traditional parameters, such as stress and strain, considered in destructive testing but rather extends into parameters derived from non-destructive testing, which may allow further insight into what may be happening within a material’s microstructure. To evaluate the non-destructive quantities measured within the StrainLife procedure and to correlate them with the aging process in a material, several fatigue tests were performed on unnotched and notched specimens under cyclic loading at room and elevated temperatures, as well as under various media conditions, such as distilled water and reactor pressure vessel boiling water (BWR) conditions

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Decentralized User Modeling with UserML and GUMO

We present a new architecture for decentralized user modeling and briefly discuss the user model markup language USERML, the general user model ontology GUMO for the uniform interpretation of decentralized user models, and the integration of ubiquitous applications with the u2m.org user model service. The motivation is that ubiquitous evaluation of user behavior with a variety of systems in the web or the physical world might lead to attractive new services

Economic crises as critical junctures for policy and structural changes towards decarbonization – the cases of Spain and Germany

Crises may act as tipping points for decarbonization pathways by triggering structural economic change or offering windows of opportunity for policy change. We investigate both types of effects of the global financial and COVID-19 crises on decarbonization in Spain and Germany through a quantitative Kaya-decomposition analysis of CO2 emissions and through a qualitative review of climate and energy policy changes. We show that the global financial crisis resulted in a critical juncture for Spanish CO2 emissions due to the combined effects of the deep economic recession and crisis-induced structural change, resulting in reductions in carbon and energy intensities and shifts in the economic structure. However, the crisis also resulted in a rollback of renewable energy policy, halting progress in the transition to green electricity. The impacts were less pronounced in Germany, where pre-existing decarbonization and policy trends continued after the crisis. Recovery packages had modest effects, primarily due to their temporary nature and the limited share of climate-related spending. The direct short-term impacts of the COVID-19 crisis on CO2 emissions were more substantial in Spain than in Germany. The policy responses in both countries sought to align short-term economic recovery with the long-term climate change goals of decarbonization, but it is too soon to observe their lasting effects. Our findings show that crises can affect structural change and support decarbonization but suggest that such effects depend on pre-existing trends, the severity of the crisis and political manoeuvring during the crisis. The short-term effects of the Global Financial and COVID-19 crises on CO2 emission drivers were larger in Spain than in Germany because of pre-existing trends and the crises´ magnitudeThe Global Financial Crisis represented a tipping point for emissions in Spain, affecting the structural drivers of emissions. However, renewable energy policy was temporarily negatively affected by the crisis.Both countries’ policy responses to the COVID-19 crisis sought to align short-term economic recovery with long-term decarbonization goals, moving beyond the ‘environment vs economic recovery’ dichotomy that prevailed during the Global Financial Crisis.Green recovery packages should prioritize measures with a higher potential to support structural change consistent with deep decarbonization.It is crucial to adapt energy and climate policy ex-ante to make them more resilient to political and fiscal pressures which may emerge in the wake of crises. The short-term effects of the Global Financial and COVID-19 crises on CO2 emission drivers were larger in Spain than in Germany because of pre-existing trends and the crises´ magnitude The Global Financial Crisis represented a tipping point for emissions in Spain, affecting the structural drivers of emissions. However, renewable energy policy was temporarily negatively affected by the crisis. Both countries’ policy responses to the COVID-19 crisis sought to align short-term economic recovery with long-term decarbonization goals, moving beyond the ‘environment vs economic recovery’ dichotomy that prevailed during the Global Financial Crisis. Green recovery packages should prioritize measures with a higher potential to support structural change consistent with deep decarbonization. It is crucial to adapt energy and climate policy ex-ante to make them more resilient to political and fiscal pressures which may emerge in the wake of crises.</p

Investigation on wall and gas temperatures inside a swirled oxy-fuel combustion chamber using thermographic phosphors, O2 rotational and vibrational CARS

Wall and gas phase temperatures inside a swirled oxy-fuel combustion chamber are important to characterize the combustion process. Wall temperatures were measured by thermographic phosphors and discussed in combination with gas phase temperatures. For gas phase temperatures an O2 vibrational coherent anti-Stokes Raman scattering (CARS) approach was compared to a N2/O2 rotational CARS (RCARS) system. The vibrational CARS (VCARS) setup was favorable due to higher signal strength at high temperatures. With this system gas phase temperature profiles inside a swirled oxy-fuel combustion chamber were measured and discussed for different operation conditions. The location of intermittent reaction zones could be determined. In order to provide a measurement tool for gas-assisted pulverized solid fuel flames the developed O2-VCARS system was successfully tested in such a harsh environment. Possible error sources related to particles within the probe volume are discussed