Framing vulnerability, risk and societal responses: the MOVE framework

The paper deals with the development of a general as well as integrative and holistic framework to systematize and assess vulnerability, risk and adaptation. The framework is a thinking tool meant as a heuristic that outlines key factors and different dimensions that need to be addressed when assessing vulnerability in the context of natural hazards and climate change. The approach underlines that the key factors of such a common framework are related to the exposure of a society or system to a hazard or stressor, the susceptibility of the system or community exposed, and its resilience and adaptive capacity. Additionally, it underlines the necessity to consider key factors and multiple thematic dimensions when assessing vulnerability in the context of natural and socio-natural hazards. In this regard, it shows key linkages between the different concepts used within the disaster risk management (DRM) and climate change adaptation (CCA) research. Further, it helps to illustrate the strong relationships between different concepts used in DRM and CCA. The framework is also a tool for communicating complexity and stresses the need for societal change in order to reduce risk and to promote adaptation. With regard to this, the policy relevance of the framework and first results of its application are outlined. Overall, the framework presented enhances the discussion on how to frame and link vulnerability, disaster risk, risk management and adaptation concepts

Conceptualizing community resilience to natural hazards - the emBRACE framework

Abstract. The level of community is considered to be vital for building disaster resilience. Yet, community resilience as a scientific concept often remains vaguely defined and lacks the guiding characteristics necessary for analysing and enhancing resilience on the ground. The emBRACE framework of community resilience presented in this paper provides a heuristic analytical tool for understanding, explaining and measuring community resilience to natural hazards. It was developed in an iterative process building on existing scholarly debates, on empirical case study work in five countries and on participatory consultation with community stakeholders, where the framework was applied and ground-tested in different contexts and for different hazard types. The framework conceptualizes resilience across three core domains: resources and capacities; actions; and learning. These three domains are conceptualized as intrinsically conjoined within a whole. Community resilience is influenced by these integral elements as well as by extra-community forces, comprising disaster risk governance and thus laws, policies and responsibilities on the one hand and on the other, the general societal context, natural and human-made disturbances and system change over time. The framework is a graphically rendered heuristic, which through application can assist in guiding the assessment of community resilience in a systematic way and identifying key drivers and barriers of resilience that affect any particular hazard-exposed community

Climate Risk Sourcebook

The Climate Risks Sourcebook provides an updated methodological approach on how to design and conduct climate risk assessments and provides the necessary and state-of-the-art knowledge incorporating findings of the sixth Assessment Report of the IPCC. It is a user-friendly, step-by-step guide to operationalizing the theoretical concept of risk. The approach is location and context-specific and gives guidance on how climate risk assessments can inform and support evidence-based decision making. This includes impact chains as tailor-made conceptual models that illustrate key risks and their drivers for a specific context. The Climate Risk Sourcebook additionally offers expert material for further in-depth knowledge. Another novelty is its focus on communication, gender and vulnerable groups

Computational Fluid Dynamics of Catalytic Reactors

Today, the challenge in chemical and material synthesis is not only the development of new catalysts and supports to synthesize a desired product, but also the understanding of the interaction of the catalyst with the surrounding flow field. Computational Fluid Dynamics or CFD is the analysis of fluid flow, heat and mass transfer and chemical reactions by means of computer-based numerical simulations. CFD has matured into a powerful tool with a wide range of applications in industry and academia. From a reaction engineering perspective, main advantages are reduction of time and costs for reactor design and optimization, and the ability to study systems where experiments can hardly be performed, e.g., hazardous conditions or beyond normal operation limits. However, the simulation results will always remain a reflection of the uncertainty in the underlying models and physicochemical parameters so that in general a careful experimental validation is required. This chapter introduces the application of CFD simulations in heterogeneous catalysis. Catalytic reactors can be classified by the geometrical design of the catalyst material (e.g. monoliths, particles, pellets, washcoats). Approaches for modeling and numerical simulation of the various catalyst types are presented. Focus is put on the principal concepts for coupling the physical and chemical processes on different levels of details, and on illustrative applications. Models for surface reaction kinetics and turbulence are described and an overview on available numerical methods and computational tools is provided

Progress in Applied CFD. Selected papers from 10th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries

Iron-ore reduction has attracted much interest in the last three decades since it can be considered as a core process in steel industry. The iron-ore is reduced to iron with the use of blast furnace and fluidized bed technologies. To investigate the harsh conditions inside fluidized bed reactors, computational tools can be utilized. One such tool is the CFD-DEM method, in which the gas phase reactions and governing equations are calculated in the Eulerian (CFD) side, whereas the particle reactions and equation of motion are calculated in the Lagrangian (DEM) side. In this work, this method has been extended to cover the most common types of representation models for the reactions of solids submerged in fluids. These models are the Shrinking Particle Model (SPM) and the Unreacted Shrinking Core Model (USCM). With the use of the SPM, the implemented communication framework between the CFD and DEM sides have been verified by running some preliminary cases and comparing the species mass balances. In the modelling of iron-oxide reduction the SPM is insufficient to represent the different reaction steps, therefore a three-layered USCM is utilized. The implemented USCM is validated by running some preliminary cases.publishedVersio

Modelling of chemical reactions in metallurgical processes

Iron-ore reduction has attracted much interest in the last three decades since it can be considered as a core process in steel industry. The iron-ore is reduced to iron with the use of blast furnace and fluidized bed technologies. To investigate the harsh conditions inside fluidized bed reactors, computational tools can be utilized. One such tool is the CFD-DEM method, in which the gas phase reactions and governing equations are calculated in the Eulerian (CFD) side, whereas the particle reactions and equation of motion are calculated in the Lagrangian (DEM) side. In this work, this method has been extended to cover the most common types of representation models for the reactions of solids submerged in fluids. These models are the Shrinking Particle Model (SPM) and the Unreacted Shrinking Core Model (USCM). With the use of the SPM, the implemented communication framework between the CFD and DEM sides have been verified by running some preliminary cases and comparing the species mass balances. In the modelling of iron-oxide reduction the SPM is insufficient to represent the different reaction steps, therefore a three-layered USCM is utilized. The implemented USCM is validated by running some preliminary cases