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Material compiled for analysis in this paper: Ueckerdt F, Bauer C, Dirnaichner A, Everall J, Sacchi R, Luderer R (2021) Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nature Climate Change. The material includes: 1) a spreadsheet file with technoeconomic data 2) an R Markdown script which is the source code for an interactive dashboard used to visualise (1) 3) a README file to assist with navigation of the data in (1) 1) The spreadsheet data contains CAPEX, efficiency and other supplementary data for small to large scale electrolysers for current, and future years. Data was collected based on a Literature Review of a variety of academic and industry sources conducted during the course of the title paper development. The data are differentiated by several categories including electrolysis method, source publication year and literature type. Care was taken to avoid recycled cost values, and to focus on the currency of the data, with values included to indicate the oldest reference year of any cited literature. 2) The R Markdown script in combination with the spreadsheet data is used as a basis for an interactive dashboard which can be run with an R installation and the supporting packages, or viewed online at https://h2.pik-potsdam.de/H2Dash/

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Purpose – Find optimal flight and design parameters for three objectives: minimum fuel consumption, Direct Operating Costs (DOC), and environmental impact of a passenger jet aircraft. --- Approach – Combining multiple models (this includes aerodynamics, specific fuel consumption, DOC, and equivalent CO2 mass) into one generic model. In this combined model, each objective's importance is determined by a weighting factor. Additionally, the possibility of further optimizing this model by altering an aircraft's wing loading is analyzed. --- Findings – When optimizing for a compromise between economic and ecologic benefits, the general outcome is a reduction in cruise altitude and an unaltered cruise Mach number compared to common practice. Decreasing cruise speed would benefit the environmental impact but has a negative effect on seat-mile cost. An increase in wing loading could further optimize the general outcome. Albeit at the cost of a greater required landing distance, therefore limiting the operational opportunities of this aircraft. --- Research limitations – Most models use estimating equations based on first principles and statistical data. --- Practical implications – The optimal cruise altitude and speed for a specific objective can be approximated for any passenger jet aircraft. --- Social implications – By using a simple approach, the discussion of optimizing aircraft opens up to a level where everyone can participate. --- Value – To find a general answer on how to optimize aviation, operational and design-wise, by using a simple approach. Digital Library - Projects and Theses - Prof. Dr. Scholz Excel, null

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c08e08aa-3055-4651-801b-0383610c1987}.

The potential of deoiled Azolla pinnata biomass (DAB) as electrode and substrate was evaluated for microbial fuel cell (MFC) operation. The anode electrode was fabricated using biochar obtained by subjecting DAB to pyrolysis at 600 °C, while the reducing sugars after hydrolysis of DAB by acid pretreatment was used as substrate. The post pyrolyzed biochar (P-DAB) was characterized for structural and elemental functionalities using SEM, XRD and Raman spectroscopy, whereas the reducing sugar obtained from hydrolyzed DAB (H-DAB) was analyzed for its composition. Experimental results indicated that at a given 3 g COD/L resulted in a voltage of 382 mV with 65.6% of COD reduction in closed circuit (CC) mode of operation. Cyclic voltammetric analysis depicted maximum oxidative and reductive peak currents of 3.42 mA and -4.0 mA. Noticeable peaks were also identified in CC (-0.2 V to +0.2 V and -0.19 V to -0.3 V) and OC (+0.2 V to +0.4 V and -0.1 V to -0.3 V) corresponding to complex IV cytochrome c couples (cytochrome Cox (Cyt Cox)/cytochrome Crd (Cyt Crd)), signifying the participation of electron carriers during electron transfer. The microbiome diversity showed dominance of Proteobacteria, a phylum known for exo-electrogenic bacterial species. The DAB-derived products account to environmental sustainability and support circular bioeconomy in a biorefinery mode.

Abstract SRM (Switched Reluctance motor) is gaining much interest in industries due to its straightforward structure, low-cost manufacturability and dependability which makes it better than other electric machines. SRM drive is the most appropriate for variable speed tasks. Additionally, the performance of the current and speed control for the SRM driver framework could be negatively influenced by noise, disturbances, and inactivity of load torque. To solve this difficulty, this paper develop an ANFIS based Speed and Current control with Torque Ripple Minimization using Hybrid SSD-SFO for SRM. The main goal of this work is to obtain preferred current and speed performance of SRM with minimum torque ripple. For concurrent regulator of the speed and current, an ANFIS (Adaptive Neuro-Fuzzy Inference System) structure is employed which includes two controlling loops. The inside loop is regulated for control of current and the outside loop is regulated for control of speed even with perfect choice of switching angles. The dynamic conduct of SRM is studied to restrict the current and speed that reduces the ripple of torque. Hybrid SSD-SFO (social ski-diver based sunflower optimization) procedure is employed to achieve the parameter values of current and speed control of SRM. The proposed scheme is accomplished by MATLAB/ Simulink environment. The results show that the hybrid SSD-SFO scheme given the better performance compared to the SSD and SFO algorithm.

AbstractTwenty sodium-cooled fast reactors (SFRs) have provided valuable experience in design, licensing, and operation. This paper summarizes the important safety criteria and safety guidelines of intermediate sodium systems, steam generators, decay heat removal systems and associated construction materials and in-service inspection. The safety criteria and guidelines provide a sufficient framework for design and licensing, in particular by new entrants in SFRs.

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.