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Nowadays, a large part of energy is provided by steam turbines; thus, increasing the efficiency and improving the steam turbines performance are of special importance. The presence of the liquid phase in the low-pressure stage of the steam turbine can cause energy loss, efficiency drop, and erosion/corrosion problems; therefore, one of the essential issues is to identify wet steam flow and try to reduce condensation loss. In order to decrease the liquid fraction, the drainage groove technique can be applied. The drainage groove sucks the water droplets from the turbine blade surface and drains them into the condenser. In this study, the effect of the drainage groove location on the surface of steam turbine blades has been investigated on the condensation, droplet radius, inlet mass flow, erosion rate, liquid drainage ratio, condensation losses, and total drainage ratio. For modeling the condensing flow, the Eulerian–Eulerian approach has been applied. The results show that the location of the drainage groove affects the groove performance and flow pattern in the turbine blade. In the selected drainage, the liquid drainage ratio, condensation losses, and erosion rate are reduced by 7.6%, 12%, and 88%, respectively, compared with the no-drainage groove case. Also, the total drainage ratio is 7.2% in the selected drainage. The outcomes of the present work have been a major step forward in the techniques having a great influence on the lifetime, repair and maintenance, and the output power of steam power generation facilities.

Two stage premixed combustor with variable geometry has been developed to meet stringent NOx goals in Japan without the use of water or steam injection. This combustion system is planned to be applied for 120-MW gas turbine in 1090-MW LNG combined cycle plant. The full-pressure, full-scale combustion tests were conducted over a wide range of operating conditions for this gas turbine. The combustion tests proved that NOx levels as well as mechanical characteristics were well within the goals.

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.NIMS-KMA.KACE-1-0-G.ssp370' 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 KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

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.DAMIP.MIROC.MIROC6' 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 MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

This file contains the raw dataset used in the manuscript "Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy" published in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659) Data has been acquired using Nion Swift (https://nionswift.readthedocs.io/en/stable/). Experimental details can be found in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659). The dataset has been analyzed using the following Python libraries: Numpy, Scipy, Hyperspy, Matplotlib EELS hyperspectral images have been aligned using the Hyperspy "align1D" method. Aligned EELS hyperspectral images are saved in files finished with "_Aligned.hspy": For the strong coupling experiments: Tip 1 is on hBN Tip 2 is on vacuum For each of the nanowires tips, a file with the fitted coefficients are available, as well as a plot of the data and the fitted curve. Datasets have been fitted with gaussian and/or lorentizan functions, as described in the published text. Any question can be forwarded to the corresponding authors of the published text. Other funding: 1) National Agency for Researchunder the program of future investment TEMPOS-CHROMATEM (reference no. ANR-10-EQPX-50); 2) Spanish MINECO (MAT2017-88492-R and SEV2015-0522); 3) the Catalan CERCA Program; 4) Fundació Privada Celle;

This study aims to identify potential hydropower sites and calculate the theoretical potential hydropower capacity based on watershed modelling of the Mindanao River Basin (MRB) in the Philippines for the sustainable development of a previously unstudied region. The Soil and Water Assessment Tool (SWAT) was applied to delineate the watershed of the MRB and simulate the river discharges with inputs from observed precipitation and global gridded precipitation datasets. Observed weather data, such as temperature, humidity, and solar radiation, from four meteorological stations in the Philippines were also used as inputs for SWAT modelling. Simulated discharges were calibrated at three river gauges on the Nituan, Libungan and Pulangi Rivers. However, due to limited river discharge records, model validations were conducted in proxy basins: the calibrated model parameters in river A were used in the watershed modelling of proxy river B. Of the delineated 107 sub-basins in the MRB watershed, only 33 were identified as having potential sites for hydropower development. These potential sub-basins hosted a total of 154 potential sites with an estimated monthly average power capacity of 5,551.35 MW for all 33 sub-basins. The estimated theoretical power capacity of 15,266.22 MW for all sites in the MRB is approximately equivalent to the Philippines’ total available power capacity in 2017 of 15,393 MW. These sites were classified into 16 mini-scale hydropower sites, 114 small-scale hydropower sites and 24 medium-scale hydropower sites based on the simulated river discharges and potential power capacities. Based on these results, hydropower development could be an alternative to strengthen the exploration of renewable energy resources and improve the energy situation in Mindanao; hydropower development could also have mitigation effects on frequent floods in flat, low-lying downstream areas.