Jun Fu (163683), Han He (220292), Feibin Yan (22290971) et al. · Figshare · 2025
<div> Ammonia is a green zero-carbon fuel, yet its low reactivity poses challenges, including difficult ignition and slow combustion rates. Compared to diesel or biodiesel, dimethyl ether (DME) has no C-C bonds, which means it produces almost no soot and has a high cetane number that helps it ignite easily. So, using the highly reactive DME to help ignite liquid ammonia is a good way to make it burn better. This study uses computer simulations to look at how well liquid ammonia and DME work together as fuel with different injection setups. Results indicate optimal DME ignition enhancement at injector spacing <i><i>L</i> </i>= 6 cm, injection angle 180°, and ammonia energy share 70%, outperforming cases with spacings of 7–8 cm and angles of 150°, 120°, 90°, and 60°. At the same time, having shorter spacing during DME combustion leads to smaller areas for OH but more NH₂ formation, showing that ammonia is more effective at cooling the flame and that more ammonia is being used as fuel. Additionally, when DME is not burning, both OH and NH₂ areas grow larger at shorter spacings, showing that the fuel mixes sooner, the reaction areas get bigger, and the burning process is more complete. Regarding the bimodal NH₂ peaks, the initial peak reflects partial ammonia oxidation that is flame-entrained during DME combustion, while the secondary peak indicates the onset of autoignition, which is characterized by diminished reaction rates and reduced combustion intensity. </div>
Cited by 0Open access
Jun Fu (163683), Han He (220292), Feibin Yan (22290971) et al. · Figshare · 2025
<div> Ammonia is a green zero-carbon fuel, yet its low reactivity poses challenges, including difficult ignition and slow combustion rates. Compared to diesel or biodiesel, dimethyl ether (DME) has no C-C bonds, which means it produces almost no soot and has a high cetane number that helps it ignite easily. So, using the highly reactive DME to help ignite liquid ammonia is a good way to make it burn better. This study uses computer simulations to look at how well liquid ammonia and DME work together as fuel with different injection setups. Results indicate optimal DME ignition enhancement at injector spacing <i><i>L</i> </i>= 6 cm, injection angle 180°, and ammonia energy share 70%, outperforming cases with spacings of 7–8 cm and angles of 150°, 120°, 90°, and 60°. At the same time, having shorter spacing during DME combustion leads to smaller areas for OH but more NH₂ formation, showing that ammonia is more effective at cooling the flame and that more ammonia is being used as fuel. Additionally, when DME is not burning, both OH and NH₂ areas grow larger at shorter spacings, showing that the fuel mixes sooner, the reaction areas get bigger, and the burning process is more complete. Regarding the bimodal NH₂ peaks, the initial peak reflects partial ammonia oxidation that is flame-entrained during DME combustion, while the secondary peak indicates the onset of autoignition, which is characterized by diminished reaction rates and reduced combustion intensity. </div>
Cited by 0Open access
Jun Fu (163683), Han He (220292), Feibin Yan (22290971) et al. · Figshare · 2025
<div> Ammonia is a green zero-carbon fuel, yet its low reactivity poses challenges, including difficult ignition and slow combustion rates. Compared to diesel or biodiesel, dimethyl ether (DME) has no C-C bonds, which means it produces almost no soot and has a high cetane number that helps it ignite easily. So, using the highly reactive DME to help ignite liquid ammonia is a good way to make it burn better. This study uses computer simulations to look at how well liquid ammonia and DME work together as fuel with different injection setups. Results indicate optimal DME ignition enhancement at injector spacing <i><i>L</i> </i>= 6 cm, injection angle 180°, and ammonia energy share 70%, outperforming cases with spacings of 7–8 cm and angles of 150°, 120°, 90°, and 60°. At the same time, having shorter spacing during DME combustion leads to smaller areas for OH but more NH₂ formation, showing that ammonia is more effective at cooling the flame and that more ammonia is being used as fuel. Additionally, when DME is not burning, both OH and NH₂ areas grow larger at shorter spacings, showing that the fuel mixes sooner, the reaction areas get bigger, and the burning process is more complete. Regarding the bimodal NH₂ peaks, the initial peak reflects partial ammonia oxidation that is flame-entrained during DME combustion, while the secondary peak indicates the onset of autoignition, which is characterized by diminished reaction rates and reduced combustion intensity. </div>
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Jamal Hassan · Figshare · 2022
Temperature data
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Arganaraz, Carina Ines · Figshare · 2025
<b>The present datasets includes the mean daily maximum temperature (TX) in degrees Celsius. The analysis is conducted for the Abruzzo Region using Universal Kriging.</b><b>Further methodological details can be found in</b><b> </b><b><i>Building a high-resolution climate gridded dataset in complex terrain: validating different methods in the Abruzzo region in Italy</i></b><b> </b><b>by Argañaraz et al., 2025.</b>
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Hassan, Jamal · Figshare · 2022
Temperature data
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Jamal Hassan Ougahi (13513875), Mark E. J. Cutler (13513878), Simon J. Cook (7190372) · Figshare · 2022
<p>Average monthly Tmax over the UJRB during 1982 to 2015.</p> <p>(TIFF)</p>
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Peter C. Uden · IUPAC Standards Online · 2016
This paper defines terms and definitions used in analytical methods of pyrolysis and includes expressions for coupled systems and for the description of the temperature profiles and the products that are obtained.
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R. Maine · Australian meteorological magazine · 1958
A set of 74 January daily maximum temperatures at Adelaide, South Australia, can be shown to correspond with reasonable precision to an empirically predicted set based on three homogeneous governing parameters available at 6 a.m. local time. Selection of governing parameters is accomplished statistically.
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Thermocouple Grade · 2013
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Jack G. Calvert · IUPAC Standards Online · 2016
Over 620 atmospheric chemistry terms which are in common use by the international research community in the atmospheric sciences are defined. The diversified nature of the scientists involved in research in atmospheric chemistry (chemists, meteorologists, physicists, biologists, engineers, and others) requires that special attention be paid to the consistency of the terminology employed. Where alternative terms are in common use to describe a given process, piece of equipment, feature, or quantity used in atmospheric chemistry, recommended choices of the IUPAC Commission on Atmospheric Chemistry are given; these are consistent with the other IUPAC publications and recommendations on nomenclature. The range of terms includes composition of the atmosphere, clouds, solar radiation, chemical and physical changes in atmospheric components, instrumentation employed in analysis of the atmospheric components and in air quality control, and in effects of certain trace gases on materials, plants, and animals.
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Arganaraz, Carina Ines · Figshare · 2025
<b>The present datasets includes the mean daily maximum temperature (TX) in degrees Celsius. The analysis is conducted for the Abruzzo Region using Universal Kriging.</b><b>Further methodological details can be found in</b><b> </b><b><i>Building a high-resolution climate gridded dataset in complex terrain: validating different methods in the Abruzzo region in Italy</i></b><b> </b><b>by Argañaraz et al., 2025.</b>
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Jan W. Gooch · 2011
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Ana Carolina Carioca da Costa (4648237), Cláudia Torres Codeço (257349), Elias Teixeira Krainski (5714705) et al. · Figshare · 2018
<p>Effect of the maximum temperature on the risk of influenza A (H1N1) in Paraná State from April 5 to September 26 in 2009 (corresponding to epidemiological weeks 14 to 38).</p>
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Olivier Broennimann · Zenodo (CERN European Organization for Nuclear Research) · 2023
Maximum air temperature above 2m is derived from daily 1 km resolution gridded dataset from the Swiss federal agency for meteorology and climate (MeteoSwiss) using a downscaling procedure based on local regressions with a 25 m resolution elevation model. Monthly and yearly individual and current average (1981-2010) and future average (2020-2049, 2045-2074, and 2070-2099)<strong> </strong>geoTIFF layers can be downloaded from separate zip files. Future layers are based on the transient daily time series of gridded climate scenarios of temperature at 0.02°D (~2.2 km) provided by the CH2018 initiative. Anomalies between monthly temperature for 1981-2010 and monthly temperature for the future period at 2.2 km were downscaled at 25 m using bilinear interpolation. Anomalies were then added to 1981-2010 average monthly variables. We calculated future climatic layers for 4 GCMs (HADGEM, ECEARTH, MPIESM, and IPSL), 3 time slices (2020-2049, 2045-2074, and 2070-2099) and 3 representative concentration pathways (RCP 2.6, 4.5 and 8.5) The layer files are stored in compressed GeoTIFF format with the “deflate” algorithm with option “predictor2” from the GDAL. This format has a high compression ratio but allows direct import in most GIS softwares. All the maps are projected in the Swiss coordinate system CH 1903+ LV95 (epsg:2056) with a resolution of 25x25m using the extent of the digital height model DHM25 of the Swiss office for topography (swisstopo).
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Andrew R. Spanjer (8795999), Andrew S. Gendaszek (5240750), Elyse J. Wulfkuhle (12575258) et al. · Figshare · 2022
<p>Water temperature predicted at each node under two future climate emission scenarios, RCP 4.5 and 8.5, and three riparian vegetation scenarios, as compared to current conditions.</p>
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Huerta, Adrian · Figshare · 2023
Maximum air temperature from PISCOt v1.2. (i) Monthly means (normals) are provided in one file. (ii) Daily values are divided per year from 1981 to 2020. (i) and (ii) are the results from the climate-aided interpolation at normal- and daily-scale, respectively.
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Greiser, Caroline, Meineri, Eric, Luoto, Miska et al. · DRYAD · 2018
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Adrian Huerta · Figshare · 2022
Maximum air temperature from PISCOt v1.2. (i) Monthly means (normals) are provided in one file. (ii) Daily values are divided per year from 1981 to 2020. (i) and (ii) are the results from the climate-aided interpolation at normal- and daily-scale, respectively.
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Mestewat Simeon (19233200), Desalegn Wana (10269199), Zerihun Woldu (5866397) · Figshare · 2024
<div><p>Climate variability is one of the major factors affecting the supply of ecosystem services and the well-being of people who rely on them. Despite the substantial effects of climate variability on ecosystem goods and services, empirical researches on these effects are generally lacking. Thus, this study examines the spatiotemporal impacts of climate variability on selected ecosystem services in Maze National Park and its surroundings, in southwestern Ethiopia. We conducted climate trend and variability analysis by using the Mann-Kendall (MK) trend test, Sen’s slope estimator, and innovative trend analysis (ITA). Relationships among ecosystem services and climate variables were evaluated using Pearson’s correlation coefficient (r), while partial correlation was used to evaluate the relationship among key ecosystem services and potential evapotranspiration (PET). The MK tests show a decreasing trend for both mean annual and main rainy season rainfall, with Sen’s slope (β) = -0.721 and β = -0.1.23, respectively. Whereas, the ITA method depicted a significant increase in the second rainy season rainfall (Slope(s) = 1.487), and the mean annual (s = 0.042), maximum (s = 0.024), and minimum (s = 0.060) temperature. Spatial correlations revealed significant positive relationships between ecosystem services and the mean annual rainfall and Normalized Difference Vegetation Index (NDVI), while negative correlations with the mean annual temperature. Additionally, temporal correlations highlighted positive relationships among key ecosystem services and the main rainy season rainfall. The maximum and minimum temperatures and ecosystem services were negatively correlated; whereas, there was strong negative correlations between annual (r = -0.929), main rainy season (r = -0.990), and second rainy season (r = -0.814) PET and food production. Thus, understanding the spatiotemporal variability of climate and the resulting impacts on ecosystem services helps decision-makers design ecosystem conservation and restoration strategies to increase the potential of the ecosystems to adapt to and mitigate the impacts of climate variability.</p></div>
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