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Scale-aware parameterization of cloud fraction and condensate for a global atmospheric model machine-learned from coarse-grained kilometer-scale simulations
No full textKilometer grid-length simulations over a variety of different locations worldwide are used as training data for a deep-learning model designed to predict clouds in a global climate model. The inputs to the neural network are profiles of temperature, humidity and pressure from the high-resolution model, averaged to the scale of the climate model. The outputs are profiles of cloud fraction and in-cloud liquid and ice water contents. The high-resolution data is coarse-grained to a range of sizes, allowing the model to learn how the cloud formation depends on the size of the area being considered. The machine-learned cloud fraction and cloud condensate scheme is coupled to a global climate model and used to run multi-year simulations where the clouds predicted by the neural-network are fully interacting with the rest of the model.Kilometer grid-length simulations over a variety of different locations worldwide are used as training data for a deep-learning model designed to predict clouds in a global climate model. The inputs to the neural network are profiles of temperature, humidity and pressure from the high-resolution model, averaged to the scale of the climate model. The outputs are profiles of cloud fraction and in-cloud liquid and ice water contents. The high-resolution data is coarse-grained to a range of sizes, allowing the model to learn how the cloud formation depends on the size of the area being considered. The machine-learned cloud fraction and cloud condensate scheme is coupled to a global climate model and used to run multi-year simulations where the clouds predicted by the neural-network are fully interacting with the rest of the model.A
The multi-wavelength Tully-Fisher relation in the TNG50 cosmological simulation
No full textContext. The Tully-Fisher relation (TFR) is one of the most important and widely used empirical correlations in extragalactic astronomy. Apart from its importance as a secondary distance indicator, the TFR relation serves as a test for galaxy evolution models, because it connects the baryonic and dark matter components of galaxies. Aims. We aimed at simulating the multi-wavelength TFR relation from UV to mid-infrared (MIR) wavelengths for the TNG50 cosmological simulation at z = 0, and at comparing the results with observational TFR studies. Our goal was to compare the wavelength dependence of the slope and scatter with the observed values, and to search for secondary parameters that reduce the scatter in the TFR. Methods. We selected a large sample of simulated late-type, disc-dominated galaxies from the TNG50 simulation. For each galaxy, we used the SKIRT radiative transfer code to generate realistic synthetic global fluxes in 12 UV to MIR broadbands and synthetic integrated H I line profiles. We used bivariate linear regression to determine the TFR in each band, and we searched for a second TFR parameter by correlating the residuals with different physical parameters. Results. Our TNG50 TFR reproduces the characteristic behaviour of the observed TFR in many studies: the TFR becomes steeper and tighter as we move from UV/optical to infrared wavelengths. The slope changes from ‑7.46 ± 0.14 mag dex‑1 in the NUV band to ‑9.66 ± 0.09 mag dex‑1 in the IRAC [4.5] band. Quantitatively, our slopes are well within the spread of different observational results. The u ‑ r colour or the sSFR can significantly reduce the scatter in the UV and optical bands. Using u ‑ r colour as second parameter, the modified TFR has a roughly constant intrinsic tightness of over the entire UV to MIR range. Conclusions. The combination of the TNG50 cosmological simulation and the SKIRT radiative transfer postprocessing is capable of broadly reproducing the multi-wavelength TFR. A better matched sample selection, the use of different characteristic velocity scales, and more advanced internal dust attenuation correction are steps towards a more stringent comparison of the simulated and observed multi-wavelength TFR.Context. The Tully-Fisher relation (TFR) is one of the most important and widely used empirical correlations in extragalactic astronomy. Apart from its importance as a secondary distance indicator, the TFR relation serves as a test for galaxy evolution models, because it connects the baryonic and dark matter components of galaxies. Aims. We aimed at simulating the multi-wavelength TFR relation from UV to mid-infrared (MIR) wavelengths for the TNG50 cosmological simulation at z = 0, and at comparing the results with observational TFR studies. Our goal was to compare the wavelength dependence of the slope and scatter with the observed values, and to search for secondary parameters that reduce the scatter in the TFR. Methods. We selected a large sample of simulated late-type, disc-dominated galaxies from the TNG50 simulation. For each galaxy, we used the SKIRT radiative transfer code to generate realistic synthetic global fluxes in 12 UV to MIR broadbands and synthetic integrated H I line profiles. We used bivariate linear regression to determine the TFR in each band, and we searched for a second TFR parameter by correlating the residuals with different physical parameters. Results. Our TNG50 TFR reproduces the characteristic behaviour of the observed TFR in many studies: the TFR becomes steeper and tighter as we move from UV/optical to infrared wavelengths. The slope changes from ‑7.46 ± 0.14 mag dex‑1 in the NUV band to ‑9.66 ± 0.09 mag dex‑1 in the IRAC [4.5] band. Quantitatively, our slopes are well within the spread of different observational results. The u ‑ r colour or the sSFR can significantly reduce the scatter in the UV and optical bands. Using u ‑ r colour as second parameter, the modified TFR has a roughly constant intrinsic tightness of over the entire UV to MIR range. Conclusions. The combination of the TNG50 cosmological simulation and the SKIRT radiative transfer postprocessing is capable of broadly reproducing the multi-wavelength TFR. A better matched sample selection, the use of different characteristic velocity scales, and more advanced internal dust attenuation correction are steps towards a more stringent comparison of the simulated and observed multi-wavelength TFR.A
Éditer, traduire et commenter les textes vétérinaires de l’antiquité et du moyen âge : contributions préparées pour le VIe colloque international de médecine vétérinaire antique et médiévale (Liège, 26-28 mars 2020) /
No full textExperimental investigation on the effect of natural fire exposure on the post‐fire behavior of reinforced concrete beams using electric radiant panel
No full textIn this study, the effects of natural fire exposure on the post‐fire behavior of concrete beams are investigated. The study is based on laboratory tests where three reinforced concrete beams were subjected to fire exposure using an electric radiant panel. This panel enables a precise application of radiative heat exposure closely mimicking natural fire exposure in a safe manner. During the test, the deflections, deformations and temperature changes are measured for all three concrete beams. Additionally, finite element modeling (FEM) is applied to supplement these tests, demonstrating the performance of existing structural fire engineering calculation tools in evaluating the burnout performance of concrete beams. The results of the tests show that the electric radiant panel provide a novel approach for fire simulation which is effective in replicating natural fire conditions, by applying the heat flux as specified in the Eurocode Parametric Fire Curve in a highly controlled manner. The uniformity of the temperature field measured inside the beams and the consistent deformations observed during the heat exposure across all three tests underscores the accuracy of the fire simulation. Furthermore, post‐fire assessments reveal that while the exposed beams suffered some reduction in load‐bearing capacity, they retained a significant portion of their original strength that was consistent across all three beams. The numerical simulations conducted in this study demonstrate a high level of accuracy in predicting the behavior of the concrete beams during fire exposure. These simulations effectively mirrored the experimental results, validating that they are a valuable tool for assessing concrete structures' performance in fire scenarios.In this study, the effects of natural fire exposure on the post‐fire behavior of concrete beams are investigated. The study is based on laboratory tests where three reinforced concrete beams were subjected to fire exposure using an electric radiant panel. This panel enables a precise application of radiative heat exposure closely mimicking natural fire exposure in a safe manner. During the test, the deflections, deformations and temperature changes are measured for all three concrete beams. Additionally, finite element modeling (FEM) is applied to supplement these tests, demonstrating the performance of existing structural fire engineering calculation tools in evaluating the burnout performance of concrete beams. The results of the tests show that the electric radiant panel provide a novel approach for fire simulation which is effective in replicating natural fire conditions, by applying the heat flux as specified in the Eurocode Parametric Fire Curve in a highly controlled manner. The uniformity of the temperature field measured inside the beams and the consistent deformations observed during the heat exposure across all three tests underscores the accuracy of the fire simulation. Furthermore, post‐fire assessments reveal that while the exposed beams suffered some reduction in load‐bearing capacity, they retained a significant portion of their original strength that was consistent across all three beams. The numerical simulations conducted in this study demonstrate a high level of accuracy in predicting the behavior of the concrete beams during fire exposure. These simulations effectively mirrored the experimental results, validating that they are a valuable tool for assessing concrete structures' performance in fire scenarios.A
Die rijck vol kaerßen zynde : proza, poëzie en prentkunst van Dirck Volckertszoon Coornhert (1522-1590) /
No full textMore than a void? The detection and characterization of cavities in a simulated galaxy’s interstellar medium
No full textPerformance evaluation of a variable-speed contra-rotating pump-turbine for low-head pumped hydro energy storage: An experimental study
No full textCapturing complexity of the foot arch bones : evaluation of a statistical modelling framework for learning shape, pose and intensity Features in a Continuous Domain
No full textAdvances in medical imaging have enabled detailed digitisation and representation of human anatomy, but challenges remain when modelling complex structures. Statistical models have been developed to capture variations in shape, pose, and intensity features of anatomical structures. However, these models often embed a single feature. This paper investigates how a novel dynamic multi-feature-class Gaussian process modelling framework (DMFC-GPM) designed to learn shape, pose, and intensity features in continuous domains, facilitates the modelling of complex anatomical structures. The work evaluates the framework’s ability to capture multi-feature variations within complex anatomy. Computed tomography image data was processed to build and validate a statistical shape, pose and intensity neutral-arched foot model (12 bones). Framework evaluation was done by validation of the model using specificity, and generality. Fitting the model globally to all objects resulted in specificity and generality reported as average root mean square (RMS) of 0.61 ± 0.11 mm and 1.02 ± 0.21 mm, and average Hausdorff distance (Hd) of 3.47 ± 0.98 mm and 7.56 ± 1.33 mm, respectively. Further validation of the model marginalised to the talus bone resulted in specificity and generality of 0.81 ± 0.25 mm and 1.20 ± 0.48 mm average RMS and 3.24 ± 1.10 mm and 6.50 ± 2.34 mm average Hd, respectively. The talus model variations were consistent with literature. Thus, the novel DMFC-GPM framework can model complex anatomies such as the foot arch.Advances in medical imaging have enabled detailed digitisation and representation of human anatomy, but challenges remain when modelling complex structures. Statistical models have been developed to capture variations in shape, pose, and intensity features of anatomical structures. However, these models often embed a single feature. This paper investigates how a novel dynamic multi-feature-class Gaussian process modelling framework (DMFC-GPM) designed to learn shape, pose, and intensity features in continuous domains, facilitates the modelling of complex anatomical structures. The work evaluates the framework’s ability to capture multi-feature variations within complex anatomy. Computed tomography image data was processed to build and validate a statistical shape, pose and intensity neutral-arched foot model (12 bones). Framework evaluation was done by validation of the model using specificity, and generality. Fitting the model globally to all objects resulted in specificity and generality reported as average root mean square (RMS) of 0.61 ± 0.11 mm and 1.02 ± 0.21 mm, and average Hausdorff distance (Hd) of 3.47 ± 0.98 mm and 7.56 ± 1.33 mm, respectively. Further validation of the model marginalised to the talus bone resulted in specificity and generality of 0.81 ± 0.25 mm and 1.20 ± 0.48 mm average RMS and 3.24 ± 1.10 mm and 6.50 ± 2.34 mm average Hd, respectively. The talus model variations were consistent with literature. Thus, the novel DMFC-GPM framework can model complex anatomies such as the foot arch.C
