MILD combustion of hydrogen and air – An efficient modelling approach in CFD validated by experimental data

2021 ◽  
Author(s):  
Markus Mayrhofer ◽  
Michael Koller ◽  
Peter Seemann ◽  
Hadi Bordbar ◽  
Rene Prieler ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mild Combustion ◽  
Combustion Of Hydrogen ◽  
Modelling Approach

scholarly journals A Population Modelling Approach to Studying Age-Related Effects on Excitation-Contraction Coupling in Human Cardiomyocytes

2019 ◽  
Author(s):  
Arsenii Dokuchaev ◽  
Svyatoslav Khamzin ◽  
Olga Solovyova
Keyword(s):  
Experimental Data ◽  
Transient Outward Current ◽  
Model Parameters ◽  
Maximal Velocity ◽  
Population Modelling ◽  
Individual Parameter ◽  
Human Cardiomyocytes ◽  
Age Related ◽  
Age Related Changes ◽  
Modelling Approach

AbstractAgeing is the dominant risk factor for cardiovascular diseases. A great body of experimental data has been gathered on cellular remodelling in the Ageing myocardium from animals. Very few experimental data are available on age-related changes in the human cardiomyocyte. We have used our combined electromechanical model of the human cardiomyocyte and the population modelling approach to investigate the variability in the response of cardiomyocytes to age-related changes in the model parameters. To generate the model population, we varied nine model parameters and excluded model samples with biomarkers falling outside of the physiological ranges. We evaluated the response to age-related changes in four electrophysiological model parameters reported in the literature: reduction in the density of the K+ transient outward current, maximal velocity of SERCA, and an increase in the density of NaCa exchange current and CaL-type current. The sensitivity of the action potential biomarkers to individual parameter variations was assessed. Each parameter modulation caused an increase in APD, while the sensitivity of the model to changes in GCaL and Vmax_up was much higher than to those in the effects of Gto and KNaCa. Then 60 age-related sets of the four parameters were randomly generated and each set was applied to every model in the control population. We calculated the frequency of model samples with repolarisation anomalies (RA) and the shortening of the electro-mechanical window in the ageing model populations as an arrhythmogenic ageing score. The linear dependence of the score on the deviation of the parameters showed a high determination coefficient with the most significant impact due to the age-related change in the CaL current. The population-based approach allowed us to classify models with low and high risk of age-related RA and to predict risks based on the control biomarkers.

Pesticide desorption from soils facilitated by dissolved organic matter coming from composts: experimental data and modelling approach

2010 ◽  
Vol 106 (1) ◽  
pp. 117-133 ◽  
Author(s):  
Enrique Barriuso ◽  
Maria-Soledad Andrades ◽  
Pierre Benoit ◽  
Sabine Houot
Keyword(s):  
Experimental Data ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Modelling Approach

Sustainability of mild combustion of hydrogen-containing hybrid fuels

2007 ◽  
Vol 31 (2) ◽  
pp. 3393-3400 ◽  
Author(s):  
Marco Derudi ◽  
Alessandro Villani ◽  
Renato Rota
Keyword(s):  
Mild Combustion ◽  
Combustion Of Hydrogen

Effect of Droplet Starting Conditions on the Spray Dispersion Resulting From a Swirl Cup Injector

2021 ◽  
Author(s):  
Niklas Bürkle ◽  
Simon Holz ◽  
Enrico Bärow ◽  
Rainer Koch ◽  
Hans-Jörg Bauer
Keyword(s):  
Experimental Data ◽  
Droplet Diameter ◽  
Three Dimensional ◽  
Fuel Injector ◽  
Viable Option ◽  
Lagrange Method ◽  
Precessing Vortex Core ◽  
Starting Conditions ◽  
Good Agreement ◽  
Modelling Approach

Abstract In this work a numerical investigation of the sensitivities of the spray dispersion to different droplet starting parameters in a realistic three-dimensional fuel injector geometry is presented. The simulations are carried out using an Euler-Lagrange method. An extended version of the primary atomization model PAMELA [1,2] is used to predict the droplet diameter and to set the droplet starting conditions. Spray characteristics are compared to experimental data [3]. Thereby, a strong influence of the initial droplet velocities, the recirculation zone, the precessing vortex core as well as the turbulence modelling approach on the spray dispersion was identified. Droplet starting conditions which provide good agreement to the experimental data are determined. The study demonstrates that the presented approach is a viable option to predict the spray dispersion in combustors. Moreover, valuable insights on necessary improvements for modeling primary atomization are given.

Modelling on-axis microdiffraction to investigate crystallographic texture within high-performance polymer fibres

2008 ◽  
Vol 41 (3) ◽  
pp. 563-571 ◽  
Author(s):  
Richard J. Davies ◽  
Manfred Burghammer ◽  
Christian Riekel
Keyword(s):  
Experimental Data ◽  
Crystallographic Texture ◽  
High Performance ◽  
Radial Direction ◽  
Cell Axis ◽  
It Impacts ◽  
High Performance Polymer ◽  
Processing Differences ◽  
Common Structural Feature ◽  
Modelling Approach

Radial crystallographic texture in high-performance polymeric fibres is a common structural feature. Quite why such preferred orientation should exist and how it impacts upon the mechanical properties of the fibre is still not fully understood. This study reports the use of a modelling approach to investigate radial texture in the poly(p-phenylene benzobisoxazole) fibre type. The model allows azimuthal scattering profiles to be calculated corresponding to an on-axis microdiffraction geometry. The results show that, in order to model experimental data successfully, an offset is required between theaunit-cell axis and the fibre radial direction. The origin of this offset is tentatively attributed to solvent outflow during coagulation, which aligns the planar molecular chains. Meanwhile, textural differences between different fibre types can be explained by processing differences.

Dry deposition velocities of submicron aerosols on water surfaces: Laboratory experimental data and modelling approach

2017 ◽  
Vol 105 ◽  
pp. 179-192 ◽  
Author(s):  
N. Calec ◽  
P. Boyer ◽  
F. Anselmet ◽  
M. Amielh ◽  
H. Branger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Dry Deposition ◽  
Deposition Velocities ◽  
Water Surfaces ◽  
Modelling Approach

scholarly journals Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

2018 ◽  
Vol 15 (149) ◽  
pp. 20180600 ◽  
Author(s):  
Sabrina Hross ◽  
Fabian J. Theis ◽  
Michael Sixt ◽  
Jan Hasenauer
Keyword(s):  
Experimental Data ◽  
Imaging Techniques ◽  
Quantitative Information ◽  
Integrated Modelling ◽  
Parameter Estimates ◽  
Published Data ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Modelling Approach

Spatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems; however, mechanisms causing spatial patterning often remain elusive. In recent years, spatio-temporal mathematical modelling has helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum-likelihood approach. We used this integrated modelling approach to studyin vivogradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parametrization and subsequent refinement of the model using additional mechanisms. Among other results, model-based hypothesis testing predicted lymphatic vessel-dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.

Modelling of static aerodynamics of helicopter underslung loads

2011 ◽  
Vol 115 (1166) ◽  
pp. 201-219 ◽  
Author(s):  
D. I. Greenwell
Keyword(s):  
Experimental Data ◽  
Reynolds Number ◽  
Wind Tunnel ◽  
Bluff Body ◽  
Flight Dynamics ◽  
Normal Pressure ◽  
Stability Problems ◽  
Wide Range ◽  
Dynamics Models ◽  
Modelling Approach

AbstractThe complex aerodynamics of rectangular underslung helicopter loads can lead to severe stability problems, but are difficult to represent in flight dynamics models. Current models for box aerodynamics are highly unsatisfactory, being entirely empirical and requiring large amounts of experimental data to generate. This paper presents a new modelling approach, which takes account of the bluff-body nature of the flow, where loads are dominated by normal pressure forces. Existing experimental data is recast in body-axes form, with α and β replaced by velocity components perpendicular and parallel to the box faces. Force and moment data for a wide range of boxes then collapse onto a set of simple generic characteristics, with features that can be related directly to the underlying flow physics. Modelling of container aerodynamics is greatly simplified, and allowance for effects of turbulence, Reynolds Number, wind tunnel interference and geometry modifications becomes possible.

scholarly journals Mechanistic description of spatial processes using integrative modelling of noise-corrupted imaging data

2018 ◽  
Author(s):  
Sabrina Hross ◽  
Fabian J. Theis ◽  
Michael Sixt ◽  
Jan Hasenauer
Keyword(s):  
Experimental Data ◽  
Imaging Techniques ◽  
Quantitative Information ◽  
Integrated Modelling ◽  
Parameter Estimates ◽  
Published Data ◽  
Imaging Data ◽  
Microscopy Imaging ◽  
Modelling Approach

AbstractSpatial patterns are ubiquitous on the subcellular, cellular and tissue level, and can be studied using imaging techniques such as light and fluorescence microscopy. Imaging data provide quantitative information about biological systems, however, mechanisms causing spatial patterning often remain illusive. In recent years, spatio-temporal mathematical modelling helped to overcome this problem. Yet, outliers and structured noise limit modelling of whole imaging data, and models often consider spatial summary statistics. Here, we introduce an integrated data-driven modelling approach that can cope with measurement artefacts and whole imaging data. Our approach combines mechanistic models of the biological processes with robust statistical models of the measurement process. The parameters of the integrated model are calibrated using a maximum likelihood approach. We used this integrated modelling approach to study in vivo gradients of the chemokine (C-C motif) ligand 21 (CCL21). CCL21 gradients guide dendritic cells and are important in the adaptive immune response. Using artificial data, we verified that the integrated modelling approach provides reliable parameter estimates in the presence of measurement noise and that bias and variance of these estimates are reduced compared to conventional approaches. The application to experimental data allowed the parameterisation and subsequent refinement of the model using additional mechanisms. Among others, model-based hypothesis testing predicted lymphatic vessel dependent concentration of heparan sulfate, the binding partner of CCL21. The selected model provided an accurate description of the experimental data and was partially validated using published data. Our findings demonstrate that integrated statistical modelling of whole imaging data is computationally feasible and can provide novel biological insights.

scholarly journals Modelling runoff at the plot scale taking into account rainfall partitioning by vegetation: application to stemflow of banana (<i>Musa</i> spp.) plant

2009 ◽  
Vol 13 (11) ◽  
pp. 2151-2168 ◽  
Author(s):  
J.-B. Charlier ◽  
R. Moussa ◽  
P. Cattan ◽  
Y.-M. Cabidoche ◽  
M. Voltz
Keyword(s):  
Experimental Data ◽  
Soil Surface ◽  
Plant Canopy ◽  
Mean Square ◽  
Flood Events ◽  
Runoff Generation ◽  
Flood Hydrograph ◽  
Two Parameters ◽  
Runoff Production ◽  
Modelling Approach

Abstract. Rainfall partitioning by vegetation modifies the intensity of rainwater reaching the ground, which affects runoff generation. Incident rainfall is intercepted by the plant canopy and then redistributed into throughfall and stemflow. Rainfall intensities at the soil surface are therefore not spatially uniform, generating local variations of runoff production that are disregarded in runoff models. The aim of this paper was to model runoff at the plot scale, accounting for rainfall partitioning by vegetation in the case of plants concentrating rainwater at the plant foot and promoting stemflow. We developed a lumped modelling approach, including a stemflow function that divided the plot into two compartments: one compartment including stemflow and the related water pathways and one compartment for the rest of the plot. This stemflow function was coupled with a production function and a transfer function to simulate a flood hydrograph using the MHYDAS model. Calibrated parameters were a "stemflow coefficient", which compartmented the plot; the saturated hydraulic conductivity (Ks), which controls infiltration and runoff; and the two parameters of the diffusive wave equation. We tested our model on a banana plot of 3000 m2 on permeable Andosol (mean Ks=75 mm h−1) under tropical rainfalls, in Guadeloupe (FWI). Runoff simulations without and with the stemflow function were performed and compared to 18 flood events from 10 to 140 rainfall mm depth. Modelling results showed that the stemflow function improved the calibration of hydrographs according to the error criteria on volume and on peakflow, to the Nash and Sutcliffe coefficient, and to the root mean square error. This was particularly the case for low flows observed during residual rainfall, for which the stemflow function allowed runoff to be simulated for rainfall intensities lower than the Ks measured at the soil surface. This approach also allowed us to take into account the experimental data, without needing to calibrate the runoff volume on Ks parameter. Finally, the results suggest a rainwater redistribution module should be included in distributed runoff models at a larger scale of the catchment.

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