06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Modeling freezing and BioGeoChemical processes in Antarctic sea ice(2024) Pathak, Raghav; Seyedpour, Seyed Morteza; Kutschan, Bernd; Thom, Andrea; Thoms, Silke; Ricken, TimThe Antarctic sea ice, which undergoes annual freezing and melting, plays a significant role in the global climate cycle. Since satellite observations in the Antarctic region began, 2023 saw a historically unprecedented decrease in the extent of sea ice. Further ocean warming and future environmental conditions in the Southern Ocean will influence the extent and amount of ice in the Marginal Ice Zones (MIZ), the BioGeoChemical (BGC) cycles, and their interconnected relationships. The so‐called pancake floes are a composition of a porous sea ice matrix with interstitial brine, nutrients, and biological communities inside the pores. The ice formation and salinity are both dependent on the ambient temperature. To realistically model these multiphasic and multicomponent coupled processes, the extended Theory of Porous Media (eTPM) is used to develop Partial Differential Equations (PDEs) based high‐fidelity models capable of simulating the different seasonal variations in the region. All critical variables like salinity, ice volume fraction, and temperature, among others, are considered and have their equations of state. The phase transition phenomenon is approached through a micro‐macro linking scheme. In this paper, a phase‐field solidification model [4] coupled with salinity is used to model the microscale freezing processes and up‐scaled to the macroscale eTPM model. The evolution equations for the phase field model are derived following Landau‐Ginzburg order parameter gradient dynamics and mass conservation of salt allowing to model the salt trapped inside the pores. A BGC flux model for sea ice is set up to simulate the algal species present in the sea ice matrix. Ordinary differential equations (ODE) are employed to represent the diverse environmental factors involved in the growth and loss of distinct BGC components. Processes like photosynthesis are dependent on temperature and salinity, which are derived through an ODE‐PDE coupling with the eTPM model. Academic simulations and results are presented as validation for the mathematical model. These high‐fidelity models eventually lead to their incorporation into large‐scale global climate models.Item Open Access Application of magnetic resonance imaging in liver biomechanics : a systematic review(2021) Seyedpour, Seyed M.; Nabati, Mehdi; Lambers, Lena; Nafisi, Sara; Tautenhahn, Hans-Michael; Sack, Ingolf; Reichenbach, Jürgen R.; Ricken, TimMRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.Item Open Access Optimization of the groundwater remediation process using a coupled genetic algorithm-finite difference method(2021) Seyedpour, Seyed Morteza; Valizadeh, Iman; Kirmizakis, Panagiotis; Doherty, Rory; Ricken, TimIn situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design.Item Open Access Transient high-frequency spherical wave propagation in porous medium using fractional calculus technique(2023) Soltani, Kamran; Seyedpour, Seyed Morteza; Ricken, Tim; Rezazadeh, GhaderTransient high-frequency spherical wave propagation in the porous medium is studied using the Biot-JKD theory. The porous media is considered to be a composed of deformable solid skeleton and viscous incompressible fluid inside the pores. In order to treat the fractional proportionality of the dynamic tortuosity to the frequency (or equivalently, to time) due to the viscous interaction between solid and fluid phases, the fractional calculus theory along with the Laplace and Fourier transforms are used to solve the coupled governing partial differential equations of the scaler and vector potential functions obtained from the Helmholtz’s decomposition in the Laplace domain. Both the longitudinal and transverse waves, additionally the reflection and transmission kernels are determined in detail at the Laplace domain. For the Laplace-to-time inversion transform, Durbin’s numerical formula is used and the independence of the results from the involved tuning and accuracy parameters is checked. The effects of the porosity, dynamic tortuosity, characteristics length, etc. on the reflected pressure and stress are investigated. The general pattern of the results is similar to our previous knowledge of wave propagation. Further works and experiments may be conducted in future works for various applications.Item Open Access In silico modeling of coupled physical‐biogeochemical (P‐BGC) processes in Antarctic Sea ice(2021) Thom, Andrea; Ricken, TimAntarctic sea ice formation in the Marginal Ice Zone (MIZ) and its mutual effects on ocean biology and chemistry is very sensitive to climate change. The formation of ’pancake’ ice floes and the coupled physical‐biogeochemical (P‐BGC) processes can be modeled by using the continuum mechanical multi‐phase description of the extended Theory of Porous Media (eTPM), and simulated with the Finite Element Method (FEM). The ice formation is described via a salinity‐temperature equilibrium of the enclosed ocean water. The growth of the thriving ice algae, enclosed and attached to the pancake ice floes, is modeled with a phenomenological approach given with an ordinary differential equation.Item Open Access Model order reduction for deformable porous materials in thin domains via asymptotic analysis(2021) Armiti-Juber, Alaa; Ricken, TimWe study fluid-saturated porous materials that undergo poro-elastic deformations in thin domains. The mechanics in such materials are described using a biphasic model based on the theory of porous media (TPM) and consisting of a system of differential equations for material’s displacement and fluid’s pressure. These equations are in general strongly coupled and nonlinear, such that exact solutions are hard to obtain and numerical solutions are computationally expensive. This paper reduces the complexity of the biphasic model in thin domains with a scale separation between domain’s width and length. Based on standard asymptotic analysis, we derive a reduced model that combines two sub-models. Firstly, a limit model consists of averaged equations that describe the fluid pore pressure and displacement in the longitudinal direction of the domain. Secondly, a corrector model re-captures the mechanics in the transverse direction. The validity of the reduced model is finally tested using a set of numerical examples. These demonstrate the computational efficiency of the reduced model, while maintaining reliable solutions in comparison with original biphasic TPM model in thin domain.Item Open Access Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem(2023) Mandl, Luis; Mielke, André; Seyedpour, Seyed Morteza; Ricken, TimPhysics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential equations. A major area of research on PINNs is the application to coupled partial differential equations in particular, and a general breakthrough is still lacking. In coupled equations, the optimization operates in a critical conflict between boundary conditions and the underlying equations, which often requires either many iterations or complex schemes to avoid trivial solutions and to achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, effectively accelerating the training process. These affine physics-informed neural networks (AfPINNs) then produce nontrivial and accurate field solutions even in parameter spaces with diverging orders of magnitude. On average, AfPINNs show the ability to improve the L2relative error by 64.84%after 25,000 epochs for a one-dimensional consolidation problem based on Biot’s theory, and an average improvement by 58.80%with a transfer approach to the theory of porous media.Item Open Access The effect of Caspian Sea water on mechanical properties and durability of concrete containing rice husk ash, nano SiO2, and nano Al2O3(2022) Arasteh-Khoshbin, Omolbanin; Seyedpour, Seyed Morteza; Ricken, TimVarious studies have been recently conducted aiming at developing more sustainable cementitious systems so that concrete structures may not have a negative effect on the environment and are decomposed. It has been attempted to build sustainable binders by substituting silica fume, cement with fly ash, nano-silica, nano-alumina, and rice husk ash. In this paper, a series of experiments on concrete with different contents of rice husk ash (10%, 15%, and 20%), nano SiO2(1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%), and nano Al2O3(1%, 2%, 3%, 4%) are performed to analyze the durability and mechanical properties of samples under the curing condition of Caspian seawater. The workability, density, water penetration, chloride ion penetration, and compressive strength (at 7, 14, 28, and 90 day) of the samples were determined. The experimental results showed that workability decreased gradually with increasing additives content, while the compressive gradually increased. Among the additives, adding 8% of the nano SiO2had the most significant effect on the improvement of compressive strength. Adding 8% nano SiO2and 4% nano Al2O3 reduced the depth of water permeability by 53% and 30%, respectively. Furthermore, adding 8% nanoSiO2 reduced chloride ion penetration by 85%.Item Open Access Quantifying fat zonation in liver lobules : an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach(2024) Lambers, Lena; Waschinsky, Navina; Schleicher, Jana; König, Matthias; Tautenhahn, Hans-Michael; Albadry, Mohamed; Dahmen, Uta; Ricken, TimMetabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.Item Open Access Uncertainty with varying subsurface permeabilities reduced using coupled Random Field and extended Theory of Porous Media contaminant transport models(2022) Seyedpour, Seyed Morteza; Henning, Carla; Kirmizakis, Panagiotis; Herbrandt, Swetlana; Ickstadt, Katja; Doherty, Rory; Ricken, TimTo maximize the usefulness of groundwater flow models for the protection of aquifers and abstraction wells, it is necessary to identify and decrease the uncertainty associated with the major parameters such as permeability. To do this, there is a need to develop set of estimates representing subsurface heterogeneity or representative soil permeability estimates. Here, we use a coupled Random Field and extended Theory of Porous Media (eTPM) simulation to develop a robust model with a good predictive ability that reduces uncertainty. The coupled model is then validated with a physical sandbox experiment. Uncertainty is reduced by using 500 realisations of the permeability parameter using the eTPM approach. A multi-layer contaminant transport scenario with varying permeabilities, similar to what could be expected with shallow alluvial sediments, is simulated. The results show that the contaminant arrival time could be strongly affected by random field realizations of permeability compared with a modelled homogenous permeability parameter. The breakthrough time for heterogeneous permeabilities is shorter than the homogeneous condition. Using the 75% confidence interval (CI), the average contaminant concentration shows 4.4% variation from the average values of the considered area and 8.9% variation in the case of a 95% confidence interval.