Universität Stuttgart
Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1
Browse
95 results
Search Results
Item Open Access Comparison study of phase-field and level-set method for three-phase systems including two minerals(2022) Kelm, Mathis; Gärttner, Stephan; Bringedal, Carina; Flemisch, Bernd; Knabner, Peter; Ray, NadjaWe investigate reactive flow and transport in evolving porous media. Solute species that are transported within the fluid phase are taking part in mineral precipitation and dissolution reactions for two competing mineral phases. The evolution of the three phases is not known a-priori but depends on the concentration of the dissolved solute species. To model the coupled behavior, phase-field and level-set models are formulated. These formulations are compared in three increasingly challenging setups including significant mineral overgrowth. Simulation outcomes are examined with respect to mineral volumes and surface areas as well as derived effective quantities such as diffusion and permeability tensors. In doing so, we extend the results of current benchmarks for mineral dissolution/precipitation at the pore-scale to the multiphasic solid case. Both approaches are found to be able to simulate the evolution of the three-phase system, but the phase-field model is influenced by curvature-driven motion.Item Open Access Stochastic model for energy propagation in disordered granular chains(2021) Taghizadeh, Kianoosh; Shrivastava, Rohit; Luding, StefanEnergy transfer is one of the essentials of mechanical wave propagation (along with momentum transport). Here, it is studied in disordered one-dimensional model systems mimicking force-chains in real systems. The pre-stressed random masses (other types of disorder lead to qualitatively similar behavior) interact through (linearized) Hertzian repulsive forces, which allows solving the deterministic problem analytically. The main goal, a simpler, faster stochastic model for energy propagation, is presented in the second part, after the basic equations are re-visited and the phenomenology of pulse propagation in disordered granular chains is reviewed. First, the propagation of energy in space is studied. With increasing disorder (quantified by the standard deviation of the random mass distribution), the attenuation of pulsed signals increases, transiting from ballistic propagation (in ordered systems) towards diffusive-like characteristics, due to energy localization at the source. Second, the evolution of energy in time by transfer across wavenumbers is examined, using the standing wave initial conditions of all wavenumbers. Again, the decay of energy (both the rate and amount) increases with disorder, as well as with the wavenumber. The dispersive ballistic transport in ordered systems transits to low-pass filtering, due to disorder, where localization of energy occurs at the lowest masses in the chain. Instead of dealing with the too many degrees of freedom or only with the lowest of all the many eigenmodes of the system, we propose a stochastic master equation approach with reduced complexity, where all frequencies/energies are grouped into bands. The mean field stochastic model, the matrix of energy-transfer probabilities between bands, is calibrated from the deterministic analytical solutions by ensemble averaging various band-to-band transfer situations for short times, as well as considering the basis energy levels (decaying with the wavenumber increasing) that are not transferred. Finally, the propagation of energy in the wavenumber space at transient times validates the stochastic model, suggesting applications in wave analysis for non-destructive testing, underground resource exploration, etc.Item Open Access Linking cortex and contraction : integrating models along the corticomuscular pathway(2023) Haggie, Lysea; Schmid, Laura; Röhrle, Oliver; Besier, Thor; McMorland, Angus; Saini, HarnoorComputational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson’s disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.Item Open Access Determination of muscle shape deformations of the tibialis anterior during dynamic contractions using 3D ultrasound(2024) Sahrmann, Annika S.; Vosse, Lukas; Siebert, Tobias; Handsfield, Geoffrey G.; Röhrle, OliverPurpose: In this paper, we introduce a novel method for determining 3D deformations of the human tibialis anterior (TA) muscle during dynamic movements using 3D ultrasound. Materials and Methods: An existing automated 3D ultrasound system is used for data acquisition, which consists of three moveable axes, along which the probe can move. While the subjects perform continuous plantar- and dorsiflexion movements in two different controlled velocities, the ultrasound probe sweeps cyclically from the ankle to the knee along the anterior shin. The ankle joint angle can be determined using reflective motion capture markers. Since we considered the movement direction of the foot, i.e., active or passive TA, four conditions occur: slow active, slow passive, fast active, fast passive. By employing an algorithm which defines ankle joint angle intervals, i.e., intervals of range of motion (ROM), 3D images of the volumes during movement can be reconstructed. Results: We found constant muscle volumes between different muscle lengths, i.e., ROM intervals. The results show an increase in mean cross-sectional area (CSA) for TA muscle shortening. Furthermore, a shift in maximum CSA towards the proximal side of the muscle could be observed for muscle shortening. We found significantly different maximum CSA values between the fast active and all other conditions, which might be caused by higher muscle activation due to the faster velocity. Conclusion: In summary, we present a method for determining muscle volume deformation during dynamic contraction using ultrasound, which will enable future empirical studies and 3D computational models of skeletal muscles.Item Open Access Reconstruction of μXRCT data sets using the ASTRA toolbox(2020) Voland, PaulItem Open Access Effect of neglecting passive spinal structures : a quantitative investigation using the forward-dynamics and inverse-dynamics musculoskeletal approach(2023) Meszaros-Beller, Laura; Hammer, Maria; Schmitt, Syn; Pivonka, PeterInverse-dynamics (ID) analysis is an approach widely used for studying spine biomechanics and the estimation of muscle forces. Despite the increasing structural complexity of spine models, ID analysis results substantially rely on accurate kinematic data that most of the current technologies are not capable to provide. For this reason, the model complexity is drastically reduced by assuming three degrees of freedom spherical joints and generic kinematic coupling constraints. Moreover, the majority of current ID spine models neglect the contribution of passive structures. The aim of this ID analysis study was to determine the impact of modelled passive structures (i.e., ligaments and intervertebral discs) on remaining joint forces and torques that muscles must balance in the functional spinal unit. For this purpose, an existing generic spine model developed for the use in the demoa software environment was transferred into the musculoskeletal modelling platform OpenSim. The thoracolumbar spine model previously used in forward-dynamics (FD) simulations provided a full kinematic description of a flexion-extension movement. By using the obtained in silico kinematics, ID analysis was performed. The individual contribution of passive elements to the generalised net joint forces and torques was evaluated in a step-wise approach increasing the model complexity by adding individual biological structures of the spine. The implementation of intervertebral discs and ligaments has significantly reduced compressive loading and anterior torque that is attributed to the acting net muscle forces by −200% and −75%, respectively. The ID model kinematics and kinetics were cross-validated against the FD simulation results. This study clearly shows the importance of incorporating passive spinal structures on the accurate computation of remaining joint loads. Furthermore, for the first time, a generic spine model was used and cross-validated in two different musculoskeletal modelling platforms, i.e., demoa and OpenSim, respectively. In future, a comparison of neuromuscular control strategies for spinal movement can be investigated using both approaches.Item Open Access A deep learning approach for large-scale groundwater heat pump temperature prediction(2022) Scheurer, StefaniaHeating and cooling buildings is one of the most energy-intensive aspects of modern life. To minimize the impact on global warming and decelerate climate change, more efficient and carbon emission-mitigating technologies such as openloop groundwater heat pumps (GWHP) for heating and cooling buildings are being used and quickly adopted. Nowadays, in order to guarantee their optimal use and prevent negative interactions, city planners need to optimize their placement in the urban landscape. This optimization process requires fast models that simulate the effect of a GWHP on the groundwater temperature. Considering a large domain with multiple GWHPs, this work introduces a framework for the groundwater temperature prediction. While using a learned local surrogate model, a convolutional neural network, to predict the local temperature field around every single GWHP, a physics-informed neural network (PINN) is employed afterwards to correct the global initial solution of stitched together local predictions. As the violations of the physical laws described by the underlying partial differential equation(s) are spatially unevenly distributed, two different methods for drawing sampling points, on the basis of which the training of the PINN to correct the global initial solution takes place, are investigated and compared. This work shows that it is possible for a PINN to correct the global initial solution of stitched together local predictions in a domain with multiple GWHPs. However, there are still opportunities to improve the quality and decrease the computational time of the presented framework. The best method for drawing sampling points depends on the scenario and the placement of the GWHPs. Thus, no general statement can be made, which of the two methods is more suitable. This work provides a good basis for further investigation of the presented framework.Item Open Access Autonomous adaption of intelligent humidity‐programmed hydrogel patches for tunable stiffness and drug release(2023) Pflumm, Stephan; Wiedemann, Yvonne; Fauser, Dominik; Safaraliyev, Javidan; Lunter, Dominique; Steeb, Holger; Ludwigs, SabineIntelligent humidity‐programmed hydrogel patches with high stretchability and tunable water‐uptake and ‐release are prepared by copolymerization and crosslinking of N‐isopropylacrylamide and oligo(ethylene glycol) comonomers. These intelligent elastomeric patches strongly respond to different humidities and temperatures in terms of mechanical properties which makes them applicable for soft robotics and smart skin applications where autonomous adaption to environmental conditions is a key requirement. It is shown that beyond using the hydrogel in the conventional state in aqueous media, new patches can be controlled by relative humidity. This humidity programming of the patches allows to tune drug release kinetics, opening potential application fields such as skin wound therapy and personalized medication. In situ dynamic‐mechanical measurements show a huge dependence on temperature and humidity. The glass transition temperature Tg shifts from around 60 °C at dry conditions to below 0 °C for 75% r.h. and higher. The storage modulus is tunable over more than four orders of magnitude from 0.6 up to 400 MPa. Time‐temperature superposition in master curves allows to extract relaxation times over 14 orders of magnitude. With strains at break of over 200% the patches are compliant with human skin and therefore patient‐friendly in terms of adapting to movements.Item Open Access Editorial - rapid, reproducible, and robust environmental modeling for decision support : worked examples and open-source software tools(2023) White, Jeremy T.; Fienen, Michael N.; Moore, Catherine R.; Guthke, AnneliItem Open Access On the accurate estimation of information-theoretic quantities from multi-dimensional sample data(2024) Álvarez Chaves, Manuel; Gupta, Hoshin V.; Ehret, Uwe; Guthke, AnneliUsing information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k -nearest neighbors ( k -NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback–Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators’ performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k -NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.