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Current-to-field prediction for non-linear magnetic systems via neural networks
No full textAccurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction
Field-scale soil moisture dynamics predicted by deep learning
No full textSoil moisture plays a critical role in land–atmosphere interactions. Prediction of its dynamics is still a grand challenge. While in-situ measurements using sensors offer highly temporally resolved and accurate information compared to satellite observations, existing sensor networks are sparse and scarce. Here we propose a deep learning model for bridging the gap between infrequent satellite observations and sparse in-situ sensor network to improve near-term soil moisture predictions. The Long Short-Term Memory (LSTM)-based deep learning model was used to forecast soil moisture dynamics using soil parameters and climatic variables (e.g. air temperature, relative humidity, pressure, wind speed, turbulent fluxes, solar and terrestrial waves) collected from a dense network of sensors in a field located in Germany in an area of about 20 hectares. The dynamic time-lagged cross-correlation between soil moisture and other co-located soil and climatic features was calculated and a set of optimal predictors for training the LSTM model was selected. To efficiently learn the long-term dependency of soil moisture on its historical trends and to improve the prediction capability of the model, we optimized the LSTM structure, hyperparameters, and the size of the sliding window based on the goodness of fit (R2 score) of the model. We also examined the feasibility of employing the model developed using temporal data from one location for the prediction of soil moisture at other locations across the landscape. The results illustrate the robustness and efficiency of the proposed model for the spatio-temporal prediction of soil moisture. Plain Language Summary Understanding and monitoring soil moisture dynamics is crucial affecting ecosystem health, climate and extreme weather patterns, and the agricultural sector. However, predicting the temporal and spatial variation of soil moisture is challenging because of the complex interactions between the land and atmosphere. While soil moisture measurement with in-situ ground-based sensors provide a high level of temporal frequency in comparison to satellite data, the implementation of dense monitoring networks to capture spatial variability of soil moisture is not economically viable. To address this problem, we utilized machine learning techniques to predict temporal and spatial variation of soil moisture using data we measured in a field in Germany. The developed model was examined against the experimental data with the results illustrating that AI-based solutions could offer a powerful tool to predict soil moisture dynamics
Application of machine learning in predicting quality parameters in Metal Material Extrusion (MEX/M)
No full textAdditive manufacturing processes such as the material extrusion of metals (MEX/M) enable the production of complex and functional parts that are not feasible to create through traditional manufacturing methods. However, achieving high-quality MEX/M parts requires significant experimental and financial investments for suitable parameter development. In response, this study explores the application of machine learning (ML) to predict the surface roughness and density in MEX/M components. The various models are trained with experimental data using input parameters such as layer thickness, print velocity, infill, overhang angle, and sinter profile enabling precise predictions of surface roughness and density. The various ML models demonstrate an accuracy of up to 97% after training. In conclusion, this research showcases the potential of ML in enhancing the efficiency in control over component quality during the design phase, addressing challenges in metallic additive manufacturing, and facilitating exact control and optimization of the MEX/M process, especially for complex geometrical structures
RBF-FD discretization of the Oseen equations
No full textThe radial basis function - finite difference (RBF-FD) method is a (meshless) technique for the discretization of differential operators on scattered node sets. In recent years, it has been successfully applied mostly to scalar partial differential equations (PDEs). The extension to the application to the steady state Oseen equations on (several) scattered node sets is not straightforward but requires novel components which are the subject of this paper. We consider the steady-state Oseen equations in three spatial dimensions, and as a radial basis function, we restrict ourselves to the polyharmonic spline (PHS) with polynomial augmentation. However, the following contributions of our paper may also be applied to other model problems and RBFs. In particular, we will consider the selection of two node sets for the two types of unknowns, velocity and pressure, and subsequent (flexible order) RBF-FD discretization of the various differential operators in the coupled system. We discuss variants for the discretization of the pressure constraint as well as the influence of the viscosity parameter on the convergence of the RBF-FD discretization. Finally, we provide numerical tests for the Oseen equations in three dimensions on complex domains using several node arrangements, convection directions and parameters inherent to the PHS RBF-FD method. The tests demonstrate that the proposed method is stable for discretization step widths between ℎ = 0.01 and ℎ = 0.5 and viscosities in the range of 10−3 to 1 not just on the unit cube but also on a more complicated three-dimensional bunny-shaped domain. In particular, for even degrees of polynomial augmentation of the Laplacian (and lower degrees for involved first order differential operators), we can reach convergence of the same (even) order
Sinter-based perovskites for energy-related applications
No full textThe transition to a low-carbon economy stands as one of the most pressing global challenges today. Developing innovative, more efficient, and sustainable material systems for energy generation, conversion, or storage are key-enabling technologies to solve this challenge. Perovskites, with their unique structural and functional properties, emerge as next-generation materials capable of addressing various energy-related applications. This review focuses on recent progress in their synthesis via sintering and emphasizes how processing parameters modify their microstructure and consequently, can enhance their performance in diverse energy storage technologies, including capacitors, supercapacitors, batteries, and energy conversion systems. Moreover, by identifying current challenges of sinter-based perovskites and outlining future directions this review offers a comprehensive overview of the latest advancements and potential of sinter-based perovskites in the field of energy materials research
Radiometric measurement of the effect of nonpharmacological interventions on vital signs of patients with palliative care using the example of music therapy
No full textBackground: Nonpharmacological, psychosocial interventions such as music therapy (MT) are common in palliative care. However, measuring the effects of these interventions is challenging. Contactless and therefore burden-free vital sign monitoring may provide a feasible solution. Aim: The aim is to investigate the use of a radar system for measuring the effect of nonpharmacological interventions on heart and respiratory rate in patients with palliative care utilizing MT as an example. Design: Radar devices were installed under patients’ mattresses of the palliative care ward to record heart and respiratory rate data. The pre-, peri-, and post-intervention heart rates were compared for 10-minute intervals, respectively. Heart rate changes were assessed for all interventions and two subgroups (receiving MT and MT with physiotherapy). Data from 77 patients were recorded as part of the GUARDIAN project performed at the palliative care unit of the University Hospital Erlangen-Nürnberg. Results: The heart rate of patients with palliative care was monitored continuously, prior to and following a complex intervention. Significant changes in heart rate were recorded depending on the intervention: A reduction of heart rate by −3.342 bpm (−3.913%, ±6.011 bpm, p = 0.0229) was found in the first 10 minutes after the MT intervention. The monitoring of the respiratory rate was only possible on an intermittent basis. Conclusions: Our study shows that radiometric heart rate monitoring is feasible during MT, highlighting the radar system’s potential for assessing complex interventions in palliative care. However, reliability issues in respiratory rate measurement call for further research
The role of material properties in modeling maximal surface temperatures and heat distribution in milling of UD CFRP
No full textIn order to meet the precision requirements for components made of carbon fibre reinforced plastics (CFRP), the edges are often trimmed by milling. However, this can lead to detrimental thermal damage to the machined surface. The aim of the study was to investigate in detail the maximum temperatures and characteristic thermal parameters for various unidirectional CFRP materials under different cutting conditions. During upcut milling using a PCD cutter an infrared camera, thermocouples and a dynamometer were employed to monitor temperatures and the cutting power. An analytical heat flow model suitable for arbitrary fibre orientation angles was used to determine, based on thermal material properties, the temperature change at the machined surface and
the heat flow parameters from experiments. Material influence on the cutting power was considered by its specific elastic energy at fracture depending on the volume content and mechanical properties of the fibres. At the machined surface, the resin glass transition temperatures were frequently exceeded, and the highest temperature changes were observed at a fibre orientation angle of Φ = 135◦. In most cases, higher cutting speeds were accompanied by greater temperature changes. Phenomenological models of the thermal parameters of the machining process were developed, which take into account both the thermal and mechanical CFRP properties and show a good correlation with the experimental results. They provide benefits in order to predict the temperature fields for materials with differing properties and under varying cutting conditions
Compensating connectivity restrictions in quantum annealers via splitting and linearization techniques
No full textCurrent quantum annealing experiments often suffer from restrictions in connectivity in the sense that only certain qubits can be coupled to each other. The most common strategy to overcome connectivity restrictions so far is by combining multiple physical qubits into a logical qubit with higher connectivity, which is achieved by adding terms to the Hamiltonian. Practically, this strategy is implemented by finding a so-called minor embedding, which is in itself an NP-hard problem. In this work, we present an iterative algorithm that does not need additional qubits but instead efficiently uses the available connectivity for different parts of the problem graph in every step. We present a weak monotonicity proof and benchmark our algorithm against the default minor-embedding algorithm on the D-Wave quantum annealer and multiple simple local search variants. While most of the experiments to compare the different iterative methods are performed with simulated annealing solvers, we also confirm the practicality of our method with experiments on the D-Wave Advantage quantum annealer
Finite element analysis of stem migration after total hip replacement
No full textAfter total hip replacement, the primary and secondary implant stability is critical to ensure long-term success. Excessive migration of the femoral stem can cause implant loosening. In this work, a novel approach for the simulation of the femoral stem migration using the finite element method is presented. Currently, only a few mostly contact-based models exist for this purpose. Instead, a bio-active interface model is used for the bone-stem interface which transforms from the Drucker–Prager to the von Mises plasticity criterion during the osseointegration process. As the position of the implant generally stabilises within one week after the implantation, the migration and osseointegration simulations are decoupled. To understand the effects on the migration, various parameter combinations are examined and a sensitivity analysis is performed. The results indicate that the joint force and the adhesion parameter have the most substantial influence on the migration. Furthermore, the influence of the migration on the subsequent osseointegration process is explored for a numerical example. The proposed model is able to depict the femoral stem migration with values up to 0.27 mm, which are in the order of magnitude of clinically observed values. Further, the model is provided as an open-source Abaqus user material subroutine. Numerical simulation of the stem migration could assist in clinical decision-making by identifying optimal parameter combinations to improve implant stability
Scaling behavior of Poisson's ratio in hierarchical nanoscale network materials
No full textHierarchical nanoscale network materials have gained increasing interest over the last decade attributing to their enhanced functional and mechanical performance, combined with reduced density. However, investigations into their Poisson's ratio, a key fundamental mechanical property, remain lacking. In this work, monolithic hierarchical nanoporous gold with tunable structure size and solid volume fraction were prepared via a two-step electrochemical dealloying method. By using in-situ digital image correlation technique, we measured their elastic and plastic Poisson's ratios during macroscopic compression. The effects of solid fraction, upper level ligament size and strain were explored systematically. We found that both the elastic and plastic Poisson's ratios are independent of the upper level ligament size and compressive strain. Notably, we introduced a novel scaling law of elastic Poisson's ratio with solid fraction in hierarchical nanoscale network materials and verified it experimentally. This study addresses a knowledge gap in the mechanics of hierarchical nanoscale network materials, offering a comprehensive understanding of their structure–mechanical property relationships. This insight provides a foundation for the design of novel materials and the optimization of their functional and mechanical properties