bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application and damping mechanism of particle dampers
Authors: Prasad Braj Bhushan , Duvigneau Fabian , Woschke Elmar , Juhre Daniel 
Published: March 2023

AbstractA particle damper is a passive damping technique, which is based on the high damping properties of granular materials. The energy dissipation rate of a particle damper depends on several factors, like the type of granular materials, filling ratios, particle size, and shape, etc. Out of all these factors, the type of granular materials used to design a particle damper plays a major role. Therefore, this contribution aims to investigate the influence of eleven different granular materials on vibration attenuation. Furthermore, the application of particle dampers for reducing the low‐frequency vibration amplitude of a wind turbine generator has been demonstrated. For this, two different approaches, namely the damping plate concept and the existing cavity concept have been introduced. It has been found that both concepts are capable of reducing the vibration amplitude tremendously. The material investigation has shown that rubber granulate can reduce the vibration amplitude of the test specimen significantly higher in comparison to the other materials by increasing the additional mass to the entire system marginally. The material test is independent of any particular application. Hence, the results can be used to reduce the vibration amplitude of any mechanical structure.
A new matrix equation expression for the solution of non‐autonomous linear systems of ODEs
Authors: Pozza Stefano , Van Buggenhout Niel 
Published: March 2023

AbstractThe solution of systems of non‐autonomous linear ordinary differential equations is crucial in a variety of applications, such us nuclear magnetic resonance spectroscopy. A new method with spectral accuracy has been recently introduced in the scalar case. The method is based on a product that generalizes the convolution. In this work, we show that it is possible to extend the method to solve systems of non‐autonomous linear ordinary differential equations (ODEs). In this new approach, the ODE solution can be expressed through a linear system that can be equivalently rewritten as a matrix equation. Numerical examples illustrate the method's efficacy and the low‐rank property of the matrix equation solution.
A study of energies in pressure dependent plasticity – mathematical model and mechanical analogies
Authors: Jezdan Ghina , Behr Florian , Dolzmann Georg , Hackl Klaus 
Published: March 2023

AbstractExperiments conducted in granular media like clay, silt, or sand show complex structures which cannot be explained as simple shear bands. Materials in this class are usually heterogeneous mixtures of fluids, usually air and water, and particles that have little to no cementation. The shear strength is provided by friction and interlocking of the particles. This generally leads to pressure dependent plastic behavior.Consequently, models in soil mechanics require pressure‐dependent yield surfaces resulting, in contrast to models in crystal plasticity, in non‐associated flow rules, which have not been studied as extensively as associated ones. In this study, we introduce a model capable of explaining the occurrence of the complex structures mentioned above starting from a time‐incremental variational formulation.We consider a basic model giving a reduced description for plasticity in soil mechanics with as few parameters as possible while retaining the essential features of the full model. The goal is to capture the complexity seen in experiments by considering the stability properties of the emerging microstructures. As expected, these complex structures lead to challenging questions concerning numerical simulations, which can be resolved employing a relaxed model in the implementation.
Which neural networks can be computed by an algorithm? – Generalised hardness of approximation meets Deep Learning
Authors: Thesing Laura , Hansen Anders C. 
Published: March 2023

AbstractClassical hardness of approximation (HA) is the phenomenon that, assuming P ≠ NP, one can easily compute an ϵ‐approximation to the solution of a discrete computational problem for ϵ > ϵ0 > 0, but for ϵ < ϵ0 – where ϵ0 is the approximation threshold – it becomes intractable. Recently, a similar yet more general phenomenon has been documented in AI: Generalised hardness of approximation (GHA). This phenomenon includes the following occurrence: For any approximation threshold ϵ1 > 0, there are AI problems for which provably there exist stable neural networks (NNs) that solve the problem, but no algorithm can compute any NN that approximates the AI problem to ϵ1‐accuracy. Moreover, this issue is independent of the P vs NP question and thus is a rather different mathematical phenomenon than HA. GHA implies that the universal approximation theorem for NNs only provides a partial understanding of the power of NNs in AI. Thus, a classification theory describing which NNs can be computed by algorithms to particular accuracies is needed to fill this gap. We initiate such a theory by showing the correspondence between the functions that can be computed to ϵ‐accuracy by an algorithm and those functions that can be approximated by NNs which can be computed to ϵ̂‐accuracy by an algorithm. In particular, the approximation thresholds ϵ and ϵ̂ cannot differ by more than a factor of 12. This means that computing function approximations through NNs will be optimal – in the sense of best approximation accuracy achievable by an algorithm – up to a small constant, compared to any other computational technique.
First Order Limiting Optimality Conditions in the Coefficient Control of an Obstacle Problem
Authors: Simon Nicolai , Wollner Winnifried 
Published: March 2023

AbstractWe consider an optimal control problem governed by an obstacle problem. The novelty and focus of this work will be the introduction of a control variable into the coefficients of this optimal control problem.In particular, we discuss the effects of the multiplicative coupling between control and, derivatives of, the state. This prevents the use of standard weak convergence arguments in limit arguments. As it is known, H‐convergence needs to be utilized, e.g., to prove existence of a solution to the problem. To obtain optimality conditions, we present a regularization approach to circumvent the non‐differentiability of the solution operator of the obstacle problems. We then discuss the limiting optimality conditions obtained by the proposed regularization.
Steep resonance of parametrically excited active MEMS cantilevers for dynamic mode in Atomic Force Microscopy
Authors: Ehrmann Jonathan , Reichert Robert , Gutschmidt Stefanie , Sattel Thomas 
Published: March 2023

AbstractOngoing developments in nanotechnology demand higher spatial resolution and thus, higher amplitude sensitivity in Atomic Force Microscopy (AFM). In this work, active cantilevers with integrated sensor and actuator systems are parametrically excited using a novel, analog feedback circuit. With that it is possible to adapt the strength and sign of a cubic nonlinearity which provides a bound to the amplitudes in resonance operation . The system response shows steeper resonance curves and therefore higher amplitude sensitivities compared to forced excited cantilevers. Theoretical findings are validated experimentally.
A Python toolbox for the numerical solution of the Maxey‐Riley equation
Authors: Urizarna-Carasa Julio , Ruprecht Daniel , von Kameke Alexandra , Padberg-Gehle Kathrin 
Published: March 2023

AbstractThe Maxey‐Riley equation (MRE) models the motion of a finite‐sized, spherical particle in a fluid. It is a second‐order integro‐differential equation with a kernel with a singularity at initial time. Because solving the integral term is numerically challenging, it is often neglected despite its often non‐negligible impact. Recently, Prasath et al. showed that the MRE can be rewritten as a time‐dependent heat equation on a semi‐infinite domain with a nonlinear, Robin‐type boundary condition. This approach avoids the need to deal with the integral term. They also describe a numerical approach for solving the transformed MRE based on Fokas method. We provide a Python toolbox implementing their approach, verify it against some of their numerical examples and demonstrate its flexibility by computing the trajectory of a particle in a velocity field given by experimental data.
Towards neural network‐based numerical friction models
Authors: Vater Kerstin , Stender Merten , Hoffmann Norbert 
Published: March 2023

AbstractFriction contacts can be found in almost all mechanical systems and are often of great technical importance. However, they are usually difficult to describe, and their behavior and influence on the whole system are hard to predict accurately. Modern product design and system operation strongly benefit from numerical simulation approaches today, but reliable friction models still represent a major challenge in this context.To tackle this problem, we employ neural network regression to capture the characteristics of frictional contacts and make them accessible for numerical methods in a minimal intrusive fashion. In particular, we test our approach using a Finite Element model of a 2D cantilever beam subject to stick‐slip vibrations induced by a moving conveyor belt at its free end. As a reference solution, we perform a transient analysis based on a simple analytical friction model, where the kinetic friction force only depends on the normal load and the relative sliding velocity. We take the same friction model, add some artificial noise to mimic uncertainties coming with experimental measurements, and pick a limited set of data points to train a regression neural network. The machine learning friction model is then deployed in the Finite Element code to predict the kinetic friction force acting on the beam tip during the slip phases.The deflection curves obtained by the transient numerical analysis using the new neural network friction model agree well with the reference solution based on the underlying analytical model. The results indicate that data‐driven approaches may also be capable of capturing more complex frictional contacts, including effects of temperature, humidity, and load history. The trained neural network friction models can then be employed in numerical simulations in a minimally intrusive manner. This approach opens up new possibilities to predict individual mechanical system behavior as accurately as possible.
Shape Optimization Algorithms for Fluid Dynamics Applications
Authors: Pinzon Escobar Jose Alfonso , Siebenborn Martin 
Published: March 2023

AbstractIn this work we present a comparison between shape optimization algorithms in different vector spaces. The main goal is to optimize the surface of an object with respect to a physical quantity. The main focus is on applications that require large element deformations as part of the optimization process, as for instance the removal and creation of geometric singularities such as edges and corners. The algorithms take into account the prevention of element degeneracy and overlapping, for instance by enforcing inequality constraints. For this purpose, an approach in the Hilbert space is compared to another in Banach spaces. The former is based on a nonlinear extension equation, whereas the p‐Laplace operator is used in the latter. Computational results are presented in the context of fluid dynamics applications, where the contour of an object is optimized with respect to the energy dissipation.
On the energy dissipation in confined concrete subjected to shear cyclic loading
Authors: Aguilar Mario , Baktheer Abedulgader , Chudoba Rostislav 
Published: March 2023

AbstractThe establishment of a simple engineering rule for predicting the fatigue failure of concrete has been pursued over the past decades. An energetic approach to the matter seems to be an attractive option that many researchers have embraced. In the present work, the authors attempt to contribute to the establishment of such a rule. In particular, the energy dissipation of confined concrete subjected to shear cyclic loading is studied and quantified. For this purpose, a microplane fatigue model recently introduced by the authors, referred to as MS1, is used. It aims to capture the fundamental inelastic mechanisms driving the tri‐axial stress redistribution within a material zone during the fatigue damage process in concrete. To this end, the fatigue damage evolution is linked to a measure of cumulative inelastic shear strain at the microplane level, reflecting the accumulation of fatigue damage due to internal shear/sliding between aggregates at subcritical pulsating load levels. To isolate the dissipative mechanism mentioned above, test configurations with dominant shear stress seem to be more appropriate. In the present work, a punch‐through shear test (PTST) FE model is used to induce shear‐dominated stresses and strains along the ligament of a specimen. Numerical studies are first presented to evaluate the behavior and energy dissipation at the elemental interface level. The interface is introduced in the MS1 microplane material model, which is capable of reproducing the concrete behavior under monotonic, cyclic, and fatigue loading with consistent set of material parameters. Quantification of the energy dissipation for each introduced dissipative mechanism is performed at each microplane and integrated via a well‐established homogenization scheme to evaluate the macroscopic energy dissipation. Later, an analysis of the energy dissipation of the PTST process zone is performed for cyclic loading under two different subcritical cyclic load amplitudes.
Behavior of additively manufactured lattice structures unter compressive loading
Authors: Bieler Sören , Weinberg Kerstin 
Published: March 2023

AbstractLattice structures are attractive in additive manufacturing technology as they are small, lightweight, and especially scaleable. Closely spaced diagonal elements absorb energy through elastic and plastic deformation within a lattice, but the performance depends on many factors like design, material properties, printing process, post‐treatment, etc. Here, we determine the material properties of two types of stereolithography printed specimens and investigate the truss‐radius dependent performance of bcc, fcc, and octet lattice structures. A simulation using the experimental parameters shows that the maximum arising stresses are located at the positions where material failure was observed.
Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks
Authors: Westmeier Tobias , Kreuter Daniel , Bäuerle Simon , Hetzler Hartmut 
Published: March 2023

AbstractIn this work, we propose a novel approach to the data‐driven prediction of vibration responses of nonlinear systems. The main idea is based on Autoregressive Neural Networks (ARNN) to model the nonlinear transfer behaviour between an external excitation and the system response. We propose an autoregressive network architecture with embedded symmetry using bias‐free tanh activation and guarantee Input‐to‐State‐Stability (ISS) by enforcing a special penalty term to the weights. The resulting training procedure is analysed for the example of a DUFFING oscillator with white noise excitation. In a BAYESian optimisation, it is found that beyond enforcing input‐to‐state‐stability, the stabilising penalty term also decreases sensitivity with respect to other training parameters compared to other classical techniques. Furthermore, we show that the stabilised ARNN is able to give excellent approximations of the nonlinear response of the DUFFING oscillator for a wide range of excitation intensities. In contrast, linear models, such as autoregressive models with exogenous input (ARX) in time domain or linear transfer functions in frequency domain, will only find some linear approximation. In particular, by construction, they will not be able to capture nonlinear effects for arbitrary amplitudes and excitation levels.
Machine learning based prediction of the corrosion behaviour of biodegradable magnesium alloys with PEO coatings
Authors: Zhang Dawei , Ma Songyun , Markert Bernd 
Published: March 2023

AbstractThe present work focuses on the application of deep learning methods for predicting the in vitro pitting corrosion behaviour of coated magnesium alloys with different microstructures. The datasets are generated by phase field simulation of pitting corrosion. Performance analysis shows that physics‐informed convolutional neural network (CNN) is superior to regular CNN in predicting corrosion curve. A good agreement between deep learning predictions and the ground truths indicates excellent predictive performance of the current encoder‐decoder architecture regardless of the variation of microstructures. Furthermore, the computational time is reduced by 4 to 6 orders of magnitude compared to the phase field simulation. The computational results demonstrate that machine learning based approach is promising alternative to traditional modelling methods for predicting pitting corrosion of coated magnesium alloys.
Optimizing artificial neural networks for mechanical problems by physics‐based Rao‐Blackwellization: Example of a hyperelastic microsphere model
Authors: Geuken Gian-Luca , Mosler Jörn , Kurzeja Patrick 
Published: March 2023

AbstractThe Rao‐Blackwell scheme provides an algorithm on how to implement sufficient information into statistical models and is adopted here to deterministic material modeling. Even crude initial predictions are improved significantly by Rao‐Blackwellization, which is proven by an error inequality. This is first illustrated by an analytical example of hyperelasticity utilizing knowledge on principal stretches. Rao‐Blackwellization improves a 1‐d uniaxial strain‐energy relation into a 3‐d relation that resembles the classical micro‐sphere approach. The presented scheme is moreover ideal for data‐based approaches, because it supplements existing predictions with additional physical information. A second example hence illustrates the application of Rao‐Blackwellization to an artificial neural network to improve its prediction on load paths, which were absent in the original training process.
In vitro degradation behaviour of biodegradable magnesium alloys
Authors: Nachtsheim Julia , Burja Jaka , Ma Songyun , Markert Bernd 
Published: March 2023

AbstractBiodegradable magnesium alloys are promising materials for orthopaedic implants. With their mechanical properties similar to native bone, magnesium alloys can overcome some significant disadvantages of conventional metallic implants. Nonetheless, the material degrades too fast for clinical approval. Therefore, the corrosion rate of the implant needs to be controlled and decelerated to guarantee sufficient mechanical support during the entire bone healing process. In this work, we analyse the corrosion behaviour of the magnesium alloy WE43 in a physiological environment and assess the influence of a plasma electrolytic coating to the corrosion process. The experimental results show that the coating significantly decreases the mass loss rate as well as the degradation of the material strength of the WE43 alloy.
A novel electrochemical sensor based on sodium alginate‐decorated single‐walled carbon nanotubes for the direct electrocatalysis of heavy metals ions
Authors: Chrouda Amani 
Published: March 2023

AbstractIn recent years, the presence of heavy metal ions in food chains and water poses serious environmental and human health problems. Therefore, there has been a pressing need for fast simple, sensitive, and accurate methods for their detection. In this study a novel electrochemical sensor based on alginate sodium biopolymer decorating single‐walled carbon nanotubes was elaborated for the detection of trace level of heavy metal ions. Different brown algae were used to extract sodium alginate, with Cystoseira being the most common. Due to its high extraction yield and high mannuronate/guluronate (M/G) ratio, Compress a brown algae was chosen to disperse with single‐walled carbon nanotubes. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), scanning electron microscopy were monitored to investigate each surface modification. Differential pulse anodic stripping voltammetry (DPASV) was used to follow the electrochemical heavy metal ions determination. Moreover, the performance of the optimized heavy metal ions sensor in terms of sensitivity, linear range, limit of detection, and reproducibility was investigated. The detection limits were found to be 0.1, 31, and 1 nM for Pb2+, Cd2+, and Cu2+, respectively. The results reported in this work showed good stability, high sensitivity, and appealing inter‐electrode repeatability. Finally, lead detection in tap water was carried out. The metrological performance of the present sensor makes it a good alternative for heavy metal ions detection.
Like parent, like offspring: Intergenerational association of parental scarcity mindset with offspring's cooperative behaviors
Authors: Zheng Lei , Peng Baozhong , Ye Changchun , Zhang Xiuqin , Qi Wei , Miao Miao 
Published: March 2023

AbstractA sense of resource availability creates a scarcity mindset, leading people to behave in a more competitive way instead of in a collaborative way. This study aims to examine the relationship between scarcity mindset and cooperative behaviors among parents and offspring, and to explore whether parents' scarcity mindset is related to their offspring's scarcity mindset and cooperative behaviors. We collected 239 parent–offspring pairs from several universities in northwest China. They completed the scarcity mindset scale and a one‐shot public good game (PGG). A path model analysis was conducted to examine the relationship between scarcity mindset and cooperative behaviors for offspring and their parents, respectively. The relationship between parental scarcity mindset and offspring's cooperative behaviors was also examined. The results showed that there was a high correlation of scarcity mindset between parents and their offspring. Scarcity mindset was negatively related to cooperative behaviors among both parents and offspring. In addition, both parents' cooperative behaviors and offspring's scarcity mindset mediated the relationship between the parental scarcity mindset and offspring's cooperative behaviors. Our results supported the idea that scarcity mindset triggers competitive rather than cooperative orientation. The present findings reveal an intergenerational relationship between scarcity mindset and cooperative behaviors by highlighting the impacts of parental scarcity mindset on that of their offspring's and its additional effects on offspring's cooperative behaviors.