arXiv subjects

Stefan Schramm

Publications and source records attributed to Stefan Schramm.

At least 19 recordsLinked to original sources

arXiv2025

Photothermomechanicaly Efficient, Low-Cost, High-Cycle-Life, Hybrid MXene-Polymer Actuators

Photothermomechanical polymer film actuators stand out among the dynamic components available for soft robotics due to a combination of assets, such as capability for rapid energy transduction, wireless control, and ease of miniaturization. Despite their anticipated superior performance, several design challenges remain. These include high operational temperatures, inadequate mechanical output relative to the radiation energy provided, limited durability during repeated use, and high production costs; such factors hinder the scalability of these actuating materials in practical applications. Here, we report a viable solution by substituting performance-enhancing nanoparticles with MXenes--carbon-based, two-dimensional materials known for their theoretical photothermal conversion efficiency of up to 100%. This led to the development of MXene-dispersed polymer trilayer actuators (MPTAs). Extensive photothermal and thermomechanical characterization demonstrated superior performance compared to previously reported actuators, with a reduced shed power demand (0.1 mW cm$^{-2}$ $^\circ$C$^{-1}$), substantial bending capacity per irradiation power per time (0.1$^\circ$ mW$^{-1}$ cm$^{2}$ s$^{-1}$), and enhanced cyclic longevity, with fatigueless operation of at least 1,000 cycles. We demonstrate three applications: A kirigami-inspired flower, parallel manipulator, and soft gripper. Additionally, these materials are cost-effective; thus, they are the optimal choice for long-term, reversible operation with efficient heat-to-work transduction.

cond-mat.soft↗

arXiv2025

Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems

Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain of thought without considering dependencies among target classes. To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods. The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class. These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-to-end learnable. In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues. The Re-HKCM is derived from the SCM through a series of mathematical transformations. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG and one existing publicly) for FID, all showing superior results and outperform recent state-of-the-art FID methods.

cs.AI↗

arXiv2023

SMTNet: Hierarchical cavitation intensity recognition based on sub-main transfer network

With the rapid development of smart manufacturing, data-driven machinery health management has been of growing attention. In situations where some classes are more difficult to be distinguished compared to others and where classes might be organised in a hierarchy of categories, current DL methods can not work well. In this study, a novel hierarchical cavitation intensity recognition framework using Sub-Main Transfer Network, termed SMTNet, is proposed to classify acoustic signals of valve cavitation. SMTNet model outputs multiple predictions ordered from coarse to fine along a network corresponding to a hierarchy of target cavitation states. Firstly, a data augmentation method based on Sliding Window with Fast Fourier Transform (Swin-FFT) is developed to solve few-shot problem. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is presented to capture sensitive features of the frequency domain valve acoustic signals. Thirdly, hierarchical multi-label tree is proposed to assist the embedding of the semantic structure of target cavitation states into SMTNet. Fourthly, experience filtering mechanism is proposed to fully learn a prior knowledge of cavitation detection model. Finally, SMTNet has been evaluated on two cavitation datasets without noise (Dataset 1 and Dataset 2), and one cavitation dataset with real noise (Dataset 3) provided by SAMSON AG (Frankfurt). The prediction accurcies of SMTNet for cavitation intensity recognition are as high as 95.32%, 97.16% and 100%, respectively. At the same time, the testing accuracies of SMTNet for cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, SMTNet has also been tested for different frequencies of samples and has achieved excellent results of the highest frequency of samples of mobile phones.

cs.SD↗

arXiv2022

A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals

With the rapid development of smart manufacturing, data-driven machinery health management has received a growing attention. As one of the most popular methods in machinery health management, deep learning (DL) has achieved remarkable successes. However, due to the issues of limited samples and poor separability of different cavitation states of acoustic signals, which greatly hinder the eventual performance of DL modes for cavitation intensity recognition and cavitation detection. In this work, a novel multi-task learning framework for simultaneous cavitation detection and cavitation intensity recognition framework using 1-D double hierarchical residual networks (1-D DHRN) is proposed for analyzing valves acoustic signals. Firstly, a data augmentation method based on sliding window with fast Fourier transform (Swin-FFT) is developed to alleviate the small-sample issue confronted in this study. Secondly, a 1-D double hierarchical residual block (1-D DHRB) is constructed to capture sensitive features from the frequency domain acoustic signals of valve. Then, a new structure of 1-D DHRN is proposed. Finally, the devised 1-D DHRN is evaluated on two datasets of valve acoustic signals without noise (Dataset 1 and Dataset 2) and one dataset of valve acoustic signals with realistic surrounding noise (Dataset 3) provided by SAMSON AG (Frankfurt). Our method has achieved state-of-the-art results. The prediction accurcies of 1-D DHRN for cavitation intensitys recognition are as high as 93.75%, 94.31% and 100%, which indicates that 1-D DHRN outperforms other DL models and conventional methods. At the same time, the testing accuracies of 1-D DHRN for cavitation detection are as high as 97.02%, 97.64% and 100%. In addition, 1-D DHRN has also been tested for different frequencies of samples and shows excellent results for frequency of samples that mobile phones can accommodate.

cs.SD↗

arXiv2022

An acoustic signal cavitation detection framework based on XGBoost with adaptive selection feature engineering

Valves are widely used in industrial and domestic pipeline systems. However, during their operation, they may suffer from the occurrence of the cavitation, which can cause loud noise, vibration and damage to the internal components of the valve. Therefore, monitoring the flow status inside valves is significantly beneficial to prevent the additional cost induced by cavitation. In this paper, a novel acoustic signal cavitation detection framework--based on XGBoost with adaptive selection feature engineering--is proposed. Firstly, a data augmentation method with non-overlapping sliding window (NOSW) is developed to solve small-sample problem involved in this study. Then, the each segmented piece of time-domain acoustic signal is transformed by fast Fourier transform (FFT) and its statistical features are extracted to be the input to the adaptive selection feature engineering (ASFE) procedure, where the adaptive feature aggregation and feature crosses are performed. Finally, with the selected features the XGBoost algorithm is trained for cavitation detection and tested on valve acoustic signal data provided by Samson AG (Frankfurt). Our method has achieved state-of-the-art results. The prediction performance on the binary classification (cavitation and no-cavitation) and the four-class classification (cavitation choked flow, constant cavitation, incipient cavitation and no-cavitation) are satisfactory and outperform the traditional XGBoost by 4.67% and 11.11% increase of the accuracy.

cs.SD↗

arXiv2022

Regional-Local Adversarially Learned One-Class Classifier Anomalous Sound Detection in Global Long-Term Space

Anomalous sound detection (ASD) is one of the most significant tasks of mechanical equipment monitoring and maintaining in complex industrial systems. In practice, it is vital to precisely identify abnormal status of the working mechanical system, which can further facilitate the failure troubleshooting. In this paper, we propose a multi-pattern adversarial learning one-class classification framework, which allows us to use both the generator and the discriminator of an adversarial model for efficient ASD. The core idea is learning to reconstruct the normal patterns of acoustic data through two different patterns of auto-encoding generators, which succeeds in extending the fundamental role of a discriminator from identifying real and fake data to distinguishing between regional and local pattern reconstructions. Furthermore, we present a global filter layer for long-term interactions in the frequency domain space, which directly learns from the original data without introducing any human priors. Extensive experiments performed on four real-world datasets from different industrial domains (three cavitation datasets provided by SAMSON AG, and one existing publicly) for anomaly detection show superior results, and outperform recent state-of-the-art ASD methods.

cs.SD↗

arXiv2020

Skyrme-Hartree-Fock calculations of nuclear properties in the drip-point region of neutron star crust

In the present paper we explore the neutron-drip region of cold non-rotating isolated neutron stars. We have performed extended nuclear-structure calculations for nuclei embedded in the electron gas. For modeling the outer crust we use a set of Wigner-Seitz cells, where every cell contains one nucleus surrounded by a cloud of relativistic electrons. Above the drip point a non-relativistic neutron gas occurs in the cell. These calculations are carried out within the Hartree-Fock approach in combination with Skyrme effective interactions. For every baryon density we have determined the configuration with a minimal total energy. The drip elements and corresponding drip densities have been determined for about 240 different parametrizations of Skyrme forces used in the literature. We demonstrate that the calculated drip-point densities depend essentially on the Skyrme parametrization used. Even the drip elements and the occupied shells in the drip region differ for different parametrizations. We have found that the number of neutrons building the neutron gas at the drip point also depends essentially on the Skyrme force chosen. Nevertheless, the number density of the neutron gas in the drip-region is more or less the same ($\sim 10^{-5} \frac{neutrons}{{fm}^{3}}$). The drip densities obtained within our approach are generally lower than predicted earlier.

nucl-th↗

arXiv2020

QCD equation of state at vanishing and high baryon density: Chiral Mean Field model

The thermodynamic properties of high temperature and high density QCD-matter are studied using the Chiral SU(3)-flavor parity-doublet Polyakov-loop quark-hadron mean-field model, CMF. The CMF model provides a proper description of lattice QCD data, heavy-ions physics, and static neutron stars. The behavior of lines of constant pressure with increase of baryon density is discussed. The rapid change of pressure behavior at $μ_B/T\approx3$ suggests a strong contribution of baryons to thermodynamic properties at this region. The position of this region is very close to the radius of convergence for a Taylor expansion of the QCD pressure. The role of mesons and unstable hadrons in the hydrodynamic expansion of strongly interacting matter is also discussed.

hep-ph↗

arXiv2019

Generalized correction to embedded-atom potentials for modeling equilibrium and non-equilibrium properties of metals

A modification of an embedded-atom method (EAM)-type potential is proposed for a quantitative description of equilibrium and non-equilibrium properties of metal systems within the molecular-dynamics framework. The modification generalizes the previously developed linear correction to EAM-type potentials [Sushko et al., J. Phys.: Condens. Matter \textbf{28}, 145201 (2016)] and asymptotically approaches zero at large interatomic distances. A general procedure for constructing this modification is outlined and its relation to the linear correction is elaborated. To benchmark this procedure, we examine the melting phase transition and several equilibrium properties of nanosystems made of silver, gold, and titanium. The simulations performed with the modified potential predict higher bulk melting temperatures of the metals and agree better with experimental values as compared to the original EAM-type potential. Our results show that the modification works well for metals with both cubic and hexagonal crystalline lattices. The Gupta potential is chosen as an illustrative case study but the modification proposed is general and can be applied to other widely-used potentials of the EAM type.

cond-mat.mes-hall↗

arXiv2019

Matter And Gravitation In Collisions of heavy ions and neutron stars: equation of state

The gravitational waves emitted from a binary neutron star merger, as predicted from general relativistic magneto-hydrodynamics calculations, are sensitive to the appearance of quark matter and the stiffness of the equation of state of QCD matter present in the inner cores of the stars. This is a new messenger observable from outer space, which does provide direct signals for the phase structure of strongly interacting QCD matter at high baryon density and high temperature. These astrophysically created extremes of thermodynamics do match, to within 20\%, the values of densities and temperatures which we find in relativistic hydrodynamics and transport theory of heavy ion collisions at the existing laboratories, if though at quite different rapidity windows, impact parameters and bombarding energies of the heavy nuclear systems. We demonstrate how one unified equation of state can be constructed and used for both neutron star physics and hot QCD matter excited at laboratory facilities. The similarity in underlying QCD physics allows the gravitational wave signals from future advanced LIGO and Virgo events to be combined with the analysis of high multiplicity fluctuations and flow measurements in heavy ion detectors in the lab to pin down the EoS and the phase structure of dense matter.

hep-ph↗

arXiv2019

Neutron-Star-Merger Equation of State

In this work, we discuss the dense matter equation of state (EOS) for the extreme range of conditions encountered in neutron stars and their mergers. The calculation of the properties of such an EOS involves modeling different degrees of freedom (such as nuclei, nucleons, hyperons, and quarks), taking into account different symmetries, and including finite density and temperature effects in a thermodynamically consistent manner. We begin by addressing subnuclear matter consisting of nucleons and a small admixture of light nuclei in the context of the excluded volume approach. We then turn our attention to supranuclear homogeneous matter as described by the Chiral Mean Field (CMF) formalism. Finally, we present results from realistic neutron-star-merger simulations performed using the CMF model that predict signatures for deconfinement to quark matter in gravitational wave signals.

nucl-th↗

arXiv2019

Equation of state for hot QCD and compact stars from a mean field approach

The thermodynamic properties of high temperature and high density QCD-matter are explored within the Chiral SU(3)-flavor parity-doublet Polyakov-loop quark-hadron mean-field model, CMF. The quark sector of the CMF model is tuned to describe the $μ_B=0$ thermodynamics data of lattice QCD. The resulting lines of constant physical variables as well as the baryon number susceptibilities are studied in some detail in the temperature/chemical potential plane. The CMF model predicts three consecutive transitions, the nuclear first-order liquid-vapor phase transition, chiral symmetry restoration, and the cross-over transition to a quark-dominated phase. All three phenomena are cross-over, for most of the $T-μ_B$-plane. The deviations from the free ideal hadron gas baseline at $μ_B=0$ and $T\approx 100-200$ MeV can be attributed to remnants of the liquid-vapor first order phase transition in nuclear matter. The chiral crossing transition determines the baryon fluctuations at much higher $μ_B\approx1.5$ GeV, and at even higher baryon densities $μ_B\approx2.4$ GeV, the behavior of fluctuations is controlled by the deconfinement cross-over. The CMF model also describe well the static properties of high $μ_B$ neutron stars as well as the new neutron star merger observations. The effective EoS presented here describes simultaneously lattice QCD results at $μ_B=0$, as well as observed physical phenomena (nuclear matter and neutron star matter) at $T\cong0$ and high densities, $μ_B>1$ GeV.

hep-ph↗

arXiv2019

Signatures of quark-hadron phase transitions in general-relativistic neutron-star mergers

Merging binaries of neutron stars are not only strong sources of gravitational waves, but also have the potential of revealing states of matter at densities and temperatures not accessible in laboratories. A crucial and long-standing question in this context is whether quarks are deconfined as a result of the dramatic increase in density and temperature following the merger. We present the first fully general-relativistic simulations of merging neutron stars including quarks at finite temperatures that can be switched off consistently in the equation of state. Within our approach, we can determine clearly what signatures a quark-hadron phase transition would leave in the gravitational-wave signal. In particular, we show that if the conditions are met for a phase transition to take place at several times nuclear saturation density, they would lead to a post-merger signal considerably different from the one expected from the inspiral, that can only probe the hadronic part of the equations of state, and to an anticipated collapse of the merged object. We also show that the phase transition leads to a very hot and dense quark core that, when it collapses to a black hole, produces a ringdown signal different from the hadronic one. Finally, in analogy with what is done in heavy-ion collisions, we use the evolution of the temperature and density in the merger remnant to illustrate the properties of the phase transition in a QCD phase diagram.

astro-ph.HE↗

arXiv2019

What do we learn about vector interactions from GW170817?

We analyze the role played by vector-isovector meson interaction in dense matter present in the interior of neutron stars in the light of new measurements made during the double neutron-star merger GW170817. These concern measurements of tidal deformability from gravitational waves and electromagnetic observations. Our study includes three different equations of state that contain different physical assumptions and matter compositions, namely the NL3 family, MBF, and CMF models. Other related quantities/relations analyzed are the neutron matter pressure, symmetry energy slope, stellar masses and radii, and Urca process threshold for stellar cooling.

nucl-th↗

arXiv2018

The Impact of Climate Change on a Cost-Optimal Highly Renewable European Electricity Network

We use three ensemble members of the EURO-CORDEX project and their data on surface wind speeds, solar irradiation as well as water runoff with a spatial resolution of 12 km and a temporal resolution of 3 hours under representative concentration pathway 8.5 (associated with a temperature increase of 2.6 to 4.8 degrees C until the end of the century) until 2100 to investigate the impact of climate change on wind, solar and hydro resources and consequently on a highly renewable and cost-optimal European power system. The weather data is transformed into power, different aspects such as capacity factors and correlation lengths are investigated and the resulting implications for the European power system are discussed. In addition, we compare a 30-node model of Europe with historical and climate change-affected data, where investments in generation, transmission and storage facilities are optimised for deep carbon dioxide reductions. Differences in capacity factors among European countries are more strongly emphasized at the end of the century compared to historic data. This results in a significantly increased photovoltaic share in the cost-optimal power system. System costs increase by 5% until the end of the century and the impact of climate change on these costs is of similar magnitude as differences between the ensemble members.

physics.soc-ph↗

arXiv2018

Principal Flow Patterns across renewable electricity networks

Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to describe 95$\%$ of the nodal power injection variance first increases with the spatial resolution of the system representation. The number of relevant components then saturates at around 76 components for network sizes larger than 512 nodes, which can be related to the correlation length of wind patterns over Europe. Remarkably, the application of PCA to the transmission line power flow statistics shows that irrespective of the spatial scale of the system representation a very low number of only 8 principal flow patterns is sufficient to capture 95$\%$ of the corresponding spatio-temporal variance. This result can be theoretically explained by a particular alignment of some principal injection patterns with topological patterns inherent to the network structure of the European transmission system.

eess.SP↗

arXiv2018

QCD at high density: Equation of state for nuclear collisions and neutron stars

A unified chiral mean field approach is presented for QCD thermodynamics in a wide range of temperatures and densities. The model simultaneously gives a satisfactory description of lattice QCD thermodynamics and fulfills nuclear matter and astrophysical constraints. The resulting equation of state can be incorporated in relativistic fluid-dynamical simulations of heavy-ion collisions and neutron stars mergers. Access to different regions of the QCD phase diagram can be obtained in simulations of heavy-ion data and observations of neutron star mergers.

hep-ph↗

arXiv2018

Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints

Cost optimal scenarios derived from models of a highly renewable electricity system depend on the specific input data, cost assumptions and system constraints. Here this influence is studied using a techno-economic optimisation model for a networked system of 30 European countries, taking into account the capacity investment and operation of wind, solar, hydroelectricity, natural gas power generation, transmission, and different storage options. A considerable robustness of total system costs to the input weather data and to moderate changes in the cost assumptions is observed. Flat directions in the optimisation landscape around cost-optimal configurations often allow system planners to choose between different technology options without a significant increase in total costs, for instance by replacing onshore with offshore wind power capacity in case of public acceptance issues. Exploring a range of carbon dioxide emission limits shows that for scenarios with moderate transmission expansion, a reduction of around 57% compared to 1990 levels is already cost optimal. For stricter carbon dioxide limits, power generated from gas turbines is at first replaced by generation from increasing renewable capacities. Non-hydro storage capacities are only built for low-emission scenarios, in order to provide the necessary flexibility to meet peaks in the residual load.

physics.soc-ph↗