publications
publications in reversed chronological order.
publications empowered by dtool
A list of publications that have been made possible by managing the underlying data with dtool and dserver. 2024
- Why soft contacts are stickier when breaking than when making themAntoine Sanner, Nityanshu Kumar, Ali Dhinojwala, and 2 more authorsScience Advances, Mar 2024
Soft solids are sticky. They attract each other and spontaneously form a large area of contact. Their force of attraction is higher when separating than when forming contact, a phenomenon known as adhesion hysteresis. The common explanation for this hysteresis is viscoelastic energy dissipation or contact aging. Here, we use experiments and simulations to show that it emerges even for perfectly elastic solids. Pinning by surface roughness triggers the stick-slip motion of the contact line, dissipating energy. We derive a simple and general parameter-free equation that quantitatively describes contact formation in the presence of roughness. Our results highlight the crucial role of surface roughness and present a fundamental shift in our understanding of soft adhesion.
10.1126/sciadv.adl1277
2023
- Entwicklung einer Multiskalenmethode für die Simulation von SchmierprozessenHannes HoleyKarlsruher Institut für Technologie (KIT), Mar 2023
Reibung und Schmierung sind Multiskalenprobleme, d.h. Prozesse auf unterschiedlichen Zeit- und Längenskalen beeinflussen einander und bestimmen die makroskopische Antwort eines Systems. Für Schmierungsprozesse trifft dies insbesondere im Grenzreibungsbereich zu, in dem die Dicke des Schmierspalts in der Größenordnung molekularer Interaktionslängen liegt. Makroskopische Schmierungsmodellierung basiert fast ausschließlich auf der Anwendung der Reynoldsgleichung, während auf atomarer Skala vermehrt Molekulardynamik-Simulationen in den Vordergrund treten. Multiskalenmethoden für Schmierungsphänomene, die über sequentielle Ansätze hinausgehen, sind bisher noch nicht etabliert. Im Rahmen dieser Arbeit wird ein Multiskalenansatz vorgestellt, welcher die Lösung der makroskopischen Bilanzgleichungen in ein Mikro- und Makroproblem aufteilt. Das Makroproblem entsteht durch Mittelung der Bilanzgleichungen über der Spalthöhe, ähnlich zur konventionellen Reynoldsgleichung, und wird mittels expliziter Finite-Volumen-Diskretisierung gelöst, während das Mikroproblem das konstitutive Verhalten des Schmierfilms enthält. Die numerische Implementierung des Makroproblems wird mithilfe gewöhnlicher Konstitutivgesetze validiert und anhand konkreter Beispiele wird gezeigt, dass diese in Zukunft durch Molekulardynamik-Simulationen ersetzt werden können. Außerdem lassen sich analytische Lösungen der linearisierten Grundgleichungen des Makroproblems herleiten, die mit Autokorrelationsfunktionen fluktuierender Zustandsvariablen aus Molekulardynamik-Simulationen verglichen werden. Daraus ergibt sich eine Methode zur simultanen Bestimmung von Viskosität und Schlupflänge aus Gleichgewichts-Simulationen, sowie die Beschreibung des überkritischen Schalltransports in Fluidspalten. Für eine effiziente Umsetzung des vorgestellten Multiskalenansatzes wird eine Ersatzmodellierung benötigt, die zwischen einzelnen Mikrosimulationen interpoliert. Anhand von einfachen Beispielen wird das Anwendungspotential der Gaußprozess-Regression als mögliches Ersatzmodell evaluiert. Die vorliegende Arbeit liefert somit die theoretischen Grundlagen einer simultanen Multiskalensimulation von Schmierungsprozessen, welche in Zukunft zu einem besseren Verständnis der Dissipationsmechanismen im Grenzreibungsbereich beitragen kann.
- Molecular simulations of sliding on SDS surfactant filmsJohannes L. Hörmann, Chenxu (刘宸旭) Liu, Yonggang (孟永钢) Meng, and 1 more authorThe Journal of Chemical Physics, Jun 2023
We use molecular dynamics simulations to study the frictional response of monolayers of the anionic surfactant sodium dodecyl sulfate and hemicylindrical aggregates physisorbed on gold. Our simulations of a sliding spherical asperity reveal the following two friction regimes: at low loads, the films show Amonton’s friction with a friction force that rises linearly with normal load, and at high loads, the friction force is independent of the load as long as no direct solid–solid contact occurs. The transition between these two regimes happens when a single molecular layer is confined in the gap between the sliding bodies. The friction force at high loads on a monolayer rises monotonically with film density and drops slightly with the transition to hemicylindrical aggregates. This monotonous increase of friction force is compatible with a traditional plowing model of sliding friction. At low loads, the friction coefficient reaches a minimum at the intermediate surface concentrations. We attribute this behavior to a competition between adhesive forces, repulsion of the compressed film, and the onset of plowing.
10.1063/5.0153397
- How surface roughness affects adhesionAntoine SannerUniversity of Freiburg, Jun 2023
2022
- Height-Averaged Navier–Stokes Solver for Hydrodynamic LubricationHannes Holey, Andrea Codrignani, Peter Gumbsch, and 1 more authorTribology Letters, Mar 2022
The cornerstone of thin-film flow modeling is the Reynolds equation—a lower-dimensional representation of the Navier–Stokes equation. The derivation of the Reynolds equation is based on explicit assumptions about the constitutive behavior of the fluid that prohibit applications in multiscale scenarios based on measured or atomistically simulated data. Here, we present a method that treats the macroscopic flow evolution and the calculation of local cross-film stresses as separate yet coupled problems—the so-called macro and micro problem. The macro problem considers mass and momentum balance for compressible fluids in a height-averaged sense and is solved using a time-explicit finite-volume scheme. Analytical solutions for the micro problem are derived for common constitutive laws and implemented into the Height-averaged Navier–Stokes (HANS) solver. We demonstrate the validity of our solver on examples, including mass-conserving cavitation, inertial effects, wall slip, and non-Newtonian fluids. The presented method is not limited to these fixed-form relations and may therefore be useful for testing constitutive relations obtained from experiment or simulation.
10.1007/s11249-022-01576-5
- Crack-front model for adhesion of soft elastic spheres with chemical heterogeneityAntoine Sanner, and Lars PastewkaJournal of the Mechanics and Physics of Solids, Mar 2022
Adhesion hysteresis can be caused by elastic instabilities that are triggered by surface roughness or chemical heterogeneity. However, the role of these instabilities in adhesion hysteresis remains poorly understood because we lack theoretical and numerical models accounting for realistic roughness. Our work focuses on the adhesion of soft elastic spheres with low roughness or weak heterogeneity, where the indentation process can be described as a Griffith-like propagation of a nearly circular external crack. We discuss how to describe the contact of spheres with chemical heterogeneity that leads to fluctuations in the local work of adhesion. We introduce a variational first-order crack-perturbation model and validate our approach using boundary-element simulations. The crack-perturbation model faithfully predicts contact shapes and hysteretic force-penetration curves, provided that the contact perimeter remains close to a circle and the contact area is simply connected. Computationally, the crack-perturbation model is orders of magnitude more efficient than the corresponding boundary element formulation, allowing for realistic heterogeneity fields.
10.1016/j.jmps.2022.104781
2020
- dtoolAI: Reproducibility for Deep LearningMatthew Hartley, and Tjelvar S. G. OlssonPatterns, Aug 2020
Deep learning, a set of approaches using artificial neural networks, has generated rapid recent advancements in machine learning. Deep learning does, however, have the potential to reduce the reproducibility of scientific results. Model outputs are critically dependent on the data and processing approach used to initially generate the model, but this provenance information is usually lost during model training. To avoid a future reproducibility crisis, we need to improve our deep-learning model management. The FAIR principles for data stewardship and software/workflow implementation give excellent high-level guidance on ensuring effective reuse of data and software. We suggest some specific guidelines for the generation and use of deep-learning models in science and explain how these relate to the FAIR principles. We then present dtoolAI, a Python package that we have developed to implement these guidelines. The package implements automatic capture of provenance information during model training and simplifies model distribution.
10.1016/j.patter.2020.100073
dtool core publications
Core publications describing components of the dtool and dserver RDM ecosystem. 2024
- dtool and dserver: A flexible ecosystem for findable dataJohannes L. Hörmann, Luis Yanes, Ashwin Vazhappilly, and 5 more authorsPLOS ONE, Jun 2024
Making data FAIR—findable, accessible, interoperable, reproducible—has become the recurring theme behind many research data management efforts. dtool is a lightweight data management tool that packages metadata with immutable data to promote accessibility, interoperability, and reproducibility. Each dataset is self-contained and does not require metadata to be stored in a centralised system. This decentralised approach means that finding datasets can be difficult. dtool’s lookup server, short dserver, as defined by a REST API, makes dtool datasets findable, hence rendering the dtool ecosystem fit for a FAIR data management world. Its simplicity, modularity, accessibility and standardisation via API distinguish dtool and dserver from other solutions and enable it to serve as a common denominator for cross-disciplinary research data management. The dtool ecosystem bridges the gap between standardisation-free data management by individuals and FAIR platform solutions with rigid metadata requirements.
10.1371/journal.pone.0306100
2022
- Lightweight research data management with dtool : a use caseJohannes L. Hörmann, and Lars PastewkaIn Proceedings of the 7th bwHPC Symposium, Nov 2022
With the dtool data management framework, we adhere to the FAIR principles – findability, accessibility, interoperability, reusability – beginning at an early stage of the data lifecycle without introducing overwhelming administrative overhead. We show how the use of dtool has been implemented within IMTEK Simulation. In particular, we make data accessible, interoperable and reusable by packaging data and descriptive metadata in dtool datasets. The dtool lookup server makes data on a group-wide S3 object storage repository findable. The dtool ecosystem has proven applicable both for manual research data management as well as rapid generation of thousands of datasets in automized workflows.
10.18725/OPARU-46062
2019
- Lightweight data management with dtoolTjelvar S. G. Olsson, and Matthew HartleyPeerJ, Mar 2019
The explosion in volumes and types of data has led to substantial challenges in data management. These challenges are often faced by front-line researchers who are already dealing with rapidly changing technologies and have limited time to devote to data management. There are good high-level guidelines for managing and processing scientific data. However, there is a lack of simple, practical tools to implement these guidelines. This is particularly problematic in a highly distributed research environment where needs differ substantially from group to group and centralised solutions are difficult to implement and storage technologies change rapidly. To meet these challenges we have developed dtool, a command line tool for managing data. The tool packages data and metadata into a unified whole, which we call a dataset. The dataset provides consistency checking and the ability to access metadata for both the whole dataset and individual files. The tool can store these datasets on several different storage systems, including a traditional file system, object store (S3 and Azure) and iRODS. It includes an application programming interface that can be used to incorporate it into existing pipelines and workflows. The tool has provided substantial process, cost, and peace-of-mind benefits to our data management practices and we want to share these benefits. The tool is open source and available freely online at http://dtool.readthedocs.io.
10.7717/peerj.6562
