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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Motion of a viscous slug on heterogeneous surfaces: crossover from stick–slip to steady sliding

We present a theoretical study of viscous slug motion inside a microscopically rough capillary tube, where pronounced stick–slip motion can emerge at slow displacement rates. The mathematical description of this intermittent motion can be reduced to a system of ordinary differential equations, which also describe the motion of a pendulum inside a fluid-filled rotating drum. We use this analogy to show that the stick–slip motion transitions to steady sliding at high displacement rates. We characterize this crossover with a simple scaling relation and show that the crossover is accompanied by a shift in the dominant energy dissipation mechanisms within the system.

Mechanics↗
DOE OSTI2025

Marine Toxicity, Biodegradability, and Rolling-Sliding Tribological Performance of Ionic Liquid-Enhanced Environmentally-Acceptable Lubricants for Tidal Turbomachinery

Environmentally acceptable lubricants (EALs) are increasingly being recognized in many fields including waterpower, hydraulics, water transport, agricultural machinery, offshore wind turbines, etc. Specifically, high-performance EALs are demanded for tidal turbomachinery to ensure high efficiency and reliability and avoid the significant risk of direct contamination of the marine ecosystem upon leaks. Here we report a new development of ionic liquid (IL)-enhanced EALs for tidal energy. One short-chain phosphonium phosphate and one short-chain ammonium phosphate ILs were used as the candidate additives and the IL-containing EALs demonstrated significantly improved lubricating performance, much lower toxicity, and increased biodegradability compared with commercial baselines. Specifically, in a rolling-sliding test simulating the operation of a model tidal turbine gearbox bearing, an EAL containing the ILs at a 0.5 wt % concentration demonstrated 40% lower friction, 45% less wear loss, substantially reduced rolling contact fatigue-induced surface damage, and one order of magnitude lower vibration noise compared with a commercial gear oil. In an EPA standard toxicity test, 90 and 70% survival of marine biota was observed when exposed to an EAL containing 5 wt % of the short-chain phosphonium phosphate and ammonium phosphate ILs, respectively, while the selected commercial gear oil and bioderived additive killed all marine biota. In a standard biodegradability test, 2 wt % addition of the phosphonium phosphate IL not only retained the EAL’s ready biodegradability but further boosted the oil decomposition from a range of 60–80% to a higher level of 80–95%. Conversely, adding the commercial bioderived additive downgraded the EAL from readily to inherently biodegradable. Furthermore, this work offers scientific insights for development of ILs as potential EAL additives for marine energy and broader applications.

additives↗
DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Room-temperature multiferroicity in sliding van der Waals semiconductors with sub-0.3 V switching

The search for van der Waals (vdW) multiferroic materials has been challenging but also holds great potential for the next-generation multifunctional nanoelectronics. The group-IV monochalcogenide, with an anisotropic puckered structure and an intrinsic in-plane polarization at room temperature, manifests itself as a promising candidate with coupled ferroelectric and ferroelastic order as the basis for multiferroic behavior. Unlike the intrinsic centrosymmetric AB stacking, we demonstrate a multiferroic phase of tin selenide (SnSe), where the inversion symmetry breaking is maintained in AA-stacked multilayers over a wide range of thicknesses. We observe that an interlayer-sliding-induced out-of-plane (OOP) ferroelectric polarization couples with the in-plane (IP) one, making it possible to control out-of-plane polarization via in-plane electric field and vice versa. Notably, thickness scaling yields a sub-0.3 V ferroelectric switching, which promises future low-power-consumption applications. Furthermore, coexisting armchair- and zigzag-like structural domains are imaged under electron microscopy, providing experimental evidence for the degenerate ferroelastic ground states theoretically predicted. Non-centrosymmetric SnSe, as the first layered multiferroic at room temperature, provides a novel platform not only to explore the interactions between elementary excitations with controlled symmetries, but also to efficiently tune the device performance via external electric and mechanical stress.

Chen, Rui [University of California, Berkeley, CA ↗
DOE OSTI2021

Conductive and anti-freezing hydrogels constructed by pseudo-slide-ring networks

In this work, stretchable, tough, and anti-freezing hydrogels were prepared using partially carboxymethylated polyrotaxanes and polyacrylamides. The carboxylic acid groups of α-cyclodextrins in the polyrotaxane and the amide groups in polyacrylamide are hydrogen-bonded, affording a pseudo-slide-ring network, greatly enhancing the hydrogels’ macroscale mechanical properties, anti-freezing features, and electrical conductivity for the fabrication of a cold-temperature strain sensor.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Large-scale atomistic studies of sliding friction in polycrystalline aluminum interfaces

In this work, we discuss large-scale non-equilibrium molecular dynamics (NEMD) simulations of ductile metal sliding comprising up to 1.8 × 10 9 atoms over time scales of 100 ns. The results of these simulations have identified a variety of physical mechanisms that are important in determining the steady-state frictional force for a wide range of velocities at compressed metal–metal interfaces. These include grain growth and refinement, the evolution of large plastic strains and strain rates, material mixing, and melting. These phenomena can be included in a strain, strain rate, and grain size model that gives good agreement with the NEMD simulations and can be applied to macroscopic continua.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Drive dependence of the Hall angle for a sliding Wigner crystal in a magnetic field

We numerically examine the depinning and sliding dynamics of a Wigner crystal in the presence of quenched disorder and a magnetic field. In the disorder-free limit, the Wigner crystal Hall angle is independent of crystal velocity, but when disorder is present, we find that the Hall angle starts near zero at the depinning threshold and increases linearly with increasing drive before reaching a saturation close to the disorder free value at the highest drives. The drive dependence is the result of a side jump effect produced when the charges move over pinning sites. The magnitude of the side jump is reduced at the higher velocities. Here, the drive dependent Hall angle is robust for a wide range of disorder parameters and should be a generic feature of classical charges driven in the presence of quenched disorder and a magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Granular packings with sliding, rolling, and twisting friction

Intuition tells us that a rolling or spinning sphere will eventually stop due to the presence of friction and other dissipative interactions. The resistance to rolling and spinning or twisting torque that stops a sphere also changes the microstructure of a granular packing of frictional spheres by increasing the number of constraints on the degrees of freedom of motion. We perform discrete element modeling simulations to construct sphere packings implementing a range of frictional constraints under a pressure-controlled protocol. Mechanically stable packings are achievable at volume fractions and average coordination numbers as low as 0.53 and 2.5, respectively, when the particles experience high resistance to sliding, rolling, and twisting. Only when the particle model includes rolling and twisting friction were experimental volume fractions reproduced.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple res-olutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple met-ric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/Oand memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues; correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.

Dash, Sajal↗
DOE OSTI2020

Efficient Generalized Boundary Detection Using a Sliding Information Distance

In this work, we present a general machine learning algorithm for boundary detection within general signals based on an efficient, accurate, and robust approximation of the universal normalized information distance. Our approach uses an adaptive sliding information distance (SLID) combined with a wavelet-based approach for peak identification to locate the boundaries. Special emphasis is placed on developing an adaptive formulation of SLID to handle general signals with multiple unknown and/or drifting section lengths. Although specialized algorithms may outperform SLID when domain knowledge is available, these algorithms are limited to specific applications and do not generalize. SLID excels in these cases. We demonstrate the versatility and efficacy of SLID on a variety of signal types, including synthetically generated sequences of tokens, binary executables for reverse engineering applications, and time series of seismic events.

42 ENGINEERING↗
DOE OSTI2023

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks

Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.

Tsaris, Aristeidis (aris)↗
DOE OSTI2020

Scalable Linked Dynamic Equilibrium (SLiDE)

SLiDE (Scalable Linked Dynamic Equilibrium) replicates the blueNOTE (National Open source Tools for general Equilibrium analysis) datastream from the Wisconsin National Data Consortium. From there, it produces a recursive dynamic computable general equilibrium model of the US states and eventually the global/national economies. The datastream operations have been set up to allow for scalability of both regions and sectors represented. The model has the capability to flexibly define functional forms similar to other general equilibrium model construction software.

Hughes, Caroline↗
DOE OSTI2020

Research Highlights [Slides]

Personal research highlight slides to be incorporated into a Nuclear Science and Security Consortium overview presentation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Basic OpenMP and Profiling [Slides]

Report contains slides for Lecture 1: Introduction to OpenMP and Lecture 2: Profiling OpenMP applications with Vtune.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

PAIN Schubert Review: Experiment [Slides]

These slides detail the experimental section of the PAIN Schubert Review. The PAIN project is investigating the utility of portable neutron radiography for emergency response. Further sections give an overview of the PAIN project and detail the simulation performed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗