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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Object Proxy Patterns for Accelerating Distributed Applications

Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer—optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns—distributed futures, streaming, and ownership—that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. As a result, we evaluate our implementations through a suite of benchmarks and by applying them in three meaningful scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage.

Distributed Computing↗
DOE OSTI2023

Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ∼6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%–2%) is required to dramatically improve the wetting (5–20 times) compared to an unstructured electrode.

25 ENERGY STORAGE↗
DOE OSTI2020

Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. In this study, we investigate intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the West Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
DOE OSTI2020

Understanding the Impact of Memory Access Patterns in Intel Processors

Because of increasing complexity in the memory hierarchy, predicting the performance of a given application in a given processor is becoming more difficult. The problem is worsened by the fact that the hardware needed to deal with more complex memory traffic also affects energy consumption. Moreover, in a heterogeneous system with shared main memory, the memory traffic between the last level cache (LLC) and the memory creates contention between other processors and accelerator devices. For these reasons, it is important to investigate and understand the impact of different memory access patterns on the memory system. This study investigates the interplay between Intel processors' memory hierarchy and different memory access patterns in applications. The authors explore sequential streaming and strided memory access patterns with the objective of predicting LLC-dynamic random access memory (DRAM) traffic for a given application in given Intel architectures. Moreover, the impact of prefetching is also investigated in this study. Experiments with different Intel micro-architectures uncover mechanisms to predict LLC-DRAM traffic that can yield up to 99% accuracy for sequential streaming access patterns and up to 95% accuracy for strided access patterns.

Alaul haque monil, Mohammad↗
DOE OSTI2024

Solid-state information pattern discriminating switch

A maze-based switch generally having three functional blocks is disclosed. The first functional block handles communications by accepting an entered maze pattern from an external system controller and outputting the entered maze pattern (and optionally its directional complement) to the second functional block. The second functional block stores the maze pattern (and optionally its directional complement) to a permanent storage element and outputs the stored, entered maze pattern and its directional complement to a series of transistors in the third functional block. The third functional block is an electronic maze in which a correct maze pattern and its directional complement must be received by the transistors for the transistors to pass electrical power through the electronic maze to a connected element. The third functional block may alternatively be implemented with optical elements, optoelectronic elements, microelectromechanical elements, or elements formed by other microsystem technologies.

Galambos, Paul C.↗
DOE OSTI2021

Island scanning pattern optimization for residual deformation mitigation in laser powder bed fusion via sequential inherent strain method and sensitivity analysis

Laser powder bed fusion (L-PBF) has emerged as one of the mainstream additive manufacturing approaches for fabricating metal parts with complex geometries and intricate internal structures. However, large deformation associated with rapid heating and cooling can lead to build failure and requires post-processing which may increase manufacturing cost and prolong the production period. Here in this work, an island scanning pattern design method is proposed to optimize the scanning direction of each island in order to reduce part deformation after cutting off the build platform. The objective of this optimization is to minimize the upward bending of the part after sectioning, which allows the part deformation to satisfy the tolerance requirement or reduce the post heat treatment time. Inherent strain method is employed in the sequential finite element analysis consisting of layer-by-layer activations and sectioning for fast residual distortion prediction. Full sequential sensitivity analysis for the formulated optimization is provided to update the island scanning directions. To show the feasibility and effectiveness of the proposed method, the scanning patterns of a block structure and a connecting rod were designed and parts were fabricated using an open architecture L-PBF machine. The fabrication experiments demonstrated that the residual deformation of both parts fabricated by optimized scanning pattern can be reduced by over 50% compared to the initial scanning patterns, which demonstrate the effectiveness of the proposed method.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A new flow pattern map for flow boiling of R410A in horizontal micro-fin tubes considering the effect of the helix angle

This paper presents an improved flow pattern map for flow boiling in horizontal micro-fin tubes considering the effect of helix angle. The proposed map is modified from the one developed for horizontal smooth tubes by Wojtan et al. (2005) and takes the early transition to annular flow due to micro-fin geometry into account. Visualization of R410A flow boiling is carried out through the transparent smooth and micro-fin tubes with 0°, 10° and 18° helix angle and plotted on the proposed map for comparison. The result shows that the flow pattern map for micro-fin tube with 0° helix angle is generally similar to that for the smooth tube. For the tube with 10° and 18° helix angle, the annular flow occurs at lower vapor quality and lower mass flux. The proposed map shows good agreements when compared with the visualization and reflects the downward shift (to lower mass flux and vapor quality conditions) of the SW-WA transition with the increase of the helix angle. In order to further validate the proposed model, the flow pattern transitions calculated from the equations are also compared with the experimental data of the micro-fin tube in literature. The results show that the modified map increase the prediction accuracy of the flow patterns in the micro-fin tube.

42 ENGINEERING↗
DOE OSTI2024

3D Temperature-Controlled Interchangeable Pattern for Size-Selective Nanoparticle Capture

Patterned surfaces with distinct regularity and structured arrangements have attracted great interest due to their extensive promising applications. Although colloidal patterning has conventionally been used to create such surfaces, herein, we introduce a novel 3D patterned poly(N-isopropylacrylamide) (PNIPAM) surface, synthesized by using a combination of colloidal templating and surface-initiated photoinduced electron transfer-reversible addition–fragmentation chain transfer (SI-PET-RAFT) polymerization. In order to investigate the temperature-driven 3D morphological variations at a lower critical solution temperature (LCST) of ~32 °C, multifaceted characterization techniques were employed. Atomic force microscopy confirmed the morphological transformations at 20 and 40 °C, while water contact angle measurements, upon heating, revealed distinct trends, offering insights into the correlation between surface wettability and topography adaptations. Moreover, quartz crystal microbalance with dissipation monitoring and electrochemical measurements were employed to detect the topographical adjustments of the unique hollow capsule structure within the LCST. Tests using different sizes of PSNPs shed light on the size-selective capture–release potential of the patterned PNIPAM, accentuating its biomimetic open–close behavior. Notably, our approach negates the necessity for expensive proteins, harnessing temperature adjustments to facilitate the noninvasive and efficient reversible capture and release of nanostructures. Finally, this advancement hopes to pave the way for future innovative cellular analysis platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ratchet effects in cyclic pattern formation systems with competing interactions

Ratchet effects can appear for particles interacting with an asymmetric potential under ac driving or for a thermal system in which a substrate is periodically flashed. Here, we show that a different type of collective ratchet effect can arise for a pattern-forming system coupled to an asymmetric substrate when the interaction potential between the particles is periodically oscillated in order to cycle the system through different patterns. We consider particles with competing short-range attraction and long-range repulsion subjected to time-dependent oscillations of the ratio between the attractive and repulsive interaction terms, which causes the system to cycle periodically between crystal and bubble states. In the presence of the substrate, this system exhibits both a positive and a reversed ratchet effect, and we show that there is a maximum in the ratchet efficiency as a function of interaction strength, ac drive frequency, and particle density. In conclusion, our results could be realized for a variety of pattern-forming systems on asymmetric substrates where the pattern type or particle interactions can be oscillated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defining upstream enhancing and inhibiting sequence patterns for plant peroxisome targeting signal type 1 using large–scale in silico and in vivo analyses

Peroxisomes are universal eukaryotic organelles essential to plants and animals. Most peroxisomal matrix proteins carry peroxisome targeting signal type 1 (PTS1), a C-terminal tripeptide. Studies from various kingdoms have revealed influences from sequence upstream of the tripeptide on peroxisome targeting, supporting the view that positive charges in the upstream region are the major enhancing elements. However, a systematic approach to better define the upstream elements influencing PTS1 targeting capability is needed. Here, we used protein sequences from 177 plant genomes to perform large-scale and in-depth analysis of the PTS1 domain, which includes the PTS1 tripeptide and upstream sequence elements. We identified and verified 12 low-frequency PTS1 tripeptides and revealed upstream enhancing and inhibiting sequence patterns for peroxisome targeting, which were subsequently validated in vivo. Follow-up analysis revealed that nonpolar and acidic residues have relatively strong enhancing and inhibiting effects, respectively, on peroxisome targeting. However, in contrast to the previous understanding, positive charges alone do not show the anticipated enhancing effect and that both the position and property of the residues within these patterns are important for peroxisome targeting. We further demonstrated that the three residues immediately upstream of the tripeptide are the core influencers, with a ‘basic-nonpolar-basic’ pattern serving as a strong and universal enhancing pattern for peroxisome targeting. These findings have significantly advanced our knowledge of the PTS1 domain in plants and likely other eukaryotic species as well. The principles and strategies employed in the present study may also be applied to deciphering auxiliary targeting signals for other organelles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

V‐Groove Si Nanopatterning for the Direct Epitaxy of Orientation‐Patterned III–V Nonlinear Optical Crystals

Orientation‐patterned (OP) III–V semiconductors—used as quasi‐phase‐matched crystals for nonlinear optics applications—are typically epitaxially grown on expensive III–V substrates using a complex process requiring three separate epitaxy steps. In this work, a method is demonstrated for growing orientation‐patterned III–V crystals on Si substrates through V‐groove nanopatterning and a single epitaxial growth. V‐groove Si allows for suppression of random antiphase domain formation that is typical of III–V growth on (001)‐oriented Si through the use of (111)‐faceted trenches patterned on (001)‐oriented Si substrates. By alternating the directions of the V‐groove trenches between [110] and [10], antiphase boundaries can be selectively induced at the boundaries between the two directions of trenches due to the difference in symmetry between the III–V material and Si. This approach allows for a greatly simplified process for growing OP‐III‐Vs and more broadly is a new, robust approach for precisely patterning antiphase boundaries of arbitrary shape and length scales down to 50 nm.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Multi-Scale Temporal Patterns in Stream Biogeochemistry Indicate Linked Permafrost and Ecological Dynamics of Boreal Catchments

Temporal patterns in stream chemistry provide integrated signals describing the hydrological and ecological state of whole catchments. However, stream chemistry integrates multi-scale signals of processes occurring in both the catchment and stream. Deconvoluting these signals could identify mechanisms of solute transport and transformation and provide a basis for monitoring ecosystem change. Here, we applied trend analysis, wavelet decomposition, multivariate autoregressive state-space modeling, and analysis of concentration-discharge relationships to assess temporal patterns in high-frequency (15 min) stream chemistry from permafrost-influenced boreal catchments in Interior Alaska at diel, storm, and seasonal time scales. We compared catchments that varied in spatial extent of permafrost to identify characteristic biogeochemical signals. Catchments with higher spatial extents of permafrost were characterized by increasing nitrate concentration through the thaw season, an abrupt increase in nitrate and fluorescent dissolved organic matter (fDOM) and declining conductivity in late summer, and flushing of nitrate and fDOM during summer rainstorms. In contrast, these patterns were absent, of lower magnitude, or reversed in catchments with lower permafrost extent. Solute dynamics revealed a positive influence of permafrost on fDOM export and the role of shallow, seasonally dynamic flowpaths in delivering solutes from high-permafrost catchments to streams. Lower spatial extent of permafrost resulted in static delivery of nitrate and limited transport of fDOM to streams. Shifts in concentration-discharge relationships and seasonal trends in stream chemistry toward less temporally dynamic patterns might therefore indicate reorganized catchment hydrology and biogeochemistry due to permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns

Abstract This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties. In particular, image classification and regression studies are conducted by means of convolutional neural networks (CNNs) and NPs. First, the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, case studies are conducted with the prototype microelastic brittle (PMB), linear peridynamic solid (LPS), and viscoelastic solid (VES) models obtained by using the peridynamic theory. The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB, LBS, and VES models. Finally, a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns. The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs. The training results gradually improve, and the variance ranges decrease to less than 0.035. The main finding of this study is that the NPs enable accurate predictions, even with missing or insufficient training data. The results demonstrate that if the context points are set to the 10th, 100th, 300th, and 784th, the training information is deliberately omitted for the context points of the 10th, 100th, and 300th, and the predictions are different when the context points are significantly lower. However, the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs. Therefore, if the NPs are employed for training, the missing information of the training data can be supplemented to predict the results.

Mathematics↗
DOE OSTI2022

Netostat: analyzing dynamic flow patterns in high-speed networks

Understanding flow traffic patterns in networks, such as the Internet or service provider networks, is crucial to improving their design and building them robustly. However, as networks grow and become more complex, it is increasingly cumbersome and challenging to study how the many flow patterns, sizes and the continually changing source-destination pairs in the network evolve with time. Here, we present Netostat, a visualization-based network analysis tool that uses visual representation and a mathematics framework to study and capture flow patterns, using graph theoretical methods such as clustering, similarity and difference measures. Netostat generates an interactive graph of all traffic patterns in the network, to isolate key elements that can provide insights for traffic engineering. We present results for U.S. and European research networks, ESnet and GEANT, demonstrating network state changes, to identify major flow trends, potential points of failure, and bottlenecks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine learning for interpreting coherent X-ray speckle patterns

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Supply sunspots and shadows: Business siting patterns and inequitable rooftop solar adoption in the United States

Entrepreneurs in certain industries tend to locate new businesses in relatively affluent areas. Preferences to site businesses in affluent areas can reduce low-income household access to certain products. Some argue that business siting patterns could explain inequitable consumption patterns, though such causal claims are often empirically weak. Here, we explore whether business siting patterns partly explain inequitable adoption of rooftop solar photovoltaics in California. We show that solar business formation drives an immediate and sustained increase in local solar adoption, including in low-income areas. However, solar business siting patterns have only weak impacts on solar adoption equity. The data show how solar businesses headquartered in low-income areas nonetheless install solar for relatively affluent customers. Customer-level adoption inequity partly offsets the potential equity gains of siting more businesses in low-income areas. We discuss how the emergent nature and unique business model of rooftop solar help explain these nuanced results.

14 SOLAR ENERGY↗