Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Extreme scale”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Variability and Extremes of Precipitation in the Global Climate as Determined by the 25-Year GEWEX/GPCP Data Set

The Global Precipitation Climatology Project (GPCP) 25-year precipitation data set is used to evaluate the variability and extremes on global and regional scales. The variability of precipitation year-to-year is evaluated in relation to the overall lack of a significant global trend and to climate events such as ENSO and volcanic eruptions. The validity of conclusions and limitations of the data set are checked by comparison with independent data sets (e.g., TRMM). The GPCP data set necessarily has a heterogeneous time series of input data sources, so part of the assessment described above is to test the initial results for potential influence by major data boundaries in the record. Regional trends, or inter-decadal changes, are also analyzed to determine validity and correlation with other long-term data sets related to the hydrological cycle (e.g., clouds and ocean surface fluxes). Statistics of extremes (both wet and dry) are analyzed at the monthly time scale for the 25 years. A preliminary result of increasing frequency of extreme monthly values will be a focus to determine validity. Daily values for an eight-year are also examined for variation in extremes and compared to the longer monthly-based study.

Adler, R. F.↗
DOE OSTI2023

Annealing in Argon Universally Upgrades the Na‐Storage Performance of Mn‐Based Layered Oxide Cathodes by Creating Bulk Oxygen Vacancies

Abstract Manganese‐rich layered oxide cathodes of sodium‐ion batteries (SIBs) are extremely promising for large‐scale energy storage owing to their high capacities and cost effectiveness, while the Jahn–Teller (J–T) distortion and low operating potential of Mn redox largely hinder their practical applications. Herein, we reveal that annealing in argon rather than conventional air is a universal strategy to comprehensively upgrade the Na‐storage performance of Mn‐based oxide cathodes. Bulk oxygen vacancies are introduced via this method, leading to reduced Mn valence, lowered Mn 3 d‐ orbital energy level, and formation of the new‐concept Mn domains. As a result, the energy density of the model P2‐Na 0.75 Mg 0.25 Mn 0.75 O 2 cathode increases by ≈50 % benefiting from the improved specific capacity and operating potential of Mn redox. The Mn domains can disrupt the cooperative J–T distortion, greatly promoting the cycling stability. This exciting finding opens a new avenue towards high‐performance Mn‐based oxide cathodes for SIBs.

Chemistry↗
DOE OSTI2023

Understanding and Controlling Photothermal Responses in MXenes

MXenes have the potential for efficient light-to-heat conversion in photothermal applications. To effectively utilize MXenes in such applications, it is important to understand the underlying nonequilibrium processes, including electron–phonon and phonon–phonon couplings. Here, we use transient electron and X-ray diffraction to investigate the heating and cooling of photoexcited MXenes at femtosecond to nanosecond time scales. Our results show extremely strong electron–phonon coupling in Ti 3 C 2 -based MXenes, resulting in lattice heating within a few hundred femtoseconds. We also systematically study heat dissipation in MXenes with varying film thicknesses, chemical surface terminations, flake sizes, and annealing conditions. We find that the thermal boundary conductance (TBC) governs the thermal relaxation in films thinner than the optical penetration depth. In conclusion, we achieve a 2-fold enhancement of the TBC, reaching 20 MW m –2 K –1 , by controlling the flake size or chemical surface termination, which is promising for engineering heat dissipation in photothermal and thermoelectric applications of the MXenes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Development of the Senseiver for efficient field reconstruction from sparse observations

Abstract The reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly nonlinear phenomena. While numerical simulations can model some of these phenomena using partial differential equations, the reconstruction problem is ill-posed. Data-driven-strategies provide crucial disambiguation, but these suffer in cases with small amounts of data, and struggle to handle large domains. Here we present the Senseiver, an attention-based framework that excels in reconstructing complex spatial fields from few observations with low overhead. The Senseiver reconstructs n -dimensional fields by encoding arbitrarily sized sparse sets of inputs into a latent space using cross-attention, producing uniform-sized outputs regardless of the number of observations. This allows efficient inference by decoding only a sparse set of output observations, while a dense set of observations is needed to train. This framework enables training of data with complex boundary conditions and extremely large fine-scale simulations. We build on the Perceiver IO by enabling training models with fewer parameters, which facilitates field deployment, and a training framework that allows a flexible number of sensors as input, which is critical for real-world applications. We show that the Senseiver advances the state-of-the-art of field reconstruction in many applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Forecasting for the Weather Driven Energy System - A New Task under IEA Wind

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled "Foreacsting for the Weather Drive Energy System") the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.

geophysics computing↗
DOE OSTI2023

An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City

In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning are required to generate good forecasts for bikeshare demand. Here, this study proposes a machine learning modeling framework to estimate hourly demand in a large-scale bikesharing system. Two Extreme Gradient Boosting models were developed: one using data from before the COVID-19 pandemic (March 2019 to February 2020) and the other using data from during the pandemic (March 2020 to February 2021). Furthermore, a model interpretation framework based on SHapley Additive exPlanations was implemented. Based on the relative importance of the explanatory variables considered in this study, share of female users and hour of day were the two most important explanatory variables in both models. However, the month variable had higher importance in the pandemic model than in the pre-pandemic model.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

A Review of Machine Learning Applications in Power System Resilience

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Deep Learning↗
DOE OSTI2024

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth’s surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

Science & Technology - Other Topics↗
DOE OSTI2022

Incoherent range-based holography of far-distant objects using LIDAR

Los Alamos National Laboratory has developed an incoherent, long-range, sub-centimeter resolution (LIDAR) with which we achieve centimeter-scale reflection holography at extremely long ranges. The system consists of a pulsed laser and photon-counting receiver. This combination yields round-trip time of flight data to illuminate parts of the object of interest. The aggregation of these data for many LIDAR pulses yields a plot with a range on the X axis and reflectance on the Y axis, which we refer to as a range profile. Observing that the range profile is a projection of the reflection map of the object onto the view vector, we collect profiles from a variety of viewing angles and invert these data to form an image. We adapt imaging algorithms from the field of computer aided tomography to suit our application and present results from imaging demonstrations at a 10 km range.

Hoffmann, Mitchell P. (ORCID:0000000170551324)↗
DOE OSTI2021

Automation is all you need: Faster Earth system models with AI/ML

Focal Area: Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI) and advanced methods. Science Challenge: Tropical cyclones can in- duce extreme water cycle events through dramatic precipitation and storm surge. More reliable models of intensity will translate into better prediction of the impact of extreme events in large scale Earth systems simulations. We demonstrate and describe AI/ML methodologies for rapid assimilation of new, in situ data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

All-optical, Microwave-free NV-Diamond Magnetometer for High Magnetic Field Environment

Nitrogen vacancy (NV) magnetometry has commonly utilized microwave sources to manipulate the sensor's spin state. However, this technique becomes technically challenging under an environment with a Tesla-scale magnetic field or extreme conditions that prohibit the usage of microwave. By leveraging the effect that an off-axis magnetic field to the NV axis quenches its emitted photoluminescence, we present an all-optical, microwave-free broadband quantum diamond magnetometer. We demonstrate magnetic sensing over an extended bias magnetic field range — beyond the ground-state level anti-crossing (GSLAC) region — with a more integrated design.

Zheng, Xiechen↗
DOE OSTI2023

Extreme Decentralized Water Treatment: Exploring the Future of Premise-Scale Water Treatment and Reuse

Access to an adequate quantity of piped water and management of wastewater produced in homes and businesses is an expectation of city dwellers in wealthy countries, and an aspiration for many people living in rapidly developing cities in low- and middle-income countries. It is also crucial to public health and protection of the environment. For well over a century, municipal drinking water provision and wastewater management have been made possible by large investments in centralized systems in which fresh water passes through a small number of drinking water treatment plants before being distributed through a vast underground pipe network to buildings throughout the city (Sedlak 2014). After it is used, wastewater is collected in underground sewers that route it to treatment plants prior to its discharge to the environment. In some water-stressed cities, a fraction of the treated wastewater undergoes additional treatment prior to reuse. This recycled water often is returned to users through another dedicated water distribution system, which is designated in many places with purple pipes (non-potable water reuse). Alternatively, treated wastewater may be subjected to advanced treatment (e.g., reverse osmosis followed by advanced oxidation) prior to being returned to the drinking water supply (potable water reuse).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Characterizing Long Island’s Extreme Precipitation and Its Relationship to Tropical Cyclones

Since extreme precipitation impacts society on small scales (i.e., a few kilometers and smaller, it is worthwhile to explore extreme precipitation trends in localized regions, such as Long Island (LI), New York. Its coastal location makes it vulnerable to various extreme events, such as tropical cyclones (TCs). This work aimed to quantify the extreme precipitation events on LI that are caused by TCs, as well as the percentage of TCs passing close to LI that cause extreme precipitation events. Both gauge-based and satellite-based precipitation datasets of varying resolutions (DAYMET, IMERG, and CPC) were used to understand the impact of dataset selection. Results are shown for the common time period of 2001–2020, as well as the full time periods of each dataset. DAYMET shows the highest percentage of extreme precipitation events linked to TCs for 2001–2020 (a maximum of 7.2%) and the highest number of TCs that caused extreme precipitation events (36.5%), with IMERG showing similar results. For the full and common time periods, the highest percentage of extreme precipitation events caused by TCs was found in eastern LI. TC-related extreme precipitation averaged over LI varied year to year, and amounts were dependent on the resolution of the observational dataset, but most datasets showed an increasing trend in the last 19 years that is larger than the trend in mean precipitation. Current infrastructure in the region is likely inadequately prepared for future impacts from TC-related extreme precipitation events in such a population-dense region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8×10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid-cell areas, super-resolve lognormally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high-resolution simulations and ensembles at a fraction of the computational cost.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Tunneling in thin MOS structures

Recent results on tunneling in thin MOS structures are described. Thermally grown SiO2 films in the thickness range of 22-40 A have been shown to be effectively uniform on an atomic scale and exhibit an extremely abrupt oxide-silicon interface. Resonant reflections are observed at this interface for Fowler-Nordheim tunneling and are shown to agree with the exact theory for a trapezoidal barrier. Tunneling at lower fields is consistent with elastic tunneling into the silicon direct conduction band and, at still lower fields, inelastic tunneling into the indirect conduction band. Approximate dispersion relations are obtained over portions of the silicon-dioxide energy gap and conduction band.

Maserjian, J.↗
NASA NTRS1978

Arecibo radar observations of Martian surface characteristics near the equator

Mars radar observations at 12.6-cm wavelength indicate that many of what were potential Viking landing sites along the planet's equator are rougher than interpretations of Mariner 9 images suggested. Root mean square surface slopes are typically 5 degrees in the region bounded by 160 and 200 degrees W, 0 and 12 degrees S. From Tharsis Montes west to 160 degrees W, radar-scattering characteristics suggest extreme roughness on small scales, perhaps exceeding 10 degrees in rms magnitude. East of Tharsis and north of Valles Marineris the surface is smooth, with values of rms slope as low as 1 degree; the elevation of this plateau was too high for a Viking landing. Study of spectral shapes indicates the Hagfors scattering law remains the best descriptor of quasi-specular surface scattering properties in an average sense; widespread variations in the surface argue against its indiscriminate use, however. Backscattering at moderate (25-40 degrees) incidence angles was studied qualitatively and was found to be significantly above the level predicted by a strictly quasi-specular (e.g., Hagfors) process; it also is variable over the surface.

Simpson, R. A.↗
NASA NTRS1987

On the role of convective motion during dendrite growth: Experiments under variable gravity, revised

Experiments show the effect of self induced convection on individual dendrite growth in uniformly supercooled samples and solidification of the resulting mush under conditions of high and low g. Convection is visualized by a Schlieren optical system or a Mach Zender interferometer. For ice crystals growing from the vapor in air, a slight reduction in linear growth rate occur under low g. For ice crystals growing from NaCl solution, dendrite tip velocities are unchanged, but subsequent mush solidification is enhanced through drainage channels under higher g. By contrast, sodium sulfate decahydrate dendrites growing from solution produce convective plumes which lead to higher tip growth rate only as the crystal growth direction approaches that of gravity. Convective plumes are laminar for small crystals under conditions of these experiments; the rise velocity of such plumes is greater than individual vortex rings under identical conditions. Convection effects are only present in solution under a critical supercooling less than about 5 C for sodium sulfate and 2 C for ice in NaCl since at higher supercooling the crystallization velocity, proportional to the square of the supercooling, exceeds the convective velocity, proportional to the square root of the supercooling. The role of convective velocity in bulk solidification is to give a large scale flow which under extreme cases may lead to extensive secondary crystal production, which alters the resulting crystal texture of the completely solidified melt.

Hallett, J.↗