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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multifunctional fiber-reinforced composites for passive sensing and energy harvesting with enhanced mechanical performance

Fiber-reinforced polymer composites (FRPCs) are valid alternatives to metals, especially in the aerospace industry, for their superior strength-to-weight ratio. FRPCs are generally manufactured by stacking layers of fibers and infusing them with epoxy-based adhesives to result in laminates. Such layer-by-layer structure often ensues a poor interlaminar property of the composites. Additionally, FRPCs can sustain complex damage modes because of their inherent anisotropic characteristics. For example, delamination can occur due to low-velocity impact at a subsurface level, which can result in premature catastrophic failures if undetected. Thus, structural monitoring is crucial to identify such damage. Point-based sensors (e.g., strain gauges, accelerometers, among many) can be embedded in the FRPCs for structural monitoring. However, their electrical power demand often embroils their usage. Therefore, the main objective of this research is to design and implement multifunctional composites that can simultaneously perform passive sensing and energy harvesting while exhibiting better mechanical performance. Previous research efforts have demonstrated that a continuous feed-through deposition of functional materials on fiber surfaces simultaneously enhanced the mechanical and sensing properties of the FRPCs. This study explores a similar approach to encode passive sensing and energy harvesting properties in the FRPCs by integrating ferroelectric microparticles on the fiber surfaces. Upon ensuring their superior interlaminar shear strength, sensing and energy harvesting properties were characterized through experimental studies. The outcome is a multifunctional composite fabricated by coating the fibers with functional microparticles through a high throughput, scalable, and low-cost approach that enables passive sensing, energy harvesting, and improved mechanical performance.

Gupta, Sumit↗
DOE OSTI2021

Hierarchical crack buffering triples ductility in eutectic herringbone high-entropy alloys

In human-made malleable materials, microdamage such as cracking usually limits material lifetime. Some biological composites, such as bone, have hierarchical microstructures that tolerate cracks but cannot withstand high elongation. Here, we demonstrate a directionally solidified eutectic high-entropy alloy (EHEA) that successfully reconciles crack tolerance and high elongation. The solidified alloy has a hierarchically organized herringbone structure that enables bionic-inspired hierarchical crack buffering. This effect guides stable, persistent crystallographic nucleation and growth of multiple microcracks in abundant poor-deformability microstructures. Hierarchical buffering by adjacent dynamic strain–hardened features helps the cracks to avoid catastrophic growth and percolation. Our self-buffering herringbone material yields an ultrahigh uniform tensile elongation (~50%), three times that of conventional nonbuffering EHEAs, without sacrificing strength.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Save the Mekong Delta from drowning

Owing to only a few decades of human influence and unsustainable management of the Mekong River basin’s natural resources, the Mekong Delta is receding rapidly. Most of the delta landform, home to 17 million people and an economic powerhouse, could slip below sea level by 2100. Avoiding such a catastrophic impact will require concerted actions that acknowledge root causes for land loss and the global importance of the delta landform. Deltas persist and grow if sediment supply from an upstream river basin builds delta land at the same or greater rates than land is submerged by relative sea level rise and erosion. Further, with more rapid sea level rise, more sediment resources are needed to maintain the current extent of the delta. Only improved coordination of governance and investments, informed by science, will provide the delta with those critical resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Chemically recyclable polyolefin-like multiblock polymers

Polyolefins are the most important and largest volume plastics produced. Unfortunately, the enormous use of plastics and lack of effective disposal or recycling options have created a plastic waste catastrophe. Here in this work, we report an approach to create chemically recyclable polyolefin-like materials with diverse mechanical properties through the construction of multiblock polymers from hard and soft oligomeric building blocks synthesized with ruthenium-mediated ring-opening metathesis polymerization of cyclooctenes. The multiblock polymers exhibit broad mechanical properties, spanning elastomers to plastomers to thermoplastics, while integrating a high melting transition temperature (T m ) and low glass transition temperature (T g ), making them suitable for use across diverse applications (T m as high as 128°C and T g as low as –60°C). After use, the different plastics can be combined and efficiently deconstructed back to the fundamental hard and soft building blocks for separation and repolymerization to realize a closed-loop recycling process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Why even source-free gravity must be quantized

I note, on the most elementary grounds: its classical ultraviolet catastrophe, that even source-free General Relativity must be quantized, despite some eminent opinions to the contrary and with a little fine print.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

From carbon to climate action: harnessing basalt for geological carbon dioxide (CO 2 ) storage onshore and offshore

Abating emissions and reducing atmospheric concentrations of carbon dioxide (CO 2 ) are crucial in avoiding catastrophic climate change. Basalt reservoirs offer suitable CO 2 storage sites and are present onshore and offshore. This study compares the geological, economic and social aspects of CO 2 storage in both domains using two basalt formations from the Republic of Ireland: the Limerick Igneous Suite and the Druid Formation. Offshore reservoirs offer larger storage resources but are more expensive to develop. Nevertheless, both offer gigatonne-scale theoretical CO 2 storage. Reviewing public perception studies on wind energy demonstrates that the commonly held belief that offshore projects face less opposition owing to their distance from communities has been dispelled, while similar studies on carbon capture and removal show no preference for onshore or offshore development. Instead, a general scepticism towards carbon removal is observed, with concerns that it hinders broader decarbonization efforts. The desire of all stakeholders to be involved throughout project development is consistently observed. Lessons from offshore natural gas projects highlight the consequences of failing to engage with local communities. The Republic of Ireland has significant potential to deploy geological CO 2 storage in basalt reservoirs, but policy hurdles such as legal limits on storage volumes must be overcome to reconcile climate ambition and legislative capacity.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2021

Can Applications Recover from fsync Failures?

We analyze how file systems and modern data-intensive applications react to fsync failures. First, we characterize how three Linux file systems (ext4, XFS, Btrfs) behave in the presence of failures. We find commonalities across file systems (pages are always marked clean, certain block writes always lead to unavailability) as well as differences (page content and failure reporting is varied). Next, we study how five widely used applications (PostgreSQL, LMDB, LevelDB, SQLite, Redis) handle fsync failures. Our findings show that although applications use many failure-handling strategies, none are sufficient: fsync failures can cause catastrophic outcomes such as data loss and corruption. Our findings have strong implications for the design of file systems and applications that intend to provide strong durability guarantees.

Computer Science↗
DOE OSTI2022

Large Scale Caching and Streaming of Training Data for Online Deep Learning

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples and the streaming I/O patterns for which traditional HPC data storage (e.g., parallel file systems) are designed. In addition, as more data are obtained, it is feasible neither simply to train learning models incrementally, due to catastrophic forgetting (i.e., bias towards new samples), nor to train frequently from scratch, due to prohibitive time and/or resource constraints. In this paper, we study data management techniques that combine caching and streaming with rehearsal support in order to enable efficient access to training samples in both offline training and continual learning. We revisit state-of-art streaming approaches based on data pipelines that transparently handle prefetching, caching, shuffling, and data augmentation, and discuss the challenges and opportunities that arise when combining these methods with data-parallel training techniques. We also report on preliminary experiments that evaluate the I/O overheads involved in accessing the training samples from a parallel file system (PFS) under several concurrency scenarios, highlighting the impact of the PFS on the design of the data pipelines.

data pipelines↗
DOE OSTI2020

Model-Instructed Design of Novel Charging Protocols for the Extreme Fast Charging of Lithium-Ion Batteries Without Lithium Plating

Relatively long recharge time of high-energy density lithium-ion batteries (LIBs) is one of the major obstacles to the widespread deployment of electric vehicles (EVs). Excessive fast charge of LIBs can cause severe safety issues, the most significant of which is lithium plating. In addition to the consumption of cyclable lithium, dendrite-like lithium plating can cause short-circuiting within the cell, which could lead to catastrophic failure. In addition to the developments of advanced electrolytes, active materials and electrodes, the fast charging capability of LIBs can be improved by using optimized charging protocol. In this paper, the Pseudo2D battery model is first used to screen some conventional charging protocols proposed for fast charging, including the constant-current constant-voltage charging (CCCV), constant-power constant-voltage charging (CPCV), multi-stage constant-current charging, and pulse current charging. A semi-optimal charging protocol is then developed by automatically modifying the charge current such that the cell is charged at close-to-maximum current without lithium plating. Based on the features of the semi-optimal protocol, two practical novel fast charging protocols are proposed which allow charging the cell at a higher rate without lithium plating. Through simulation, we show that the no-plating capacity after 10 min charge can be improved by up to ~10.7% compared to CCCV charging for a 2.5 mAh cm -2 cell and ~18.8% for a 4 mAh cm -2 cell, when the proposed charging protocols are used.

25 ENERGY STORAGE↗
DOE OSTI2022

A Comprehensive Numerical and Experimental Study for the Passive Thermal Management in Battery Modules and Packs

Cooling plates in battery packs of electric vehicles play critical roles in passive thermal management systems to reduce risks of catastrophic thermal runaway. In this work, a series of numerical simulations and experiments are carried out to unveil the role of cooling plates (both between cells and a bottom plate parallel to the cell stack) on the thermal behavior of battery modules and packs under nail penetrations. First, we investigated the role of side cooling plates on the thermal runaway propagation mitigation in battery modules (1S3P) and packs (3S3P) by varying the key parameters of the side cooling plates, such as plate thicknesses, thermal contact resistances, and materials. Then, three important factors for passive thermal management systems are identified: (i) thermal mass of side cooling plates, (ii) interfacial thermal contact resistances, and (iii) the effective heat transfer coefficients at exterior surfaces. The roles of bottom cooling plates on thermal runaway propagation mitigation in 1S3P and 1S5P battery modules are numerically investigated by comparing the thermal behavior of the modules with only side cooling plates and with both side and bottom cooling plates.

25 ENERGY STORAGE↗
DOE OSTI2024

Early Detection of Li-Ion Battery Thermal Runaway Using Commercial Diagnostic Technologies

The rate of electric vehicle (EV) adoption, powered by the Li-ion battery, has grown exponentially; largely driven by technological advancements, consumer demand, and global initiatives to reduce carbon emissions. As a result, it is imperative to understand the state of stability (SoS) of the cells inside an EV battery pack. That understanding will enable the warning of or prevention against catastrophic failures that can lead to serious injury or even, loss of life. The present work explores rapid electrochemical impedance spectroscopy (EIS) coupled with gas sensing technology as diagnostics to monitor cells and packs for failure markers. These failure markers can then be used for onboard assessment of SoS. Experimental results explore key changes in single cells and packs undergoing thermal or electrical abuse. Rapid EIS showed longer warning times, followed by VOC sensors, and then H 2 sensors. While rapid EIS gives the longest warning time, with the failure marker often appearing before the cell vents, the reliability of identifying impedance changes in single cells within a pack decreases as the pack complexity increases. This provides empirical evidence to support the significant role that cell packaging and battery engineering intricacies play in monitoring the SoS.

25 ENERGY STORAGE↗
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2023

NED_Asteroid_Energy_Deposition

In the event of a potentially catastrophic asteroid impact, with sufficient warning time, deploying a nuclear device remains a powerful option for planetary defense if a kinetic impactor proves insufficient. Predicting the effectiveness of a potential nuclear deflection or disruption mission depends on accurate multiphysics simulations of the device's x-ray energy deposition into the asteroid and the resulting material ablation. These simulations span many orders of magnitude, require a variety of different complex physics packages, and are computationally expensive. Having an efficient and accurate way of modeling this system is necessary for exploring a mission's sensitivity to the asteroid's range of physical properties. To expedite future simulations, we present a completed x-ray energy deposition model developed using the radiation-hydrodynamics code Kull which can be used to initiate a nuclear mitigation mission hydrocode. The model spans a wide variety of possible mission initial conditions: four different asteroid-like materials (Silicon Dioxide, Forsterite, Iron, and Ice), two different source spectra (1 and 2 keV blackbodies), and then a broad range of radiation fluences (0.0001 to 1 kt per square meter), source durations (10 to 100 ns), and asteroid porosities (0 to 80 percent). Using blowoff momentum as the primary metric, the modelinitiated simulation results match the full radiation-hydrodynamics results to within 10 percent. Please reference the journal article: Burkey et al., X-Ray Energy Deposition Model for Simulating Asteroid Response to a Nuclear Planetary Defense Mitigation Mission, Planetary Science Journal, (2023) for more information.

Managan, RobertA↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (T b ) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) T b –rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Record-Breaking Atmospheric River Drives April 2024 Extreme Precipitation in the United Arab Emirates and the Surrounding Gulf Region

In mid-April 2024, the United Arab Emirates (UAE) and the surrounding Gulf region experienced unprecedented rainfall and catastrophic flooding, causing widespread damage, loss of life, and significant economic costs. During the 3-day period from 15 to 17 April, rainfall in the UAE exceeded 100 mm in the hardest-hit areas, with more than 170% of the typical annual precipitation recorded in just 72 h. The event was associated with exceptionally strong integrated water vapor transport (IVT), driven by a persistent low pressure system and a focused corridor of moisture transport. This environment, combined with favorable dynamical and convective conditions, triggered intense thunderstorms and widespread flooding. This article examines the role of an atmospheric river (AR) in the April 2024 extreme precipitation event, emphasizing the contribution of extreme IVT to preconditioning and amplifying the heavy rainfall. While this event has previously been described primarily in terms of a mesoscale convective system (MCS) and potential vorticity (PV) streamers, here we document and illustrate its close relationship to a record-breaking moisture transport corridor. This AR-based perspective highlights how large-scale moisture-transport frameworks can complement synoptic and mesoscale analyses in understanding extreme rainfall in arid and semiarid regions such as the UAE and the Gulf. The event serves as a stark example of the vulnerabilities faced by arid regions, where atmospheric conditions conducive to extreme flooding may become more frequent in the future.

Massoud, Elias [ORNL] (ORCID:0000000217725361)↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Validation of a lightning protection system for a fusion-welded thermoplastic composite wind turbine blade tip

Fusion welding for thermoplastic composite wind turbine blades is being investigated as a replacement for adhesives in bond lines to reduce manufacturing time and cost and to promote ease of recyclability. Resistance welding uses an electrically conductive material, such as carbon fiber, embedded in the bond line to heat the joining surfaces and fuse them together under pressure. One main challenge with fusion joining for large wind blades is the ability to use this conductive heating element in the bond line while still protecting the blade from the catastrophic effects of a lightning strike. This paper outlines the design and manufacturing of a lightning protection system for a 5-m-long thermally welded blade tip. The outcomes of high voltage and high current lightning testing applied to the 5 m blade tip show that the proposed design was able to protect the fusion welded bond lines from significant lightning damage.

17 WIND ENERGY↗