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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of green-colour-emitting pyrotechnics as a core for 3D temperature imaging sensors inside coal boilers

Efficiency in the control operation of the boilers for coal and coal with biomass can be further improved if the flue gas temperature distribution can be better characterized. This is very difficult in these harsh environmental systems, where spatially resolved measurements are nearly impossible with solid-state sensors. Here, in this work, we evaluate the development of pyrotechnic compounds that would serve as the basis for a novel optical mapping of the temperature inside coal boilers. For this purpose, various green-colour-emitting pyrotechnics using BaCl 2 · 2H 2 O and Ba(NO 3 ) 2 as the green light source were prepared, as this colour offers a distinct signal from the combustion-based background in the boiler. These pyrotechnics were characterized using thermogravimetric analysis (TGA) and X-ray diffraction (XRD) and tested using a flat-flame burner. Furthermore, the composition was varied to evaluate the effect of different metal fuels such as Sn, Co, and Mg, as well as various binders such as ethylcellulose, shellac, parlon, and PVC on green light emission. The emission intensity and the apparent ignition temperature were strongly dependent on the metal type, with Mg showing higher intensities. On the other hand, the effect of the binder showed that the ignition behaviour, emission intensity, and spectral purity were influenced by the nature and exothermicity of the binder. The addition of other potential green light-producing materials, such as boric acid, increased the intensity of emission by 17% for a BaCl 2 · 2H 2 O-based composition. This study identified prospective compositions with intense and bright green-colour emissions that have high spectral purities.

42 ENGINEERING↗
DOE OSTI2020

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

The execution of agent-based simulations (ABSs) on hardware accelerator devices such as graphics processing units (GPUs) has been shown to offer great performance potentials. However, in heterogeneous hardware environments, it can become increasingly difficult to find viable partitions of the simulation and provide implementations for different hardware devices. To automate this process, we present OpenABLext, an extension to OpenABL, a model specification language for ABSs. By providing a device-aware OpenCL backend, OpenABLext enables the co-execution of ABS on heterogeneous hardware platforms consisting of central processing units, GPUs, and field programmable gate arrays (FPGAs).We present a novel online dispatching method that efficiently profiles partitions of the simulation during run-time to optimize the hardware assignment while using the profiling results to advance the simulation itself. In addition, OpenABLext features automated conflict resolution based on user-specified rules, supports graph-based simulation spaces, and utilizes an efficient neighbor search algorithm. We show the improved performance of OpenABLext and demonstrate the potential of FPGAs in the context of ABS. We illustrate how co-execution can be used to further lower execution times. OpenABLext can be seen as an enabler to tap the computing power of heterogeneous hardware platforms for ABS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Assessing data change in scientific datasets

Summary Scientific datasets are growing rapidly and becoming critical to next‐generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance. Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad‐hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real‐time (online) and offline analyses. We employ a variety of fast‐to‐compute numerical analyses, graphical data change representations, and more resource‐intensive recomputations of a subset of the data product. We illustrate the application of our approach using three scientific diverse use cases, namely, satellite, cosmological, and x‐ray data. The results show that a variety of data change metrics should be employed to enable a comprehensive representation and qualitative evaluation of data changes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Structure‐Aware Representation Learning for Effective Performance Prediction

ABSTRACT Application performance is a function of several unknowns stemming from the interactions between the application, runtime, OS, and underlying hardware, making it challenging to model performance using deep learning techniques, especially without a large labeled dataset. Collecting such labeled longitudinal datasets can take weeks. Intuitively, developers could save analysis time during code development by taking a comparative approach between multiple applications. However, the unknown dynamic interactions between applications and execution environments make it difficult for deep learning‐based models to predict the performance of new applications. In this paper, we address these problems by presenting a labeled dataset for the community and taking a comparative analysis approach to explore the source code differences between different correct implementations of the same problem. This paper assesses the feasibility of using purely static information, for example, Abstract Syntax Tree (AST), of applications to predict performance change based on code structure. We evaluate several deep learning‐based representation learning techniques for source code and propose an architecture for the tree‐based Long Short‐Term Memory (LSTM) models to discover latent representations for a source code's hierarchical structure. We demonstrate that our proposed architecture enables feed‐forward predictive models to predict change in performance using source code with up to 84% accuracy.

Ramadan, Tarek [Department of Computer Science Tex↗
DOE OSTI2024

Quantum optimization algorithms: Energetic implications

Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. Here in this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2x to 4x more energy efficient than their classical counterparts; why feedback-based quantum optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥2 0 x. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Volcano‐like Activity Trends in Au@Pd Catalysts: The Role of Pd Loading and Nanoparticle Size

The addition of palladium (Pd) to preformed gold nanoparticles (Au NPs) enables the formation of core‐shell structures with enhanced catalytic performance in oxidation reactions. However, predicting the precise palladium content required to achieve maximum catalytic activity remains difficult based on current understanding. Herein, Pd was systematically introduced onto titania‐supported Au NPs (2, 6, and 10 nm) to evaluate their performance in benzyl alcohol oxidation. A volcano‐like trend in catalytic activity was observed, where activity increased with Pd addition, peaked, and then declined. The Pd loading required for maximum activity depended on Au NP size: ≈40 at% Pd/Au for 2.6 nm, ≈20 at% Pd/Au for 6.4 nm, and ≈12.5 at% Pd/Au for 10.6 nm. For Au NPs > 6 nm, peak activity aligned with monolayer Pd coverage, while for smaller NPs (2–3 nm), optimal Pd content was below monolayer predictions. X‐ray absorption spectroscopy revealed a core‐shell structure at low Pd content, but higher Pd loadings led to Pd diffusion into the Au core. This structural transformation likely caused activity decline, indicating that AuPd alloying negatively impacts catalysis. These results highlight that core‐shell Au@Pd catalysts outperform AuPd alloys and provide crucial insights for designing highly active bimetallic catalysts.

X-ray absorption spectroscopy↗
DOE OSTI2022

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first‐responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro , hypoxia‐induced cardiac arrest in vivo , and opioid‐induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid‐induced respiratory depression, including those caused by newly emerging synthetic opioids.

Pharmacology & Pharmacy↗
DOE OSTI2024

Progress on Photo‐, Electro‐, and Photoelectro‐Catalytic Conversion of Recalcitrant Polyethylene, Polypropylene, and Polystyrene ‐ A Review

Abstract Recalcitrant waste plastics such a polyethylene, polypropylene, and polystyrene are difficult to recycle and are mostly disposed of in landfills and eventually leached into the environmental as micro‐ and nano‐plastics. This review explores how photo‐, electro‐, and combined photoelectro‐catalytic processes can assist in the degradation and upcycling of waste plastic into different chemicals and mitigate their release to the environment. In this work, we discuss how the different reaction mechanisms proceed, explore the current relevant literature, and highlight the developments needed to advance the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluation of Aliphatic Alcohols for CO 2 Capture Using the Characteristic Carbonate Frequency

Control over CO 2 capture and utilization are important scientific and technological challenges. Although a variety of amine absorbents are used for capture, releasing the captured CO 2 is often difficult and limits their recyclability. Therefore, it is crucial to control the strength of the CO 2 bond with the absorbent. Furthermore, it is desirable to use a method that can conveniently report the strength of this bond. This motivates exploring adducts of CO 2 with alcohols in the presence of a base, using vibrational spectroscopy to report on the bond strength. Although reactions of alcohols with CO 2 to form alkyl carbonates are known, a systematic study of these adducts has not been conducted. Here we show formation of alkyl carbonates by a series of alcohols spanning the pK a range 9.5 to 16.8. We show experimental and computational results for the frequency of the characteristic asymmetric stretch of the carbonate and demonstrate that it correlates inversely with the pK a of the alcohol. Based on computations of the bond lengths and previous work, we propose that this frequency also correlates inversely with the adduct strength. As a result, this work extends the scope of CO 2 capture reagents and inspires further research in tuning alcohols as reversible absorbents.

Alcohols↗
DOE OSTI2026

Evidence of Free-Bound Transitions in Warm Dense Matter and Their Impact on Equation-of-State Measurements

Warm dense matter (WDM) is now routinely created and probed in laboratories around the world, providing unprecedented insights into conditions achieved in stellar atmospheres, planetary interiors, and inertial confinement fusion experiments. However, the interpretation of these experiments is often filtered through models with systematic errors that are difficult to quantify. Due to the simultaneous presence of quantum degeneracy and thermal excitation, transitions in which free electrons are de-excited into thermally unoccupied bound states transferring momentum and energy to a scattered x-ray photon become viable. Here we show that such free-bound transitions are a particular feature of WDM and vanish in the limits of cold and hot temperatures. The inclusion of these transitions into the analysis of recent X-ray Thomson Scattering experiments on WDM at the National Ignition Facility and the Linac Coherent Light Source is required to obtain a physically consistent temperature from the Chihara decomposition. This interpretation is corroborated by agreement with a recently developed model-free thermometry technique and presents an important step for precisely characterizing and understanding the complex WDM state of matter.

Boehme, M [HZDR Dresden]↗
DOE OSTI2022

Nitrification, denitrification, and competition for soil N : Evaluation of two Earth System Models against observations

Abstract Earth System Models (ESMs) have implemented nitrogen (N) cycles to account for N limitation on terrestrial carbon uptake. However, representing inputs, losses, and recycling of N in ESMs is challenging. Here, we use global rates and ratios of key soil N fluxes, including nitrification, denitrification, mineralization, leaching, immobilization, and plant uptake (both NH 4 + and NO 3 − ), from the literature to evaluate the N cycles in the land model components of two ESMs. The two land models evaluated here, E3SM Land Model version 1 (ELMv1)‐ECA and CLM5.0, originated from a common model but have diverged in their representation of plant–microbe competition for soil N. The models predict similar global rates of gross primary productivity (GPP) but have approximately two‐fold to three‐fold differences in their underlying global mineralization, immobilization, plant N uptake, nitrification, and denitrification fluxes. Both models dramatically underestimate the immobilization of NO 3 − by soil bacteria compared with literature values and predict dominance of plant uptake by a single form of mineral nitrogen (NO 3 − for ELM, with regional exceptions, and NH 4 + for CLM5.0). CLM5.0 strongly underestimates the global ratio of gross nitrification:gross mineralization and both models are likely to substantially underestimate the ratio of nitrification:denitrification. Few experimental data exist to evaluate this last ratio, in part because nitrification and denitrification are quantified using different techniques and because denitrification fluxes are difficult to measure at all. More observational constraints on soil nitrogen fluxes such as nitrification and denitrification, as well as greater scrutiny of the functional impact of introducing separate NH 4 + and NO 3 − pools into ESMs, could help to improve confidence in present and future simulations of N limitation on the carbon cycle.

Nevison, Cynthia↗
DOE OSTI2023

Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem

Abstract Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface‐level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface‐ and shallow‐foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator–prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface‐level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species‐ and community‐level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub‐mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Seasonal grassland productivity forecast for the U.S. Great Plains using Grass–Cast

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge —trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season above ground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in larger scale decision-making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-of growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass-Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Drought legacies mediated by trait trade‐offs in soil microbiomes

Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Trade-offs of forest management scenarios on forest carbon exchange and threatened and endangered species habitat

Management of forest lands under climate warming poses challenges to managers, some of which are difficult to predict. Examining the trade-offs associated with forest stewardship choices is essential to avoid consequences with loss of natural capital. We utilized LANDIS-II process model simulations for three forested sites located in disparate parts of the United States with the purpose of understanding the trade-offs imposed by management choices under climate warming and associated wildfire. There were only small trade-offs that emerged from the simulations, between habitat area for threatened and endangered species (TES), net ecosystem exchange for CO2 (NEE), and risk of wildfire. Stand management in the form of thinning and prescribed burning typically increased NEE while simultaneously increasing habitat for TES, while reducing the risk of wildfire. These benefits were also observed under a climate warming scenario; however, the benefits were greatly outweighed by the negative impacts f warming on both TES habitat and NEE. Balancing these ecosystem services via thinning and burning treatments is a strategic approach to mitigate risks of wildfire both currently and under a warming future climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling Heterogeneity in UO 2 Nanoparticles Using X‐ray Absorption Spectroscopy

Abstract EXAFS provides the capability to interrogate nanoparticle (NP) structure in atomistic detail without relying on long‐range crystallinity. There is a limitation in that EXAFS provides averaged structural information, making it difficult to separate a small amount of heterogeneous structure from bulk. In this work, models were developed to extract surface‐specific information from conventional EXAFS measurements collected on UO 2 NPs of varying size. Specifically, the surface terminating species of UO 2 NPs was determined from comparison of coordination numbers with geometric models while the origin of static disorder was interrogated from user‐defined simulations. Results show that the degree of oxygenation on the NP surface does not significantly deviate from bulk surface and that static disorder is highly enhanced in NP surface layers but cannot be attributed to surface relaxation effects alone. The approach described herein has the potential to be adapted to a range of inorganic NP systems to interrogate surface structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗