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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Heterogeneity‐enhanced gas phase formation in shallow aquifers during leakage of CO 2 ‐saturated water from geologic sequestration sites

Abstract A primary concern for geologic carbon storage is the potential for leakage of stored carbon dioxide (CO 2 ) into the shallow subsurface where it could degrade the quality of groundwater and surface water. In order to predict and mitigate the potentially negative impacts of CO 2 leakage, it is important to understand the physical processes that CO 2 will undergo as it moves through naturally heterogeneous porous media formations. Previous studies have shown that heterogeneity can enhance the evolution of gas phase CO 2 in some cases, but the conditions under which this occurs have not yet been quantitatively defined, nor tested through laboratory experiments. This study quantitatively investigates the effects of geologic heterogeneity on the process of gas phase CO 2 evolution in shallow aquifers through an extensive set of experiments conducted in a column that was packed with layers of various test sands. Soil moisture sensors were utilized to observe the formation of gas phase near the porous media interfaces. Results indicate that the conditions under which heterogeneity controls gas phase evolution can be successfully predicted through analysis of simple parameters, including the dissolved CO 2 concentration in the flowing water, the distance between the heterogeneity and the leakage location, and some fundamental properties of the porous media. Results also show that interfaces where a less permeable material overlies a more permeable material affect gas phase evolution more significantly than interfaces with the opposite layering.

Plampin, Michelle R.↗
DOE OSTI2024

Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data

ABSTRACT In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests.

Desai, Ronak↗
DOE OSTI2022

Transverse Λ polarization in e + e – processes within a TMD factorization approach and the polarizing fragmentation function

We perform a re-analysis of Belle data for the transverse Λ and Λ¯ polarization in e + e – annihilation processes within a transverse momentum dependent (TMD) factorization approach. We consider two data sets, one referring to the associated production of Λ’s with a light unpolarized hadron in an almost back-to-back configuration, and one for the inclusive Λ production, with the reconstruction of the thrust axis. We adopt the Collins-Soper-Sterman framework and employ the recent formulation on the factorization of single-inclusive hadron production. This extends a previous phenomenological study carried out in a more simplified TMD approach, leading to a new extraction of the polarizing fragmentation function (FF). While confirming several features of the previous analysis, here we include the proper QCD scale dependence of this TMD FF and carefully exploit the role of its nonperturbative component. Here, the compatibility, within a unique TMD factorization scheme, of double and single-inclusive hadron production is discussed in detail. Moreover, we consider another data set for inclusive Λ production, at much larger energies, from the OPAL Collaboration, in order to test the consistency of the entire approach. Finally, we elaborate on some fundamental issues like the role of SU(2) isospin symmetry and the heavy quark contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Organizing environmental flow frameworks to meet hydropower mitigation needs

The global recognition of the importance of natural flow regimes to sustain the ecological integrity of river systems has led to increased societal pressure on the hydropower industry to change plant operations to improve downstream aquatic ecosystems. However, a complete reinstatement of natural flow regimes is often unrealistic when balancing water needs for ecosystems, energy production, and other human uses. Thus, stakeholders must identify a prioritized subset of flow prescriptions that meet ecological objectives in light of realistic constraints. Yet, isolating aspects of flow regimes to restore downstream of hydropower facilities is among the greatest challenges of environmental flow science due, in part, to the sheer volume of available environmental flow tools in conjunction with complex negotiation-based regulatory procedures. Here, we propose an organizational framework that structures information and existing flow paradigms into a staged process that assists stakeholders in implementing environmental flows for hydropower facilities. The framework identifies areas where regulations fall short of the needed scientific process, and provide suggestions for stakeholders to ameliorate those situations through advanced preparation. We highlight the strengths of existing flow paradigms in their application to hydropower settings and suggest when and where tools are most applicable. In conclusion, our suggested framework increases the effectiveness and efficiency of the e-flow implementation process by rapidly establishing a knowledge base and decreasing uncertainty so more time can be devoted to filling knowledge gaps. As a result, the framework provides the structure for a coordinated research agenda to further the science of environmental flows related to hydropower environments.

13 HYDRO ENERGY↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Iron isotope fractionation during pyrite formation in a sulfidic Precambrian ocean analogue

We present that the chemical response of the Precambrian oceans to rising atmospheric O 2 levels remains controversial. The iron isotope signature of sedimentary pyrite is widely used to trace the microbial and redox states of the ocean, yet the iron isotope fractionation accompanying pyrite formation in nature is difficult to constrain due to the complexity of the pyrite formation process, difficulties in translating the iron isotope systematics of experimental studies to natural settings, and insufficient iron isotope datasets for natural euxinic (i.e. anoxic and sulfidic) marine basins where pyrite formation occurs. Herein we demonstrate, that a large, permil-level shift in the isotope composition of dissolved iron occurs in the Black Sea euxinic water column during syngenetic pyrite formation. Specifically, iron removal to syngenetic pyrite gives rise to an iron isotope fractionation factor between Fe(II) and FeS 2 of 2.75 permil (‰), the largest yet reported for reactions under natural conditions that do not involve iron redox chemistry. These iron isotope systematics offer the potential to generate permil-level shifts in the sedimentary pyrite iron isotope record due to partial drawdown of the oceanic iron inventory. The implication is that the iron stable isotope signatures of sedimentary pyrites may record fundamental regime shifts between pyrite formation under sulfur-limited conditions and pyrite formation under iron-limited conditions. To this end, the iron isotope signatures of sedimentary pyrite may best represent the extent of euxinia in the past global ocean, rather than its oxygenation state. On this basis, the reinterpreted sedimentary pyrite Fe isotope record suggests a fundamental shift towards more sulfidic oceanic conditions coincident with the ‘Great Oxidation Event’ around 2.3 billion years ago. Importantly, this does not require the chemical state of the ocean to shift from mainly de-oxygenated to predominantly oxygenated in parallel with the permanent rise in atmospheric oxygen, contrary to other interpretations based on iron isotope systematics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Aberrant FBXW7-mediated ubiquitination and degradation of ZMYND8 enhances tumor progression and stemness in bladder cancer

ZMYND8, an epigenetic regulator, was identified as a common oncogene across various tumors. However, little was reported about the association between ZMYND8 and bladder cancer. Besides, aberrant mechanisms that contribute to abnormal ZMYND8 expressions still remain unclear. In the current study, we first found that ZMYND8 protein levels were significantly elevated in Bca samples versus normal tissues, but not the mRNA levels. We then utilized the Cell Counting Kit-8 (CCK-8) assay, clone formation assay and transwell analysis to confirm that ZMYND8 could remarkably promote the tumor progression in vitro, including growth capacity and migration. Bioinformatic predictive analysis revealed that E3 ubiquitin ligase FBXW7 interacts directly with ZMYND8 and degrades ZMYND8 in a polyubiquitination manner. Low FBXW7 was a hazard factor for promoting and depending on accumulated ZMYND8 proteins to promote Bca progression. Gene set enrichment analysis (GSEA) further indicated that ZMYND8 was notably associated with stemness process, which was well functionally validated. Lastly, ZMYND8 deficiency was observed to inhibit tumor growth of Bca in vivo, revealing a promising translational significance in Bca treatment. In conclusion, our study for the first time provided evidence for a novel mechanism of FBXW7/ZMYND8 axis in Bca, providing therapeutic vulnerability for individualized cancer treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Constitutive Model of Radiation Aging Effects in Filled Silicone Elastomers under Strain

Filled silicone elastomers, an essential component in many technological applications, are often subjected to controlled or unintended radiation for a variety of reasons. Radiation exposure can lead to permanent mechanical and structural changes in the material, which is manifested as altered mechanical response, and in some cases, a permanent set. For unfilled elastomers, network theories developed and refined over decades can explain these effects in terms of chain-scission and cross-link formation and a hypothesis involving independent networks formed at different strain levels of the material. In this work, we expose a filled silicone rubber to gamma radiation while being under finite elongational strain and show that the observed mechanical and structural changes can be quantitatively modeled within the same theoretical framework developed for unfilled elastomers as long as nuances associated with the Mullins effect are accounted for in a consistent manner. In this work, we employ Ogden’s incompressible hyperelastic model within the framework of Tobolsky’s two-network scheme to describe the observed permanent set and mechanical modulus changes as a function of radiation dosage. In the process, we conclude that gamma radiation induces both direct cross-linking at chain crossings (H-links) and main-chain-scission followed by cross-linking (Y-links). We provide an estimate of the ratio of chain-scission to cross-linking rates, which is in reasonable agreement with previous experimental estimate from Charlesby–Pinner analysis. We use density functional theory (DFT)-based quantum mechanical calculations to explore the stability of –Si and –SiO radicals that form upon a radiation-induced chain-scission event, which sheds light on the relative rates of Y-linking and H-linking processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Toward Carbon Monoxide Methanation at Mild Conditions on Dual-Site Catalysts

The catalytic carbon monoxide (CO) methanation is an ideal model reaction for the fundamental understanding of catalysis on the gas–solid interface and is crucial for various industrial processes. However, the harsh operating conditions make the reaction unsustainable, and the limitations set by the scaling relations between the dissociation energy barrier and dissociative binding energy of CO further increase the difficulty in designing high-performance methanation catalysts operating under milder conditions. In this work, we proposed a theoretical strategy to circumvent the limitations elegantly and achieve both facile CO dissociation and C/O hydrogenation on the catalyst containing a confined dual site. The DFT-based microkinetic modeling (MKM) reveals that the designed Co-Cr 2 /G dual-site catalyst could provide 4–6 orders of magnitude higher turnover frequency for CH 4 production than the cobalt step sites. We believe that the proposed strategy in the current work will provide essential guidance for designing state-of-the-art methanation catalysts under mild conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sterols are required for the coordinated assembly of lipid droplets in developing seeds

Abstract Lipid droplets (LDs) are intracellular organelles critical for energy storage and lipid metabolism. They are typically composed of an oil core coated by a monolayer of phospholipids and proteins such as oleosins. The mechanistic details of LD biogenesis remain poorly defined. However, emerging evidence suggest that their formation is a spatiotemporally regulated process, occurring at specific sites of the endoplasmic reticulum defined by a specific set of lipids and proteins. Here, we show that sterols are required for formation of oleosin-coated LDs in Arabidopsis. Analysis of sterol pathway mutants revealed that deficiency in several ∆ 5 -sterols accounts for the phenotype. Importantly, mutants deficient in these sterols also display reduced LD number, increased LD size and reduced oil content in seeds. Collectively, our data reveal a role of sterols in coordinating the synthesis of oil and oleosins and their assembly into LDs, highlighting the importance of membrane lipids in regulating LD biogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

The GSAM software: A global search algorithm of minima exploration for the investigation of low lying isomers of clusters

The study of atomic clusters has become an increasingly active area of research in the recent years because of the fundamental interest in studying a completely new area that can bridge the gap between atomic and solid state physics. Due to their specific properties, such compounds are of great interest in the field of nanotechnology [1,2]. Here, we would present our GSAM algorithm based on a DFT exploration of the PES to find the low lying isomers of such compounds. This algorithm includes the generation of an intial set of structure from which the most relevant are selected. Moreover, an optimization process, called raking optimization, able to discard step by step all the non physically reasonnable configurations have been implemented to reduce the computational cost of this algorithm. Structural properties of Ga{sub n}Asm clusters will be presented as an illustration of the method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Origin of the performances degradation of two-dimensional-based metal-oxide-semiconductor field effect transistors in the sub-10 nm regime: A first-principles study

The impact of the scaling of the channel length on the performances of metal-oxide-semiconductor field effect transistors, based on two-dimensional (2D) channel materials, is theoretically investigated, using density functional theory combined with the non-equilibrium Green's function method. It is found that the scaling of the channel length below 10 nm leads to strong device performance degradations. Our simulations reveal that this degradation is essentially due to the tunneling current flowing between the source and the drain in these aggressively scaled devices. It is shown that this electron tunneling process is modulated by the effective mass of the 2D channel material, and sets the limit of the scaling in future transistor designs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Ideas that Work!. Retuning the Building Automation System

A building automation system (BAS) can save considerable energy by effectively and efficiently operating building energy systems (fans, pumps, chillers boilers, etc.), but only when the BAS is properly set up and operated. Tuning, or retuning, the BAS is a cost effective process worthy of your time and attention.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

CONNECTING ANGULAR MOMENTUM AND GALACTIC DYNAMICS: THE COMPLEX INTERPLAY BETWEEN SPIN, MASS, AND MORPHOLOGY

The evolution and distribution of the angular momentum of dark matter (DM) halos have been discussed in several studies over the past decades. In particular, the idea arose that angular momentum conservation should allow us to infer the total angular momentum of the entire DM halo from measuring the angular momentum of the baryonic component, which is populating the center of the halo, especially for disk galaxies. To test this idea and to understand the connection between the angular momentum of the DM halo and its galaxy, we use a state-of-the-art, hydrodynamical cosmological simulation taken from the set of Magneticum Pathfinder simulations. Thanks to the inclusion of the relevant physical processes, the improved underlying numerical methods, and high spatial resolution, we successfully produce populations of spheroidal and disk galaxies self-consistently. Thus, we are able to study the dependence of galactic properties on their morphology. We find that (1) the specific angular momentum of stars in disk and spheroidal galaxies as a function of their stellar mass compares well with observational results; (2) the specific angular momentum of the stars in disk galaxies is slightly smaller compared to the specific angular momentum of the cold gas, in good agreement with observations; (3) simulations including the baryonic component show a dichotomy in the specific stellar angular momentum distribution when splitting the galaxies according to their morphological type (this dichotomy can also be seen in the spin parameter, where disk galaxies populate halos with slightly larger spin compared to spheroidal galaxies); (4) disk galaxies preferentially populate halos in which the angular momentum vector of the DM component in the central part shows a better alignment to the angular momentum vector of the entire halo; and (5) the specific angular momentum of the cold gas in disk galaxies is approximately 40% smaller than the specific angular momentum of the total DM halo and shows a significant scatter.

79 ASTRONOMY AND ASTROPHYSICS↗

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