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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Methane emissions from MBT landfills

Highlights: • Compilation of methane generation potential of mechanical biological treated (MBT) municipal solid waste. • Impacts and kinetics of landfill gas production of MBT landfills, approach with differentiated half-lives. • Methane oxidation in the waste itself and in soil covers. • Estimation of methane emissions from MBT landfills in Germany. - Abstract: Within the scope of an investigation for the German Federal Environment Agency (“Umweltbundesamt”), the basics for the estimation of the methane emissions from the landfilling of mechanically and biologically treated waste (MBT) were developed. For this purpose, topical research including monitoring results regarding the gas balance at MBT landfills was evaluated. For waste treated to the required German standards, a methane formation potential of approximately 18–24 m{sup 3} CH{sub 4}/t of total dry solids may be expected. Monitoring results from MBT landfills show that a three-phase model with differentiated half-lives describes the degradation kinetics in the best way. This is due to the fact that during the first years of disposal, the anaerobic degradation processes still proceed relatively intensively. In addition in the long term (decades), a residual gas production at a low level is still to be expected. Most of the soils used in recultivation layer systems at German landfills show a relatively high methane oxidation capacity up to 5 l CH{sub 4}/(m{sup 2} h). However, measurements at MBT disposal sites indicate that the majority of the landfill gas (in particular at non-covered areas), leaves the landfill body via preferred gas emission zones (hot spots) without significant methane oxidation. Therefore, rather low methane oxidation factors are recommended for open and temporarily covered MBT landfills. Higher methane oxidation rates can be achieved when the soil/recultivation layer is adequately designed and operated. Based on the elaborated default values, the First Order Decay (FOD) model of the IPCC Guidelines for National Greenhouse Gas Inventories, 2006, was used to estimate the methane emissions from MBT landfills. Due to the calculation made by the authors emissions in the range of 60,000–135,000 t CO{sub 2-eq.}/a for all German MBT landfills can be expected. This wide range shows the uncertainties when the here used procedure and the limited available data are applied. It is therefore necessary to generate more data in the future in order to calculate more precise methane emission rates from MBT landfills. This is important for the overall calculation of the climate gas production in Germany which is required once a year by the German Government.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Paths to superheavy nuclei

This document summarizes the discussions and outcomes of the Facility for Rare Isotope Beams (FRIB) Theory Alliance topical program ‘The path to Superheavy Isotopes’ held in June 2024 at FRIB. Its content is non-exhaustive, reflecting topics chosen and discussed by the participants. The program aimed to assess the current status of theory in superheavy nuclei (SHN) research and identify necessary theoretical developments to guide experimental programs and determine fruitful production mechanisms. This report details the intersection of SHN research with other fields, provides an overview of production mechanisms and theoretical models, discusses future needs in theory and experiment, explores other potential avenues for SHN synthesis, and highlights the importance of building a strong theory community in this area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Fluctuations in strongly coupled cosmologies

In the early Universe, a dual component made of coupled CDM and a scalar field Φ, if their coupling β > (3){sup 1/2}/2, owns an attractor solution, making them a stationary fraction of cosmic energy during the radiation dominated era. Along the attractor, both such components expand ∝a{sup −4} and have early density parameters Ω{sub d} = 1/(4β{sup 2}) and Ω{sub c} = 2 Ω{sub d} (field and CDM, respectively). In a previous paper it was shown that, if a further component, expanding ∝a{sup −3}, breaks such stationary expansion at z ∼ 3–5 × 10{sup 3}, cosmic components gradually acquire densities consistent with observations. This paper, first of all, considers the case that this component is warm. However, its main topic is the analysis of fluctuation evolution: out of horizon modes are then determined; their entry into horizon is numerically evaluated as well as the dependence of Meszaros effect on the coupling β; finally, we compute: (i) transfer function and linear spectral function; (ii) CMB C{sub l} spectra. Both are close to standard ΛCDM models; in particular, the former one can be so down to a scale smaller than Milky Way, in spite of its main DM component being made of particles of mass < 1 keV. The previously coupled CDM component, whose present density parameter is O(10{sup −3}), exhibits wider fluctuations δρ/ρ, but approximately β-independent δρ values. We discuss how lower scale features of these cosmologies might ease quite a few problems that ΛCDM does not easily solve.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Conditional random fields for pattern recognition applied to structured data

Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building) or “natural” (such as a tree). Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X) is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs) model structured data using the conditional distribution P(Y|X = x), without specifying a model for P(X), and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches) in the output domain. Second, we identify research topics and present numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Advances in the studies of the nucleon resonance spectrum in experiments with electromagnetic probes

The studies of the nucleon resonance spectrum with the focus on the search for new baryon states for a long time remain the challenging topic in hadron physics. Full resonance spectrum plays a critical role in the evolution of ~ millisecond age Universe in the transition from the quark-gluon to the hadron gas phases. Recent LQCD evaluations of the N*-spectrum from the first QCD principles suggest existence as much new nucleon resonances as expected in the quark models employed SU(6)*O(3) symmetry. Discovery of several new nucleon resonances in the global analyses of exclusive meson photoproduction off protons data with decisive impact from the CLAS measurements of exclusive KY photoproduction will be presented. Combined studies of charged double pion photo- and electroproduction data from CLAS revealed strong evidence for another N'(1720)3/2+ new baryon state. The information on electroexcitation amplitudes of this state has become available for the first time offering the insight to the new baryon state structure. The prospects for the search of the new states of baryon matter with glue as the structural component in the experiments with the CLAS12 detector will be outlined.

Mokeev, Viktor↗
DOE OSTI2019

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations

The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Three-state majority-vote model on small-world networks

Abstract In this work, we study the opinion dynamics of the three-state majority-vote model on small-world networks of social interactions. In the majority-vote dynamics, an individual adopts the opinion of the majority of its neighbors with probability 1- q , and a different opinion with chance q , where q stands for the noise parameter. The noise q acts as a social temperature, inducing dissent among individual opinions. With probability p , we rewire the connections of the two-dimensional square lattice network, allowing long-range interactions in the society, thus yielding the small-world property present in many different real-world systems. We investigate the degree distribution, average clustering coefficient and average shortest path length to characterize the topology of the rewired networks of social interactions. By employing Monte Carlo simulations, we investigate the second-order phase transition of the three-state majority-vote dynamics, and obtain the critical noise $$q_c$$ q c , as well as the standard critical exponents $$\beta /\nu$$ β / ν , $$\gamma /\nu$$ γ / ν , and $$1/\nu$$ 1 / ν for several values of the rewiring probability p . We conclude that the rewiring of the lattice enhances the social order in the system and drives the model to different universality classes from that of the three-state majority-vote model in two-dimensional square lattices.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.

Remote sensing↗
DOE OSTI2023

HP1-driven phase separation recapitulates the thermodynamics and kinetics of heterochromatin condensate formation

The spatial segregation of pericentromeric heterochromatin (PCH) into distinct, membrane-less nuclear compartments involves the binding of Heterochromatin Protein 1 (HP1) to H3K9me2/3-rich genomic regions. While HP1 exhibits liquid–liquid phase separation properties in vitro, its mechanistic impact on the structure and dynamics of PCH condensate formation in vivo remains largely unresolved. Here, using a minimal theoretical framework, we systematically investigate the mutual coupling between self-interacting HP1-like molecules and the chromatin polymer. We reveal that the specific affinity of HP1 for H3K9me2/3 loci facilitates coacervation in nucleo and promotes the formation of stable PCH condensates at HP1 levels far below the concentration required to observe phase separation in purified protein assays in vitro. These heterotypic HP1–chromatin interactions give rise to a strong dependence of the nucleoplasmic HP1 density on HP1-H3K9me2/3 stoichiometry, consistent with the thermodynamics of multicomponent phase separation. The dynamical cross talk between HP1 and the viscoelastic chromatin scaffold also leads to anomalously slow equilibration kinetics, which strongly depend on the genomic distribution of H3K9me2/3 domains and result in the coexistence of multiple long-lived, microphase-separated PCH compartments. The morphology of these complex coacervates is further found to be governed by the dynamic establishment of the underlying H3K9me2/3 landscape, which may drive their increasingly abnormal, aspherical shapes during cell development. These findings compare favorably to 4D microscopy measurements of HP1 condensate formation in live Drosophila embryos and suggest a general quantitative model of PCH formation based on the interplay between HP1-based phase separation and chromatin polymer mechanics.

3D genomics↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution

Projections of precipitation extremes in simulations with global climate models are very uncertain in the tropics, in part because of the use of parameterizations of deep convection and model deficiencies in simulating convective organization. Here, we analyse precipitation extremes in high-resolution simulations that are run without a convective parameterization on a quasi-global aquaplanet. The frequency distributions of precipitation rates and precipitation cluster sizes in the tropics of a control simulation are similar to the observed distributions. In response to climate warming, 3 h precipitation extremes increase at rates of up to 9 % K - 1 in the tropics because of a combination of positive thermodynamic and dynamic contributions. The dynamic contribution at different latitudes is connected to the vertical structure of warming using a moist static stability. When the precipitation rates are first averaged to a daily timescale and coarse-grained to a typical global climate-model resolution prior to calculating the precipitation extremes, the response of the precipitation extremes to warming becomes more similar to what was found previously in coarse-resolution aquaplanet studies. However, the simulations studied here do not exhibit the high rates of increase of tropical precipitation extremes found in projections with some global climate models. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Science & Technology - Other Topics↗
DOE OSTI2016

Filling-enforced quantum band insulators in spin-orbit coupled crystals

An early triumph of quantum mechanics was the explanation of metallic and insulating behavior based on the filling of electronic bands. A complementary, classical picture of insulators depicts electrons as occupying localized and symmetric Wannier orbitals that resemble atomic orbitals. We report the theoretical discovery of band insulators for which electron filling forbids such an atomic description. We refer to them as filling-enforced quantum band insulators (feQBIs) because their wave functions are associated with an essential degree of quantum entanglement. Like topological insulators, which also do not admit an atomic description, feQBIs need spin-orbit coupling for their realization. However, they do not necessarily support gapless surface states. Instead, the band topology is reflected in the insulating behavior at an unconventional filling. We present tight binding models of feQBIs and show that they only occur in certain nonsymmorphic, body-centered cubic crystals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Primordial black hole dark matter from single field inflation

We propose a model of inflation capable of generating a population of light black holes (about 10 –16 –10 –14 solar masses) that might account for a significant fraction of the dark matter in the Universe. The effective potential of the model features an approximate inflection point arising from two-loop order logarithmic corrections in well-motivated and perturbative particle physics examples. This feature decelerates the inflaton before the end of inflation, enhancing the primordial spectrum of scalar fluctuations and triggering efficient black hole production with a peaked mass distribution. At larger field values, inflation occurs thanks to a generic small coupling between the inflaton and the curvature of spacetime. We compute accurately the peak mass and abundance of the primordial black holes using the Press-Schechter and Mukhanov-Sasaki formalisms, showing that the slow-roll approximation fails to reproduce the correct results by orders of magnitude. We study as well a qualitatively similar implementation of the idea, where the approximate inflection point is due to competing terms in a generic polynomial potential. In both models, requiring a significant part of the dark matter abundance to be in the form of black holes implies a small blue scalar tilt with a sizable negative running and a tensor spectrum that may be detected by the next-generation probes of the cosmic microwave background. We also comment on previous works on the topic.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

REPORT OF RESEARCH ACCOMPLISHMENTS AND FUTURE GOALS HIGH ENERGY PHYSICS

Caltech High Energy Physics (HEP) has a broad program in both experimental and theoretical physics. We are known for our creativity and leadership. The future is uncertain and we strive to be involved in all the major areas of experimental and theoretical HEP physics so no matter where the important discoveries occur we are well positioned to play an important role. An outstanding group of postdoctoral scholars, graduate students, staff scientists, and technical and administrative personnel support our efforts in experimental and theoretical physics. The PI’s on this grant are involved in the following program of experimental and theoretical activities: I) EXPERIMENTAL PHYSICS Our CMS group, led by Harvey Newman and Maria Spiropulu, has played a key role in the discovery and interpretation of the Higgs boson and in searches for new physics. They have important hardware responsibilities in both ECAL and HCAL and are also involved in the upgrades needed for the High Luminosity LHC. Newman's group also develops and operates Grid-based computing, networking, and collaborative systems for CMS and the US HEP community. The charged lepton (Mu2e) and quark BaBar flavor physics group is led by David Hitlin and Frank Porter. On Mu2e they have been instrumental in the design of the calorimeter. Construction responsibilities include one third of the crystals and associated readout as well as the calibration system. They also will have responsibility for a major part of the online system software. Although data taking ceased in 2008 the Caltech BaBar group is active on several new forefront analyses. The neutrino group is led by Ryan Patterson. They are central to NOvA's core oscillation physics program, to calibration, and to detector readiness being responsible for the production and installation of 12,000 APD arrays. They have key roles in neutrino appearance and disappearance analysis in MINOS and MINOS+. Sunil Golwala leads the dark matter direct detection effort. Areas of activity include: CDMS II data analysis, contributions to SuperCDMS Soudan operations and analysis, R&D towards SuperCDMS SNOLAB, development of a novel screener for radiocontamination (the BetaCage), and development of new WIMP detector concepts. Ren-Yuan Zhu leads the HEP crystal laboratory for the advanced detector R&D effort. The crystal lab is involved in development of novel scintillating crystals and has proposed several crystal based detector concepts for future HEP experiments at the energy and intensity frontiers. Its current research effort is concentrated on development of fast crystal scintillators with good radiation hardness and low cost. II) THEORETICAL PHYSICS The main theme of Sergei Gukov's current research is the relation between the geometry of quantum group invariants and their categorification, on the one hand, and the physics of supersymmetric gauge theory and string theory, on the other. Anton Kapustin's research spans a variety of topics in non-perturbative Quantum Field Theory (QFT). His main areas of interest are supersymmetric gauge theories, non-perturbative dualities in QFT, disorder operators, Topological Quantum Field Theory, and non-relativistic QFT. He is also interested in the foundations and possible generalizations of Quantum Mechanics. Hirosi Ooguri's current research has two main components. One is to find exact results in Calabi-Yau compactification of string theory. Another is to explore applications of the AdS/CFT correspondence. He also plans to continue his project with Caltech postdoctoral fellows on BPS spectra of supersymmetric gauge theories in diverse dimensions. John Preskill works on quantum information science. This field may lead to important future technologies, and also lead to new understanding of issues in fundamental physics John Schwarz has been exploring a number of topics in superstring theory/M-theory, supersymmetric gauge theory, and their AdS/CFT relationships. Much of the motivation for these studies is the desire to gain a deeper understanding of superstring theory and M-theory. The research interests of Mark Wise span particle physics, cosmology and nuclear physics. His recent work has centered on extensions of the standard model where baryon number and lepton number are gauged and extensions of the standard model that have novel sources of baryon number violation and new sources of charged lepton flavor violation

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗