Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hidden”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simultaneous Development of Antiferromagnetism and Local Symmetry Breaking in a Kagome Magnet (Co 0.45 Fe 0.55 )Sn

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to knowledge of the average crystal structure. Here, we report the Fe-doping induced coemergence of the antiferromagentic (AFM) order and local symmetry breaking in (Co 0.45 Fe 0.55 )Sn. Rietveld analysis on the neutron and synchrotron X-ray diffraction data indicates A-type antiferromagnetic order with the moment pointing perpendicular to the kagome layers, associated with the anomaly in the MSn(1) 2 Sn(2) 4 (M = Co/Fe) octahedral distortion and the lattice constant c. Reverse Monte Carlo (RMC) modeling of the synchrotron X-ray total scattering results captured the subtle local orthorhombic distortion involving off-axis displacements of Sn(2). Our results indicate that the stable hexagonal lattice above T N becomes unstable once the A-type AFM order is formed below T N . Here we argue that the local symmetry breaking has a magnetic origin, since the spatially varied M–Sn(2) bond lengths arise from out-of-plane magnetic exchange coupling J c via the exchange pathway M–Sn(2)–M. Our study provides comprehensive information on the crystal structure in both long-range scale and local scale, unveiling unique coupling between AFM order, octahedral distortion, and hidden local symmetry breaking.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules

Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal–organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

Abstract A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

58 GEOSCIENCES↗
DOE OSTI2022

Short–Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

Short–term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high–passenger–volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short–term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper–air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗
DOE OSTI2023

Floods and Heavy Precipitation at the Global Scale: 100‐Year Analysis and 180‐Year Reconstruction

Floods and heavy precipitation have disruptive impacts worldwide, but their historical variability remains only partially understood at the global scale. This article aims at reducing this knowledge gap by jointly analyzing seasonal maxima of streamflow and precipitation at more than 3,000 stations over a 100‐year period. The analysis is based on Hidden Climate Indices (HCIs). Like standard climate indices (e.g., Nino 3.4, NAO), HCIs are used as covariates explaining the temporal variability of data, but unlike them, HCIs are estimated from the data. In this work, a distinction is made between common HCIs, that affect both heavy precipitation and floods, and specific HCIs, that exclusively affect one or the other. Overall, HCIs do not show noticeable autocorrelation, but some are affected by noticeable trends. In particular, strong and wide‐ranging trends are identified in precipitation‐specific HCIs, while trends affecting flood‐specific HCIs are weaker and have more localized effects. A probabilistic model is then derived to link HCIs and large‐scale atmospheric variables (pressure, wind, temperature) and to reconstruct HCIs since 1836 using the 20CRv3 reanalysis. In turn this allows estimating the probability of occurrence of floods and heavy precipitation at the global scale. This 180‐year reconstruction highlights flood hot‐spots and hot‐moments in the distant past, well before the establishment of perennial monitoring networks. Finally, the approach presented in this study is generic and paves the way for an improved characterization of historical variability by making a better use of long but highly irregular station data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models

Applications of process-based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high-fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM-surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high-fidelity and physics-agnostic models for a wide range of prediction problems in geosciences.

58 GEOSCIENCES↗
DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2020

Targeted assemblies of cas1 suggest CRISPR-Cas’s response to soil warming

Abstract There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus–host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus–host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Molecular design principles of Lysine-DOPA wet adhesion

The mussel byssus has long been a source of inspiration for the adhesion community. Recently, adhesive synergy between flanking lysine (Lys, K) and 3,4-Dihydroxyphenylalanine (DOPA, Y ) residues in the mussel foot proteins (Mfps) has been highlighted. However, the complex topological relationship of DOPA and Lys as well as the interfacial adhesive roles of other amino acids have been understudied. Herein, we study adhesion of Lys and DOPA-containing peptides to organic and inorganic substrates using single-molecule force spectroscopy (SMFS). We show that a modest increase in peptide length, from K Y to (K Y ) 3 , increases adhesion strength to TiO 2. Surprisingly, further increase in peptide length offers no additional benefit. Additionally, comparison of adhesion of dipeptides containing Lys and either DOPA (K Y ) or phenylalanine (KF) shows that DOPA is stronger and more versatile. We furthermore demonstrate that incorporating a nonadhesive spacer between (K Y ) repeats can mimic the hidden length in the Mfp and act as an effective strategy to dissipate energy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Longitudinal unzipping of 2D transition metal dichalcogenides

Unzipping of the basal plane offers a valuable pathway to uniquely control the material chemistry of 2D structures. Nonetheless, reliable unzipping has been reported only for graphene and phosphorene thus far. The single elemental nature of those materials allows a straightforward understanding of the chemical reaction and property modulation involved with such geometric transformations. Here we report spontaneous linear ordered unzipping of bi-elemental 2D MX 2 transition metal chalcogenides as a general route to synthesize 1D nanoribbon structures. The strained metallic phase (1T') of MX 2 undergoes highly specific longitudinal unzipping owing to the self-linearized oxygenation at chalcogenides. Stable dispersions of 1T' MoS 2 nanoribbons with widths of 10–120 nm and lengths up to ~4 µm are produced in water. Edge abundant 1T' MoS 2 nanoribbons reveal the hidden potential of idealized electrocatalysis for hydrogen evolution reactions at a competitive level with the precious Pt catalyst.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Light quantum control of persisting Higgs modes in iron-based superconductors

The Higgs mechanism, i.e., spontaneous symmetry breaking of the quantum vacuum, is a cross-disciplinary principle, universal for understanding dark energy, antimatter and quantum materials, from superconductivity to magnetism. Unlike one-band superconductors (SCs), a conceptually distinct Higgs amplitude mode can arise in multi-band, unconventional superconductors via strong interband Coulomb interaction, but is yet to be accessed. Here we discover such hybrid Higgs mode and demonstrate its quantum control by light in iron-based high-temperature SCs. Using terahertz (THz) two-pulse coherent spectroscopy, we observe a tunable amplitude mode coherent oscillation of the complex order parameter from coupled lower and upper bands. The nonlinear dependence of the hybrid Higgs mode on the THz driving fields is distinct from any known SC results: we observe a large reversible modulation of resonance strength, yet with a persisting mode frequency. Together with quantum kinetic modeling of a hybrid Higgs mechanism, distinct from charge-density fluctuations and without invoking phonons or disorder, our result provides compelling evidence for a light-controlled coupling between the electron and hole amplitude modes assisted by strong interband quantum entanglement. Such light-control of Higgs hybridization can be extended to probe many-body entanglement and hidden symmetries in other complex systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Nonequilibrium charge-density-wave order beyond the thermal limit

The interaction of many-body systems with intense light pulses may lead to novel emergent phenomena far from equilibrium. Recent discoveries, such as the optical enhancement of the critical temperature in certain superconductors and the photo-stabilization of hidden phases, have turned this field into an important research frontier. Here, we demonstrate nonthermal charge-density-wave (CDW) order at electronic temperatures far greater than the thermodynamic transition temperature. Using time- and angle-resolved photoemission spectroscopy and time-resolved X-ray diffraction, we investigate the electronic and structural order parameters of an ultrafast photoinduced CDW-to-metal transition. Tracking the dynamical CDW recovery as a function of electronic temperature reveals a behaviour markedly different from equilibrium, which we attribute to the suppression of lattice fluctuations in the transient nonthermal phonon distribution. A complete description of the system’s coherent and incoherent order-parameter dynamics is given by a time-dependent Ginzburg-Landau framework, providing access to the transient potential energy surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Moisture-induced autonomous surface potential oscillations for energy harvesting

A variety of autonomous oscillations in nature such as heartbeats and some biochemical reactions have been widely studied and utilized for applications in the fields of bioscience and engineering. Here, we report a unique phenomenon of moisture-induced electrical potential oscillations on polymers, poly([2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide-co-acrylic acid), during the diffusion of water molecules. Chemical reactions are modeled by kinetic simulations while system dynamic equations and the stability matrix are analyzed to show the chaotic nature of the system which oscillates with hidden attractors to induce the autonomous surface potential oscillation. Using moisture in the ambient environment as the activation source, this self-excited chemoelectrical reaction could have broad influences and usages in surface-reaction based devices and systems. As a proof-of-concept demonstration, an energy harvester is constructed and achieved the continuous energy production for more than 15,000 seconds with an energy density of 16.8 mJ/cm 2 . A 2-Volts output voltage has been produced to power a liquid crystal display toward practical applications with five energy harvesters connected in series.

42 ENGINEERING↗
DOE OSTI2022

Quadrupolar magnetic excitations in an isotropic spin-1 antiferromagnet

The microscopic origins of emergent behaviours in condensed matter systems are encoded in their excitations. In ordinary magnetic materials, single spin-flips give rise to collective dipolar magnetic excitations called magnons. Likewise, multiple spin-flips can give rise to multipolar magnetic excitations in magnetic materials with spin S ≥ 1. Unfortunately, since most experimental probes are governed by dipolar selection rules, collective multipolar excitations have generally remained elusive. For instance, only dipolar magnetic excitations have been observed in isotropic S = 1 Haldane spin systems. Here, we unveil a hidden quadrupolar constituent of the spin dynamics in antiferromagnetic S = 1 Haldane chain material Y 2 BaNiO 5 using Ni L 3 -edge resonant inelastic x-ray scattering. Our results demonstrate that pure quadrupolar magnetic excitations can be probed without direct interactions with dipolar excitations or anisotropic perturbations. Originating from on-site double spin-flip processes, the quadrupolar magnetic excitations in Y 2 BaNiO 5 show a remarkable dual nature of collective dispersion. While one component propagates as non-interacting entities, the other behaves as a bound quadrupolar magnetic wave. This result highlights the rich and largely unexplored physics of higher-order magnetic excitations.

36 MATERIALS SCIENCE↗