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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 325 records · Page 18

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NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗

DOE OSTI2018

Deep Learning for Scientific Discovery

Deep learning has positively impacted fields were perceptive tasks are paramount, such as computer vision and natural language processing. Tremendous strides have also been made in deep reinforcement learning towards robotics and gaming. Given the success in these fields, it is apparent that deep learning may also accelerate scientific discovery. Herein, we discuss several topic areas where modern representation learning is driving innovation. This article presents a) learning non-linear models from data, b) learning scientific laws from models, c) deep learning on graphs, and d) approaches to scalable deep learning.

deep learning, scientific computing, koopman, mate↗

DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Trends and Future Challenges in Sampling the Deep Terrestrial Biosphere

Research in the deep terrestrial biosphere is driven by interest in novel biodiversity and metabolisms, biogeochemical cycling, and the impact of human activities on this ecosystem. As this interest continues to grow, it is important to ensure that when subsurface investigations are proposed, materials recovered from the subsurface are sampled and preserved in an appropriate manner to limit contamination and ensure preservation of accurate microbial, geochemical, and mineralogical signatures. On February 20th, 2014, a workshop on “Trends and Future Challenges in Sampling The Deep Subsurface” was coordinated in Columbus, Ohio by The Ohio State University and West Virginia University faculty, and sponsored by The Ohio State University and the Sloan Foundation’s Deep Carbon Observatory. The workshop aims were to identify and develop best practices for the collection, preservation, and analysis of terrestrial deep rock samples. This document summarizes the information shared during this workshop.

contamination↗

DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Root-driven weathering impacts on mineral-organic associations in deep soils over pedogenic time scales

Plant roots are primary weathering agents in deep soils, yet the impact of resulting mineral transformations on the vast deep soil carbon (C) reservoir are largely unknown. Root-driven weathering of primary minerals causes the formation of reactive secondary minerals, which may protect mineral-organic associations (MOAs) for centuries or millennia. Conversely, root-driven weathering may also transform secondary minerals, potentially enhancing the bioavailability of C previously protected in MOAs. Here we examined the impact of root-driven weathering on MOAs and their capacity to store C over pedogenic time scales. To accomplish this, we compared soil that experienced root-driven weathering, resulting in the formation of discrete rhizosphere zones in deep soil horizons (100-160 cm) of the Santa Cruz Marine Terrace chronosequence (65ka-226ka), with adjacent soil that experienced no root growth. Using a combination of radiocarbon, mass spectrometry, Mössbauer spectroscopy, and X-ray spectromicroscopy approaches, we characterized transformations of mineral-organic associations in relation to changes in C content, turnover and chemistry across four soils ranging in age from 65 to 226 ka. We found that the onset of root-driven weathering (65-90ka) increased the amount of C associated with poorly crystalline iron (Fe) and aluminum (Al) phases, particularly highly-disordered np-goethite. This increase coincided with greater C concentrations, longer C residence times, and a greater abundance of microbially-derived C. Continued root-driven weathering (137-226ka) did not significantly change the amount of C associated with crystalline Fe and Al phases, but resulted in a decline in the amount of C associated with poorly crystalline metal phases. This decline coincided with a decrease in C concentrations and potential turnover rates, and a shift toward plant-derived C. In contrast, soil not affected by root-driven weathering showed comparatively low amounts of C bound in poorly crystalline Fe and Al phases regardless of soil age and, correspondingly, lower C concentrations and estimated residence times. Furthermore, our results demonstrate that root driven-formation and disruption of poorly crystalline Fe and Al phases is a direct control on both C accrual and loss in deep soil. This finding suggests that root impacts on soil C storage are dependent on soil weathering stage, a consideration that is critical for future predictions of the vulnerability of deep soil C to global change.

58 GEOSCIENCES↗

DOE OSTI2020

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

In order to improve the physical consistency between shallow and deep convection, we modify the deep convective closure in the Community Atmosphere Model version 5 (CAM5) coupled with a third-order turbulence closure parameterization (i.e., Cloud Layers Unified by Binormals; CLUBB). The revised closure reserves a portion of the total convective available potential energy for shallow convection via utilizing the heating and moistening profiles from CLUBB to distribute moisture and energy between shallow and deep convection. Simulations at two resolutions (i.e. 2° and 0.5°, respectively) are conducted to investigate the impacts of convective closure on the simulated precipitation diurnal variations. Results from low-resolution simulations show that the revised closure suppresses deep convection until the lower troposphere is sufficiently moistened by shallow convection, which improves the precipitation diurnal variation simulations compared to the default closure, with the precipitation diurnal peak over tropical lands delayed from 12LST to 19LST. The revised closure better simulates the diurnal variations for precipitation over the Asian monsoon region, such as the delayed precipitation onset, but still fails to well capture the nighttime peak for precipitation there. This deficiency is alleviated to some extent when applying the revised closure in highresolution simulations, but nighttime precipitation is still underestimated probably because key processes responsible for nighttime convection are missing. Altogether, our results indicate that establishing the consistency between shallow and deep convection is critical for the precipitation diurnal cycle simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Influence of the Kuroshio Intrusion on Deep Flow Intraseasonal Variability in the Northern South China Sea

Abstract Interactions between the open ocean and marginal seas have been suggested to be critical to the redistribution and dissipation of global energy. Here, we propose a mechanism for the upper open ocean influencing the deep flow in marginal seas that hinges on the formation and propagation of topographic Rossby waves (TRWs). Observations and high‐resolution simulations suggest substantial intraseasonal variability with periods of 5–60 days associated with the deep flow over continental slopes of the northern South China Sea (NSCS). These fluctuations generally account for over 40% of the total deep kinetic energy variability and the number can reach 70% over the slopes to the west of the Luzon Strait, southwest of the Dongsha Islands, and northeast of the Xisha Islands. By examining the spatiotemporal features of the fluctuations, we demonstrate that the intraseasonal variability of the deep flow in the NSCS is closely associated with TRWs. Using a recently developed multiscale energetics analysis in combination with a ray tracing model, we show that the energy sources of TRWs can be traced back to the east of the Dongsha Islands, where the Kuroshio intrusion and related eddies energize the TRWs primarily through pressure work. These waves propagate westward across the NSCS and drive the intraseasonal variability of the deep flow over continental slopes. Our findings highlight an energy pathway from the open ocean western boundary current to the abyssal marginal sea that could modulate regional circulation as well as exchanges between major ocean basins.

Quan, Qi↗

DOE OSTI2022

Differences in Tropical Rainfall in Aquaplanet Simulations With Resolved or Parameterized Deep Convection

Abstract This study investigates the effects of resolved deep convection on tropical rainfall and its multi‐scale variability. A series of aquaplanet simulations are analyzed using the Model for Prediction Across Scales‐Atmosphere with horizontal cell spacings from 120 to 3 km. The 3‐km experiment uses a novel configuration with 3‐km cell spacing between 20°S and 20°N and 15‐km cell spacing poleward of 30°N/S. A comparison of those experiments shows that resolved deep convection yields a narrower, stronger, and more equatorward intertropical convergence zone, which is supported by stronger nonlinear horizontal momentum advection in the boundary layer. There is also twice as much tropical rainfall variance in the experiment with resolved deep convection than in the experiments with parameterized convection. All experiments show comparable precipitation variance associated with Kelvin waves; however, the experiment with resolved deep convection shows higher precipitation variance associated with westward propagating systems. Resolved deep convection also yields at least two orders of magnitude more frequent heavy rainfall rates (>2 mm hr −1 ) than the experiments with parameterized convection. A comparison of organized precipitation systems demonstrates that tropical convection organizes into linear systems that are associated with stronger and deeper cold pools and upgradient convective momentum fluxes when convection is resolved. In contrast, parameterized convection results in more circular systems, weaker cold pools, and downgradient convective momentum fluxes. These results suggest that simulations with parameterized convection are missing an important feedback loop between the mean state, convective organization, and meridional gradients of moisture and momentum.

Rios‐Berrios, Rosimar↗

DOE OSTI2022

Precipitation–Moisture Coupling Over Tropical Oceans: Sequential Roles of Shallow, Deep, and Mesoscale Convective Systems

We report Precipitation over tropical oceans rapidly increases when the environmental column saturation fraction (CSF) increases past a critical value of ~0.7. Past studies suggested that increased stratiform rainfall greatly contributes to the rapid rainfall enhancement. In this study, the sequential roles of non-deep convection, deep convection, and mesoscale convective system (MCS) in precipitation-moisture interactions are examined using 19 years of satellite observations. When CSF is below ~0.5, non-deep convection dominates total rainfall, and predominantly contributes to moistening of the environment. Between the CSF range of 0.5–0.7, transition to deep convective rainfall begins. Meanwhile, MCS contribution to total rain rapidly increases, and the environment is further moistened. MCS becomes the major rainfall type above the critical CSF value (~0.7), with the rapid increase of total rain mostly explained by the rapid increase in MCS rain area. Rainfall reduction at high CSF values is jointly contributed by MCS and non-deep convection.

58 GEOSCIENCES↗

DOE OSTI2022

Combining Deep Learning With Physics Based Features in Explosion-Earthquake Discrimination

This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics-based parametric features. These features are high-frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. Further, we also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad-CAM). Such visualization provides a window into the black-box nature of the machine-learning models and offers new insight into how the deep learning derived models use data to make decisions.

58 GEOSCIENCES↗

DOE OSTI2015

High-pressure orthorhombic ferromagnesite as a potential deep-mantle carbon carrier

In this study, knowledge of the physical and chemical properties of candidate deep-carbon carriers such as ferromagnesite [(Mg,Fe)CO 3 ] at high pressure and temperature of the deep mantle is necessary for our understanding of deep-carbon storage as well as the global carbon cycle of the planet. Previous studies have reported very different scenarios for the (Mg,Fe)CO 3 system at deep-mantle conditions including the chemical dissociation to (Mg,Fe)O+CO 2 , the occurrence of the tetrahedrally-coordinated carbonates based on CO 4 structural units, and various high-pressure phase transitions. Here we have studied the phase stability and compressional behavior of (Mg,Fe)CO 3 carbonates up to relevant lower-mantle conditions of approximately 120 GPa and 2400 K. Our experimental results show that the rhombohedral siderite (Phase I) transforms to an orthorhombic phase (Phase II with Pmm2 space group) at approximately 50 GPa and 1400 K. The structural transition is likely driven by the spin transition of iron accompanied by a volume collapse in the Fe-rich (Mg,Fe)CO 3 phases; the spin transition stabilizes the high-pressure phase II at much lower pressure conditions than its Mg-rich counterpart. It is conceivable that the low-spin ferromagnesite phase II becomes a major deep-carbon carrier at the deeper parts of the lower mantle below 1900 km in depth.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Atomic force microscopy deep trench and sidewall imaging with an optical fiber probe

We report a method to measure critical dimensions of micro- and nanostructures using the atomic force microscope (AFM) with an optical fiber probe (OFP). This method is capable of scanning narrow and deep trenches due to the long and thin OFP tip, as well as imaging of steep sidewalls with unique profiling possibilities by laterally tilting the OFP without any modifications of the optical lever. A switch control scheme is developed to measure the sidewall angle by flexibly transferring feedback control between the Z- and Y-axis, for a serial scan of the horizontal surface (raster scan on XY-plane) and sidewall (raster scan on the YZ-plane), respectively. In experiments, a deep trench with tapered walls (243.5 μm deep) and a microhole (about 14.9 μm deep) have been imaged with the orthogonally aligned OFP, as well as a silicon sidewall (fabricated by deep reactive ion etching) has been characterized with the tilted OFP. Moreover, the sidewall angle of TGZ3 (AFM calibration grating) was accurately measured using the switchable scan method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Capacitance behavior of InAlN Schottky diodes in presence of large concentrations of shallow and deep states related to oxygen

The capacitance-voltage-temperature characteristics of nonintentionally doped In{sub 0.16}Al{sub 0.84 }N/n{sup +}-GaN Schottky diodes were measured at 1 MHz and in the 90–400 K range. They are discussed in the framework of existing theories, which properly treat the Poisson's equation, especially near the edge of the space-charge region, the so-called transition region. The concentration of a shallow donor and of a deep DX-like center, previously reported, is properly determined. The key parameter to discuss the temperature dependence of the capacitance is the ratio between the frequency of the small ac modulating signal and the temperature-dependent emission rate associated to each level. The capacitance-voltage C-V{sub a} curves were successfully fitted using a three parameters expression over the full range of temperatures. The concentration of both shallow and deep levels exceeds a few 10{sup 18} cm{sup −3}. Based on secondary ion mass spectrometry profiling, we assign both levels to the dominant oxygen impurity. This result supports our previous assignment of the shallow donor to a substitutional oxygen atom on a nitrogen site and the deep state to an O-related DX center, naturally explaining its high concentration. The sluggish kinetics at low temperatures, associated to the large concentration of deep levels located near the transition region, is illustrated by hysteresis loops in the C-V{sub a} curves below 270 K. Furthermore, the contribution of free carriers to the capacitance is revealed below 150 K, when both shallow and deep donors cannot respond anymore due to an emission rate lower than the 1 MHz modulating frequency. Finally, the presence of a highly doped thin surface barrier, as already reported in other III-nitrides, finds further support.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

High temperature annealing effects on deep-level defects in a high purity semi-insulating 4H-SiC substrate

Effects of high-temperature annealing on deep-level defects in a high-purity semi-insulating 4H silicon carbide substrate have been studied by employing current-voltage, capacitance-voltage, junction spectroscopy, and chemical impurity analysis measurements. Secondary ion mass spectrometry data reveal that the substrate contains boron with concentration in the mid 10{sup 15 }cm{sup −3} range, while other impurities including nitrogen, aluminum, titanium, vanadium and chromium are below their detection limits (typically ∼10{sup 14 }cm{sup −3}). Schottky barrier diodes fabricated on substrates annealed at 1400–1700 °C exhibit metal/p-type semiconductor behavior with a current rectification of up to 8 orders of magnitude at bias voltages of ±3 V. With increasing annealing temperature, the series resistance of the Schottky barrier diodes decreases, and the net acceptor concentration in the substrates increases approaching the chemical boron content. Admittance spectroscopy results unveil the presence of shallow boron acceptors and deep-level defects with levels in lower half of the bandgap. After the 1400 °C annealing, the boron acceptor still remains strongly compensated at room temperature by deep donor-like levels located close to mid-gap. However, the latter decrease in concentration with increasing annealing temperature and after 1700 °C, the boron acceptor is essentially uncompensated. Hence, the deep donors are decisive for the semi-insulating properties of the substrates, and their thermal evolution limits the thermal budget for device processing. The origin of the deep donors is not well-established, but substantial evidence supporting an assignment to carbon vacancies is presented.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Evolution of AlGaN deep level defects as a function of alloying and compositional grading and resultant impact on electrical conductivity

AlGaN:Si epilayers with uniform Al compositions of 60%, 70%, 80%, and 90% were grown by metal-organic vapor phase epitaxy along with a compositionally graded, unintentionally doped (UID) AlGaN epilayer with the Al composition varying linearly between 80% and 100%. The resistivity of AlGaN:Si with a uniform composition increased significantly for the Al content of 80% and greater, whereas the graded UID-AlGaN film exhibited resistivity equivalent to 60% and 70% AlGaN:Si owing to polarization-induced doping. Deep level defect studies of both types of AlGaN epilayers were performed to determine why the electronic properties of uniform-composition AlGaN:Si degraded with increased Al content, while the electronic properties of graded UID-AlGaN did not. The deep level density of uniform-composition AlGaN:Si increased monotonically and significantly with the Al mole fraction. Conversely, graded-UID AlGaN had the lowest deep level density of all the epilayers despite containing the highest Al composition. These findings indicate that Si doping is an impetus for point defect incorporation in AlGaN that becomes stronger with the increasing Al content. However, the increase in deep level density with the Al content in uniform-composition AlGaN:Si was small compared to the increase in resistivity. This implies that the primary cause for increasing resistivity in AlGaN:Si with the increasing Al mole fraction is not compensation by deep levels but rather increasing activation energy for the Si dopant. As a result, the graded UID-AlGaN films maintained low resistivity because they do not rely on thermal ionization of Si dopants.

36 MATERIALS SCIENCE↗