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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 OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Superstructures generated from truncated tetrahedral quantum dots

The assembly of uniform nanocrystal building blocks into well ordered superstructures is a fundamental strategy for the generation of meso- and macroscale metamaterials with emergent nanoscopic functionalities(1-)(10). The packing of spherical nanocrystals, which frequently adopt dense, face-centred-cubic or hexagonal-close-packed arrangements at thermodynamic equilibrium, has been much more widely studied than that of non-spherical, polyhedral nanocrystals, despite the fact that the latter have intriguing anisotropic properties resulting from the shapes of the building blocks(11-13). Here we report the packing of truncated tetrahedral quantum dot nanocrystals into three distinct superstructures-one-dimensional chiral tetrahelices, two-dimensional quasicrystal-approximant superlattices and three-dimensional cluster-based body-centred-cubic single supercrystals- by controlling the assembly conditions. Using techniques in real and reciprocal spaces, we successfully characterized the superstructures from their nanocrystal translational orderings down to the atomic-orientation alignments of individual quantum dots. Our packing models showed that formation of the nanocrystal superstructures is dominated by the selective facet-to-facet contact induced by the anisotropic patchiness of the tetrahedra. This study provides information about the packing of non-spherical nanocrystals into complex superstructures, and may enhance the potential of self-assembled nanocrystal metamaterials in practical applications.

Nagaoka, Yasutaka↗
DOE OSTI2019

Producing a BOSS CMASS sample with DES imaging

We present a sample of galaxies with the Dark Energy Survey (DES) photometry that replicates the properties of the BOSS CMASS sample. The CMASS galaxy sample has been well characterized by the Sloan Digital Sky Survey (SDSS) collaboration and was used to obtain the most powerful redshift-space galaxy clustering measurements to date. A joint analysis of redshift-space distortions (such as those probed by CMASS from SDSS) and a galaxy-galaxy lensing measurement for an equivalent sample from DES can provide powerful cosmological constraints. Unfortunately, the DES and SDSS-BOSS footprints have only minimal overlap, primarily on the celestial equator near the SDSS Stripe 82 region. Using this overlap, we build a robust Bayesian model to select CMASS-like galaxies in the remainder of the DES footprint. The newly defined DES-CMASS (DMASS) sample consists of 117,293 effective galaxies covering $1,244 {\rm deg}^2$. Through various validation tests, we show that the DMASS sample selected by this model matches well with the BOSS CMASS sample, specifically in the South Galactic cap (SGC) region that includes Stripe 82. Combining measurements of the angular correlation function and the clustering-z distribution of DMASS, we constrain the difference in mean galaxy bias and mean redshift between the BOSS CMASS and DMASS samples to be $\Delta b = 0.010^{+0.045}_{-0.052}$ and $\Delta z = \left( 3.46^{+5.48}_{-5.55} \right) \times 10^{-3}$ for the SGC portion of CMASS, and $\Delta b = 0.044^{+0.044}_{-0.043} $ and $\Delta z= ( 3.51^{+4.93}_{-5.91}) \times 10^{-3}$ for the full CMASS sample. These values indicate that the mean bias of galaxies and mean redshift in the DMASS sample is consistent with both CMASS samples within $1\sigma$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Calorimetric Study of Functionalized Uranyl Peroxide Nanoclusters and Their Monomeric Building Block

The description of the energy landscape of polyoxometalates (POMs) enriches our understanding of their solution behavior through quantitative information that can be used to predict their existence, stability, and properties. Herein, we report thermodynamic values that describe the energy landscape of four uranyl peroxide nanoclusters (UPCs) (LiRb–U@U 24 , LiNa–U 24 Pp 12 , NaK–U 24 Pp 12 , and U 60 Ox 30 ) and a building block of UPCs, lithium uranyl triperoxide monomer (Li–UT). Furthermore, the results reveal relationships between the affinity of counter cations and the anionic uranyl peroxide units, enthalpy of dissolution, as well as enthalpy of crystallization and cluster solubility. Additionally, the calorimetric measurements of Li–UT allowed us to calculate a favorable enthalpy of formation of Li–U 24 and Li–U 28 from their monomeric units.

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

Toward Quantum Confinement in Graphitic Carbon Nitride-Based Polymeric Monolayers

raphitic carbon nitride (g-C 3 N 4 ) has garnered much attention due to its potential as an efficient metal-free photocatalyst. This study examines the evolution of properties in zero-dimensional quantum dots up to sizable clusters that mimic extended g-C 3 N 4 monolayers. We employ density functional theory to investigate systematically the structural, electronic, and optical properties of the g-C 3 N 4 -based melamine and heptazine building blocks using a “bottom-up” construction of polymeric monolayers. The results from our computations indicate that the melamine- and heptazine-based polymeric g-C 3 N 4 systems must be reduced to at least 2.74 and 4.00 nm, respectively, to observe an increase of its optical gap with a size reduction. The present study also examines the nature of the electronic transitions exhibited by g-C 3 N 4 -based monolayers through full natural transition orbital and density of state analyses. Furthermore, the most promising sites for water splitting and subsequent chemical doping studies are identified, which generally correspond to the nitrogen and carbon atoms, respectively.

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

Integrating particle flavor into deep learning models for hadronization

Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there have been multiple proposals to improve the accuracy of hadronization models by utilizing more flexible parametrizations based on neural networks. These recent proposals have focused on the kinematic properties of hadrons, but a full model must also include particle flavor. In this paper, we show how to build a deep learning-based hadronization model that includes both kinematic (continuous) and flavor (discrete) degrees of freedom. Our approach is based on generative adversarial networks and we show the performance within the context of the cluster hadronization model within the erwig event generator.

Chan, Jay↗
DOE OSTI2025

DNABERT-S: pioneering species differentiation with species-aware DNA embeddings

SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 28 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. AVAILABILITY AND IMPLEMENTATION: Model, codes, and data are publically available at https://github.com/MAGICS-LAB/DNABERT_S.

Zhou, Zhihan↗
DOE OSTI2018

From cluster structures to nuclear molecules: The role of nodal structure of the single-particle wave functions

In this study, the nodal structure of the density distributions of the single-particle states occupied in rod-shaped, hyper- and megadeformed structures of non-rotating and rotating N ~ Z nuclei has been investigated in detail. The single-particle states with the Nilsson quantum numbers of the [NN0]1/2 (with N from 0 to 5) and [N,N – 1,1]Ω (with N from 1 to 3 and Ω = 1/2, 3/2) types are considered. These states are building blocks of extremely deformed shapes in the nuclei with mass numbers A ≤ 50. Because of (near) axial symmetry and large elongation of such structures, the wave functions of the single-particle states occupied are dominated by a single basis state in cylindrical basis. This basis state defines the nodal structure of the single-particle density distribution. The nodal structure of the single-particle density distributions allows us to understand in a relatively simple way the necessary conditions for α clusterization and the suppression of the α clusterization with the increase of mass number. It also explains in a natural way the coexistence of ellipsoidal mean-field-type structures and nuclear molecules at similar excitation energies and the features of particle-hole excitations connecting these two types of the structures. Lastly, our analysis of the nodal structure of the single-particle density distributions does not support the existence of quantum liquid phase for the deformations and nuclei under study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Intersecting heuristic adaptive strategies, building design and energy saving intentions when facing discomfort environment: A cross-country analysis

Occupants' adaptive strategies play an important role in the energy consumption of office buildings. Previous research has mostly focused on the adaptive strategies triggered by occupants' indoor discomfort; however, it is crucial to understand if specific adaptive strategies are linked to occupants' energy-saving intentions. This study explores the relationships among employees’ heuristic decision-making in their first choice of adaptive strategies (technological solutions or personal adjustments) when facing extreme discomfort conditions, and their energy-saving intentions, then links these patterns with building design, workplace contextual factors, and demographics. A cross-sectional survey was collected among university employees from China, Brazil, Italy, Poland, Switzerland, and the US. Our results demonstrated that the accessibility to indoor environmental controls (IECs) and office type are the significant factors for adaptive strategies. There was a positive relationship between the number of IEC features and the percentage of employees choosing a technological solution. When feeling too hot, occupants in private offices are more likely to adopt a technological solution, whereas occupants in cubicles are more likely to choose a personal adjustment. Occupants with energy-saving intentions are less likely to choose thermostat adjustments or use portable devices as adaptive strategies than their counterparts. Lastly, the cluster analysis suggests females were more likely to use adaptive strategies for energy-saving purposes than males. The majority of occupants would turn on/off lighting to save energy. The study provides the contributions in the connection between the heuristic decision-making process and energy-saving intentions and recommendations on design strategies for building architects, engineers, and managers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hilbert-space fragmentation, multifractality, and many-body localization

Investigating many-body localization (MBL) using exact numerical methods is limited by the exponential growth of the Hilbert space. However, localized eigenstates display multifractality and only extend over a vanishing fraction of the Hilbert space. Here, building on this remarkable property, we develop a simple yet efficient decimation scheme to discard the irrelevant parts of the Hilbert space of the random-field Heisenberg chain. This leads to a Hilbert space fragmentation in small clusters, allowing to access larger systems at strong disorder. The MBL transition is quantitatively predicted, together with a geometrical interpretation of MBL multifractality as a shattering of the Hilbert space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ICAT: The Interactive Corpus Analysis Tool

The Interactive Corpus Analysis Tool (ICAT) is a Python library for creating dashboards to explore textual datasets and build simple binary classification models to help filter through them and focus on entries of interest. This tool uses a form of interactive machine learning (IML), a paradigm of “machine teaching” (Simard et al., 2017) that sits at the intersection of the fields of human computer interaction (HCI), visual analytics, and machine learning. The intent of ICAT is to allow subject matter experts (SME) with limited to no experience in machine learning to benefit from an iterative human-in-the-loop (HITL) approach to building their own model without needing to understand the details of the underlying algorithm. This interactivity is achieved by allowing the user to create features, label data points, and visually manipulate a representation of the features to manually cluster and investigate data, while a model is trained on the fly based on these actions. ICAT is built on top of the Panel (Holoviz, 2018) library, using a combination of Vega, a custom IPyWidget using D3, and ipyvuetify, and is intended to be used inside of a Jupyter environment.

Martindale, Nathan [Oak Ridge National Laboratory ↗
DOE OSTI2021

A medium-range structure motif linking amorphous and crystalline states

Amorphous materials have no long-range order, but there are ordered structures at short range (2–5 Å), medium range (5–20 Å) and even longer length scales. While regular and semiregular polyhedra are often found as short-range ordering in amorphous materials, the nature of medium-range order has remained elusive. Consequently, it is difficult to determine whether there exists any structural link at medium range or longer length scales between the amorphous material and its crystalline counterparts. Moreover, an amorphous material often crystallizes into a phase of different composition, with very different underlying structural building blocks, further compounding the issue. Here, we capture an intermediate crystalline cubic phase in a Pd-Ni-P amorphous alloy and reveal the structure of the medium-range order, a six-membered tricapped trigonal prism cluster (6M-TTP) with a length scale of 12.5 Å. We find that the 6M-TTP can pack periodically to several tens of nanometres to form the cube phase. Our experimental observations provide evidence of a structural link between the amorphous and crystalline phases in a Pd-Ni-P alloy at the medium-range length scale and suggest that it is the connectivity of the 6M-TTP clusters that distinguishes the crystalline and amorphous phases. These findings will shed light on the structure of amorphous materials at extended length scales beyond that of short-range order.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synopsis of SURF and SURFplus

Developed by Menikoff and Shaw, the SURF reactive burn model builds on the Ignition and Growth concept by incorporating the lead shock pressure directly into the volumetric hot-spot burn rate. The plus-extension augments the model with a late time surface burn rate due to carbon clustering. Together, SURF and SURFplus allow for robust modeling of both conventional and insensitive high explosives. Practically, the SURF burn models require shock detection and the advection of the lead shock pressure as an additional material field. Calibrated parameters for given equations of state and thermodynamic closure are dependent on initial temperature and density. An implementation in Python is given.

42 ENGINEERING↗
NASA NTRS2009

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

Bhaduri, Kanishka↗
NASA NTRS2017

Anthropometry and Biomechanics Facility Presentation to Open EVA Research Forum

NASA is required to accommodate individuals who fall within a 1st to 99th percentile range on a variety of critical dimensions. The hardware the crew interacts with must therefore be designed and verified to allow these selected individuals to complete critical mission tasks safely and at an optimal performance level. Until now, designers have been provided simpler univariate critical dimensional analyses. The multivariate characteristics of intra-individual and inter-individual size variation must be accounted for, since an individual who is 1st percentile in one body dimension will not be 1st percentile in all other dimensions. A more simplistic approach, assuming every measurement of an individual will fall within the same percentile range, can lead to a model that does not represent realistic members of the population. In other words, there is no '1st percentile female' or '99th percentile male', and designing for these unrealistic body types can lead to hardware issues down the road. Furthermore, due to budget considerations, designers are normally limited to providing only 1 size of a prototype suit, thus requiring other possible means to ensure that a given suit architecture would yield the necessary suit sizes to accommodate the entire user population. Fortunately, modeling tools can be used to more accurately model the types of human body sizes and shapes that will be encountered in a population. Anthropometry toolkits have been designed with a variety of capabilities, including grouping the population into clusters based on critical dimensions, providing percentile information given test subject measurements, and listing measurement ranges for critical dimensions in the 1st-99th percentile range. These toolkits can be combined with full body laser scans to allow designers to build human models that better represent the astronaut population. More recently, some rescaling and reposing capabilities have been developed, to allow reshaping of these static laser scans in more representative postures, such as an abducted shoulder. All of the hardware designed for use with the crew must be sized to accommodate the user population, but the interaction between subject size and hardware fit is complicated with multi-component, complex systems like a space suit. Again, prototype suits are normally only provided in a limited size range, and suited testing is an expensive endeavor; both of these factors limit the number and size of people who can be used to benchmark a spacesuit. However, modeling tools for assessing suit-human interaction can allow potential issues to be modeled and visualized. These types of modeling tools can be used for analysis of a larger combination of anthropometries and hardware types than could feasibly be done with actual human subjects and physical mockups.

Rajulu, Sudhakar↗
DOE OSTI2017

Potential impacts of climate change on the built environment: ASHRAE climate zones, building codes and national energy efficiency

Statement of the Problem: ASHRAE releases updates to 90.1 “Energy Standard for Buildings except Low-Rise Residential Buildings” every three years resulting in a 3.7%-17.3% increase in energy efficiency for buildings with each release. This is adopted by or informs building codes in nations across the globe, is the National Standard for the US, and individual states elect which release year of the standard they will enforce. These codes are built upon Standard 169 “Climatic Data for Building Design Standards,” the latest 2017 release of which defines climate zones based on 8, 118 weather stations throughout the world and data from the past 8-25 years. This data may not be indicative of the weather that new buildings built today, will see during their upcoming 30-120 year lifespan. Methodology & Theoretical Orientation: Using more modern, high-resolution datasets from climate satellites, IPCC climate models (PCM and HadGCM), high performance computing resources (Titan) and new capabilities for clustering and optimization the authors briefly analyzed different methods for redefining climate zones. Using bottom-up analysis of multiple meteorological variables which were the subject matter, experts selected as being important to energy consumption, rather than the heating/cooling degree days currently used. Findings: We analyzed the accuracy of redefined climate zones, compared to current climate zones and how the climate zones moved under different climate change scenarios, and quantified the accuracy of these methods on a local level, at a national scale for the US. Conclusion & Significance: There is likely to be a significant annual, national energy and cost (billions USD) savings that could be realized by adjusting climate zones to take into account anticipated trends or scenarios in regional weather patterns.

New, Joshua Ryan↗