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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Concerning colour: The effect of environment on type Ia supernova colour in the dark energy survey

ABSTRACT Recent analyses have found intriguing correlations between the colour (c) of type Ia supernovae (SNe Ia) and the size of their ‘mass-step’, the relationship between SN Ia host galaxy stellar mass (Mstellar) and SN Ia Hubble residual, and suggest that the cause of this relationship is dust. Using 675 photometrically classified SNe Ia from the Dark Energy Survey 5-yr sample, we study the differences in Hubble residual for a variety of global host galaxy and local environmental properties for SN Ia subsamples split by their colour. We find a 3σ difference in the mass-step when comparing blue (c < 0) and red (c > 0) SNe. We observe the lowest r.m.s. scatter (∼0.14 mag) in the Hubble residual for blue SNe in low mass/blue environments, suggesting that this is the most homogeneous sample for cosmological analyses. By fitting for c-dependent relationships between Hubble residuals and Mstellar, approximating existing dust models, we remove the mass-step from the data and find tentative ∼2σ residual steps in rest-frame galaxy U − R colour. This indicates that dust modelling based on Mstellar may not fully explain the remaining dispersion in SN Ia luminosity. Instead, accounting for a c-dependent relationship between Hubble residuals and global U − R, results in ≤1σ residual steps in Mstellar and local U − R, suggesting that U − R provides different information about the environment of SNe Ia compared to Mstellar, and motivating the inclusion of galaxy U − R colour in SN Ia distance bias correction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

National Park Service Bus Electrification Study: 2020 Report

This report summarizes important considerations for implementing BEBs in the three national park fleets, detailing information about current buses at each fleet, electric bus demonstration vehicles, as well as performance evaluations of BEBs in Zion, Bryce, and Yosemite. Results include in-use data collection results reporting metrics such as average bus speed, energy usage per trip, and daily distance traveled, as well as effects of high heating, ventilation, and air conditioning (HVAC) system use to both heat and cool the buses, emissions estimations before and after use of electric buses, operating costs, electric vehicle infrastructure, maintenance, and bus driver user experience survey information. Analysis results from this project will help the NPS understand how BEBs and future expansion of BEBs could assist in meeting their bottom line and operational goals and assist NPS in choosing appropriate locations for future BEB deployments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model

Abstract Background Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter‐reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case. Purpose This study aims to develop a deep learning‐based technique to generate multiple CTV contours for each case, simulating the inter‐reader variability in the clinical practice. Methods We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG‐PET), x‐ray computed tomography (CT), and pre‐contrast T1‐weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi‐modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model‐based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter‐reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation. Results The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state‐of‐the‐art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state‐of‐the‐art models). It also achieved the second‐highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation. Conclusions The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter‐reader variability in CTV delineation.

Dong, Yafei [Yale Biomedical Imaging Institute Yal↗
DOE OSTI2023

Standoff inspection using geometry-informed full-wavefield response measurements

A vibrometer may measure acoustic responses in portions of a structure along a scan path to acoustic excitation of the structure. A ranging device may measure distances to the portions of the structure along the scan path. A three-dimensional point cloud may be generated based on the acoustic responses in the portions of the structure and the distances to the portions of the structure. The three-dimensional point cloud may include points representing geometry of the portions of the structure. The points may be associated with the acoustic responses in corresponding portions of the structure. One or more properties of the structure may be determined based on an analysis of the three-dimensional point cloud.

Flynn, Eric Brian↗
DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Improved Accuracy in Semi-Experimental Structure Determination by Resolving Problems Associated with Rotation of Principal Inertial Axes of Isotopologues: Structures of 1,3-Oxazole ( c -C 3 H 3 NO)

The rotational spectrum of the normal isotopologue of 1,3-oxazole (c-C 3 H 3 NO) was observed from 43 to 750 GHz. Over 3900 transitions for the ground vibrational state are measured, assigned, and least-squares fit to sextic centrifugally distorted-rotor Hamiltonians. The measured frequencies and resulting spectroscopic constants from this extended spectral range, combined with previous measurements of the nuclear quadrupole coupling constants, will facilitate astronomical searches for oxazole across the majority of the range of modern radiotelescopes. Spectra for a set of 30 oxazole isotopologues, which include multiple isotopic substitutions of each atom, are used to determine the first semi-experimental equilibrium ($r$$^{SE}_{e}$) structure and semi-experimental substitution structure ($r$$^{SE}_{e}$), each using CCSD(T) computed values for the vibration–rotation interaction and electron-mass corrections. The large number of isotopologues, including 21 isotopologues observed for the first time, and the redundant substitutions of each atom provide sufficient spectroscopic information to determine the $r$$^{SE}_{e}$ structure with the expected high level of accuracy and precision (0.0001 or 0.0002 Å in bond distances and 0.013 to 0.025° in bond angles). In the course of this study, we analyzed a known issue for some $r$$^{SE}_{e}$ structure determinations of near-oblate asymmetric tops in which inclusion of individual isotopologues degrades the structure determination. We demonstrate that this problem primarily arises from the difference in the values of the computed vibration–rotation interaction corrections as evaluated at the computed re geometry vs the $r$$^{SE}_{e}$ geometry of the “real” molecule. Our solution to this problem substantially improves the $r$$^{SE}_{e}$ structure of oxazole and likely can be generalized to many other molecules.

Chemical structure↗
DOE OSTI2021

Spectral Addressability in a Modular Two Qubit System

The combination of structural precision and reproducibility of synthetic chemistry is perfectly suited for the creation of chemical qubits, the core units of a quantum information science (QIS) system. By exploiting the atomistic control inherent to synthetic chemistry, we address a fundamental question of how the spin–spin distance between two qubits impacts electronic spin coherence. To achieve this goal, in this study we designed a series of molecules featuring two spectrally distinct qubits, an early transition metal, Ti 3+ , and a late transition metal, Cu 2+ with increasing separation between the two metals. The spectral separation between the two metals enables us to probe each metal individually in the bimetallic species and compare it with monometallic control samples. Coherence times found to be governed by the distance to nuclear spins on the other qubit’s ligand framework. This finding offers guidance for the design of spectrally addressable molecular qubits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Transitions in the morphology and critical stresses of gliding dislocations in multiprincipal element alloys

Refractory multiprincipal element alloys (MPEAs) are promising material candidates for high-temperature, high-strength applications. However, their deformation mechanisms, particularly at the dislocation scale, are unusual compared to those of conventional alloys, making it challenging to understand the origin of their high strength. Here, using an atomistically informed phase-field dislocation dynamics model, we study transitions in the morphology and critical stresses of long gliding screw dislocations over extended distances in a refractory MPEA. The model MPEA crystal accounts for the atomic-scale fluctuations in chemical composition across the glide planes via spatially correlated lattice energies and for the differences in glide resistances between screw and edge dislocations of unit length. Here, we show that the dislocation moves in a stop-start motion, alternating between wavy morphology in free flight and nearly recovered straight screw orientation in full arrest. The periods of wavy glide are due to variable kink-pair formation and migration rates along the length, where portions with higher rates glide more quickly. The critical stress to initiate motion corresponds to the stress required to form and migrate a kink pair at the weakest region along the length of the dislocation. Heterogeneity in lattice energy leads to variability in the local stress-strain response and to a strain hardening-like response, in which the critical stress to reactivate glide increases with glide distance. Statistical assessment of hundreds of realizations of dislocations indicates that the amount of hardening directly scales with the dispersion in underlying lattice energy.

3-dimensional systems↗
DOE OSTI2021

Commissioning of a Cask Enabling Characterization of Irradiated Nuclear Fuels with Pulsed Neutrons

Irradiation tests are a key component of nuclear fuel development and identifying typical and atypical regions in the irradiated fuel volume relies on very few characterization techniques. The goal of the effort reported here is to provide complementary measurements adding to the available parameter space for post irradiation examination as well as to inform subsequent hot cell PIE examinations by identifying typical and atypical regions with respect to microstructure, tomographic data, or isotope densities. Pulsed neutrons, enabling diffraction as well as energy-resolved neutron imaging and neutron absorption resonance spectroscopy, offer unique capabilities for this purpose. Time-of-flight neutron diffraction has the potential to offer efficient, non-destructive and non-contact microstructural characterization of irradiated fuel pins with spatial resolution of 1 mm 3 to 1 cm 3 while for energy-resolved neutron imaging (and by extension tomography) a resolution of 100 μm was demonstrated. The potential results include crystallinity vs. amorphous volumes and microstructural information such as phase compositions, lattice strains (indicative of residual stresses or chemistry variations) and textures from the diffraction data as well as distances (e.g. pellet to cladding), cracks, and isotopic distributions of minor actinides, fission products as well as fission gas partial pressures e.g. in the plenum from energy resolved neutron imaging.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Progress Report on Mockup Irradiation Capsule Fuel Measurements at LANSCE

Irradiation tests are a key component of nuclear fuel development and identifying typical and atypical regions in the irradiated fuel volume relies on very few characterization techniques. The goal of the effort reported here is to provide complementary measurements adding to the available parameter space for post irradiation examination as well as to inform subsequent hot cell PIE examinations by identifying typical and atypical regions with respect to microstructure, tomographic data, or isotope densities. Pulsed neutrons, enabling diffraction as well as energy-resolved neutron imaging and neutron absorption resonance spectroscopy, offer unique capabilities for this purpose. Time-of-flight neutron diffraction has the potential to offer efficient, non-destructive and non-contact microstructural characterization of irradiated fuel specimen with spatial resolution of 1 mm 3 to 1 cm 3 while for energy-resolved neutron imaging (and by extension tomography) a resolution of 100 μm 3 was demonstrated. The potential results include crystallinity vs. amorphous volumes and microstructural information such as phase compositions, lattice strains (indicative of residual stresses or chemistry variations) and textures from the diffraction data as well as distances (e.g. pellet to cladding), cracks, and isotope densities of minor actinides, fission products as well as fission gas partial pressures e.g. in the plenum from energy resolved neutron imaging.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2021

Geometrical defect detection for additive manufacturing with machine learning models

This study proposed a scheme based on Machine Learning (ML) models to detect geometric defects of additively manufactured objects. The ML models are trained with synthetic 3D point clouds with defects and then applied to detect defects in actual production. Using synthetic 3D point clouds rather than experimental data could save a huge amount of training time and costs associated with many prints for each design. Besides distance differences of individual points between source and target point clouds, this scheme uses a new concept called “patch” to capture macro-level information about nearby points for ML training and implementation. Numerical comparisons of prediction results on experimental data with different shapes showed that the proposed scheme outperformed the existing Z-difference method in the literature. Five ML methods (Bagging of Trees, Gradient Boosting, Random Forest, K-nearest Neighbors and Linear Supported Vector Machine) were compared under various conditions, such as different point cloud densities and defect sizes. Bagging and Random Forest were found the two best models regarding predictability; and the right patch size was found to be at 20. The proposed ML-based scheme is applicable to in-situ defect detection during additive manufacturing with the aid of a proper 3D data acquisition system.

Additive manufacturing↗
DOE OSTI2021

Patch selection by bumble bees navigating discontinuous landscapes

Pollen and nectar resources are unevenly distributed over space and bees must make routing decisions when navigating patchy resources. Determining the patch selection process used by bees is crucial to understanding bee foraging over discontinuous landscapes. To elucidate this process, we developed four distinct probability models of bee movement where the size and the distance to the patch determined the attractiveness of a patch. A field experiment with a center patch and four peripheral patches of two distinct sizes and distances from the center was set up in two configurations. Empirical transition probabilities from the center to each peripheral patch were obtained at two sites and two years. The best model was identified by comparing observed and predicted transition probabilities, where predicted values were obtained by incorporating the spatial dimensions of the field experiment into each model’s mathematical expression. Bumble bees used both patch size and isolation distance when selecting a patch and could assess the total amount of resources available in a patch. Bumble bees prefer large, nearby patches. This information will facilitate the development of a predictive framework to the study of bee movement and of models that predict the movement of genetically engineered pollen in bee-pollinated crops.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Proton-coupled electron transfer across benzimidazole bridges in bioinspired proton wires

Designing molecular platforms for controlling proton and electron movement in artificial photosynthetic systems is crucial to efficient catalysis and solar energy conversion. The transfer of both protons and electrons during a reaction is known as proton-coupled electron transfer (PCET) and is used by nature in myriad ways to provide low overpotential pathways for redox reactions and redox leveling, as well as to generate bioenergetic proton currents. Herein, we describe theoretical and electrochemical studies of a series of bioinspired benzimidazole-phenol (BIP) derivatives and a series of dibenzimidazole-phenol (BI 2 P) analogs with each series bearing the same set of terminal proton-accepting (TPA) groups. The set of TPAs spans more than 6 pK a units. These compounds have been designed to explore the role of the bridging benzimidazole(s) in a one-electron oxidation process coupled to intramolecular proton translocation across either two (the BIP series) or three (the BI 2 P series) acid/base sites. These molecular constructs feature an electrochemically active phenol connected to the TPA group through a benzimidazole-based bridge, which together with the phenol and TPA group form a covalent framework supporting a Grotthuss-type hydrogen-bonded network. Infrared spectroelectrochemistry demonstrates that upon oxidation of the phenol, protons translocate across this well-defined hydrogen-bonded network to a TPA group. The experimental data show the benzimidazole bridges are non-innocent participants in the PCET process in that the addition of each benzimidazole unit lowers the redox potential of the phenoxyl radical/phenol couple by 60 mV, regardless of the nature of the TPA group. Using a series of hypothetical thermodynamic steps, density functional theory calculations correctly predicted the dependence of the redox potential of the phenoxyl radical/phenol couple on the nature of the final protonated species and provided insight into the thermodynamic role of dibenzimidazole units in the PCET process. This information is crucial for developing molecular “dry proton wires” with these moieties, which can transfer protons via a Grotthuss-type mechanism over long distances without the intervention of water molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The Atacama Cosmology Telescope: DR4 maps and cosmological parameters

In this paper, we present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013–2016 at 98 and 150 GHz. The maps cover more than 17,000 deg 2 , the deepest 600 deg 2 with noise levels below 10µK-arcmin. We use the power spectrum derived from almost 6,000 deg 2 of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H 0 . By combining ACT data with large-scale information from WMAP we measure H 0 = 67.6±1.1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently-measured Planck satellite estimate (from ACT alone we find H 0 = 67.9 ± 1.5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5–2.2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Reporter-Spin-Assisted T 1 Relaxometry

A single-spin quantum sensor can quantitatively detect and image fluctuating electromagnetic fields via their effect on the sensor spin’s relaxation time, thus revealing important information about the target solid-state or molecular structures. However, the sensitivity and spatial resolution of spin relaxometry are often limited by the distance between the sensor and target. Here, we propose an alternative approach that leverages an auxiliary reporter spin in conjunction with a single-spin sensor, a diamond nitrogen-vacancy (N-V) center. We show that this approach can realize a 100-fold measurement sensitivity improvement for realistic working conditions and we experimentally verify the proposed method using a single shallow N-V center. Our work opens up a broad path of inquiry into a range of possible spin systems that can serve as relaxation sensors without the need for optical initialization and readout capabilities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Comparing Mapper Graphs of Artificial Neuron Activations

The mapper graph is a popular tool from topological data analysis that provides a graphical summary of point cloud data. It has been used to study data from cancer research, sports analytics, neurosciences, and machine learning. In particular, mapper graphs have been used recently to visualize the topology of high-dimensional artificial neural activations from convolutional neural networks and large language models. However, a key question that arises from using mapper graphs across applications is how to compare mapper graphs to study their structural differences. In this paper, we introduce a distance between mapper graphs using tools from optimal transport. We demonstrate the utility of such a distance by studying the topological changes of neural activations across convolutional layers in deep learning, as well as by capturing the loss of structural information for multiscale mapper.

mapper graphs, computational topology, machine lea↗
DOE OSTI2023

Extending quantum key distribution through proxy re-encryption

Modern quantum key distribution (QKD) network designs are based on sending photons from one node to another and require free-space or dedicated fiber optic cables between nodes. The purpose of this is to co-generate secret key material on both sides of the quantum channel. In addition to this quantum link, there are several insecure classical channels that allow QKD algorithms to exchange book-keeping information and send symmetrically encrypted data. The attenuation of photons transmitted through fiber becomes too high to practically generate key material over fiber at distances of more than 100 km. Free-space transmission through the atmosphere or the vacuum of space can reduce attenuation, but at the cost of system complexity and sensitivity to other impairments, such as weather. To extend the effective range of QKD networks, we present a method that combines QKD algorithms with post-quantum, homomorphic key-switching to allow multiple parties to effectively share secret key material over longer distances through semi-trusted relay nodes. We define how such a system should work for arbitrary network topologies and provide proofs that our scheme is both correct and secure. We assess the feasibility of this solution by building and evaluating two implementations based on lattice-based cryptography: learning with errors.

97 MATHEMATICS AND COMPUTING↗