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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data Quality Assessment Process for Real-Time Data-Driven Traffic Microsimulation of Smart Corridor

Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Draco Dwarf Spheroidal Galaxy in the First Year of Dark Energy Spectroscopic Instrument Data

We investigate the spatial distribution, kinematics, and metallicity of stars in the Draco dwarf spheroidal galaxy using data from the Dark Energy Spectroscopic Instrument (DESI). We identify 155 high-probability members of Draco using line-of-sight velocity and metallicity information derived from DESI spectroscopy along with Gaia Data Release 3 proper motions. We find a mean line-of-sight velocity of −290.62 ± 0.80 km s −1 with dispersion = $9.57$$^{+0.66}_{–0.62}$ km s −1 and mean metallicity [Fe/H] = −2.10 ± 0.04, consistent with previous results. We also find that Draco has a steep metallicity gradient within the half-light radius, and a metallicity gradient that flattens beyond the half-light radius. We identify eight high-probability members outside the King tidal radius, four of which we identify for the first time. These extratidal stars are not preferentially aligned along the orbit of Draco. We compute an average surface brightness of 34.02 mag arsec –2 within an elliptical annulus from the King tidal radius of 48'.1–81′.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2020

Influence of Data Acquisition Algorithms on X-ray Phase Contrast Imaging Computed Tomography

X-ray phase contrast imaging (XPCI) is a non-destructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating based Talbot-Lau XPCI system to three- dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Comprehensive Characterization of Galaxy-cool CGM Connections at z < 0.4 with DESI Year 1 Data

We investigate the relationships between the cool circumgalactic medium (CGM), traced by Ca II absorption lines, and galaxy properties at z < 0.4 using ∼900,000 galaxy–quasar pairs within 200 kpc from the Year 1 data of the Dark Energy Spectroscopic Instrument (DESI). This large data set enables us to obtain composite spectra with sensitivity reaching to the mÅ level and to explore the Ca II absorption as a function of stellar mass, star formation rate (SFR), redshift, and galaxy types, including active galactic nuclei (AGNs). Our results show a positive correlation between the absorption strength and stellar mass of star-forming galaxies with $\langle$$W$$^{Ca II}_{0}$$\rangle$ α $M$$^{0.5}_{*}$ over 3 orders of magnitude in stellar mass from ∼10 8 to 10 11 M ⊙ , while such a mass dependence is weaker for quiescent galaxies. At a fixed mass, Ca II absorption is stronger around star-forming galaxies than quiescent ones especially within impact parameters <30 kpc. Among star-forming galaxies, the Ca II absorption further correlates with SFR, following ∝SFR 0.3 . However, in contrast to the results at higher redshifts, stronger absorption is not preferentially observed along the minor axis of star-forming galaxies, indicating a possible redshift evolution of CGM dynamics resulting from galactic feedback. Moreover, no significant difference between the properties of the cool gas around AGNs and galaxies is detected. Finally, we measure the absorption profiles with respect to the virial radius of dark matter halos and show that the total Ca II mass in the CGM is comparable to the Ca mass in the ISM of galaxies.

Circumgalactic medium↗
DOE OSTI2025

Tracing the Cosmic Evolution of the Cool Circumgalactic Medium of Luminous Red Galaxies with DESI Year 1 Data

We investigate the properties of the cool circumgalactic medium (CGM) of massive galaxies and their cosmic evolution. By using the year 1 dataset of luminous red galaxies (LRGs) and QSOs from the Dark Energy Spectroscopic Instrument survey, we construct a sample of approximately 600,000 galaxy-quasar pairs and measure the radial distribution and kinematics of the cool gas traced by Mg II absorption lines as a function of galaxy properties from redshift 0.4 to redshift 1.2. Our results show that the covering fraction of the cool gas around LRGs increases with redshift, following a trend similar to the global evolution of galaxy star formation rate. At small radii (< 0.3rvir), the covering fraction anti-correlates with stellar mass, suggesting that mass-dependent processes suppress the cool gas content in the inner region. In addition, we measure the gas dispersion by modeling the velocity distribution of absorbers with a narrow and a broad components -- sigma_n ~ 160 and sigma_b ~ 380 km/s -- and quantify their relative contributions. The results show that the broad component becomes more prominent in the outer region, and its relative importance in the central region grows with increasing stellar mass. Finally, we discuss possible origins of the cool gas around massive galaxies, including the contribution of satellite galaxies and the precipitation scenario.

Chang, Yu-Ling [Taiwan, Natl. Taiwan U.] (ORCID:00↗
DOE OSTI2022

Impact of W and Z Production Data and Compatibility of Neutrino DIS Data in Nuclear Parton Distribution Functions

Vector boson production and neutrino deep-inelastic scattering (DIS) data are crucial for constraining the strange quark parton distribution function (PDF) and more generally for flavor decomposition in PDF extractions. We extend the nCTEQ15 nuclear PDFs (nPDFs) by adding the recent W and Z production data from the LHC in a global nPDF fit. The new nPDF set, referred to as nCTEQ15WZ, is used as a starting point for a follow-up study in which we assess the compatibility of neutrino DIS data with charged lepton DIS data. Specifically, we re-analyze neutrino DIS data from NuTeV, Chorus, and CDHSW, as well as dimuon data from CCFR and NuTeV. To scrutinize the level of compatibility, different kinematic regions of the neutrino data are investigated. Fits to the neutrino data alone and a preliminary global fit are performed and compared to nCTEQ15WZ.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Passive Wireless Sensors for Realtime Temperature and Corrosion Monitoring of Coal Boiler Components Under Flexible Operation (Final Technical Report)

Researchers at West Virginia University (WVU) propose to demonstrate inexpensive wireless, high-temperature sensors for real-time monitoring of the temperature and corrosion of metal components, which are commonly used in coal-fired boilers. This study presents the development of cost-effective wireless high-temperature sensors for real-time temperature and corrosion monitoring in coal-fired boilers' metal components. The focus is on fabricating and evaluating chipless radio-frequency identification (RFID) sensors capable of operating between 25-1300 ºC. Efforts were directed towards designing passive RFID sensor and interrogator antenna with a broad frequency range, optimizing a microstrip patch antenna sensor integrated into a "peel-and-stick" format for efficient application to various metal specimens without altering their geometry. Additionally, this research aimed to assess the sensor responses under accelerated high-temperature conditions, correlating corrosion and cracking mechanisms with sensor data. An investigation of through-wall data acquisition techniques was also planned, facilitating unobtrusive monitoring of sensor responses housed within metal enclosures. Ultimately, this work sought to establish a robust passive wireless sensor system for the continuous health monitoring of metal components in operational settings, thereby contributing to enhanced safety and efficiency in coal-fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Microbial sensor system for monitoring the environment and surfaces

A microbial sensor, system and method that can be used to determine the biochemical conditions of waters, saturated sediments, unsaturated soils, rhizosphere and other environments is disclosed. The flexibility of the microbial sensor design allows for the monitoring of surfaces (skin, ventilation conduits, etc.). An electrochemical microbial sensor system is composed of an indicator electrode(s) and a reference electrode. The reference electrode employs a hydrogen permeable membrane. The electrochemical system is interfaced into a signal/communication module allowing the manual or automated collection of data from field deployments and laboratory investigations. The data is transmitted using various communication technologies including Bluetooth™, cellular, satellite and radio telemetry to cloud-based data management systems. The stored data may be downloaded by users using open-source dashboard and visualization software to image the various environments and/or surfaces investigated.

Burge, Scott R.↗
DOE OSTI2024

Simultaneous reweighting of Transverse Momentum Dependent distributions

The Bayesian reweighting procedure is extended to the case of multiple independent extractions of transverse momentum dependent parton distributions (TMDs). By exploiting the data on transverse single spin asymmetries, A N , for inclusive pion production in polarized proton-proton collisions measured at RHIC, we perform a simultaneous reweighting of the quark Sivers, transversity and Collins TMD functions extracted from semi-inclusive deep inelastic scattering (SIDIS) and annihilation into hadron pairs. The impact of the implementation of the Soffer bound, as well as the differences between older and newer e + e - data, are investigated. The agreement with A N data at large-x F values, a kinematical region complementary to those explored in SIDIS measurements, is enhanced, improving the knowledge of the polarized quark TMDs in the large-x region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Opacities of singly and doubly ionized neodymium and uranium for kilonova emission modeling

ABSTRACT Even though the electromagnetic counterpart AT2017gfo to the binary neutron star merger GW170817 is powered by the radioactive decay of r-process nuclei, only few tentative identifications of light r-process elements have been made so far. One of the major limitations for the identification of heavy nuclei is incomplete or missing atomic data. While substantial progress has been made on lanthanide atomic data over the last few years, for actinides there has been less emphasis, with the first complete set of opacity data only recently published. We perform atomic structure calculations of neodymium (Z = 60) as well as the corresponding actinide uranium (Z = 92). Using two different codes [flexible atomic code (fac) and hartree–fock-relativistic (hfr)] for the calculation of the atomic data, we investigate the accuracy of the calculated data (energy levels and electric dipole transitions) and their effect on kilonova opacities. For the fac calculations, we optimize the local central potential and the number of included configurations and use a dedicated calibration technique to improve the agreement between theoretical and available experimental atomic energy levels (AELs). For ions with vast amounts of experimental data available, the presented opacities agree quite well with previous estimations. On the other hand, the optimization and calibration method cannot be used for ions with only few available AELs. For these cases, where no experimental nor benchmarked calculations are available, a large spread in the opacities estimated from the atomic data obtained with the various atomic structure codes is observed. We find that the opacity of uranium is almost double the neodymium opacity.

Flörs, A. (ORCID:0000000320242819)↗
DOE OSTI2021

Investigating User Experiences with Data Abstractions on High Performance Computing Systems

Scientific exploration generates expanding volumes of data that commonly require High Performance Computing (HPC) systems to facilitate research. HPC systems are complex ecosystems of hardware and software that frequently are not user friendly. The Usable Data Abstractions (UDA) project set out to build usable software for scientific workflows in HPC environments by undertaking multiple rounds of qualitative user research. Qualitative research investigates how individuals accomplish their work and our interview-based study surfaced a variety of insights about the experiences of working in and with HPC ecosystems. This report examines multiple facets to the experiences of scientists and developers using and supporting HPC systems. We discuss how stakeholders grasp the design and configuration of these systems, the impacts of abstraction layers on their ability to successfully do work, and the varied perceptions of time that shape this work. Examining the adoption of the Cori HPC at NERSC we explore the anticipations and lived experiences of users interacting with this system’s novel storage feature, the Burst Buffer. We present lessons learned from across these insights to illustrate just some of the challenges HPC facilities and their stakeholders need to account for when procuring and supporting these essential scientific resources to ensure their usability and utility to a variety of scientific practices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
DOE OSTI2022

Müllerian mimicry and the coloration patterns of sympatric coral snakes

Abstract Coral snakes in the genus Micrurus are venomous, aposematic organisms that signal danger to predators through vivid coloration. Previous studies found that they serve as models to several harmless species of Batesian mimics. However, the extent to which Micrurus species engage in Müllerian mimicry remains poorly understood. We integrate detailed morphological and geographical distribution data to investigate if coral snakes are Müllerian mimics. We found that coloration is spatially structured and that Micrurus species tend to be more similar where they co-occur. Though long supposed, we demonstrate for the first time that coral snakes might indeed be Müllerian mimics as they show some convergence in coloration patterns. Additionally, we found that the length of red-coloured rings in Micrurus is conserved, even at large geographic scales. This finding suggests that bright red rings may be under more substantial stabilizing selection than other aspects of coloration and probably function as a generalized signal for deterring predators.

Evolutionary Biology↗
DOE OSTI2023

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗