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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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NASA NTRS2020

Battery Health Quantification for TDRS Spacecraft by Using Signature Discriminability Measurement

The NASA/GSFC Space Network Project Office (SN) currently operates a constellation of ten geosynchronous TDRS spacecraft launched over the past 30 years. The SN project collects up to 16.5 Gigabytes of telemetry every month. Generally, the spacecraft health and functionality are obtained by the use of real-time telemetry data for the multiple spacecraft subsystems, which are transmitted to the main ground station at the White Sands Complex in Las Cruces, NM. Recently, the SN has instituted a program of Big Data to analyze the large amounts of data using a variety of tools including Machine Learning, Artificial Intelligence, development of training sets, and a variety of mathematical modeling tools. The goal is to improve spacecraft management and obtain a more accurate prediction of the spacecraft end of life. The combination of these efforts with those of the Aerospace Corporation, which has a contract with the SN to produce yearly reliability estimates for the TDRS fleet, will be performed. This paper presents a new concept called telemetry quality quantification (TQQ) and discusses the progress that has been made in battery performance estimation for the second-generation TDRS spacecraft using a signature discriminability measures (SDM) algorithm combined with the Aerospace Corp. battery life estimation models. This activity is important because many of the TDRS fleet of spacecraft have exceeded their on-orbit design lifetime and, therefore, NASA must carefully manage the spacecraft to continue operations while avoiding an end-of-mission scenario that leaves a non-functioning spacecraft in geosynchronous orbit.

Ma, Kenneth Y.↗

DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Plasma Perturbations in High-Speed Probing of Hall Thruster Discharge Chambers: Quantification and Mitigation

An experimental investigation is presented to quantify the effect of high-speed probing on the plasma parameters inside the discharge chamber of a 6-kW Hall thruster. Understanding the nature of these perturbations is of significant interest given the importance of accurate plasma measurements for characterizing thruster operation. An array of diagnostics including a high-speed camera and embedded wall probes is employed to examine in real time the changes in electron temperature and plasma potential induced by inserting a high-speed reciprocating Langmuir probe into the discharge chamber. It is found that the perturbations onset when the scanning probe is downstream of the electron temperature peak, and that along channel centerline, the perturbations are best characterized as a downstream shift of plasma parameters by 15-20% the length of the discharge chamber. A parametric study is performed to investigate techniques to mitigate the observed probe perturbations including varying probe speed, probe location, and operating conditions. It is found that the perturbations largely disappear when the thruster is operated at low power and low discharge voltage. The results of this mitigation study are discussed in the context of recommended methods for generating unperturbed measurements of the discharge chamber plasma.

Jorns, Benjamin A.↗

DOE OSTI2020

1D to 2D Transition in Tellurium Observed by 4D Electron Microscopy

A new microwave-enhanced synthesis method for the production of tellurium nanostructures is reported here—with control over products from the 1D regime (sub-5 nm diameter nanowires), to nanoribbons, to the 2D tellurene regime—along with a new methodology for local statistical quantification of the crystallographic parameters of these materials at the nanometer scale. Using a direct electron detector and image-corrected microscope, large and robust 4D scanning transmission electron microscopy datasets for accurate structural analysis are obtained. These datasets allow the adaptation of quantitative techniques originally developed for X-ray diffraction (XRD) refinement analyses to transmission electron microscopy, enabling the first demonstration of sub-picometer accuracy lattice parameter extraction while also obtaining both the size of the coherent crystallite domains and the nanostrain, which is observed to decrease as nanowires transition to tellurene. This new local analysis is commensurate with global powder XRD results, indicating the robustness of both the new synthesis approach and new structural analysis methodology for future scalable production of 2D tellurene and characterization of nanomaterials.

2D materials↗

DOE OSTI2024

Nuclear-matter saturation and symmetry energy within Δ -full chiral effective field theory

Nuclear saturation and the symmetry energy are key properties of low-energy nuclear physics that depend on fine details of the nuclear interaction. The equation of state around saturation is also an important anchor for extrapolations to higher densities and studies of neutron stars. Here we develop a unified statistical framework that uses realistic nuclear forces to link the theoretical modeling of finite nuclei and infinite nuclear matter. We construct fast and accurate emulators for nuclear-matter observables and employ an iterative history-matching approach to explore and reduce the enormous parameter domain of Δ -full chiral interactions. We perform rigorous uncertainty quantification and find that model calibration including O 16 observables gives saturation predictions that are more precise than those that only use few-body data. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Evaluate opportunities for monitoring the oxidation states of technetium in both organic and aqueous phases using Raman spectroscopy

An important isotope in the nuclear fuel cycle, 99 Tc is characterized by its long half-life of 2.1 × 10 5 years and its beta emission. It is generated through the fission of 235 U. As the burnup levels of nuclear waste continue to rise, the concentration of technetium in spent fuel increases, leading to a heightened interest in its quantification during the reprocessing of spent nuclear fuel. In HNO3 environments, technetium predominantly exists as TcO 4 − ; however, it tends to interfere with reducing agents, decreasing the separation efficiency for actinides. Therefore, accurate quantitative determination of technetium is critical to controlling its distribution and mitigating its negative effect on the reprocessing of spent fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2003

The Interaction Between an Insoluble Particle and an Advancing Solid/Liquid Interface: Micro-Gravity Experiments and Theoretical Developments

The interaction of an insoluble particle with an advancing solid/liquid interface (SLI) has been a subject of investigation for the past four decades. While the original interest stemmed from geology applications (e.g., frost heaving in soil), researchers soon realized that the complex science associated with such an interaction is relevant to many other scientific fields encompassing metal matrix composites (MMCs), high temperature superconductors, inclusion management in steel, growth of monotectics, and preservation of biological cells. During solidification of a liquid containing an insoluble particle, three distinct interaction phenomena have been experimentally observed: instantaneous engulfment of the particle, continuous pushing, and particle pushing followed by engulfment. It was also observed that for given experimental conditions and particle size there is a critical solidification velocity, V(sub cr), above which a particle is engulfed. During solidification of MMCs pushing leads to particle agglomeration at the grain boundaries and this has detrimental effects on mechanical properties of the casting. Consequently, the process must be designed for instantaneous engulfment to occur. This implies the development of accurate theoretical models to predict V(sub cr), and perform benchmark experiments to test the validity of such models. Although considerable progress has been made in understanding the pushing/engulfment phenomenon (PEP), its quantification in terms of the material and processing parameters remains a focus of research. Since natural convection currents occurring during terrestrial solidification experiments complicate the study of PEP, execution of experiments on the International Space Station (ISS) has been approved and funded by NASA. Extensive terrestrial (1g) experiments and preliminary micro-gravity (mu g) experiments on two space shuttle missions have been conducted in preparation for future experiments on the ISS. The investigated systems included metal-ceramic particles (pure aluminum - zirconia particles) and transparent organic - non-reactive particles (succinonitrile - polystyrene and biphenyl - glass). This paper will discuss the experimental results obtained in both lg and pg conditions and the influence of the natural convection on V(sub cr). A summary of past mathematical models and our recent theoretical developments will also be presented to explain the experimentally observed particle/SLI interaction.

Catalina, Adrian V.↗

NASA NTRS2015

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Leser, Patrick E.↗

DOE OSTI2023

Quantification of SARS-CoV-2 spike protein expression from mRNA vaccines using isotope dilution mass spectrometry

The advent of mRNA vaccine technology has been vital in rapidly creating and manufacturing COVID-19 vaccines at an industrial scale. To continue to accelerate this leading vaccine technology, an accurate method is needed to quantify antigens produced by the transfection of cells with a mRNA vaccine product. This will allow monitoring of protein expression during mRNA vaccine development and provide information on how changes to vaccine components affects the expression of the desired antigen. Developing novel approaches that allow for high-throughput screening of vaccines to detect changes in antigen production in cell culture prior to in vivo studies could aid vaccine development. Here, we have developed and optimized an isotope dilution mass spectrometry method to detect and quantify the spike protein expressed after transfection of baby hamster kidney cells with expired COVID-19 mRNA vaccines. Five peptides of the spike protein are simultaneously quantified and provide assurance that protein digestion in the region of the target peptides is complete since results between the five peptides had a relative standard deviation of less than 15 %. In addition, two housekeeping proteins, actin and GAPDH, are quantified in the same analytical run to account for any variation in cell growth within the experiment. IDMS allows a precise and accurate means to quantify protein expression by mammalian cells transfected with an mRNA vaccine.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Revisiting spontaneous Raman scattering for direct oxygen atom quantification

In this paper, the counterintuitive and largely unknown Raman activity of oxygen atoms is evaluated for its capacity to determine absolute densities in gases with significant O-density. The study involves C O 2 microwave plasma to generate a self-calibrating mixture and establish accurate cross sections for the 3 P 2 ↔ 3 P 1 and 3 P 2 ↔ 3 P 0 transitions. The approach requires conservation of stoichiometry, confirmed within experimental uncertainty by a 1D fluid model. The measurements yield σ J = 2 → 1 = 5.27 ± s y s : 0.53 r a n d : 0.17 × 10 - 31 c m 2 / s r and σ J = 2 → 0 = 2.11 ± s y s : 0.21 r a n d : 0.06 × 10 - 31 c m 2 / s r , and the detection limit is estimated to be 1 × 10 15 c m - 3 for systems without other scattering species.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS2000

Passive, Direct-Read Monitoring System for Selective Detection and Quantification of Hydrogen Chloride

Monitoring the exposure of an employee to hydrogen chloride or hydrochloric acid in the presence of other acids has been a challenge to the industrial hygiene community. The capability of a device to differentiate the levels of acid vapors would allow for more accurate determinations of exposure and therefore improved occupational health. In this work, a selective direct-read colorimetric badge system was validated for Short Term Exposure Limit (STEL) monitoring of hydrogen chloride. The passive colorimetric badge system consists of a direct reading badge and a color scale. The badge has a coated indicator layer with a diffusive resistance in the shape of an exclamation mark. An exclamation mark will appear if hydrogen chloride is present in the atmosphere at concentrations at or above 2.0 ppm. By using the color scale, the intensity of the color formed on the badge can be further quantified up to 25 ppm. The system was validated according to a protocol based on the NIOSH Protocol for the Evaluation of Passive Monitors. The badge was exposed to relative humidities ranging from 11% to 92%, temperatures ranging from 7 C to 400 C and air velocities ranging from 5 cm/sec to 170 cm/sec. All experiments were conducted in a laboratory vapor generation system. Hydrofluoric acid, nitric acid, sulfuric acid, chlorine, hydrogen sulfide and organic acids showed no effect on the performance of the hydrogen chloride monitoring system. The passive badge and color scale system exceeded the accuracy requirements as defined by NIOSH. At ambient conditions, the mean coefficient of variation was 10.86 and the mean bias was 1.3%. This data was presented previously at the American Industrial Hygiene Conference and Exposition in Toronto, Canada in June 1999.

Chapman, K. B.↗

DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1982

Cloud cover sampling capability of various satellite combinations for ISCCP

One of the most important objectives of the International Satellite Cloud Climatology Project (ISCCP) is the quantification of cloud cover and associated radiation parameters for climate research. The time-varying nature of cloud cover requires a measurement system that obtains cloud radiance at sufficient time intervals for determining accurate values of cloudiness for the appropriate averaging period. Current plans for the ISCCP call for such day to be acquired every 3 hours during both data and night to ensure that the diurnal cycle of cloud cover is adequately sampled. The satellite system proposed is an array of geostationary and polar-orbiting satellites. The error in mean cloud cover estimates for observation systems which do not include geostationary satellites are quantified. Candidates for such a system include two NOAA Sun-synchronous satellites with daylight equatorial-crossing times of 0730 and 1430, and a USSR satellite in an 81 deg. inclination, 900-km altitude orbit.

Harrison, E. F.↗

DOE OSTI2026

Uncertainty quantification of fireball features extracted from nuclear test films using computer vision

Films from the US’s historic nuclear testing era comprise the only extensive collection of imagery depicting high-yield detonations. These films offer unique insights into the characteristics of flows occurring on scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models used to describe nuclear detonations. In recent work, we implemented modern computer vision and machine learning techniques to extract features of the fireball following nuclear detonation. With a training dataset of fireball films, we fine-tuned a You Only Look Once 11 (YOLO11) model to detect and track the fireball. Applied to a video, the outer bounding box produced in each frame by YOLO11 is used as an input prompt to Meta’s Segment Anything Model 2 (SAM2), which is shown to accurately predict the boundary of the fireball over time with high resolution. These state-of-the-art computer vision foundation models exhibit impressive visual accuracy in their results but lack an output of values that robustly quantify uncertainty in scientific applications. In this paper, we develop procedures for uncertainty quantification of extracted fireball features. We outline the application of a parallel attention mechanism to calculate uncertainty ranges that complement and better pose model validation data. This higher quality fireball validation data may serve to improve prognostic models describing nuclear detonations in support of nuclear forensic and emergency response activities.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗

DOE OSTI2022

Assessing entropy for catalytic processes at complex reactive interfaces

When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.

Kollias, Loukas↗

DOE OSTI2020

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part II - Autonomous Navigation

Nuclear sites across the Unites States and other nations in the world, are prone to extreme radiation effects. To understand the radiation hazards, plan the cleanup efforts; and to meet the quality standards and guidelines, it is important to accurately characterize any nuclear facility. One of the current methods in characterizing facilities is the use of advanced autonomous systems. These are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. Conventional methods of taking radiation measurements by hand within or around the containment areas, and analyzing the collected data to obtain the result are: Ineffective, Puts scientists at risk of Radiation, Not cost-effective. The platform uses the Robotic Operating Systems (ROS) to integrate multiple sensors seamlessly. It can operate near real time by using a distributed processing system to handle large amounts of data: Surrounding cameras, A multi-channel 3D Lidar, Manipulator arm, High-performance gamma-ray spectrometer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An expectation–maximization framework for comprehensive prediction of isoform-specific functions

Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. We present isoform interpretation, a method that uses expectation–maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗