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DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗

NASA NTRS2019

ESD Reviews: Model Dependence in Multi-Model Climate Ensembles: Weighting, Sub-Selection and Out-Of-Sample Testing

The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates; therefore, a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near-duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognised by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.

Abramowitz, Gab↗

DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗

NASA NTRSDate not supplied

Innovations in Continuous Ultrasonic Welding of Thermoplastic Composites and Evaluation for Space Applications

The goals of NASA’s Thermoplastic Development for Exploration Applications (TDEA) Project include and assessment of thermoplastic composite joints for space structures by developing and maturing design capabilities, analysis tools and techniques, and manufacturing processes for thermoplastic composites. Through this effort the TDEA project will expand NASA’s in-house thermoplastic composite manufacturing capabilities, develop an understanding of advanced thermoplastic joining techniques relevant to space environments, evaluate the feasibility of reconfigurable composite structures, and advance structural analysis capabilities, including failure prediction of thermoplastic composites including joints. This presentation will provide an overview of the materials and manufacturing effort within the TDEA project. Three semi-crystalline and one amorphous thermoplastic composite system were selected for initial screening and provided an opportunity to gain manufacturing experience across a range of processing temperatures, rheological behavior, and composite properties. Equivalency to the National Center for Advanced Materials Performance (NCAMP) test report for Toray’s TC1225 material was established to provide confidence in in-house manufacturing and material properties were generated through baseline mechanical tests for each material. Material characterization data was generated for a subset of materials to (1) provide data for analysis and model development and (2) generate thermal, rheological and conductivity data across relevant temperatures. A key objective of the TDEA project is evaluation of thermoplastic composite joining processes suitable for in-space operations. This presentation will outline the project’s progress in fusion bonding candidate materials by common welding techniques to assess the quality, reproducibility and strength of the bond, as well as identify limitations to in-space manufacturing. The feasibility of joint disassembly and reassembly will be discussed.

thermoplastic composite↗

NASA NTRSDate not supplied

Manufacture, Characterization, and Fusion Welding of Thermoplastic Composites for Space Applications

The goals of NASA’s Thermoplastic Development for Exploration Applications (TDEA) Project include an assessment of thermoplastic composite joints for space structures by developing and maturing design capabilities, analysis tools and techniques, and manufacturing processes for thermoplastic composites. Through this effort the TDEA project will expand NASA’s in-house thermoplastic composite manufacturing capabilities, develop an understanding of advanced thermoplastic joining techniques relevant to space environments, evaluate the feasibility of reconfigurable composite structures, and advance structural analysis capabilities. This presentation will provide an overview of the materials and manufacturing effort within the TDEA project. Three semi-crystalline and one amorphous thermoplastic composite materials were selected for initial screening and provided an opportunity to gain manufacturing experience across a range of processing temperatures, rheological behavior, and composite properties. Equivalency to the National Center for Advanced Materials Performance (NCAMP) test report for Toray’s TC1225 material was established to provide confidence in in-house manufacturing and material properties were generated through baseline mechanical tests for each material. Material characterization data was generated for a subset of materials to (1) provide data for analysis and model development and (2) generate thermal, rheological and conductivity data across relevant temperatures. A key objective of the TDEA project is evaluation of thermoplastic composite joining processes suitable for in-space operations. This presentation will outline the project’s progress in fusion bonding of candidate materials to assess the quality, reproducibility and strength of the bond, as well as identify limitations to in-space manufacturing. The feasibility of joint disassembly and reassembly will be discussed.

thermoplastic composite↗

NASA NTRSDate not supplied

Manufacture, Characterization, and Fusion Welding of Thermoplastic Composites for Space Applications

thermoplastic composite↗

NASA NTRS1990

Flight crew aiding for recovery from subsystem failures

Some of the conceptual issues associated with pilot aiding systems are discussed and an implementation of one component of such an aiding system is described. It is essential that the format and content of the information the aiding system presents to the crew be compatible with the crew's mental models of the task. It is proposed that in order to cooperate effectively, both the aiding system and the flight crew should have consistent information processing models, especially at the point of interface. A general information processing strategy, developed by Rasmussen, was selected to serve as the bridge between the human and aiding system's information processes. The development and implementation of a model-based situation assessment and response generation system for commercial transport aircraft are described. The current implementation is a prototype which concentrates on engine and control surface failure situations and consequent flight emergencies. The aiding system, termed Recovery Recommendation System (RECORS), uses a causal model of the relevant subset of the flight domain to simulate the effects of these failures and to generate appropriate responses, given the current aircraft state and the constraints of the current flight phase. Since detailed information about the aircraft state may not always be available, the model represents the domain at varying levels of abstraction and uses the less detailed abstraction levels to make inferences when exact information is not available. The structure of this model is described in detail.

Hudlicka, E.↗

NASA NTRS2022

An Examination of the Recent Stability of Ozonesonde Global Network Data

The recent Assessment of Standard Operating Procedures for OzoneSondes (ASOPOS 2.0; WMO/GAW Report #268) addressed questions of homogeneity and long-term stability in global electrochemical concentration cell (ECC) ozone sounding network time series. Among its recommendations was adoption of a standard for evaluating data quality in ozonesonde time series. Total column ozone (TCO) derived from the sondes compared to TCO from Aura’s Ozone Monitoring Instrument (OMI) is a primary quality indicator. Comparisons of sonde ozone with Aura’s Microwave Limb Sounder (MLS) are used to assess the stability of stratospheric ozone. This paper provides a comprehensive examination of global ozonesonde network data stability and accuracy since 2004 in light of the sudden post-2013 TCO “dropoff” of ~3-4% that was reported previously at select stations (Stauffer et al., 2020). Comparisons with Aura OMI TCO averaged across the network of 60 stations are stable within about ±2% over the past 18 years. Sonde TCO has similar stability compared to three other TCO satellite instruments, and the stratospheric ozone measurements average to within ±5% of MLS from 50 to 10 hPa. Thus, sonde data are reliable for trends, but with a caveat applied for a subset of dropoff stations in the tropics and subtropics. The dropoff is associated with only one of two major ECC instrument types. A detailed examination of ECC serial numbers pinpoints the timing of the dropoff. However, we find that overall, ozonesonde data are stable and accurate compared to independent measurements over the past two decades.

Ryan M. Stauffer↗

NASA NTRS1985

Constitutive modeling for isotropic materials (HOST)

This report presents the results of the second year of work on a problem which is part of the NASA HOST Program. Its goals are: (1) to develop and validate unified constitutive models for isotropic materials, and (2) to demonstrate their usefulness for structural analyses of hot section components of gas turbine engines. The unified models selected for development and evaluation are that of Bodner-Partom and Walker. For model evaluation purposes, a large constitutive data base is generated for a B1900 + Hf alloy by performing uniaxial tensile, creep, cyclic, stress relation, and thermomechanical fatigue (TMF) tests as well as biaxial (tension/torsion) tests under proportional and nonproportional loading over a wide range of strain rates and temperatures. Systematic approaches for evaluating material constants from a small subset of the data base are developed. Correlations of the uniaxial and biaxial tests data with the theories of Bodner-Partom and Walker are performed to establish the accuracy, range of applicability, and integability of the models. Both models are implemented in the MARC finite element computer code and used for TMF analyses. Benchmark notch round experiments are conducted and the results compared with finite-element analyses using the MARC code and the Walker model.

Lindholm, U. S.↗

DOE OSTI2024

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies necessitates a comprehensive understanding of uncertainties in system code predictions. The forward uncertainty quantification (UQ) process involves the propagation of input uncertainties through the computational models to obtain uncertainties in the outputs. To this end, achieving an accurate estimation of input uncertainties is important, which is the focus of inverse UQ (IUQ). Traditionally, research in Bayesian IUQ within the nuclear engineering domain has largely relied on single-level Bayesian inference. While being effective for relatively small datasets, this approach encounters limitations for cases with large datasets. The use of a single-level model may prove inefficient, as the resultant posterior distributions can significantly differ when distinct subsets of data are employed. To address this issue, we employ an hierarchical Bayesian model for IUQ. Furthermore, this approach involves organizing observations into different groups based on the test conditions, thereby accommodating varying calibration parameters across these distinct groups. In this study, we developed and implemented a hierarchical Bayesian IUQ method to consider the grouping effect of critical flow measurement data from various geometries. Comparing the outcomes of IUQ under different selections of test data using hierarchical Bayesian IUQ against those obtained from single-level Bayesian IUQ, the forward propagation of hierarchical Bayesian IUQ results demonstrates a notably improved agreement with the experimental data.

42 ENGINEERING↗

DOE OSTI2022

Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning

Density functional theory (DFT) within the local or semilocal density approximations, i.e., the local density approximation (LDA) or generalized gradient approximation (GGA), has become a workhorse in the electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. The accurate prediction of band gaps using first-principles methods is time consuming, requiring hybrid functionals, quasiparticle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we, in this study, use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO 3 perovskite oxides. Relying on the results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band-gap correction of ~1.5 eV for this class of materials, where ~1 eV comes from downward shifting the valence band and ~0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band-gap correction are determined through a feature selection procedure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1992

Model reduction in a subset of the original states

A model reduction method is investigated to provide a smaller structural dynamic model for subsequent structural control design. A structural dynamic model is assumed to be derived from finite element analysis. It is first converted into the state space form, and is further reduced by the internal balancing method. Through the co-ordinate transformation derived from the states that are deleted during reduction, the reduced model is finally expressed with the states that are members of the original states. Therefore, the states in the final reduced model represent the degrees of freedom of the nodes that are selected by the designer. The procedure provides a more practical implementation of model reduction for applications in which specific nodes, such as sensor and/or actuator attachment points, are to be retained in the reduced model. Thus, it ensures that the reduced model is under the same input and output condition as the original physical model. The procedure is applied to two simple examples and comparisons are made between the full and reduced order models. The method can be applied to a linear, continuous and time-invariant model of structural dynamics with nonproportional viscous damping.

Yae, K. H.↗

NASA NTRS1993

Morphometric comparison of Icelandic lava shield volcanoes versus selected Venusian edifices

Shield volcanoes are common landforms on the silicate planets of the inner Solar System, and a wide variety have recently been documented on Venus by means of Magellan observations. In this report, we emphasize our recently completed morphometric analysis of three representative Icelandic lava shields: the classic Skjaldbreidur edifice, the low-reflief Lambahraun feature, and the monogenetic Sandfellshaed shield, as the basis for comparison with representative venusian edifices (greater than 60 km in diameter). Our detailed morphometric measurements of a representative and well-studied set of Icelandic volcanoes permits us to make comparisons with our measurements of a reasonable subset of shield-like edifices on Venus on the basis of Magellan global radar altimetry. Our study has been restricted to venusian features larger than approximately 60 km in basal diameter, on the basis of the minimum intrinsic spatial resolution (8 km) of the Magellan radar altimetry data. Finally, in order to examine the implications of landform scaling from terrestrial simple and composite shields to larger venusian varieties, we have considered the morphometry of the subaerial component of Mauna Loa, a type-locality for a composite shield edifice on Earth.

Garvin, James B.↗

DOE OSTI2022

Active Galactic Nuclei Continuum Reverberation Mapping Based on Zwicky Transient Facility Light Curves

We perform a systematic survey of active galactic nuclei (AGNs) continuum lags using ~3 days cadence gri-band light curves from the Zwicky Transient Facility. We select a sample of 94 type 1 AGNs at z < 0.8 with significant and consistent inter-band lags based on the interpolated cross-correlation function method and the Bayesian method JAVELIN. Within the framework of the "lamp-post" reprocessing model, our findings are: (1) The continuum emission (CE) sizes inferred from the data are larger than the disk sizes predicted by the standard thin-disk model. (2) For a subset of the sample, the CE size exceeds the theoretical limit of the self-gravity radius (12 lt-days) for geometrically thin disks. (3) The CE size scales with continuum luminosity as R CE ∝ L 0.48±0.04 with a scatter of 0.2 dex, analogous to the well-known radius–luminosity relation of broad Hβ. These findings suggest a significant contribution of diffuse continuum emission from the broad-line region (BLR) to AGN continuum lags. We find that the R CE –L relation can be explained by a photoionization model that assumes ~23% of the total flux comes from the diffuse BLR emission. In addition, the ratio of the CE size and model-predicted disk size anticorrelates with the continuum luminosity, which is indicative of a potential nondisk BLR lag contribution evolving with the luminosity. Finally, a robust positive correlation between the CE size and black hole mass is detected.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Coordination Chemistry in the Structural and Functional Exploration of Actinide-Based Metal-Organic Frameworks

The coordination chemistry between inorganic and organic species can be optimally exemplified by metal–organic frameworks (MOFs), whose structures and functionalities can be rationally designed from these highly tunable building blocks. The high porosity, stability, and versatile functionalities of MOFs have attracted wide-spread attention from energy-related research and pollution remediation to biomedical applications. A unique and underexplored subset of these materials are MOFs based on actinide nodes; these MOFs have distinguished themselves as a unique platform for investigating the versatile oxidation states, reactivity, and coordination chemistry of actinides. Herein, we will focus on the rational design and synthesis of actinide-based MOFs under the general guidelines of coordination chemistry for their structural and functional explorations. The dimensionality, topology, and structures of actinide-based MOFs can be controlled by selecting pre-designed building blocks of actinide-based nodes and organic linkers with certain desired coordination geometries and functionalities. Furthermore, these unique actinide-based MOFs have shown promise for applications in nuclear waste mitigation, pollution control, and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2017

Inviscid/Boundary-Layer Aeroheating Approach for Integrated Vehicle Design

A typical entry vehicle design depends on the synthesis of many essential subsystems, including thermal protection system (TPS), structures, payload, avionics, and propulsion, among others. The ability to incorporate aerothermodynamic considerations and TPS design into the early design phase is crucial, as both are closely coupled to the vehicle's aerodynamics, shape and mass. In the preliminary design stage, reasonably accurate results with rapid turn-representative entry envelope was explored. Initial results suggest that for Mach numbers ranging from 9-20, a few inviscid solutions could reasonably sup- port surface heating predictions at Mach numbers variation of +/-2, altitudes variation of +/-10 to 20 kft, and angle-of-attack variation of +/- 5. Agreement with Navier-Stokes solutions was generally found to be within 10-15% for Mach number and altitude, and 20% for angle of attack. A smaller angle-of-attack increment than the 5 deg around times for parametric studies and quickly evolving configurations are necessary to steer design decisions. This investigation considers the use of an unstructured 3D inviscid code in conjunction with an integral boundary-layer method; the former providing the flow field solution and the latter the surface heating. Sensitivity studies for Mach number, angle of attack, and altitude, examine the feasibility of using this approach to populate a representative entry flight envelope based on a limited set of inviscid solutions. Each inviscid solution is used to generate surface heating over the nearby trajectory space. A subset of a considered in this study is recommended. Results of the angle-of-attack sensitivity studies show that smaller increments may be needed for better heating predictions. The approach is well suited for application to conceptual multidisciplinary design and analysis studies where transient aeroheating environments are critical for vehicle TPS and thermal design. Concurrent prediction of aeroheating environments, coupled with the use of unstructured methods, is considered enabling for TPS material selection and design in conceptual studies where vehicle mission, shape, and entry strategies evolve rapidly.

Lee, Esther↗

DOE OSTI2020

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗