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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Test Vector Development for Verification and Validation of Heavy-Duty Autonomous Vehicle Operations

The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation. Continuing this prior work, this paper focuses on the generation of test cases based on the critical scenarios identified that can be used to prioritize either the most common nominal driving scenarios or the least common severe driving scenarios. These test cases can then be used validate, through simulation or real-world testing, the operating design domain (ODD) for a generalized driving mission and built upon to identify spatial and temporal impacts on the driving mission of an autonomous vehicle.

Siekmann, Adam↗
DOE OSTI2026

Accelerating multigrid with streaming chiral SVD for Wilson fermions in lattice QCD

A modification to the setup algorithm for the multigrid preconditioner of Wilson fermions in lattice QCD is presented. A larger basis of test vectors than that used in regular multigrid is calculated by the smoother and truncated by singular value decomposition on the chiral components of the test vectors. The truncated basis is used to form the prolongation and restriction matrices of the multigrid hierarchy. This modification of the setup method is demonstrated to increase the convergence of linear solvers on an anisotropic lattice with m π ≈ 239 MeV from the Hadron Spectrum Collaboration and an isotropic lattice with m π ≈ 220 MeV from the MILC Collaboration. The lattice volume dependence of the method is also examined. Increasing the number of test vectors improves speedup up to a point, but storing these vectors becomes impossible in limited memory resources such as GPUs. To address storage cost, we implement a streaming singular value decomposition of the basis of test vectors on the chiral components and demonstrate a decrease in the number of fine level iterations by a factor of 1.7 for m q ≈ m crit

Iterative methods↗
DOE OSTI2025

Chiral rank-$k$ truncations for the multigrid preconditioner of Wilson fermions in lattice QCD

We present a modification to the setup algorithm for the multigrid preconditioner of Wilson fermions in lattice QCD. A larger number of test vectors than that used in conventional multigrid is generated by the smoother. This set of test vectors is then truncated by a singular value decomposition on the chiral components of the test vectors, which are subsequently used to form the prolongation and restriction matrices of the multigrid hierarchy. This modification is demonstrated to improve the convergence of linear equations on an anisotropic lattice with 𝑚𝜋 ≈ 280 MeV from the Hadron Spectrum Collaboration and an isotropic lattice with 𝑚𝜋 ≈ 220 MeV from the MILC Collaboration. The lattice volume dependence of the method is also examined.

Whyte, Travis [Jülich Supercomputing Center, Jülic↗
DOE OSTI2022

Binding of adenovirus species C hexon to prothrombin and the influence of hexon on vector properties in vitro and in vivo

The majority of adenovirus (Ad) vectors are based on human Ad type 5, which is a member of Ad species C. Species C also includes the closely-related types 1, 2, 6, 57 and 89. It is known that coagulation factors bind to Ad5 hexon and play a key role in the liver tropism of Ad5 vectors, but it is unclear how coagulation factors affect vectors derived from other species C Ads. We evaluated species C Ad vectors both in vitro and following intravenous injection in mice. To assess the impact of hexon differences, we constructed chimeric Ad5 vectors that contain the hexon hypervariable regions from other species C types, including vectors with hexon mutations that decreased coagulation factor binding. After intravenous injection into mice, vectors with Ad5 or Ad6 hexon had strong liver tropism, while vectors with chimeric hexon from other Ad types had weaker liver tropism due to inhibition by natural antibodies and complement. In addition, we discovered a novel ability of hexon to bind prothrombin, which is the most abundant coagulation factor in blood, and we found striking differences in the affinity of Ads for human, mouse and bovine coagulation factors. When compared to Ad5, vectors with non-Ad5 species C hexons had considerably higher affinity for both human and mouse prothrombin. Most of the vectors tested were strongly dependent on coagulation factors for liver transduction, but vectors with chimeric Ad6 hexon showed much less dependence on coagulation factors than other vectors. We found that in vitro neutralization experiments with mouse serum predicted in vivo behavior of Ad5 vectors, but in vitro experiments did not predict the in vivo behavior of vectors based on other Ad types. In sum, hexons from different human Ad species C viruses confer diverse properties on vectors, including differing abilities to target the liver.

Tian, Jie↗
DOE OSTI2024

Reducing Operator Complexity of Galerkin Coarse-grid Operators with Machine Learning

Here, we propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of increasing operator complexity. Guided by the MG theory on spectrally equivalent coarse-grid operators, we have developed novel machine learning algorithms that utilize neural networks combined with smooth test vectors from multigrid eigenvalue problems. The proposed method demonstrates promise in reducing the complexity of coarse-grid operators while maintaining overall MG convergence for solving parametric partial differential equation problems. Numerical experiments on anisotropic rotated Laplacian and linear elasticity problems are provided to showcase the performance and comparison with existing methods for computing non-Galerkin coarse-grid operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Vector Calibration for Vertical Tests of Superconducting RF Cavities

We propose procedure for vector calibration of Q 0 vs E acc curve measurement for superconducting resonators. The proposed vector calibration procedure and hardware has several advantages over the techniques currently used for vertical cavity tests. It provides high level of automation and reduces manpower and preparation time for the test; it provides in situ, real time control of hardware parameters and allows to detect hardware failure and operator's errors; it allows to control and exclude the most significant systematic uncertainties, measured values of Q 0 (E acc ) can be made traceable to national standards.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Viral‐mediated delivery of morphogenic regulators enables leaf transformation in Sorghum bicolor (L.)

Recent advancements in monocot transformation, using leaf tissue as explant material, have expanded the number of grass species capable of transgenesis. However, the complexity of vectors and reliance on inducible excision of essential morphogenic regulators have so far limited widespread application. Plant RNA viruses, such as Foxtail Mosaic Virus (FoMV), present a unique opportunity to express morphogenic regulator genes, such as Babyboom (Bbm), Wuschel2 (Wus2), Wuschel-like homeobox protein 2a (Wox2a) and the GROWTH-REGULATING FACTOR 4 (GRF4) GRF-INTERACTING FACTOR 1 (GIF1) fusion protein transiently in leaf explant tissues. Furthermore, altruistic delivery of conventional and viral vectors could provide opportunities to simplify vectors used for leaf transformation—facilitating vector optimization and reducing reliance on morphogenic regulator gene integration. In this study, both viral and conventional T-DNA vectors were tested for their ability to promote the formation of embryonic calli, a critical step in leaf transformation protocols, using Sorghum bicolor leaf explants. Although conventional leaf transformation vectors yielded viable embryonic calli (43.2 ± 2.9%: GRF4-GIF1, 50.2 ± 3%: Bbm/Wus2), altruistic conventional vectors employing the GRF4-GIF1 morphogenic regulator resulted in improved efficiencies (61.3 ± 4.7%). Altruistic delivery was further enhanced with the use of viral vectors employing both GRF4-GIF1 and Bbm/Wus2 regulators, resulting in 75.1 ± 2.3% and 79.2 ± 2.5% embryonic calli formation, respectively. Embryonic calli generated from both conventional and viral vectors produced shoots expressing fluorescent reporters, which were confirmed using molecular analysis. This work provides an important proof-of-concept for the use of both altruistic vectors and viral-expressed morphogenic regulators for improving plant transformation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data for "Viral-mediated Delivery of Morphogenic Regulators Enables Leaf Transformation in Sorghum bicolor (L.)"

Recent advancements in monocot transformation, using leaf tissue as explant material, have expanded the number of grass species capable of transgenesis. However, the complexity of vectors and reliance on inducible excision of essential morphogenic regulators have so far limited widespread application. Plant RNA viruses, such as Foxtail Mosaic Virus (FoMV), present a unique opportunity to express morphogenic regulator genes, such as Babyboom ( Bbm ), Wuschel2 ( Wus2 ), Wuschel-like homeobox protein 2a ( Wox2a ) and the GROWTH-REGULATING FACTOR 4 (GRF4) GRF-INTERACTING FACTOR 1 (GIF1) fusion protein transiently in leaf explant tissues. Furthermore, altruistic delivery of conventional and viral vectors could provide opportunities to simplify vectors used for leaf transformation—facilitating vector optimization and reducing reliance on morphogenic regulator gene integration. In this study, both viral and conventional T-DNA vectors were tested for their ability to promote the formation of embryonic calli, a critical step in leaf transformation protocols, using Sorghum bicolor leaf explants. Although conventional leaf transformation vectors yielded viable embryonic calli (43.2 ± 2.9%: GRF4-GIF1, 50.2 ± 3%: Bbm/Wus2), altruistic conventional vectors employing the GRF4-GIF1 morphogenic regulator resulted in improved efficiencies (61.3 ± 4.7%). Altruistic delivery was further enhanced with the use of viral vectors employing both GRF4-GIF1 and Bbm/Wus2 regulators, resulting in 75.1 ± 2.3% and 79.2 ± 2.5% embryonic calli formation, respectively. Embryonic calli generated from both conventional and viral vectors produced shoots expressing fluorescent reporters, which were confirmed using molecular analysis. This work provides an important proof-of-concept for the use of both altruistic vectors and viral-expressed morphogenic regulators for improving plant transformation.

gene editing↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Efficacy of commercial recombinant HVT vaccines against a North American clade 2.3.4.4b H5N1 highly pathogenic avian influenza virus in chickens

The outbreak of clade 2.3.4.4b H5 highly pathogenic avian influenza (HPAI) in North America that started in 2021 has increased interest in applying vaccination as a strategy to help control and prevent the disease in poultry. Two commercially available vaccines based on the recombinant herpes virus of turkeys (rHVT) vector were tested against a recent North American clade 2.3.4.4b H5 HPAI virus isolate: A/turkey/Indiana/22-003707-003/2022 H5N1 in specific pathogen free white leghorn (WL) chickens and commercial broiler chickens. One rHVT-H5 vaccine encodes a hemagglutinin (HA) gene designed by the computationally optimized broadly reactive antigen method (COBRA-HVT vaccine). The other encodes an HA gene of a clade 2.2 virus (2.2-HVT vaccine). There was 100% survival of both chicken types COBRA-HVT vaccinated groups and in the 2.2-HVT vaccinated groups there was 94.8% and 90% survival of the WL and broilers respectively. Compared to the 2.2-HVT vaccinated groups, WL in the COBRA-HVT vaccinated group shed significantly lower mean viral titers by the cloacal route and broilers shed significantly lower titers by the oropharyngeal route than broilers. Virus titers detected in oral and cloacal swabs were otherwise similar among both vaccine groups and chicken types. To assess antibody-based tests to identify birds that have been infected after vaccination (DIVA-VI), sera collected after the challenge were tested with enzyme-linked lectin assay-neuraminidase inhibition (ELLA-NI) for N1 neuraminidase antibody detection and by commercial ELISA for detection of antibodies to the NP protein. As early as 7 days post challenge (DPC) 100% of the chickens were positive by ELLA-NI. ELISA was less sensitive with a maximum of 75% positive at 10DPC in broilers vaccinated with 2.2-HVT. Both vaccines provided protection from challenge to both types of chickens and ELLA-NI was sensitive at identifying antibodies to the challenge virus therefore should be evaluated further for DIVA-VI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Supplemental Data for the Manuscript: Quantification of Manganese for ChemCam Mars and Laboratory Spectra Using a Multivariate Model

This dataset includes all of the data needed to validate and/or reproduce the manganese calibration model described in the manuscript. The reference database contains metadata for the new Mn-bearing standards, minerals, and mixtures that are > 2.9 wt.% MnO. In addition, the files include the MnO composition data for all standards used, non-normalized spectral data, mean peak area spectrum, results of outlier determination, RMSECV data, regression vectors, and Test Set predictions.

58 GEOSCIENCES↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Multi-Probe Modeling Strategy and Validation

This paper details the modeling pipeline and validates the baseline analysis choices of the DES Year 3 joint analysis of galaxy clustering and weak lensing (a so-called "3$\times$2pt" analysis). These analysis choices include the specific combination of cosmological probes, priors on cosmological and systematics parameters, model parameterizations for systematic effects and related approximations, and angular scales where the model assumptions are validated. We run a large number of simulated likelihood analyses using synthetic data vectors to test the robustness of our baseline analysis. We demonstrate that the DES Year 3 modeling pipeline, including the calibrated scale cuts, is sufficiently accurate relative to the constraining power of the DES Year 3 analyses. Our systematics mitigation strategy accounts for astrophysical systematics, such as galaxy bias, intrinsic alignments, source and lens magnification, baryonic effects, and source clustering, as well as for uncertainties in modeling the matter power spectrum, reduced shear, and estimator effects. We further demonstrate excellent agreement between two independently-developed modeling pipelines, and thus rule out any residual uncertainties due to the numerical implementation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Diagnosis of Corrosion Process in Nuclear Power Plant Secondary Piping Structures

Corrosion inside secondary pipe structures in Nuclear Power Plants is difficult to detect without intrusion. Statistical and machine learning techniques are used to predict corrosion using pipe vibration data. 3D accelerometers were used to record pipe vibration data in three directions using six sensors. Vibration data were processed using the Hilbert-Huang Transformation, which is known for its adaptable basis function to non-linear and non-stationary signals. Hilbert-Huang Transformation using empirical mode functions generates multiple intrinsic mode functions of different amplitude and frequency levels from raw vibration data. From intrinsic mode functions features are extracted which include phase, energy, entropy, mean, standard deviation, skewness, and kurtosis. Logistic regression using only phase information correctly distinguished mass additions from baseline data in roughly 83% and 85% of the cases. Phase information varied substantially among different baseline runs and within trials of the same run. Random Forest resulted in >95% testing accuracy for both mass addition and mass removal data. Support Vector Machine had ~98% testing accuracy for mass removal and ~92% testing accuracy for mass additions when using Permutation Importance feature selection. When using Principle Component Analysis with Support Vector Machine, testing accuracy was ~68% testing accuracy for mass additions and ~85% testing accuracy for mass.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Vectors of Efficiency in Hybrid Poplar Genotype Testing

Abstract The Natural Resources Research Institute Hybrid Poplar Program breeds and tests genetically improved clones for bio-mass production and environmental services. The testing process progresses from Nursery Progeny Tests (NPT) to Family Field Trials (FFT) to Clone Trials (CT) to Yield Blocks (YB), with limited replication of many clones in FFT and CT and a limited number of highly selected clones set out in monoclonal blocks (YB) to approximate the conditions of commercial plantations. We used correlation vectors, R 2 (coefficient of determination) and r s (Spearman’s Coefficient) for growth (DBH 2 ) and McFadden’s Pseudo R 2 for canker severity score, to determine where testing times could be altered (age – age correlations) and whole testing steps eliminated. FFT can be shortened from 5 years to 4 years. In CT, rank correlations between age 5 (half-rotation) and age 9/10 (full rotation) were significant (R 2 = 0.39 – 0.72), but age 5 selection missed 44 % of the top ten clones at age 9/10. Clone rank in CT at full, but not half, rotation was correlated with rank at full rotation in YB. Choosing clones at 9 years in CT adds 4 years but allows possible elimination of YB for clone selection. Both FFT and CT are necessary. Canker abundance and severity in CT at full rotation cannot be determined at earlier ages. An aggressive strategy saves 6 years of testing.

Forestry↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗