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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 73 records · Page 4

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DOE OSTI2025

Part distortion monitoring in additive manufacturing using machining

In additive manufacturing, accumulation of residual stresses can result in severe part distortion from the desired preform shape. Current methods for in-situ part distortion monitoring in additive manufacturing typically require expensive sensors, or capital equipment, and require time-consuming post-processing to understand the shape deviation. This paper presents an in-situ method, in the context of hybrid manufacturing, for part distortion detection using machining of additively manufactured parts. As a surrogate, three test artifacts were used to represent different distorted geometries. The tool axis positions from the machine tool controller and the cutting power were monitored during a facing operation. Cutting power data was used to detect the tool entry and exit in the workpiece using a novel approach with power standard deviation metric. The workpiece geometry and distorted configuration was subsequently predicted for positional and rotational deviations to within 2 mm accuracy using synchronized tool position data with cutting power. The proposed method can be used in a hybrid (additive and subtractive) machine tool to periodically check part distortion in the additive build. The method is applicable for any additive process and is low-cost and computationally inexpensive.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

AmeriFlux FLUXNET-1F US-RGB Butte County Rice Farm

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RGB Butte County Rice Farm. This is the FLUXNET version of the carbon flux data for the site US-RGB Butte County Rice Farm produced by applying the standard ONEFlux (1F) software. Site Description - Commercially farmed, mid-grain japonica rice variety, field in Butte County, California. Part of a five-year, ground-truthing field study under the DOE ARPA-E SMARTFARM project. Field is approx. 30 ha in size in three checks with commercial rice over rice rotation on silty clay loam.

Schuppenhauer, Michael↗

DOE OSTI2021

Deploying Software-Defined Networking in Operational Technology Environments

Software Defined Networking for Operational Technologies, referred to as OT-SDN, is a leading technology to secure critical infrastructure and command and control (C2) systems. As the name implies, OT-SDN networks are programmable, which allows system owners to utilize the characteristics of their physical process to inform the security of their network. There are best practices for deploying OT-SDN into an environment, whether it is all at once or over time (hybrid) that the network is converted to SDN technologies. Through the development of data mining tools and standardized process control, OT-SDN can be deployed reliably. These tools will minimize or eliminate any communication failures during the transition and provide the network owner with complete documentation of their environment. This documentation could enable or facilitate the network owner to pass any audits or policy checks (Authority to Operate) before being allowed to utilize the OT-SDN infrastructure.

Software Defined Networking, Operational Technolog↗

DOE OSTI2020

Heterotic instantons for monad and extension bundles

We consider non-perturbative superpotentials from world-sheet instantons wrapped on holomorphic genus zero curves in heterotic string theory. These superpotential contributions feature prominently in moduli stabilization and large field axion inflation, which makes their presence or absence, as well as their functional dependence on moduli, an important issue. We develop geometric methods to compute the instanton superpotentials for heterotic string theory with monad and extension bundles. Using our methods, we find a variety of examples with a non-vanishing superpotential. In view of standard vanishing theorems, we speculate that these results are likely to be attributed to the non-compactness of the instanton moduli space. We test this proposal, for the case of monad bundles, by considering gauged linear sigma models where compactness of the instanton moduli space can be explicitly checked. In all such cases, we find that the geometric results are consistent with the vanishing theorems. Surprisingly, linearly dependent Pfaffians even arise for cases with a non-compact instanton moduli space. This suggests some gauged linear sigma models with a non-compact instanton moduli space may still have a vanishing instanton superpotential.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗

DOE OSTI2020

A public website for the automated assessment and validation of SARS-CoV-2 diagnostic PCR assays

Abstract Summary Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay-screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true-positive rate (recall) to simplify assay comparisons. Availability and implementation The assay evaluation website and supporting software are Open Source and freely available at https://covid19.edgebioinformatics.org/#/assayValidation, https://github.com/jgans/thermonucleotide BLAST and https://github.com/LANL-Bioinformatics/assay_validation. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Elucidating the Dynamics of Electroweak Symmetry Breaking through Study of High-Multiplicity Boson Final States at the LHC

To understand the full story of the electroweak symmetry breaking (EWSB) and how the universe arrived at today’s universe, the structure of the Higgs potential needs to be probed. We must carefully select and study processes at the LHC that are sensitive to the Higgs potential. In this context, longitudinal vector boson scattering (VBS) processes, V L V L →V L V L , and di-Higgs production, including V L V L →HH at the LHC have been studied for any potential deviation from SM prediction. However, many classes of models beyond the Standard Model (BSM) may result in the 2→2 process being perfectly well-behaved and SM-like but show interesting deviations in 2→3, 2→4, or 2→n(3) processes. If we are to complete the mission objectives the P5 laid out, then a large swath of 2→n(3) must be thoroughly searched and checked. The research supported by this award used data collected at the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) to explore VBS produced VVH process in the semi-merged channel. In addition, in the electroweak sector, there are open questions in the multi-gauge boson couplings and the Higgs couplings to the gauge bosons. The research supported by the award also studied the triboson production WWZ process at the LHC, which is a crossed diagram of a VBS process. During the funded period of 9 months, the analysis designs have been finalized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Including 238 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f) NIFFTE fission TPC Cross-sections into the Neutron Data Standards Database

The primary purpose of this report is to document how the 238 U/ 235 U and 239 Pu/ 235 U neutron induced fission cross-section ratios, 238 U(n,f)/ 235 U(n,f) and 239 Pu(n,f)/ 235 U(n,f), respectively, measured by the NIFFTE fission Time Projection Chamber (fissionTPC) were included in the most recent database (termed GMA) underlying Neutron Data Standards (NDS) evaluations. This report shows and discusses NDS input files, and underlying assumptions regarding the uncertainty estimate and necessary for including these data. This uncertainty estimate and the resulting files were based on information provided by fissionTPC experimentalists, R.J.Casperson, N.S. Bowden, L. Snyder and K.T. Schmitt for the 238 U ratio, and by L. Snyder for the 239 Pu ratio. The fissionTPC data were included twice, by D. Neudecker and V. Pronyaev, to counter-check results and exclude possible mistakes in their inclusion. It is shown in both evaluations that including fissionTPC 239 Pu(n,f)/ 235 U(n,f) data points to a lower evaluated 239 Pu(n,f) cross section above 10 MeV than the currently released NDS data. This raises the question whether a part of a previous dataset by Tovesson et al., that was previously rejected above 13 MeV for having low values, should be included in the NDS evaluation after all. The evaluated 238 U(n,f) cross section only changes significantly close to the threshold. The impact on the 235 U(n,f) cross section is minimal. fissionTPC data reduce evaluated uncertainties on both observables by 0–12% of the GMA evaluated uncertainties. However, the currently released NDS data contain in addition to these GMA evaluated uncertainties “Unrecognized Sources of Uncertainties” (USU) of 1.2%. It needs to be further discussed within the NDS project, whether the new fissionTPC data should also reduce USU.

238U(n,f)/235U(n,f)↗

DOE OSTI2020

MWRRETV2 Value-Added Product Report: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer – 3-Channel (MWR3C) Data Sets

This report provides a short description of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Microwave Radiometer – 3-Channel (MWR3C) Retrieval (MWRRETV2) Value-Added Product (VAP) algorithm. MWRRETV2 is an updated version of MWRRET, which extends the physical-iterative retrieval of liquid water path (LWP) and precipitable water vapor PWV) to 3-channel microwave radiometers beyond ARM's original standard 2-channel (23-and 31-GHz) systems. The 89-GHz channel in the 3-channel microwave radiometers has approximately three times the sensitivity to liquid water as the 31-GHz channel, thus improving LWP retrieval accuracy when LWP is less than 100 g/m2. We present a general overview of the technique, input parameters, and output products, and describe data quality checks.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Giallar: push-button verification for the qiskit Quantum compiler

This paper presents Giallar, a fully-automated verification toolkit for quantum compilers. Giallar requires no manual specifications, invariants, or proofs, and can automatically verify that a compiler pass preserves the semantics of quantum circuits. To deal with unbounded loops in quantum compilers, Giallar abstracts three loop templates, whose loop invariants can be automatically inferred. To efficiently check the equivalence of arbitrary input and output circuits that have complicated matrix semantics representation, Giallar introduces a symbolic representation for quantum circuits and a set of rewrite rules for showing the equivalence of symbolic quantum circuits. With Giallar, we implemented and verified 44 (out of 56) compiler passes in 13 versions of the Qiskit compiler, the open-source quantum compiler standard, during which three bugs were detected in and confirmed by Qiskit. Furthermore, our evaluation shows that most of Qiskit compiler passes can be automatically verified in seconds and verification imposes only a modest overhead to compilation performance.

automated verification↗

DOE OSTI2025

Dynamical dark energy from lattice quantum gravity

We study the behavior of the vacuum in Euclidean dynamical triangulations (EDT). Algorithmic improvements and better lattice spacing determinations allow us to test the properties of the emergent de Sitter geometries of our simulations to higher precision than previously possible. Although the agreement with de Sitter is good, the improved precision reveals deviations that can be interpreted as nontrivial vacuum dynamics, well described by a cosmological constant that runs with scale. The simulations show that the dominant running is quadratic and that the scale can be identified with the Hubble rate. Several key cross-checks support this picture, including consistent results across multiple lattice spacings and the fact that the null energy condition is not violated. The parameters of the running are fully determined by simulations, enabling predictions when extrapolated to the scales relevant for our Universe. This leads to a model for dark energy that is compatible with current observations, but which predicts deviations from the Standard Model of cosmology at the 𝒪⁡(10 −3 ) level in observables that could be tested with future improvements in precision measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Laser Disdrometer Quantities (LDQUANTS) and Video Disdrometer Quantities (VDISQUANTS) Value-Added Products Report

Disdrometers are useful instruments that measure the distribution of raindrop sizes (drop size distribution: DSD) and the associated rainfall rates/accumulation as those raindrops fall to the ground. These DSDs are a quantity of interest to members of the modeler and observational communities. However, use of disdrometer data for model evaluation, radar monitoring, or other activities requires careful quality control and processing for key DSD properties of interest (e.g., the number concentration of drops) to ensure appropriate physical (scattering, fall speed) assumptions. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Laser Disdrometer Quantities and Video Disdrometer Quantities Value-Added Product (LDQUANTS/VDISQUANTS VAP) uses standard methods from Tokay et al. (e.g., 2013, 2014) to filter drops with unrealistic fall speeds. Further, it estimates several microphysical/geophysical quantities of parameterized DSDs (gamma or exponential assumption type fitting methods) as in previous disdrometer studies and ARM long-term efforts (e.g., Testud et al. 2001, Giangrande et al. 2014, Thompson et al. 2015). Disdrometers are also beneficial for cross-checks with other instrumentation, including rain gauges and radars. To support research interests and related radar monitoring activities, this product calculates radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR, etc.), using T-Matrix scattering and additional wavelength, temperature, and drop shape assumptions (e.g., Thurai et al. 2007). All of these efforts allow disdrometer data sets to become more useful and easily handled by modeling and observational studies, or routine instrumentation checks.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Investigation of U content in J-swipe/PIC samples

This final report documents the results of an evaluation of the U content in J-swipes for the International Atomic Energy Agency (IAEA). J-swipes are manufactured with inked numbers (1–30) and U backgrounds that are considered consistent across different lots and manufacturing times. These characteristics make them ideal for use by IAEA inspectors for pre-inspection check samples to ensure inspectors are not cross-contaminating sites. However, recent work has found elevated U concentrations in the inked numbers compared with the J-swipe material. To determine whether the inked numbers correlate with the U backgrounds, triplicate U content measurements were made on Swipes 1, 2, 11, 22, and 28. Triplicates of each swipe were processed through the standard chemistry used by the Oak Ridge National Laboratory Network of Analytical Laboratories team and analyzed for U content by isotope dilution via multicollector inductively coupled plasma mass spectrometry. Finally, samples were made by mass consisting of only ink areas and only cotton swipe material to determine the trace element differences between the cotton and the ink.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2026

Updated constraints from electric dipole moments in the MSSM with R-parity violation

We revisit the electric dipole moments (EDMs) of quarks and leptons in the Minimal Supersymmetric Standard Model (MSSM) with trilinear R-parity violation (RPV). In this framework, EDMs are induced at the two-loop level via RPV interactions. We perform a comprehensive recalculation of several classes of Barr-Zee type diagrams in a general Rξ gauge. While we find general agreement with previous analytic results in the literature, our work provides a valuable independent cross-check of the complicated calculations. We also point out some subtleties in the intermediate steps and in the choice of the flavor basis for the numerical evaluation of the expressions. By confronting the theoretical predictions with the latest experimental limits on EDMs, we derive updated constraints on combinations of RPV couplings. We highlight an approximate, testable correlation between the proton and neutron EDM that emerges within the considered class of RPV models, offering a distinctive signature for future EDM experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

LeWRON: Agentic Analysis of Electroweak Phase Transitions

The electroweak phase transition (EWPT) is a central topic in particle physics and cosmology, connecting collider phenomenology, baryogenesis, and gravitational-wave observatories. Its analysis requires a technically demanding, convention-sensitive, and model-dependent pipeline, from constructing the finite-temperature effective potential to tracking thermal histories, computing bubble nucleation rates, and predicting gravitational-wave spectra. We present LeWRON (Learning ElectroWeak phase tRansitiON), an agentic framework that orchestrates this pipeline starting from an input Lagrangian. LeWRON combines audited toolbox construction with an Explorer module that uses the generated model-specific code for further analysis, including scans and plots. Intermediate analytic outputs are checked by auditor agents and stored as structured artifacts, enabling reproducible human inspection and downstream use through both a command-line interface and a public Python API. The framework supports a reproduction mode, which infers conventions from the literature and reproduces published results, and a discovery mode, which guides users through structured checkpoints for new models. We demonstrate LeWRON across representative beyond-the-Standard-Model scenarios and release the code on GitHub.

Wang, Isaac R. [Fermilab] (ORCID:000000030789218X)↗

DOE OSTI2024

Dark sink enhances the direct detection of freeze-in dark matter

We describe a simple dark sector structure which, if present, has implications for the direct detection of dark matter (DM); the dark sink. A dark sink transports energy density from the DM into light dark-sector states that do not appreciably contribute to the DM density. As an example, we consider a light, neutral fermion ψ which interacts solely with DM Χ via the exchange of a heavy scalar Φ. We illustrate the impact of a dark sink by adding one to a DM freeze-in model in which Χ couples to a light dark photon γ' which kinetically mixes with the Standard Model (SM) photon. This freeze-in model (absent the sink) is itself a benchmark for ongoing experiments. In some cases, the literature for this benchmark has contained errors; we correct the predictions and provide them as a public code. We then analyze how the dark sink modifies this benchmark, solving coupled Boltzmann equations for the dark-sector energy density and DM yield. We check the contribution of the dark sink ψ’s to dark radiation; consistency with existing data limits the maximum attainable cross section. For DM with a mass between MeV –Ο⁡(10 GeV), adding the dark sink can increase predictions for the direct detection cross section all the way up to the current limits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗